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BANGALORE UNIVERSITY
Department of Computer Science and Engineering
UNIVERSITY VISVESVARAYA COLLEGE OF ENGINEERING
K R Circle, Bengaluru-560 001.
Choice Based Credit System (CBCS)-2018
M. Tech in Computer Science and Engineering
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BANGALORE UNIVERSITY
VISION
―To strive for excellence in education for the realization of a vibrant and inclusive
society through knowledge creation and dissemination‖
MISSION
Impart quality education to meet national and global challenges
Blend theoretical knowledge with practical skills
Pursue academic excellence through high quality research and publications
Provide access to all sections of society to pursue higher education
Inculcate right values among students while encouraging competitiveness to
promote leadership qualities
Produce socially sensitive citizens
Hasten the process of creating a knowledge society
To contribute to nation building
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Bangalore University UNIVERSITY VISVESVARAYA COLLEGE OF ENGINEERING
K R Circle, Bengaluru – 560 001.
University Visvesvaraya College of Engineering (UVCE) was started as a School of Mechanical
Engineering by Bharat Ratna Sir. M. Visvesvaraya in the year 1913 to meet the needs of the State for
skilled workers with S V Setty as its Superintendent. Later, it was converted to a full-fledged
Engineering College in the year 1917 under the name Government Engineering College and was
affiliated to the University of Mysore. It is the fifth Engineering College to be established in the country.
After the formation of Bangalore University in 1964, UVCE became one of the Constituent
Colleges of Bangalore University. This is one of the oldest Institutions in the country imparting
technical education leading to B.E., M.E, B.Arch., M.Sc. (Engineering), M.Arch. and Ph.D. degrees in
various disciplines of Engineering and Architecture. The Institution currently offers 7 Undergraduate
(B.E. / B.Arch.) Full-time, three Undergraduate (B.E.) Part-time and 24 Postgraduate (M.E. / M.Arch.)
Programmes.
VISION
The vision of UVCE is to strive for excellence in advancing engineering education through path
breaking innovations across the frontiers of human knowledge to realize a vibrant, inclusive and humane
society.
MISSION
The mission of UVCE is to prepare human resource and global leaders to achieve the above vision
through discovery, invention and develop friendly technologies to promote scientific temper for a
healthy society. UVCE shapes engineers to respond competently and confidently to the economic, social
and organizational challenges arising from globally advancing technical needs.
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Bangalore University Bengaluru
Department of Computer Science and Engineering, UVCE, Bengaluru
M. Tech. DEGREE IN COMPUTER SCIENCE AND ENGINEERING under CBCS Scheme - 2K18
Vision of the Department
Strive for Centre of Excellence in advancing Computer Science and Engineering education to produce
highly qualified human resources to meet local and global requirement.
Mission of the Department
CSM1. Implementing effectively, the outcome based education by imparting knowledge of basics and
advances in Computer Science and Engineering and other allied disciplines.
CSM2. Preparing and equipping human resources to become global leaders through innovation,
discovery, sustainable and environment friendly technology.
CSM3. Creating conducive environment for effective teaching and learning process through
interdisciplinary research, online courses, interaction with institutions of higher learning and industries, R
and D laboratories of national importance, alumni, employers and other internal & external stake holders.
CSM4. Imbibing awareness of entrepreneurship, ethics, honesty, credibility, social and environmental
consciousness and providing opportunity to the faculty and technical staff for continuous academic
improvement and to equip them with then latest trends in Software Engineering and thereby inculcate the
habit of continuous learning in faculty, staff and students.
Program Outcomes:
Computer Science and Engineering Graduates will be able to:
CSPO1: An ability to independently carry out research/investigate and development work to solve
practical problems
CSPO2: An ability to write and present a substantial technical report/document
CSPO3: Students should be able to demonstrate a degree of mastery over the area as per the
specialization of the problem. The mastery should be at a level higher than the requirements in the
appropriate bachelor degree
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Program Educational Objectives (PEO)
M. Tech (Computer Science Engineering)
After successful completion of the program, the graduates will be
CSPEO 1: Able to apply concepts of mathematical foundation and computing to Computer Science
and Engineering
CSPEO 2: Able to design and develop interdisciplinary and innovative systems.
CSPEO 3: Able to inculcate effective communication skills, team work, ethics, leadership in
preparation for a successful career in industry and R&D organizations.
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BANGLORE UNIVERSITY
SCHEME OF STUDIES AND EXAMINATION FOR 24MONTHS COURSE FOR THE AWARD OF
M. Tech. DEGREE IN COMPUTER SCIENCE AND ENGINEERING under CBCS Scheme – 2K18
Semester I Sl. No Course Type/ Course Name Teaching scheme Teaching Total CIE *SEE Credits
Course Code Hrs/Week DPT Hrs/week Marks Marks
L T P S
1 18CS1C01 Mathematical Foundations of Computer Science 3 1 0 0 CSE 4 50 50 4
2 18CS1C02 Advances in Computer Networks 4 0 0 0 CSE 4 50 50 4
3 18CS1C03 Advanced Database Management Systems 4 0 0 0 CSE 4 50 50 4
18CS1E1A Cloud Computing 4 0 0 0 CSE
4 18CS1E1B Mobile Computing 4 0 0 0 CSE 4 50 50 4
18CS1E1C Wireless Networks 4 0 0 0 CSE
18CS1E2A Soft Computing 3 0 2 0 CSE
5 18CS1E2B Advances in Storage Area Networks 4 0 0 0 CSE 4 50 50 4
18CS1E2C Advanced Computer Architecture 4 0 0 0 CSE
6 18CS1L01 Network Programming Lab 0 0 4 0 CSE 4 50 50 2
7 18CS1M01 Research Methodology and IPR. 2 0 0 0 CSE 2 50 50 2
8 18CS1S01 Seminar -I 0 0 2 0 CSE 2 50 -- 1
9 18CS1M02 Audit Course-I (Technical Paper Writing) 2 0 0 0 English 2 50 -- 1
Total 30 450 350 26
*SEE shall be conducted for 100 marks and the marks obtained is to be reduced for 50 marks.
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Semester II Sl. No Course Type/ Course Name Teaching scheme Teaching Total CIE *SEE Credits
Course Code Hrs/Week DPT Hrs/week Marks Marks
L T P S
1 18CS2C01 Advanced Data Structures and Algorithms 4 0 0 0 CSE 4 50 50 4
2 18CS2C02 Advanced Operating Systems 4 0 0 0 CSE 4 50 50 4
3 18CS2C03 Advances in Digital Image Processing 4 0 0 0 CSE 4 50 50 4
4
18CS2E1A Data Warehousing and Mining 4 0 0 0 CSE
4
50
50
4
18CS2E1B Stochastic Process and Queuing Theory 4 0 0 0
18CS2E1C Internet of Things 3 0 2 0
5
18CS2E2A Network Security 4 0 0 0
CSE 4 50 50 4 18CS2E2B Pattern Recognition 4 0 0 0
18CS2E2C Web Security 4 0 0 0
6 18CS2L01 Advanced Data Structures and Algorithms Lab 0 0 4 0 CSE 4 50 50 2
7 18CS2S01 Seminar -II 0 0 2 0 CSE 2 50 -- 1
8 18CS2M01 Audit Course-II (Pedagogy Studies) 2 0 0 0 CSE 2 50 -- 1
Total 28 400 300 24
Semester III Sl. No Course Type/ Course Name Teaching scheme Teaching Total CIE *SEE Credits
Course Code Hrs/Week DPT Hrs/week Marks Marks
L T P S
1 2
18CS3E1A Machine Learning 4 0 0 0 CSE
CSE
CSE
4
4
50
50
50
50
4
4
18CS3E1B Big Data Analytics 3 0 2 0
18CS3E1C High Performance Computing
4 0 0 0
Open Elective
3 18CS3S01 Seminar -III 0 0 2 0 CSE 2 50 1
4 18CS3I01 Internship / Mini Project 0 0 10 0 CSE 10 50 50 5
5 18CS3D01 Dissertation Phase -I 0 0 10 0 CSE 10 50 50 5
Total 30 250 200 19
CS7
Semester IV Sl. No Course Type/ Course Name Teaching Scheme Teaching Total CIE *SEE Credits
Course Code Hrs/Week DPT Hrs/week Marks Marks
L T P S
1 18CS4S01 Seminar -IV 0 0 2 0 CSE 2 50 1
2 18CS4D01 Dissertation Phase -II 0 0 30 0 CSE 30 50 50 15
Total -- -- 32 -- 32 100 50 16
1 18CSMOOC MOOC Course 0 0 0 0 03
Grand Total of Credits 88
COURSE TYPE
CS: COMPUTER SCIENCE C: PROFESSIONAL CORE E: PROFESSIONAL ELECTIVE
P: OPEN ELECTIVE M: MANDATORY AUDIT L: LABORATORY
S: SEMINAR I: INTERNSHIP/ MINI PROJECT D: DISSERTATION
L – Theory lecture, T – Tutorial, P – Lab work, S – Self-study:
Numbers under teaching scheme indicates contact clock hours.
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Open Elective
Sl. No Course Type /
Course Code Course Name
Teaching Scheme (No. of hrs per week)
Teaching
Dept.
Total hrs
/ week
CIE
Marks
*See
Marks Credits
L T P S
1
18CS3P1A Artificial Intelligence
4 0 0 0 CSE 4 50 50 4 18CS3P1B Business Analytics
18CS3P1C Modeling and Simulation
2
18CV3P1A Significance of National Building Codes
4 0 0 0 Civil 4 50 50 4
18CV3P1B Water Laws, Rights and Administration
18CV3P1C Waste to Energy
18CV3P1D Remote Sensing and Geographic Information
System
3 18ME3P1A Composite and Smart Materials
4 0 0 0 Mech 4 50 50 4 18ME3P1B Industrial Safety
4
18EE3P1A Real Time Embedded Systems
4 0 0 0 EEE 4 50 50 4 18EE3P1B Robotics and Automation
18EE3P1C Solar and Wind Energy
5
18EC3P1A Reliability and Engineering
4 0 0 0 ECE 4 50 50 4 18EC3P1B M-Commerce and Applications
18EC3P1C Optimization Techniques
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Course Code 18CS1C01 M. Tech (Computer Science and Engineering)
Category Engineering Science Courses (Theory - Professional Core)
Course title MATHEMATICAL FOUNDATIONS OF COMPUTER SCIENCE
Scheme and
Credits
No. of Hours/Week Semester – I
L T P S Credits
3 1 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
1. Basics of probability
2. Basics of graph theory
COURSE OBJECTIVES
The course will enable the students to:
1. Understand the concepts of number theory and solve related problems.
2. Apply the concepts of stochastic process and queuing theory required to devise
analytical models for the real problems of computer science.
3. Analyse the various concepts of arranging, selecting and combining objects from a
set.
4. Understand the concept of advanced graph theory that can be used to model any
network, physical or conceptual.
UNIT -I NUMBER THEORY: 10 Hours
The Division algorithm, the Greatest Common Divisor, the Euclidean algorithm. The basic
properties of Congruencies, Binary and decimal representation of integer, linear congruence,
Chinese-Reminder Theorem, Fermat‘s Little theorem, The sum and number of Divisors, The
Mobius inversion formula, The Greatest integer function (No theorem proofs).
UNIT -II PROBABILITY AND QUEUING THEORY: 10 Hours
Random Variables, Probability Distribution, Binomial Distribution, Poisson Distribution,
Geometric Distribution, Exponential Distribution, Normal Distribution, Uniform
Distribution. Two Dimensional Random Variables. Introduction to Stochastic Processes,
Markov process, Markov chain, one step and n-step Transition Probability, Chapman
Kolmogorov theorem (Statement only), Transition Probability Matrix, Classification of
States of a Markov chain. Introduction to Markovian queuing models, Single Server Model
with Infinite system capacity, Characteristics of the Model (M/M/1): (∞/FIFO), Single
Server Model with Finite System Capacity, Characteristics of the Model (M/M/1):
(K/FIFO).
UNIT -III COMBINATORICS: 10 Hours
Basics of Counting: Permutations, Permutations with Repetitions, Circular Permutations,
Restricted Permutations, Combinations: Restricted Combinations, Generating Functions of
Permutations and Combinations, Binomial and Multinomial Coefficients, Binomial and
Multinomial Theorems, The Principles of Inclusion Exclusion, Pigeonhole Principle and its
Application.
UNIT -IV RECURRENCE RELATIONS: 09 Hours Generating Functions, Function of Sequences, Partial Fractions, Calculating Coefficient of
Generating Functions, Recurrence Relations, Formulation as Recurrence Relations, Solving
Recurrence Relations by Substitution and Generating Functions, Method of Characteristic
Roots, Solving Inhomogeneous Recurrence Relations.
UNIT –V GRAPH THEORY: 09 Hours
Basic Concepts of Graphs, Sub graphs, Matrix Representation of Graphs: Adjacency
Matrices, Incidence Matrices, Isomorphic Graphs, Paths and Circuits, Eulerian and
Hamiltonian Graphs, Multi-graphs, Planar Graphs, Euler‗s Formula, Graph Colouring and
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Covering, Chromatic Number, Spanning Trees, Algorithms for Spanning Trees (Concepts
and Problems Only, Theorems without Proofs).
UNIT-VI Recent advances and research being done in the topics mentioned above
units
REFERENCES
1. David M Burton, ―Elementary Number Theory‖, Allyn and Bacon, 1980.
2. K. S. Trivedi, ―Probability and Statistics with Reliability, Queuing for Computer
Science Applications‖, John Wiley and Sons, II Edition, 2008.
3. Gunter Bolch, Stefan Greiner, Hermann De Meer, K S Trivedi, ―Queuing Networks
and Markov Chains‖, John Wiley and Sons, II Edition, 2006.
4. Richard A Brualdi, Introductory Combinatorics 5th
Edition, Pearson 2009
5. J. A. Bondy and U. S. R. Murty, ―Graph Theory and Applications‖, Macmillan
Press, 1982.
COURSE OUTCOMES
On completion of the course, the student would be able to:
CO1. Solve problems related to number theory.
CO2: Design the analytical models using the concepts of probability and stochastic process.
CO3: Compare the various methods of counting using permutations and combinations.
CO4: Solve the problems of recurrence relations.
CO5: Apply the graph theory concepts in solving problems related to computer science.
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 9 hours shall not have internal
choice
20*2=40
Marks Total:
Marks 100 Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 1 CO2 2 CO3 1 1 CO4 1 CO5 2
1: Low 2: Medium 3:High
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Course Code 18CS1C02 M. Tech (Computer Science and Engineering)
Category Engineering Science Courses
Course title ADVANCES IN COMPUTER NETWORKS
Scheme and
Credits
No. of
Hours/Week
Semester – I
L T P SS Credits
4 0 - - 4
CIE Marks:
50
SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3
Hrs
Prerequisites (if any):
COURSE OBJECTIVE
The course will enable the student to:
1. Understand the requirement of various high speed networks
2. Learn the effect of congestion and its control.
3. Understand Network Traffic Management for reliable delivery.
4. Understand integrated and differentiated architecture and services.
5. Learn the effect of traffic in the networks on various QoS parameters
UNIT I- INTRODUCTION 9 Hours
OSI and TCP/ IP Reference Model, RS-232C, RS-449, Devices used in Physical Layers,
Framing, Flow and Error Control, Error Detection and Correction, Checksum, Sliding
Window Protocols-ARQ.
UNIT II- DATA LINK LAYER 10 Hours Multiple Access Control Protocols(MAC), ALOHA, CSMA/CD (802.3), Data Link
Protocol- HDLC,PPP, Wired LAN‘s : Fast Ethernet, Gigabit Ethernet, Fibre Channel,
Wireless LAN‘s(802.11), Broadband Wireless(802.16).
UNIT III- ROUTING AND CONGESTION CONTROL 10 Hours Design issues, Routing Algorithms, Distance Vector Routing, Link State Routing, Routing
in Mobile Host, Broadcast Routing, Reverse Path Forwarding, OSPF, IP Protocols (IPv4 -
ARP, RARP, IPv6), Queuing Analysis- Queuing Models – Single Server Queues –
Effects of Congestion – Congestion Control – Traffic Management – Congestion Control
in Packet Switching Networks.
UNIT IV – TRANSPORT LAYER AND INTEGRATED SERVICES 10 Hours
TCP Header, TCP Flow control – TCP Congestion Control – Retransmission – Timer
Management – Exponential RTO back-off – KARN‘s Algorithm – Window
management. Integrated Services Architecture – Approach, Components, Services-
Queuing Discipline, FQ, PS, BRFQ, GPS, WFQ – Random Early Detection,
Differentiated Services.
UNIT V - PROTOCOLS FOR QOS SUPPORT 09 Hours
RSVP – Goals & Characteristics, Data Flow, RSVP operations, Protocol
Mechanisms – Multiprotocol Label Switching – Operations, Label Stacking, Protocol
details – RTP – Protocol Architecture, Data Transfer Protocol, RTCP.
UNIT VI- To understand latest innovative networks such as Software Defined
Networks(SDN).
REFERENCES
1. Behrouz A Forouzan and Firouz Mosharraf, ―Computer Networks, A Top-Down
Approach‖, TMH, 2012.
2. Andrew S. Tanenbaum and David J. Wetherall, ―Computer Networks‖, Pearson Education, 5th
Edition,2011.
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3. William Stallings, ―High Speed Networks and Internet‖, , Second Edition, 2012.
4. Prakash C Guptha, ―Data Communication and Computer Networks‖, PHI , 6th
printing 2012.
5. Larry L. Peterson and Bruce S Davis , ―Computer Network A System
Approach‖, Elsevier, 5th
edition 2010.
COURSE OUTCOMES
On Completion of the course, the student will be able to:
CO1: Apply the networking principles to manage the network traffic.
CO2: Control the various anomalies in the network to improve the QoS.
CO3: Study the relation and effect of one QoS parameter on the other.
CO4: Apply the efficient techniques to achieve effective and reliable communication.
CO5: Develop new protocols to mitigate emerging problems.
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100 Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2
CO3 3 2 2
CO4 3 2
CO5 2 2 2
1. Low, 2. Medium, 3. High
CS13
Course Code 18CS1C03 M. Tech (Computer Science and Engineering)
Category Engineering Science Courses (Theory - Professional Core)
Course title ADVANCED DATABASE MANAGEMENT SYSTEMS
Scheme and Credits No. of Hours/Week Semester – I
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES:
The course will enable the students to:
1. Remembering the basics of database management systems.
2. Understanding the concepts of object relational databases and XML
3. Evaluate database security strategies.
4. Applying the concepts of Data Storage and Querying.
5. Understanding distributed, parallel databases and recent technologies
UNIT- I INTRODUCTION 09 Hours
Data models, schemas and instances, three schema architecture and data independence,
database languages and interfaces, database environment. ER model: entity types, entity sets,
attributes and keys, relationship types, relationship sets, roles and structural constraints, ER
Diagrams. SQL3 - Overview of the SQL Query Language, SQL Data Definition, Basic
Structure of SQL Queries, Additional Basic Operations, Set Operations, Null Values,
Aggregate Functions, Nested Subqueries.
UNIT-II OBJECT AND OBJECT RELATIONAL DATABASES 10 Hours Object oriented concepts, object identity, object structure and type constructors, encapsulation
of operations, methods and persistence, class hierarchies and inheritance, object model of
ODMG, object definition language, object query language.XML: Structured, Semi structured,
and Unstructured Data, Data Model, Documents, DTD, XML Schema, Storing and Extracting
XML Documents from Databases, XML Languages.
UNIT-III DATABASE SECURITY 09 Hours Issues, discretional access control and role base access control, SQL Injection, statistical
database security, public key infrastructure, privacy issues and preservation, Oracle Label-
Based Security
UNIT- IV INDEXING AND HASHING 10 Hours Basic Concepts, Ordered Indices, B + -Tree Index Files, B + -Tree Extensions, Multiple-Key
Access, Static Hashing, Dynamic Hashing, Comparison of Ordered Indexing and Hashing,
Bitmap Indices. Query Processing: Overview, Measures of Query Cost, Selection Operation,
Sorting, Join Operation, Evaluation of Expressions. Query Optimization: Overview,
Transformation of Relational Expressions, Estimating Statistics of Expression Results, Choice
of Evaluation Plans, Materialized Views.
UNIT-V PARALLEL AND DISTRIBUTED DATABASES 10 Hours Parallel Databases: Introduction, I/O Parallelism, Interquery Parallelism, Intraquery
Parallelism, Intraoperation Parallelism, Interoperation Parallelism, Query Optimization, Design
of Parallel Systems, Parallelism on Multicore Processors. Distributed Databases:
Homogeneous and Heterogeneous Databases, Distributed Data Storage, Distributed
Transactions, Commit Protocols, Concurrency Control in Distributed Databases, Availability,
Distributed Query Processing, Heterogeneous Distributed Databases, Cloud-Based Databases,
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Directory Systems.
UNIT-VI RECENT TECHNOLOGIES
Latest technologies such as NoSQL, BigData, Multimedia Databases, Mobility and Personal
Databases
REFERENCES
1. Elmasri and Navathe, Fundamentals of Database Systems, 7th
edition, Pearson, 2016.
2. A. Silberschatz, H. F. Korth and S. Sudarshan, Database system concepts 6th ed. 2011
3. Raghu Ramakrishnan, Database Management System, McGraw Hill, 3rd
edition, 2003.
4. Ceri and Pelagatti, Distributed Databases: Principles and Systems, Tata McGraw Hill, 2008,
5. C.J.Date, A.Kannan and S.Swamynathan, An introduction to Database System, Pearson
Education, 8th
edition, 2009.
6. Dr. P.S. Deshpande, SQL and PL/SQL for Oracle log, Black Books Dreamtech Press.
COURSE OUTCOMES
Upon completion of the course, the students would be able to: CO1: State and identify the key concepts of database management systems
CO2: Design and implement object relational databases.
CO3: Determine the different strategies for database security and key issues.
CO4: Apply the concepts of query optimization and indexing.
CO5: Illustrate distributed and parallel database technologies.
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit VI(AAT)=15
marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not have
internal choice. 20* 2 = 40 Marks
Total:100
marks Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks..
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 1
CO2 1
CO3 1
CO4 1 2
CO5 2
1. Low, 2. Medium, 3. High
CS15
Course Code 18CS1E1A M. Tech (Computer Science and Engineering)
Category Engineering Science Courses (Theory - Professional Elective)
Course title CLOUD COMPUTING
Scheme and Credits No. of Hours/Week Semester – I
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
1. Operating systems
2. Basics of distributed computing
COURSE OBJECTIVES
The course will enable the students to:
1. Understand the various cloud service providers and cloud interoperability
2. Apply the cloud computing applications and paradigms
3. Analyse the concept of virtualization
4. Acquire the knowledge of cloud resource management and scheduling mechanism
5. Learn various security issues in cloud computing
UNIT-I CLOUD INFRASTRUCTURE 09 Hours
Cloud computing at Amazon, Cloud computing-the Google perspective, Microsoft Windows
Azure and Online services, Open-Source Software Platforms for Private Clouds Cloud Storage
Diversity and Vendor Lock-in, Cloud Computing Interoperability: The Intercloud, Service- and
Compliance-Level Agreements, Responsibility Sharing Between User and Cloud Service
Provider, User Experience, Software Licensing.
UNIT- II CLOUD COMPUTING: APPLICATIONS AND PARADIGMS 09 Hours Challenges for Cloud Computing, Existing Cloud Applications and New Application
Opportunities Architectural Styles for Cloud Applications, Workflows: Coordination of Multiple
Activities, Coordination Based on a State Machine Model: The ZooKeeper, The MapReduce
Programming Model, A Case Study: The GrepTheWeb Application, High-Performance
Computing on a Cloud.
UNIT-III CLOUD VIRTUALIZATION 10 Hours Virtualization, Layering and Virtualization, Virtual Machine Monitors, Virtual Machines,
Performance and Security Isolation, Full Virtualization and Paravirtualization, Hardware Support
for Virtualization, Case Study: Xen, a VMM Based on Paravirtualization, Optimization of
Network Virtualization in Xen 2.0, vBlades: Paravirtualization Targeting an x86-64 Itanium
Processor, A Performance Comparison of Virtual Machines.
UNIT-IV CLOUD RESOURCE MANAGEMENT AND SCHEDULING 10 Hours Policies and Mechanisms for Resource Management, Applications of Control Theory to Task
Scheduling on a Cloud, Stability of a Two-Level Resource Allocation Architecture, Feedback
Control Based on Dynamic Thresholds, Coordination of Specialized Autonomic Performance
Managers, A Utility-Based Model for Cloud-Based Web Services, Resource Bundling:
Combinatorial Auctions for Cloud Resources, Scheduling Algorithms for Computing Clouds,
Fair Queuing, Start-Time Fair Queuing, Borrowed Virtual Time Cloud Scheduling Subject to
Deadlines, Scheduling MapReduce Applications Subject to Deadlines, Resource Management
and Dynamic Application Scaling.
UNIT-V CLOUD SECURITY 10 Hours Cloud Security Risks, Security: The Top Concern for Cloud Users, Privacy and Privacy Impact
Assessment, Trust Operating System Security, Virtual Machine Security, Security of
Virtualization, Security Risks Posed by Shared Images, Security Risks Posed by a Management
OS.
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UNIT-VI Recent developments and current research in multi cloud, cloud security, mobile
cloud computing.
REFERENCES
1. Dan C Marinescu, ―Cloud Computing: Theory and Practice‖, Morgan
Kaufmann/Elsevier. 2013.
2. George Reese, ―Cloud Application Architectures: Building Applications and
Infrastructure in the Cloud‖, O‘Reilly, 2009.
3. Rajkumar Buyya, James Broberg and Andrzej M. Goscinski, ―Cloud Computing:
Principles and Paradigms‖, Wiley, 2011.
4. Kai Hwang, Geoffrey C Fox, Jack G Dongarra, ―Distributed and Cloud Computing: From
Parallel Processing to the Internet of Things‖, Morgan Kaufmann Publishers, 2012.
COURSE OUTCOMES
Upon completion of the course, the students would be able to:
CO1: Categorize the architectures, services and delivery models in cloud computing
CO2: Implement the concept of virtualization and its management in cloud computing
CO3: Design the extended functionalities of resource management and scheduling mechanisms
CO4: Analyse the security models in cloud environment
CO5: Investigate recent developments in multi cloud, cloud security and mobile cloud computing
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III) - 15 marks Quiz / AAT = 5
marks
Unit VI(AAT)=15
marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not have
internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2
CO2 2
CO3 1 2
CO4 2 1
CO5 2 2
2. Low, 2. Medium, 3. High
CS17
Course Code 18CS1E1B M. Tech (Computer Science and Engineering)
Category Engineering Science Courses (Theory - Professional Elective)
Course title MOBILE COMPUTING
Scheme and Credits No. of Hours/Week Semester – I
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
1. Computer Networks
2. Database Management Systems
3. Operating Systems
COURSE OBJECTIVES:
The course will enable the students to:
1. Understand the GSM architecture, services and protocols.
2. Understand the wireless MAC, mobile IP and transport layer functions and protocols.
3. Analyse the concepts of mobile databases, data dissemination, broadcasting systems and data
synchronization.
4. Review various mobile technologies including WLAN, WiFi, WAP, Bluetooth, Zigbee.
5. Understand mobile application languages and mobile operating systems
UNIT- I MOBILE COMPUTING ARCHITECTURE AND GSM 09 Hours
Mobile Computing Architecture: Types of Networks, Architecture for Mobile Computing, 3-tier
Architecture, Design Considerations for Mobile Computing. GSM: Services and System Architectures,
Radio Interfaces, Protocols, Localization, Calling, Handover, General Packet Radio Service.
UNIT-II WIRELESS MAC, IP and TRANSPORT LAYER 10 Hours
Medium Access Control, Introduction to CDMA based Systems, IP and Mobile IP Network Layers,
Packet Delivery and Handover Management, Location Management, Registration, Tunnelling and
Encapsulation, Route Optimization, Dynamic Host Configuration Protocol. Indirect TCP, Snooping
TCP, Mobile TCP, Other Methods of TCP.
UNIT-III DATABASES, DATA DISSEMINATION AND BROADCASTING SYSTEMS
10 Hours
Database Hoarding Techniques, Data Caching, Client – Server Computing and Adaptation,
Transactional Models, Query Processing, Data Recovery Process, Issues relating to Quality of Service.
Communication Asymmetry, Classification of Data – Delivery Mechanisms, Data Dissemination
Broadcast Models, Selective Tuning and Indexing Techniques, Digital Audio Broadcasting, Digital
video Broadcasting.
UNIT-IV DATA SYNCHRONIZATION IN MOBILE COMPUTING SYSTEMS 09 Hours
Synchronization, Synchronization Protocols, SyncML – Synchronization Language for Mobile
Computing, Synchronized Multimedia Markup Language (SMIL). –
UNIT-V MOBILE DEVICES, SERVER AND MANAGEMENT AND MOBILE APPLICATION
LANGUAGES 10 Hours
Wireless LAN, Mobile Internet Connectivity and Personal Area Network, Mobile agent, Application
Server, Gateways, Portals, Service Discovery, Device Management, Mobile File Systems. Wireless
LAN (Wi-Fi) Architecture and Protocol Layers, WAP 1.1 and WAP 2.0 Architectures, Bluetooth –
enabled Devices Network, Zigbee. XML, JAVA, J2ME and JAVACARD, Mobile Operating Systems:
Introduction, PalmOS, Windows CE, Symbian OS, Linux for Mobile Devices.
UNIT-VI Recent trends in wireless and mobile network security, mobile cloud computing.
CS18
REFERENCES
1. Raj Kamal, ―Mobile Computing‖, Oxford University Press, 2007.
2. Ashok Talukder, Ms Roopa Yavagal, and Mr. Hasan Ahmed, ―Mobile Computing,
Technology, Applications and Service Creation‖, II Edition, Tata McGraw Hill, 2010.
3. Jochen Schiller, ―Mobile Communications‖, Addison-Wesley. II Edition, 2004.
4. Hansmann, Merk, Nicklous, Stober, ―Principles of Mobile Computing‖, Springer, II Edition,
2003.
COURSE OUTCOMES
Upon completion of the course, the student would be able to:
CO1: Demonstrate the knowledge of GSM architecture, services and protocols.
CO2: Simulate a typical GSM network and demonstrate the performance analysis.
CO3: Extending the functionalities of mobile IP and transport layer protocols.
CO4: Apply the mobile application languages to design mobile applications.
CO5: Investigate recent developments in wireless, mobile network security and mobile cloud
computing.
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III) - 15 marks Quiz / AAT = 5
marks
Unit VI(AAT)=15
marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not have
internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2
CO3 2
CO4 2 2
CO5 2 2
1. Low, 2. Medium, 3. High
CS19
Course Code 18CS1E1C M. Tech (Computer Science and Engineering)
Category Engineering Science Courses (Theory - Professional Elective)
Course title WIRELESS NETWORKS
Scheme and
Credits
No. of Hours/Week Semester – I
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks:
50
Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
Computer Networks
COURSE OBJECTIVES:
The course will enable the students to:
1. Get familiar with the wireless market and the future needs and challenges.
2. Learn the key concepts of wireless networks, standards, technologies and their
basic operations
3. Know various generations of cellular networks and learn cellular architecture
4. Understand the key concept of sensor networks
5. Analyse security techniques and vulnerabilities
UNIT- I INTRODUCTION 09 Hours
Wireless Networking Trends, Key Wireless Physical Layer Concepts, Multiple Access
Technologies -CDMA, FDMA, TDMA, Spread Spectrum technologies, Frequency reuse,
Radio Propagation and Modelling, Challenges in Mobile Computing: Resource poorness,
Bandwidth, energy etc.
UNIT-II WIRELESS LOCAL AREA NETWORKS 10 Hours
IEEE 802.11 Wireless LANs Physical & MAC layer, 802.11 MAC Modes (DCF & PCF)
IEEE 802.11 standards, Architecture & protocols, Infrastructure vs. Adhoc Modes, Hidden
Node & Exposed Terminal Problem, Fading Effects in Indoor and outdoor WLANs,
WLAN Deployment issues.
UNIT- III WIRELESS CELLULAR NETWORKS 10 Hours
1G and 2G, 2.5G, 3G, and 4G, Mobile IPv4, Mobile IPv6, TCP over Wireless Networks,
Cellular architecture, Frequency reuse, Channel assignment strategies, Handoff strategies,
Interference and system capacity, Improving coverage and capacity in cellular systems
UNIT- IV WIRELESS SENSOR NETWORKS 10 Hours
Introduction, Application, Physical, MAC layer and Network Layer, Power Management,
Tiny OS Overview. Wireless Pans Bluetooth and Zigbee, Introduction to Wireless
Sensors networks, deployment, key design challenges, network deployment, Routing
protocols, routing challenges and design issues, routing strategies.
UNIT-V SECURITY 09 Hours
Security in wireless Networks, Vulnerabilities, Security techniques, Wi-Fi Security, DoS
in wireless communication.
UNIT-VI RECENT TRENDS Recent trends in Wireless networks, Vehicular Adhoc Networks.
CS20
REFERENCES
1. Schiller J., Mobile Communications, Addison Wesley 2000
2. Stallings W., Wireless Communications and Networks, Pearson Education 2005
3. Stojmenic Ivan, Handbook of Wireless Networks and Mobile Computing, John Wiley
and Sons Inc 2002
4. Yi Bing Lin and Imrich Chlamtac, Wireless and Mobile Network Architectures, John
Wiley and Sons Inc 2000
5. Pandya Raj, Mobile and Personal Communications Systems and Services, PHI 2000
6.Feng Zhao, leonidas Guibas, ―Wireless sensor Networks: An information processing
approach‖, Elsevier, 2004
COURSE OUTCOMES
Upon completion of the course, the students will be able to:
CO1: Demonstrate advanced knowledge of networking and wireless networking
CO2: Understand various types of wireless networks, standards, operations and use cases.
CO3: Be able to design and compare cellular based upon underlying propagation and
performance analysis.
CO4: Demonstrate knowledge of WPAN and sensor networks
CO5: Assess security measure and vulnerabilities.
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit VI(AAT)=15
marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not have
internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for
50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 3
CO2 2 3
CO3 2 3
CO4 3 3
CO5 1 3
1. Low, 2. Medium, 3. High
CS21
Course Code 18CS1E2A M. Tech (Computer Science and Engineering)
Category Engineering Science Courses (Integrated - Professional
Elective)
Course title SOFT COMPUTNG
Scheme and
Credits
No. of Hours/Week Semester – I
L T P S Credits
3 0 2 0 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
1. Basic knowledge of mathematics
COURSE OBJECTIVES:
The course will enable the students to:
1. Describe soft computing concepts and techniques and foster their abilities in
designing appropriate technique for a given scenario.
2. Choose Neural network algorithms for real – world problems.
3. Analyse and compare the different Optimization techniques.
4. Develop the applications of Genetic Algorithms in Machine Learning.
5. Provide a hands-on experience on MATLAB to implement various strategies
UNIT-I INTRODUCTION TO SOFT COMPUTING AND NEURAL NETWORKS
09 Hours
Evolution of Computing: Soft Computing Constituents, Conventional AI to Computational
Intelligence: Machine Learning Basics, Hard-Margin and Soft-Margin SVMs- Concepts of
Kernel and Feature Spaces, Basics of Optimization and Quadratic programming,
Introduction to Steganography and Applications of SVMs to Steganalysis
UNIT-II NEURAL NETWORKS 10 Hours Introduction
to ANN, Architectures, Learning methods, Bayesian Networks, Back Propagation network,
Perceptrons, Hopfield Networks, Kohonen Self Organizing Feature Maps, Chaos Theory
UNIT-III OPTIMIZATION TECHNIQUES 09 Hours Introduction, Elitism based Ant Colony Optimization, Min-Max based Ant Colony
Optimization, Particle Swarm Optimization, Artificial Bee Colony Optimization, Multi-
Swarm Optimization, Cuckoo Search, Whole Optimization, Firefly algorithm, Bat
Algorithm, Introduction to missing data-Imputation techniques, Principal Component
Analysis, Gradient Descent
UNIT-IV GENETIC ALGORITHMS and FUZZY LOGIC 10 Hours
Introduction to Genetic Algorithms (GA), Applications of GA in Machine Learning:
Machine Learning Approach to Knowledge Acquisition. Fuzzy Logic: Fuzzy Sets,
Operations on Fuzzy Sets, Fuzzy Relations, Membership Functions: Fuzzy Rules and
Fuzzy Reasoning, Fuzzy Inference Systems, Fuzzy Expert Systems, Fuzzy Decision
Making, Defuzzification
UNIT-V Matlab Lib 10 Hours
Introduction to Matlab, Arrays and array operations, Functions and Files, Study of neural
network toolbox and fuzzy logic toolbox, Simple implementation of Artificial Neural
Network and Fuzzy Logic
UNIT-VI
Recent Trends in deep learning, various classifiers, neura1 networks and genetic algorithm.
Implementation of recently proposed soft computing techniques
CS22
UNIT-VII (Lab Programs)
1. a) Write a MATLAB Program for Hebb Net to classify two dimensional input
patterns in bipolar with given targets.
b) Generate XOR function and ANDNOT function using McCulloch-Pitts Neural
Network.
2. Classification of a 4-Class problem with a Perceptron using MATLAB.
3. Write a MATLAB program to apply Back Propagation network for pattern
recognition problem.
4. Develop a Kohonen Self Organizing feature map for image recognition problem.
5. Write a MATLAB program to implement Discrete Hopfield Network and test the
input pattern.
6. Write a MATLAB program for edge detection using Fuzzy logic.
7. Use a genetic algorithms approach for Travelling Salesman Problem.
8. Develop a simple Ant Colony Optimization problem with MATLAB to find the
optimum path.
9. Solve a feature selection problem using Artificial Bee Colony Optimization.
10. Implementation of minimum Spanning tree using Particle Swarm Optimization.
REFERENCES
1. S. N. Sivanandam and S. N. Deepa, ―Principles of Soft Computing‖, 2nd
Edition,
Wiley India, 2012.
2. Samir Roy, Udit Chakraborty, ―Introduction to Soft Computing- Neuro-Fuzzy and
Genetic Algorithms‖, First Edition, 2013.
3. David E Goldberg, ―Genetic Algorithms in Search Optimization and Machine
Learning‖, Addison Wesley, 1997.
4. MATLAB Toolkit Manual.
COURSE OUTCOMES
Upon completion of the course, the students would be able to:
CO1: Explain the concepts and techniques of soft computing and their roles in building
intelligent machines
CO2: Apply fuzzy logic and reasoning to handle uncertainty and solve various
engineering problems.
CO3: Differentiate the various Optimization techniques.
CO4: Implement and evaluate the genetic algorithms in Machine learning.
CO5: Evaluate and compare solutions by various soft computing approaches for a given
Problem.
SCHEME OF EXAMINATION:
CIE -
Practical
Conduction of experiments, performance of student in
every week lab and completion of lab record=50#
Total: Marks
50
Lab Test: Part-A =20, Part-B=20 and Vice-Voce=10
Marks(50##
)
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 09 hours shall not have 20*2=40
CS23
internal choice Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
# = CIE Marks for laboratory performance is to be evaluated for 50 marks and the
marks obtained shall be reduced for 25 marks
## = Lab test is to be conducted for 50 marks and the marks obtained shall be
reduced for 25 marks. Lab test shall be conducted by two examiners out of which
one examiner is the faculty taught the course. There is no SEE for the practical ‘s
portion of integrated course
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 3
CO2 3 3
CO3 3 3
CO4 3 3
CO5 3 3
1. Low, 2. Medium, 3. High
CS24
Course Code 18CS1E2B M. Tech (Computer Science and Engineering)
Category Engineering Science Courses (Theory - Professional Elective)
Course title ADVANCES IN STORAGE AREA NETWORKS
Scheme and Credits No. of Hours/Week Semester – I
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
1.Computer Networks
2.Database Management Systems
3.Operating Systems
COURSE OBJECTIVES
This course will enable the students to
1. Understand storage centric and server centric systems
2. Apply various metrics used for designing storage area networks
3. Analysis RAID concepts
4. Evaluate data maintains at data centres with the concepts of backup
5. Create techniques for data storage management at data centres
UNIT -I INTRODUCTION: 10 Hours
Server Centric IT Architecture and its Limitations; Storage – Centric IT Architecture and its
advantages. Case study: Replacing a server with Storage Networks The Data Storage and Data
Access problem; The Battle for size and access. Intelligent Disk Subsystems: Architecture of
Intelligent Disk Subsystems; Hard disks and Internal 8 Hours I/O Channels; JBOD, Storage
virtualization using RAID and different RAID levels; Caching: Acceleration of Hard Disk
Access; Intelligent disk subsystems, Availability of disk subsystems.
UNIT -II I/O TECHNIQUES: 10 Hours
The Physical I/O path from the CPU to the Storage System; SCSI; Fibre Channel Protocol
Stack; Fibre Channel SAN; IP Storage. Network Attached Storage: The NAS Architecture, The
NAS hardware Architecture, The NAS Software Architecture, Network connectivity, NAS as a
storage system. File System and NAS: Local File Systems; Network file Systems and file
servers; Shared Disk file systems; Comparison of fibre Channel and NAS.
UNIT -III STORAGE VIRTUALIZATION: 10 Hours
Definition of Storage virtualization; Implementation Considerations; Storage virtualization on
Block or file level; Storage virtualization on various levels of the storage Network; Symmetric
and Asymmetric storage virtualization in the Network.
UNIT- IV SAN ARCHITECTURE AND HARDWARE DEVICES: 09 Hours
Overview, Creating a Network for storage; SAN Hardware devices; The fibre channel switch;
Host Bus Adaptors; Putting the storage in SAN; Fabric operation from a Hardware perspective.
Software Components of SAN: The switch‘s Operating system; Device Drivers; Supporting the
switch‘s components; Configuration options for SANs.
UNIT–V MANAGEMENT OF STORAGE NETWORK: 09 Hours
System Management, Requirement of management System, Support by Management System,
Management Interface, Standardized Mechanisms, Property Mechanisms, In-band Management,
Use of SNMP, CIM and WBEM, Storage.
UNIT-VI Recent advances and research being done in the topics mentioned above units
CS25
REFERENCES
1. Ulf Troppens, Rainer Erkens and Wolfgang Muller: Storage Networks Explained, Wiley
India 2013.
2. Robert Spalding: ―Storage Networks The Complete Reference‖, Tata McGraw-Hill, 2011.
3. Marc Farley: Storage Networking Fundamentals – An Introduction to Storage Devices,
Subsystems, Applications, Management, and File Systems, Cisco Press, 2005.
4. Richard Barker and Paul Massiglia: ―Storage Area Network Essentials A Complete Guide to
understanding and Implementing SANs‖, Wiley India, 2006.
COURSE OUTCOMES :
The students should be able to:
CO1: Distinguish storage centric and server centric systems
CO2: Determine the need for performance evaluation and the metrics used for it
CO3: Extrapolate RAID and different RAID levels
CO4: Validate data maintained at data centres
CO5: Develop techniques for storage management
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Total:
Marks 100 Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 CO2 2 CO3 3 CO4 3 CO5 1 2
1: Low 2: Medium 3:High
CS26
Course Code 18CS1E2C M. Tech (Computer Science and Engineering)
Category Engineering Science Courses (Theory - Professional Elective)
Course title ADVANCED COMPUTER ARCHITECTURE
Scheme and Credits No. of Hours/Week Semester – I
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES:
This course will enable students to
1. Understand the fundamentals of computer design
2. Apply various memory technologies and optimizations
3. Analyse different multiprocessor issues
4. Evaluate homogeneous and heterogeneous multi-core architectures
5. Create vector, SIMD and GPU architectures
UNIT-I FUNDAMENTALS OF COMPUTER DESIGN AND ILP 10 Hours Fundamentals of Computer Design – Measuring and Reporting Performance – Instruction Level
Parallelism and its Exploitation – Concepts and Challenges –Exposing ILP - Advanced Branch
Prediction - Dynamic Scheduling - Hardware-Based Speculation - Exploiting ILP - Instruction
Delivery and Speculation - Limitations of ILP - Multithreading
UNIT-II MEMORY HIERARCHY DESIGN 09 Hours
Introduction – Optimizations of Cache Performance – Memory Technology and Optimizations –
Protection: Virtual Memory and Virtual Machines – Design of Memory Hierarchies – Case Studies.
UNIT-III MULTIPROCESSOR ISSUES 10 Hours
Introduction- Centralized, Symmetric and Distributed Shared Memory Architectures –Cache
Coherence Issues – Performance Issues – Synchronization – Models of Memory Consistency –
Case Study-Interconnection Networks – Buses, Crossbar and Multi-stage Interconnection Networks
UNIT-IV MULTICORE ARCHITECTURES 10 Hours
Homogeneous and Heterogeneous Multi-core Architectures – Intel Multicore Architectures – SUN
CMP architecture – IBM Cell Architecture. Introduction to Warehouse-scale computers-
Architectures- Physical Infrastructure and Costs- Cloud Computing –Case Study- Google
Warehouse-Scale Computer.
UNIT-V VECTOR, SIMD AND GPU ARCHITECTURES 09 Hours
Introduction-Vector Architecture – SIMD Extensions for Multimedia – Graphics Processing Units –
Case Studies – GP GPU Computing – Detecting and Enhancing Loop Level Parallelism-Case
Studies.
UNIT-VI Recent trends in Multicore processors
REFERENCES
1. Darryl Gove, ―Multicore Application Programming: For Windows, Linux, and Oracle
Solaris‖, Pearson, 2011
2. David B. Kirk, Wen-mei W. Hwu, ―Programming Massively Parallel Processors‖, Morgan
Kauffman, 2010
3. David E. Culler, Jaswinder Pal Singh, ―Parallel computing architecture hardware/software
approach‖ , Morgan Kaufmann /Elsevier Publishers, 1999
CS27
4. John L. Hennessey and David A. Patterson, ―Computer Architecture – A Quantitative
Approach‖, Morgan Kaufmann / Elsevier, 5th edition, 2012.
5. Kai Hwang and Zhi.Wei Xu, ―Scalable Parallel Computing‖, Tata McGraw Hill,
NewDelhi, 2003
COURSE OUTCOMES
Upon completion of this course, the students should be able to:
CO1: Recognize the fundamentals of computer design
CO2: Illustrates the memory technologies, optimizations and cache performance
CO3: Compare the different multiprocessor issues, and multi-stage interconnection networks
CO4: Assess the homogeneous and heterogeneous multi-core architectures
CO5: Investigate vector, SIMD and GPU architectures and detecting, enhancing loop level
parallelism
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit VI(AAT)=15
marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not have
internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2
CO3 3
CO4 3
CO5 2
1. Low, 2. Medium, 3. High
CS28
Course Code 18CS1L01 M. Tech (Computer Science and Engineering)
Category Engineering Science Courses ( Practical )
Course title NETWORK PROGRAMMING LAB
Scheme and
Credits
No. of Hours/Week Semester – I
L T P S Credits
- - 3 - 2
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
1. Computer Networks
2. Programming in Java and C++
3. NS-3 Simulator
COURSE OBJECTIVES
The course will enable the students to:
1. Understand the implementation of various network protocols.
2. Understand programming the network protocols using Java.
3. Analyse the programming environment of NS-3 simulator.
4. Evaluate typical wired/wireless network using the NS-3 simulator.
5. Create a typical GSM network using NS-3
PART – A
Write a Java Program to design a :
1. TCP iterative Client-Server application to reverse the given input sequence.
2. TCP concurrent Client-Server application to reverse the given input sequence.
3. TCP Client-Server application to transfer a file.
4. UDP Client-Server application to transfer a file.
5. ARP/RARP protocol.
6. DHCP protocol.
7. Distance Vector Routing protocol.
8. Dijkstra‘s shortest path routing protocol.
PART – B
1. Write a C++ program to connect two nodes on NS-3 (for practise only).
2. Write a C++ program to connect three nodes considering one as a central node on
NS-3 (for practise only).
3. Write a C++ program to implement a star topology on NS-3.
4. Write a C++ program to implement a bus topology on NS-3.
5. Write a C++ program showing the connection of two nodes and four routers such that
the extreme nodes act as client and server on NS-3.
6. Implement and study the performance of a typical GSM network on NS-3 (using
MAC layer).
COURSE OUTCOMES
On completion of the course, the student would be able to:
CO1: Design programs for any type of TCP and UDP based client-server applications using
Java and Analyze.
CO2: Implement a typical wired network using Java.
CO3: Extend the functionalities of a routing protocol using Java.
CO4: Implement and analyse the performance of a wireless/mobile network on NS-3.
CO5: Design a typical GSM network on NS-3.
CS29
SCHEME OF EXAMINATION The student has to write and implement two programs selecting ONE from each part
Continuous Internal
Evaluation (CIE) (Laboratory
– 50 Marks)
Marks Semester End Evaluation (SEE)
(Laboratory – 100 Marks) Marks
Performance of the Student in
the laboratory every week
20 Write up 10
Test at the end of the semester 20 Experiment-1 (Part - A) – 35 Marks
Experiment-2 (Part - B) – 35 Marks
70
Viva Voce 10 Viva Voce 20
Total 100
Total (CIE) 50 Total (SEE) 50*
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 1
CO2 2
CO3 2
CO4 2 2 3
CO5 2 2
1. Low, 2. Medium, 3. High
CS30
Course Code 18CS1M01 M. Tech (Computer Science and Engineering)
Category Engineering Science Courses ( Mandatory Audit)
Course title RESEARCH METHODOLOGY AND IPR
Scheme and
Credits
No. of Hours/Week Semester – I
L T P S Credits
2 0 0 0 2
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3
Hrs
Prerequisites (if any):
COURSE OBJECTIVES
This course will enable students to
1. Understand the formulation of research problem, scope and objectives of research
problem
2. Use methods for effective technical writing skills
3. Analyse Approaches of investigation of solutions for research problem
4. Evaluate the format of research proposal , intellectual property and patent
5. Create patent, research paper
UNIT -I RESEARCH PROBLEM: 03 Hours Meaning of research problem, Sources of research problem, Criteria Characteristics of a good
research problem, Errors in selecting a research problem, Scope and objectives of research
problem. Approaches of investigation of solutions for research problem, data collection,
analysis, interpretation, Necessary instrumentations
UNIT- II RESEARCH REQUIREMENTS: 03 Hours
Effective literature studies approaches, analysis Plagiarism, Research ethics,
UNIT- III EFFECTIVE TECHNICAL WRITING: 06 Hours Effective technical writing, how to write report, Paper Developing a Research Proposal, Format of research
proposal, a presentation and assessment by a review committee
UNIT- IV NATURE OF INTELLECTUAL PROPERTY: 06 Hours Patents, Designs, Trade and Copyright. Process of Patenting and Development: technological research,
innovation, patenting, development. International Scenario: International cooperation on Intellectual Property.
Procedure for grants of patents, Patenting under PCT.
UNIT- V PATENT RIGHTS: 06 Hours Scope of Patent Rights. Licensing and transfer of technology. Patent information and databases. Geographical
Indications.
UNIT- VI NEW DEVELOPMENTS IN IPR: Administration of Patent System. New developments in IPR; IPR of Biological Systems, Computer Software
etc. Traditional knowledge Case Studies, IPR and IITs.
REFERENCES
1. Stuart Melville and Wayne Goddard, ―Research methodology: an introduction for
science & engineering students‘‖
2. Wayne Goddard and Stuart Melville, ―Research Methodology: An Introduction‖
3. Ranjit Kumar, 2nd Edition, ―Research Methodology: A Step by Step Guide for
beginners‖ Halbert, ―Resisting Intellectual Property‖, Taylor & Francis Ltd ,2007.
4. Mayall, ―Industrial Design‖, McGraw Hill, 1992.
5. Niebel, ―Product Design‖, McGraw Hill, 1974.
6. Asimov, ―Introduction to Design‖, Prentice Hall, 1962.
7. Robert P. Merges, Peter S. Menell, Mark A. Lemley, ―Intellectual Property in New
CS31
Technological Age‖, 2016.
8. T. Ramappa, ―Intellectual Property Rights Under WTO‖, S. Chand, 2008
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1: Understand research problem formulation. Analyze research related information and
follow research ethics
CO2: Understanding that when IPR would take such important place in growth of
individuals and nation, it is needless to emphasis the need of information about
Intellectual Property Right to be promoted among students in general & engineering
in particular.
CO3: Understand that IPR protection provides an incentive to inventors for further research
work and investment in R & D, which leads to creation of new and better products,
and in turn brings about, economic growth and social benefits.
CO4: Analyze research related information
CO5: Follow research ethics
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 06 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 03 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 CO2 3 CO3 3 CO4 CO5 3 3
1: Low 2: Medium 3:High
CS32
Course Code 18CS1S01 M. Tech (Computer Science and Engineering)
Category Seminar Semester- I
Course title SEMINAR – I
Scheme and Credits
No. of Hours/Week
Total hours = 24 L T P S Credits
0 0 2 0 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any): NIL
COURSE LEARNING OBJECTIVES:
The objectives of the SEMINAR-I is to prepare the students to learn to:
1. Carry out the literature review of general research area/current topic and analyse the same
effectively.
2. Prepare a technical report, reflecting his/her depth of understanding, on the selected
area/topic and prepare content rich presentation.
3. Acquire communication and time management skills for effective and impactful presentation.
Interact with peers to acquire the qualities of thoughtfulness, friendliness, adaptability,
responsiveness, and politeness in-group discussion. Overcome stage fear during the
presentation.
GUIDE LINES
1. Seminar preparation and presentation is an individual student activity.
2. Topic may be of general/ specific interest to program of engineering or electives not offered in
the semester and to be selected in consultation with the faculty/Guide.
3. Select one pertinent research paper for the seminar presentation.
4. Prepare and submit a detailed technical report of the seminar topic.
COURSE OUTCOMES:
Students shall be able to:
1. Carry out the literature survey of topic of seminar.
2. Prepare a technical report on the selected area/topic.
3. Make an effective presentation with seamless flow of content within the time allocated.
Overcome inhibition in interacting with peers and hence develop the spirit of team work.
Overcome stage fear during the presentation.
CS33
SCHEME OF EXAMINATION
CIE – 50
marks
Phase -1 Marks =10 Seminar Report
Marks =20
Total:50
Marks Phase -2 Marks =20
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2 3 CO2 2 3 3 CO3 2
1. Low, 2. Medium, 3. High
Scheme of Continuous Internal Evaluation (CIE):
Evaluation would be carried out in TWO phases. The Seminar Evaluation Committee shall
comprise of Chairman of the Department, Faculty/Guide and one more faculty member nominated by
Chairman. The evaluation criteria shall be as per the rubrics given below:
Rubrics for Evaluation:
Topic - Technical Relevance, Sustainability and Societal Concerns : 15%
Review of Literature and Technical Content : 35%
Presentation Skills : 25%
Report : 25%
CS34
Course Code 18CS1M02 M. Tech (Computer Science and Engineering)
Category Engineering Science Courses ( Mandatory Audit )
Course title AUDIT COURSE-I ( TECHNICAL PAPER WRITING )
Scheme and
Credits
No. of Hours/Week Semester – I
L T P S Credits
2 0 0 0 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any):
COURSE OBJECTIVES
This course will enable students to
1. Understand the planning section of research paper and preparation of paper writing
2. Apply key skill while writing research paper and know about what to write in each
section
3. Analyse literature, methods,
4. Evaluate research paper, paraphrasing paper
5. Create good research paper
UNIT-I PLANNING AND PREPARATION: 06 Hours Planning and Preparation, Word Order, Breaking up long sentences, Structuring Paragraphs
and Sentences, Being Concise and Removing Redundancy, Avoiding Ambiguity and
Vagueness
UNIT- II CLARIFYING: 03 Hours Clarifying Who Did What, Highlighting Your Findings, Hedging and Criticising,
Paraphrasing and Plagiarism, Sections of a Paper, Abstracts. Introduction
UNIT- III REVIEW OF THE LITERATURE: 06 Hours Review of the Literature, Methods, Results, Discussion, Conclusions, The Final Check.
UNIT- IV KEY SKILLS: 06 Hours Key skills are needed when writing a Title, key skills are needed when writing an Abstract,
key skills are needed when writing an Introduction, skills needed when writing a Review of
the Literature,
UNIT- V METHODS: 03 Hours
skills are needed when writing the Methods, skills needed when writing the Results, skills are
needed when writing the Discussion, skills are needed when writing the Conclusions.
UNIT- VI NEW DEVELOPMENTS IN RESEARCH PAPER WRITING: useful phrases, how to ensure paper is as good as it could possibly be the first- time
submission
REFERENCES
1. Goldbort R (2006) Writing for Science, Yale University Press (available on Google
Books)
2. Day R (2006) How to Write and Publish a Scientific Paper, Cambridge University
Press
3. Highman N (1998), Handbook of Writing for the Mathematical Sciences, SIAM.
Highman‘sbook.
4. Adrian Wallwork, English for Writing Research Papers, Springer New York
Dordrecht Heidelberg London, 2011
CS35
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1: List of section of research paper and preparation of paper writing
CO2: Determine key skill while writing research paper
CO3: Analyse literature, methods
CO4: Assess research paper, do paraphrasing paper
CO5: Formulate research paper and results of simulation
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 CO2 3 CO3 3 CO4 3 CO5 3
1: Low 2: Medium 3:High
CS36
Course Code 18CS2C01 M. Tech (Computer Science and Engineering)
Category Engineering Science Courses ( Theory - Professional Core)
Course title ADVANCED DATA STRUCTURES AND ALGORITHMS
Scheme and
Credits
No. of Hours/Week Semester – II
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVE
The course will enable the student to:
1. Learn various data structures and its usage in designing algorithms.
2. Understand to the advanced methods of designing and analysing algorithms.
3. Learn various string matching and graph algorithms.
4. Acquire the knowledge of polynomial, non polynomial and approximation problems.
5. Understand the recent developments in the area of algorithmic design.
UNIT-1 REVIEW OF ANALYSIS TECHNIQUES 09 Hours
Growth of Functions: Asymptotic notations; Standard notations and common functions;
Recurrences -The substitution method, recursion-tree method, the master method,
Probabilistic Analysis and Randomized Algorithms.
UNIT- II BASIC DATA STRUCTURES 09 Hours Stacks and Queues, Vectors, Lists, and Sequences, Trees, Priority Queues and Heaps,
Dictionaries and Hash Tables, Search Trees and Skip lists- Ordered Dictionaries and
Binary Search Trees, AVL Trees, Bounded-Depth Search Trees, Splay Trees, Skip Lists.
UNIT -III DYNAMIC PROGRAMMING 10 Hours
Matrix-Chain multiplication, Elements of dynamic programming, longest common
subsequences. Graph algorithms: Bellman - Ford Algorithm; Single source shortest paths
in a DAG; Johnson‘s Algorithm for sparse graphs; Flow networks and Ford-Fulkerson
method. .
UNIT- IV TRIES AND STRING MACHING ALGORITHMS 10 Hours
Quad trees and K-d trees. String-Matching Algorithms: Naïve string Matching; Rabin -
Karp algorithm; String matching with finite automata; KnuthMorris-Pratt algorithm.
UNIT- V NP-COMPLETENESS 10 Hours
: Polynomial time, Polynomial time verification, NP-Completeness and reducibility, NP-
Complete problems. Approximation Algorithms: vertex cover problem, the set – covering
problem, randomization and linear programming, the subset – sum problem.
UNIT VI
Recent Trends in problem solving paradigms applying recently proposed data
structures
REFERENCES
1. Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein,‖
Introduction to Algorithms‖, Third Edition, Prentice-Hall, 2011.
2. M T Goodrich, Roberto Tamassia, ―Algorithm Design‖, John Wiley, 2002.
3. Mark Allen Weiss, ―Data Structures and Algorithm Analysis in C++‖, 4th
Edition,
Pearson, 2014.
4. Alfred V. Aho, John E. Hopcroft, Jeffrey D. Ullman, ―Data Structures and
Algorithms‖, Pearson Education, Reprint 2006.
5. Ellis Horowitz, Sartaj Sahni, Dinesh Mehta, ―Fundamentals of Data Structures in C‖,
Silicon Pr, 2007.
6. Aho, Hopcroft and Ullman, Data structures and algorithms, 1st edition, Pearson
CS37
Education, India, 2002, ISBN: 8177588265, 978817758826
COURSE OUTCOMES
On completion of the course, the student will be able to:
CO1: Develop and analyze algorithms for red-black trees, B-trees and Splay trees and for
text processing applications.
CO2: Identify suitable data structures and develop algorithms for solving a particular set of
problems
CO3: Analyze the complexity/ performance of different algorithms.
CO4: Categorize the different problems in various classes according to their complexity.
CO5: Use appropriate data structure and algorithms in real time applications.
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not have
internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for
50 marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2 2
CO3 2 2
CO4 2
CO5 2 2
1. Low, 2. Medium, 3. High
CS38
Course Code 18CS2C02 M. Tech (Computer Science and Engineering)
Category Engineering Science Courses (Theory - Professional Core)
Course title ADVANCED OPERATING SYSTEMS
Scheme and
Credits
No. of Hours/Week Semester – II
L T P S Credits
4 - - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
The course will enable the students to:
1. Understand the Design Approaches and Issues related to Advanced Operating Systems.
2. Apply the Knowledge on Distributed Operating Systems Concepts to develop Clocks,
Mutual Exclusion Algorithms.
3. Analyse the Distributed Deadlock Detection Algorithms and Agreement Protocols.
4. Evaluate the Algorithms for Distributed Scheduling, Examine the Commit Protocols
and review Concurrency Control Algorithms.
5. Create Advanced Operating Systems Applications with recent technologies
UNIT- I INTRODUCTION: 09 Hours
Concept of Batch system, Multiprogramming, Time Sharing, Parallel, Distributed and Real-
time System, Process Management: Concept of Process, Synchronization, CPU Scheduling,
IPC, Deadlock: Concept of Deadlock Prevention, Avoidance, Detection and its Recovery.
Memory Management: Contiguous allocation, Paging and Segmentation. Virtual memory:
Demand Paging, Page Replacement Algorithms. Distributed OS- Design Approaches and
Issues in DOS. Message Passing Model and RPC.
UNIT -II CLOCKS AND DISTRIBUTED MUTUAL EXCLUSION: 10 Hours
Concept of Lamport‘s Logical Clock and Vector Clocks, Termination Detection. A simple
solution to distributed mutual exclusion, Non Token based algorithms: Lamport‘s algorithm,
Ricart Agarwala‘s algorithm, Maekawa‘s algorithm, Token based algorithms: Suzuki Kasami‘s
broadcast algorithm, Raymond‘s tree based algorithm.
UNIT -III DISTRIBUTED DEADLOCK DETECTION: 10 Hours
Deadlock Handling, Strategies in Distributed Systems, Issues in Deadlock Detection And
Resolution, Control Organization for Distributed Deadlock Detection, Centralized Deadlock
Detection Algorithm: Ho-Ramamoorthy‘s Algorithm, Distributed Deadlock Detection
Algorithms: A Path- Pushing Algorithm and Edge Chasing Algorithm, Hierarchical Deadlock
Detection Algorithms: Menasce- Muntz Algorithm, Ho-Ramamoorthy‘s Algorithm.
Agreement Protocols: Byzantine Agreement Problem, Solution to The Byzantine Agreement
Problem- Lamport -Shostak- Pease Algorithm, Dolev et al.‘s Algorithm
UNIT –IV DISTRIBUTED SCHEDULING AND FAULT TOLERANCE: 10 Hours Issues in Load Distribution, Components of a Load Distributing Algorithms, Load Distributing
Algorithms, Performance Comparison, Selecting Suitable Load Sharing Algorithms,
Requirements of Load Sharing Policies. Commit Protocols, Nonblocking Commit Protocols,
Voting Protocols, Dynamic Voting Protocols, The Majority Based Dynamic Voting Protocol,
Dynamic Vote Reassignment Protocols.
UNIT -V DATABASE OS & CONCURRENCY CONTROL 09 Hours
Requirement of Database OS, A Concurrency Control Model of a Database System, The
Problem of Concurrency Control, Serializability Theory, Concurrency Control Algorithms,
Basic Synchronization Primitives, Lock Based Algorithms, Timestamp Based Algorithms,
Optimistic Algorithms, Concurrency Control Algorithms for Data Replication.
CS39
UNIT-VI Recent advances and research being done in the topics mentioned above units
REFERENCES
1. Mukesh Singhal and Niranjan G Shivaratri, Advanced Concepts in Operating Systems, Tata
Mcgraw Hill, 2002.
2. A Silberchatz and Peter Baer Galvin, Operating System Concepts, 10th Edition, John Wiley
and Sons, 2018.
3. William Stallings, Operating System: Internals and Design Principles, 9th Edition, Prentice
Hall India, 2017.
4. Andrew S Tennenbaum and Albert S Woodhull, Operating System Design and
Implementation, 3rd Edition, Pearson Education Inc., 2006.
5. Ceri S and Pelagorthi S, Distributed Databases: Principles and Systems, 1984, McGraw Hill.
COURSE OUTCOMES
On completion of the course, the student would be able to:
CO1: Explain the Fundamentals of Advanced Operating Systems, Design issues.
CO2: Determine the various Clock Synchronization Principles and Implement Mutual
Exclusion Algorithms.
CO3: Differentiate the various Distributed Deadlock Detection Algorithms and Analyze the
Agreement Protocols.
CO4: Select the appropriate Distributed Scheduling Algorithms, Commit Protocols and
Concurrency Control Algorithms.
CO5: Integrate the Concepts of Advanced Operating Systems with recent trends and
technologies to Design and Develop Applications.
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Total:
Marks 100
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs) PO1 PO2 PO3
CO1 1 - CO2 1 2 CO3 1 2 CO4 1 3 CO5 3 2 2
1: Low 2: Medium 3:High
CS40
Course code 18CS2C03 M. Tech (Computer Science and Engineering)
Category Engineering Science Courses (Theory - Professional Core)
Course title ADVANCES IN DIGITAL IMAGE PROCESSING
Scheme and Credits No. of Hours/Week Semester – II
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES:
The course will enable the students to:
1. Learn Digital Image Fundamentals and mathematical transforms necessary for image
processing
2. Apply image enhancement techniques in Spatial and Frequency Domains
3. Investigate the Image Restoration/Degradation Process
4. Demonstrate the image segmentation and representation techniques.
5. Be Familiar With Image Compression Techniques.
UNIT-I DIGITAL IMAGE FUNDAMENTALS & IMAGE TRANSFORMS 10 Hours Digital Image Fundamentals, Components of an Image Processing System, Sampling and
Quantization, Relationship between Pixels
Image Transforms Discrete Fourier Transform, Discrete Cosine Transform, Hadamard
Transform - Haar Transform - Slant Transform - KL Transform -Properties And Examples.
UNIT-II IMAGE ENHANCEMENT IN THE SPATIAL DOMAIN 10 Hours
Gray level transformations, histogram processing, Enhancement using Arithmetic/logical
operations, Basics of spatial filtering, smoothening and sharpening spatial filters.
Image Enhancement in the Frequency Domain: Filtering in Frequency Domain,
smoothening and sharpening frequency domain filters.
UNIT-III IMAGE RESTORATION 09 Hours
Degradation Model, Noise Models, Restoration in Presence of Noise Only- Spatial Filtering,
Periodic Noise Reduction by Frequency Domain Filtering, Estimation of Degradation
Function, Inverse Filtering.
UNIT-IV IMAGE SEGMENTATION AND REPRESENTATION 09 Hours Detection of Discontinuities, Edge Linking And Boundary Detection, Thresholding, Region
Oriented Segmentation.
Representation, Boundary Descriptors and Regional Descriptors
UNIT-VIMAGE COMPRESSION 10 Hours
Fundamentals, Image Compression Models, Error Free Compression, Lossy Compression,
Image Compression Standards
UNIT-VI APPLICATIONS
Character Recognition, Fingerprint Recognition, Remote Sensing. Applications using different
Imaging modalities such as acoustic Imaging, Medical imaging, electron microscopy etc.
CS41
REFERENCES
1. Digital Image Processing – Rafael C. Gonzalez, Richard E. Woods, 3rd Edition,
Pearson, 2008
2. Digital Image Processing- S Jayaraman, S Esakkirajan, T Veerakumar- TMH, 2015.
3. Digital Image Processing and Analysis-Human and Computer Vision Application with
using CVIP Tools – Scotte Umbaugh, 2nd Ed, CRC Press, 2011
4. Digital Image Processing using MATLAB — Rafael C. Gonzalez, Richard E Woods
and Steven L. Eddings, 2nd Edition, TMH, 2010.
5. Fundamentals of Digital Image Processing — A.K.Jain, PHI, 2015
COURSE OUTCOMES
Upon Completion of the course, the student would be able to:
CO1: Discuss Digital Image Fundamentals.
CO2: Apply Image Enhancement techniques in spatial and frequency domain.
CO3: Distinguish image Restoration and Degradation processes.
CO4: Design image analysis techniques in the form of image segmentation and to evaluate the
Methodologies for segmentation.
CO5: Use Image Compression Techniques.
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit VI(AAT)=15
marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not have
internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 1
CO2 3 1
CO3 1 3
CO4 3 3
CO5 3 3
1. Low, 2. Medium, 3. High
CS42
Course Code 18CS2E1A M. Tech ( Computer Science and Engineering)
Category Engineering Science Courses(Theory - Professional Elective)
Course title DATA WAREHOUSING AND MINING
Scheme and
Credits
No. of Hours/Week Semester – II
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
Course Objectives: The course will enable the students to:
1. Understand the principles of Data warehousing and data mining.
2. Perform classification and prediction of data.
3. Examine the types of data in cluster analysis with various clustering methods.
4. Illustrate the concepts of mining object, spatial, multimedia, text and web data.
5. Build a data warehouse and mapping the data warehouse to a multiprocessor
architecture.
UNIT I - INTRODUCTION TO DATA MINING: 9 Hours Data Mining Functionalities, Data Pre-processing, Data Cleaning, Data Integration and
Transformation, Data Reduction, Data Discretization and Concept Hierarchy Generation.
Association Rule Mining: Efficient and Scalable Frequent Item set Mining Methods,
Mining Various Kinds of Association Rules, Association Mining to Correlation Analysis,
Constraint-Based Association Mining, Handling categorical, Continuous Attributes,
Concept hierarchy, Sequential and Sub graph Patterns.
UNIT II - CLASSIFICATION AND PREDICTION: 10 Hours
Issues Regarding Classification and Prediction, Classification by Decision Tree
Introduction, Bayesian Classification, Rule Based Classification, Classification by Back
propagation, Support Vector Machines, Associative Classification, Lazy Learners, Other
Classification Methods, Prediction, Accuracy and Error Measures, Evaluating the
Accuracy of a Classifier or Predictor, Ensemble Methods, Model Section.
UNIT III - CLUSTER ANALYSIS: 10 Hours
Types of Data in Cluster Analysis, A Categorization of Major Clustering Methods,
Partitioning Methods, Hierarchical methods, Density-Based Methods, Grid-Based
Methods, Model-Based Clustering Methods, Clustering High-Dimensional Data,
Constraint-Based Cluster Analysis, Outlier Analysis, Quality and validity of Cluster
Analysis.
UNIT IV - MINING OBJECT, SPATIAL, MULTIMEDIA, TEXT AND WEB DATA:
9 Hours
Multidimensional Analysis and Descriptive Mining of Complex Data Objects, Spatial
Data Mining, Multimedia Data Mining, Text Mining, Mining the World Wide Web,
Stream Data Mining, Social Network Analysis.
UNIT V – DATA WAREHOUSING AND BUSINESS ANALYSIS: 10 Hours
Data warehousing Components, Building a Data warehouse, Mapping the Data Warehouse
to a Multiprocessor Architecture, DBMS Schemas for Decision Support, Data Extraction,
Cleanup, and Transformation Tools, Metadata, reporting, Query tools and Applications,
Online Analytical Processing (OLAP), OLAP and Multidimensional Data Analysis.
CS43
UNIT VI - Recent Trends in Distributed warehousing and Data Mining, Class Imbalance
Problem, Graph mining, Social Network Analysis.
REFERENCES
1. Jiawei Han and Micheline Kamber ―Data Mining Concepts and Techniques‖,
Second Edition, Elsevier, 2011.
2. Vipin Kumar, Introduction to Data Mining - Pang-Ning Tan, Michael Steinbach,
Addison Wesley, 2006.
3. G Dong and J Pei, Sequence Data Mining, Springer, 2007.
4. Alex Berson and Stephen J. Smith ―Data Warehousing, Data Mining & OLAP‖, Tata
McGraw – Hill Edition, Tenth Reprint 2007.
5. K.P. Soman, Shyam Diwakar and V. Ajay ―Insight into Data Mining Theory and
Practice‖, Easter Economy Edition, Prentice Hall of India, 2006.
G. K. Gupta ―Introduction to Data Mining with Case Studies‖, Easter Economy Edition,
Prentice Hall of India, 2006.
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1. Demonstrate the concept of data mining principles, data warehousing Architecture
and its
Implementation
CO2. Apply the association rules, design and deploy appropriate classification techniques
for
mining the data
CO3. Cluster the high dimensional data for better organization of the data
CO4. Describe stream mining, Time-Series and sequence data in high dimensional system
CO5. Acquire the concept of Mining Object, Spatial, Multimedia, Text, and Web Data
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not have
internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for
50 marks.
CS44
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3
CO2 2
CO3 3
CO4 2
CO5 3
1. Low, 2. Medium, 3. High
CS45
Course Code 18CS2E1B M. Tech (Computer Science and Engineering)
Category Engineering Science Courses (Theory - Professional Elective)
Course title STOCHASTIC PROCESS AND QUEUING THEORY
Scheme and Credits No. of Hours/Week Semester – II
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any)
1. Probability Theory
COURSE OBJECTIVES:
The course will enable the students to:
1. Understand the concepts of stochastic processes, and Markov chains.
2. Understand Markov processes with discrete and continuous state spaces.
3. Understand the concepts of queuing theory and different queues.
4. Understand open and closed queuing networks.
5. Analyse single and multi-server queuing models.
UNIT-I INTRODUCTION TO STOCHASTIC PROCESSES AND MARKOV CHAINS 09 Hours Introduction, Specifications, Classification of Stochastic Processes, Stationary Process, Poisson Processes,
Renewal Processes, Markov Chains: Transition Probabilities, Classification of States and Chains, Reducible
Chains, Statistical Inference of Markov Chains, Markov Chains with Continuous State Space, Non-
homogenous Chains.
UNIT-II MARKOV PROCESSES WITH DISCRETE AND CONTINUOUS STATE SPACE 09 Hours
Poisson Process and its Related Distributions, Generalization of Poisson Processes, Birth and Death Process,
Markov Process with Discrete State Space (Continuous Time Markov Chains), Brownian Motion, Wiener
Process, Differential Equations for Wiener Process, Kolmogorav Equations, First Passage Time Distribution
for Wiener Process.
UNIT-III QUEUING THEORY AND MARKOVIAN QUEUING MODELS 10 Hours
Introduction, Characteristics Notations, Birth and Death Processes, Single-Server Queues (M|M|1), Multi-
Server Queues (M|M|c), Choosing the Number of Servers, Queues with Truncation (M|M|c|K), Erlang‘s Loss
Formula (M|M|c|c), Queues with Unlimited Service, Finite Source Queues, State-Dependent Service, Queues
with Impatience, Transient Behaviour, Busy-Period Analysis, Bulk Input and Bulk Service.
UNIT-IV NETWORKS, SERIES, AND CYCLIC QUEUES 10 Hours
Series Queues, Open Jackson Networks, Closed Jackson Networks, Cyclic Queues, Extensions of Jackson
Networks, Non-Jackson Networks.
UNIT-V GENERAL ARRIVAL OR SERVICE PATTERNS 10 Hours General Service, Single Server (M|G|1), General Service, Multi-server (M|G|c|∙, M|G|∞), General Input
(G|M|1, G|M|c).
UNIT-VI Performance analysis of data networks.
REFERENCES
1. Jyothiprasad Medhi, ―Stochastic Processes‖, New Age International Publishers, II Edition, 2002.
2. Kishore S. Trivedi, ―Probability and Statistics with Reliability, Queuing and Computer Science
Applications‖, John Wiley and Sons, II Edition, 2008.
3. Donald Gross, John F. Shortle, James M. Thomson, and Carl M. Harris, ―Fundamentals of Queuing
Theory‖, John Wiley and Sons, IV Edition, 2008.
4. Oliver Knill, ―Probability Theory and Stochastic Processes with Applications‖, Overseas Press, 2009.
CS46
COURSE OUTCOMES
On completion of the course, the student would be able to:
CO1: Solve problems on stochastic process and Markov chains.
CO2: Analyse Markov Process for Discrete and Continuous State Spaces.
CO3: Model the Behaviour of Various Computer Networks and Distributed Systems using Queuing Models.
CO4: Analyse the Arrival and Service Patterns of any System and Solve Problems in Computer Networks
and Distributed Systems.
CO5: Investigate the performance analysis of data networks
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit VI(AAT)=15
marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks
Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 1
CO2 2
CO3 2
CO4 2
CO5 1
1. Low, 2. Medium, 3. High
CS47
Course Code 18CS2E1C M. Tech (Computer Science and Engineering)
Category Engineering Science Courses ( Integrated - Professional
Elective )
Course title INTERNET OF THINGS
Scheme and
Credits
No. of Hours/Week Semester – II
L T P S Credits
3 0 2 - 4
CIE Marks:
50
SEE Marks: 50 Total Max. Marks:
100
Duration of SEE: 3 Hrs
Prerequisites (if any):
1. Computer Networks
COURSE OBJECTIVES
The course will enable the students to:
1. Understand the IoT architecture and its enabling technologies.
2. Realize the various applications of IoT, understand the IoT system management
using NETCONF-YANG.
3. Understand the design of IoT, Python programming language, packages for IoT
and Raspberry Pi.
4. Create the various IoT protocols and their support in the implementation of
services.
5. Create a typical IoT input using the standard IT protocols.
UNIT I – INTRODUCTION TO INTERNET OF THINGS (IoT) 09 Hours
Definition and Characteristics of IoT, Physical Design of IoT, Logical Design of IoT, IoT
Enabling Technologies, IoT Levels and Deployment Templates.
UNIT II – DOMAIN SPECIFIC IoT, M2M and IoT System Management 09 Hours
Home Automation, Cities, Environment, Energy, Retail, Logistics, Agriculture, Industry,
Health and Lifestyle, M2M, Difference between IoT and M2M, SDN and NFV for IoT,
Need for IoT Systems Management, Simple Network Management Protocol, Network
Operator Requirements, IoT System Management with NETCONF-YANG.
UNIT III – DEVELOPING IoT USING PYTHON 10 Hours
IoT Design Methodology, IoT Systems – Logical Design using Python, Python Data
Types and Data Structures, Control Flow, Functions, Modules, Packages, File Handling,
Data/Time Operations. Classes, Python Packages for IoT: JSON, XML, HTTPLib and
URLLib, SMTPLib.
UNIT IV – IoT DEVICES AND PROTOCOLS 09 Hours
Basic Building Blocks of an IoT Device, Raspberry Pi, Programming Raspberry Pi using
Python, Basics of IoT Protocols: HTTP, UPnP, MQTT, CoAP and XMPP.
UNIT V – IoT PROTOCOLS 10 Hours
HTTP: Adding HTTP Support to Sensor, Adding HTTP Support to Actuator, Adding
HTTP Support to Controller. UPnP Protocol: Creating a Device Description Document,
Creating a Service Description Document, Providing a Web Interface, Creating an UPnP
Interface, Implementing the Still Image Service using Camera. CoAP Protocol: Making
HTTP Binary, Adding CoAP to Sensor, Adding CoAP to Actuator. MQTT Protocol:
Adding MQTT Support to Sensor, Adding MQTT Support to Actuator, Adding MQTT
Support to Controller. XMPP Protocol: Adding XMPP Support to a Thing, Adding
XMPP Support to Actuator, Adding XMPP Support to Camera, Adding XMPP Support
to Controller, Connecting All Together.
UNIT VI – Recent Trends in Industrial Internet of Things and Social Internet of Things.
CS48
UNIT VII- ( Lab Programs)
1. Study and Install Python in Eclipse and WAP for data types in python.
2. Write a Program for arithmetic operation in Python.
3. Write a Program for looping statement in Python.
4. Study and Install IDE of Arduino and different types of Arduino.
5. Write program using Arduino IDE for Blink LED.
6. Write Program for RGB LED using Arduino.
7. Study the Temperature sensor and Write Program foe monitor temperature using
Arduino.
8. Study and Implement RFID, NFC using Arduino.
9. Study and implement MQTT protocol using Arduino.
10. Study and Configure Raspberry Pi.
11. WAP for LED blink using Raspberry Pi.
12. Study and Implement Zigbee Protocol using Arduino / Raspberry Pi.
REFERENCES
1. Arshdeep Bahga and Vijay Madisetti, ―Internet of Things: A Hands-on
Approach‖, University Press, 2015.
2. Peter Waher, ―Learning Internet of Things‖, PACKT Publishing, 2015.
3. Adrian McEwen and Hakim Cassimally, ―Designing Internet of Things‖, John
Wiley and Sons, 2014.
COURSE OUTCOMES
On completion of the course, the student would be able to:
CO1: Demonstrate the knowledge of IoT architecture and design.
CO2: Manage the IoT system with NETCONF-YANG.
CO3: Program the Raspberry Pi using Python.
CO4: Develop an IoT application using the IoT protocol.
CO5: Investigate the standard IoT protocol.
SCHEME OF EXAMINATION:
CIE -
Practical
Conduction of experiments, performance of student in
every week lab and completion of lab record=50#
Total: Marks
50
Lab Test: Part-A =20, Part-B=20 and Vice-Voce=10
Marks(50##
)
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
CS49
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
# = CIE Marks for laboratory performance is to be evaluated for 50 marks and
the marks obtained shall be reduced for 25 marks
## = Lab test is to be conducted for 50 marks and the marks obtained shall be
reduced for 25 marks. Lab test shall be conducted by two examiners out of which
one examiner is the faculty taught the course. There is no SEE for the practical ‘s
portion of integrated course
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 1 2 CO2 1 CO3 3 CO4 1 CO5 2
1. Low, 2. Medium, 3. High
CS50
Course Code 18CS2E2A M. Tech (Computer Science and Engineering)
Category Engineering Science Courses (Theory - Professional Elective)
Course title NETWORK SECURITY
Scheme and
Credits
No. of Hours/Week Semester – II
L T P S Credits
4 0 - - 4
CIE Marks:
50
SEE Marks: 50 Total Max. Marks:
100
Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
The course will enable the students to:
1: Learn the basics of security and various types of security issues.
2: Understand cryptography techniques available and various security attacks.
3: Explore network security and how they are implemented in real world.
4: Analyse various issues of wireless security techniques.
5: Effectively design secured wireless sensor network
UNIT I- INTRODUCTION TO SECURITY 09 Hours
Need for security, Security approaches, Principles of security, Types of attacks.
Encryption Techniques: Plaintext, Cipher text, Substitution & Transposition techniques,
Encryption & Decryption, Types of attacks, Key range & Size. Symmetric &
Asymmetric Key Cryptography: Algorithm types & Modes, DES, AES, RSA, ECC;
UNIT II- SECURED HASH ALGORITHMS 09 Hours
Message Digest, Key- Distribution Algorithms, Digital signatures, User Authentication
Mechanisms, Key Management, Certificates, Kerberos.
UNIT III - DISTRIBUTED SYSTEM SECURITY 10 Hours Firewalls, Proxy-Servers, Network intrusion detection. Transport security: Mechanisms
of TLS, SSL, IPSec. Network -level solutions, Secure socket layer, IP Security, DoS
Counter measures, DNS Solutions.
UNIT IV - WIRELESS SECURITY 10 Hours
Security in wireless Networks Vulnerabilities, Security techniques, Wi-Fi Security, DoS
in wireless communication.
UNIT V - WIRELESS SENSOR NETWORKS SECURITY 10 Hours
Security in Wireless Sensor Networks, Possible attacks, countermeasures, SPINS, Static
and dynamic key Management
UNIT VI Recent trends in IOT security, IDS – 04 Hours
REFERENCES
1. W. Stallings. Cryptography and Network Security. Prentice Hall, 7th
Edition - 2017
2. W. R. Cheswick and S. M. Bellovin. Firewalls and Internet Security. Addison Wesley,
2007.
3. B. Schneier. Applied Cryptography. Wiley, 2006.
4. Stallings W., Wireless Communications and Networks, Pearson Education 2005
5. KazemSohraby, Daniel Minoli and TaiebZnati, ―wireless sensor networks -
Technology,
Protocols, and Applications‖, Wiley Interscience 2007
6. Takahiro Hara,Vladimir I. Zadorozhny, and Erik Buchmann, ―Wireless Sensor
NetworkTechnologies for the Information Explosion Era‖, springer 2010
CS51
COURSE OUTCOMES
On completion of the course, the student would be able to:
CO1: Analyse various security issues related to computer networks.
CO2: Implement various network security algorithms.
CO3: Design and implement various security algorithms for distributed environment.
CO4: Analyse the security issues and apply the relevant algorithm to mitigate the same.
CO5: Analyse various security attacks in WSN.
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not have
internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for
50 marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2 CO2 2 CO3 2 2 CO4 3 2 CO5 2 2
1. Low, 2. Medium, 3. High
CS52
Course Code 18CS2E2B M. Tech (Computer Science and Engineering)
Category Engineering Science Courses (Theory - Professional Elective)
Course title PATTERN RECOGNITION
Scheme and Credits No. of Hours/Week Semester – II
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
Course Objectives
This course will enable students to
1. Understand the various image processing and pattern recognition techniques.
2. Apply the mathematical morphology necessary for pattern recognition.
3. Analyse image representation, description and feature extraction.
4. Evaluate the principles of decision trees and clustering in pattern recognition
5. Create clustering large data sets and applications
UNIT- I INTRODUCTION 09 Hours
Definition of PR, Applications, Datasets for PR, Different paradigms for PR, Introduction to
probability, events, random variables, Joint distributions and densities, moments. Estimation
minimum risk estimators, problems
UNIT- II REPRESENTATION 09Hours
Data structures for PR, Representation of clusters, proximity measures, size of patterns,
Abstraction of Data set, Feature extraction, Feature selection, Evaluation
UNIT- III NEAREST NEIGHBOUR BASED CLASSIFIERS & BAYES CLASSIFIER 10
Hours
Nearest neighbour algorithm, variants of NN algorithms, use of NN for transaction databases,
efficient algorithms, Data reduction, prototype selection, Bayes theorem, minimum error rate
classifier, estimation of probabilities, estimation of probabilities, comparison with NNC, Naive
bayes classifier, Bayesian belief network
UNIT- IV NAIVE BAYES CLASSIFIER 10 Hours
Bayessian belief network,Decision Trees: Introduction, DT for PR, Construction of DT,
Splitting at the nodes, Over fitting & Pruning, Examples, Hidden Markov models: Markov
models for classification, Hidden Markov models and classification using HMM
UNIT- V CLUSTERING 10 Hours
Hierarchical (Agglomerative, single/complete/average linkage, wards, Partitional (Forgy‘s, k-
means, Isodata), clustering large data sets, examples, An application: Handwritten Digit
recognition
UNIT-VI Recent trends in pattern analysis
REFERENCES
1. Pattern Recognition ( An Introduction) , V Susheela Devi, M Narsimha Murthy, 2011
Universities Press, ISBN 978-81-7371-725-3
2. Pattern Recognition & Image Analysis, Earl Gose, Richard Johnsonbaugh, Steve Jost.
PH ISBN-81-203-1484-0, 1996.
3. Duda R. O., P.E. Hart, D.G. Stork., Pattern Classification, John Wiley and sons, 2000.
CS53
COURSE OUTCOMES
Upon completion of the course, the students will be able to:
CO1: Explain the various image processing and pattern recognition techniques.
CO2: Solve the mathematical morphology necessary for pattern recognition.
CO3: Review image representation and feature extraction.
CO4: Assess the principles of decision trees and clustering in pattern recognition
CO5: Design the clustering large data sets and applications
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit VI(AAT)=15
marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not have
internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 1
CO2 1
CO3 2
CO4 2
CO5 2
1. Low, 2. Medium, 3. High
CS54
Course Code 18CS2E2C M. Tech (Computer Science and Engineering)
Category Engineering Science Courses(Theory - Professional Elective)
Course title WEB SECURITY
Scheme and
Credits
No. of Hours/Week Semester – II
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
The course will enable the students to:
1. Understand web application‘s vulnerability and malicious attacks.
2. Understand basic web technologies used for web application development.
3. Analyse basic concepts of Mapping the application
4. Illustrate different attacking illustrations.
5. Emphasis various basic concepts of Attacking Data Stores.
.
UNIT I: WEB APPLICATION SECURITY 09 Hours
The Evolution of Web Applications, Common Web Application Functions, Benefits of
Web Applications, Web Application Security.
Core Defense Mechanisms: Handling User Access Authentication, Session Management,
Access Control, Handling User Input, Varieties of Input Approaches to Input Handling,
Boundary Validation.
Multistep Validation and Canonicalization: Handling Attackers, Handling Errors,
Maintaining Audit Logs, Alerting Administrators, Reacting to Attacks.
UNIT II: WEB APPLICATION TECHNOLOGIES 09 Hours The HTTP Protocol, HTTP Requests, HTTP Responses, HTTP Methods, URLs, REST,
HTTP Headers, Cookies, Status Codes, HTTPS, HTTP Proxies, HTTP Authentication,
Web Functionality, Server-Side Functionality, Client-Side Functionality, State and
Sessions, Encoding Schemes, URL Encoding, Unicode Encoding, HTML Encoding,
Base64 Encoding, Hex Encoding, Remoting and Serialization Frameworks.
UNIT III: MAPPING THE APPLICATION 10 Hours Enumerating Content and Functionality, Web Spidering, User-Directed Spidering,
Discovering Hidden Content, Application Pages Versus Functional Paths, Discovering
Hidden Parameters, Analyzing the Application, Identifying Entry Points for User Input,
Identifying Server-Side Technologies, Identifying Server-Side Functionality, Mapping the
Attack Surface.
UNIT IV: ATTACKING AUTHENTICATION 10 Hours
Authentication Technologies, Design Flaws in Authentication Mechanisms, Bad
Passwords, Brute-Forcible Login, Verbose Failure Messages, Vulnerable Transmission of
Credentials, Password Change, Functionality, Forgotten Password Functionality, User
Impersonation, Functionality Incomplete, Validation of Credentials, Nonunique
Usernames, Predictable Usernames, Predictable Initial Passwords, Insecure Distribution of
Credentials. Attacking Access Controls.
UNIT V - ATTACKING DATA STORES 10 Hours
Injecting into Interpreted Contexts, Bypassing a Login, Injecting into SQL, Exploiting a
Basic Vulnerability Injecting into Different Statement Types, Finding SQL Injection Bugs,
Fingerprinting the Database, The UNION Operator, Extracting Useful Data, Extracting
CS55
Data with UNION, Bypassing Filters, Second-Order SQL Injection, Advanced Exploitation
Beyond SQL Injection: Escalating the Database Attack, Using SQL Exploitation Tools,
SQL Syntax and Error Reference, Preventing SQL Injection.
UNIT VI
Recent trends in Web Applications and its Security
REFERENCES
1. Defydd Stuttard, Marcus Pinto, The Web Application Hacker's Handbook: Finding and
Exploiting Security, Wiley Publishing, Second Edition.
2.Andres Andreu, Professional Pen Testing for Web application, Wrox Press.
3. Carlos Serrao, Vicente Aguilera, Fabio Cerullo, ―Web Application Security‖ Springer;
1st Edition
4. Joel Scambray, Vincent Liu, Caleb Sima ,―Hacking exposed‖, McGraw-Hill; 3rd
Edition, (October, 2010).
5. OReilly Web Security Privacy and Commerce 2nd Edition 2011.
6. Software Security Theory Programming and Practice, Richard sinn, Cengage Learning.
COURSE OUTCOMES
On completion of the course, the student would be able to:
CO1: Achieve Knowledge of web application‘s vulnerability and malicious attacks.
CO2:Understand the basic web technologies used for web application development
CO3: Understands the basic concepts of Mapping the application.
CO4:Able to illustrate different attacking illustrations
C05:Investigate technique of attacking Data Stores
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not have
internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for
50 marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 1 3
CO2 2 1 3
CO3 1 3
CO4 3 1 3
CO5 1 3
1. Low, 2. Medium, 3. High
CS56
Course Code 18CS2L01 M. Tech (Computer Science and Engineering)
Category Engineering Science Courses ( Practical )
Course title ADVANCED DATS STRUCTURES AND ALGORITHMS LAB
Scheme and Credits No. of Hours/Week Semester – II
L T P S Credits
0 0 4 0 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
1. Data structures and Algorithm
2. Java Programming
Course Objectives: The course will enable the students to:
1. Acquire the knowledge of using advanced data structures
2. Acquire the knowledge of sorting and balancing the tree structure
3. Understand the usage of graph structures and string matching.
4. Understand the implementation of various string matching algorithms.
5. learn to solve the various NP complete problems
Each student has to work individually on assigned lab exercises. Lab sessions could be
scheduled as one contiguous four-hour session per week. It is recommended that all
implementations are carried out in Java. Exercises should be designed to cover the following
topics:
1. Doubly Circular Linked List
2. AVL Tree
3. Efficiency of Heap Sort & Quick Sort
4. Travelling Salesman Problem (Dynamic Programming)
5. N Queens Problem (Backtracking/ Branch & Bound)
6. Bellman-Ford algorithm
7. Shortest paths in a DAG
8. Ford-Fulkerson algorithm
9. Robin-Karp algorithm
10. Knuth-Morris-Pratt algorithms
11. String matching with Finite Automata
12. Vertex Cover problem
13. The Set Covering problem
14. The Subset-Sum problem
15. Maximum Bipartite algorithm
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1: Design and implement basic and advanced data structures extensively.
CO2: Design and apply graph structures for various applications.
CO3: Design and develop efficient algorithms with minimum complexity using design
techniques.
CO4: Design and develop advanced string matching and NP Complete problems.
CO5: Achieve proficiency in Java programming.
SCHEME OF EXAMINATION The student has to write and implement two programs selecting ONE from each part
Continuous Internal
Evaluation (CIE) (Laboratory Marks
Semester End Evaluation (SEE)
(Laboratory – 100 Marks) Marks
CS57
– 50 Marks)
Performance of the Student in
the laboratory every week
20 Write up 10
Test at the end of the semester 20 Experiment-1 (Part - A) – 35 Marks
Experiment-2 (Part - B) – 35 Marks
70
Viva Voce 10 Viva Voce 20
Total 100
Total (CIE) 50 Total (SEE) 50*
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2 CO2 2 CO3 2 CO4 2 CO5 2
1. Low, 2. Medium, 3. High
CS58
COURSE LEARNING OBJECTIVES:
The objectives of the SEMINAR-II is to prepare the students to learn to:
1.Carry out the literature review of general research area/current topic and analyse the same
effectively.
2.Prepare a technical report, reflecting his/her depth of understanding, on the selected area/topic
and prepare content rich presentation.
3.Acquire communication and time management skills for effective and impactful presentation.
Interact with peers to acquire the qualities of thoughtfulness, friendliness, adaptability,
responsiveness, and politeness in-group discussion. Overcome stage fear during the presentation.
GUIDE LINES
1.Seminar preparation and presentation is an individual student activity.
2.Topic may be of general/ specific interest to program of engineering or electives not offered in
the semester and to be selected in consultation with the faculty/Guide.
3.Select one pertinent research paper for the seminar presentation.
4.Prepare and submit a detailed technical report of the seminar topic.
COURSE OUTCOMES:
Students shall be able to:
1.Carry out the literature survey of topic of seminar.
2.Prepare a technical report on the selected area/topic.
3.Make an effective presentation with seamless flow of content within the time allocated. Overcome
inhibition in interacting with peers and hence develop the spirit of team work. Overcome stage fear
during the presentation.
SCHEME OF EXAMINATION
CIE – 50
marks
Phase -1 Marks =10 Seminar Report
Marks =20
Total:50
Marks Phase -2 Marks =20
Course Code 18CS2S01 M. Tech (Computer Science and Engineering)
Category Seminar Semester: II
Course title SEMINAR - II
Scheme and Credits
No. of Hours/Week
Total hours = 24 L T P S Credits
0 0 2 0 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any): NIL
CS59
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2 3 CO2 2 3 3 CO3 2
1. Low, 2. Medium, 3. High
Scheme of Continuous Internal Evaluation (CIE):
Evaluation would be carried out in TWO phases. The Seminar Evaluation Committee shall
comprise of Chairman of the Department, Faculty/Guide and one more faculty member nominated by
Chairman. The evaluation criteria shall be as per the rubrics given below:
Rubrics for Evaluation:
Topic - Technical Relevance, Sustainability and Societal Concerns : 15%
Review of Literature and Technical Content : 35%
Presentation Skills : 25%
Report : 25%
CS60
Course Code 18CS2M01 M. Tech (Computer Science and Engineering)
Category Engineering Science Courses ( Mandatory Audit )
Course title PEDAGOGY STUDIES
Scheme and Credits No. of Hours/Week Semester – II
L T P S Credits
2 0 0 0 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any):
COURSE OBJECTIVES
SThis course will enable students to
1. Understand the Thematic Overview and Pedagogical practices
2. Apply professional classroom practices , curriculum and assessment
3. Analyse methodology for quality assessment of school curriculum teacher
4. Evaluate pedagogic theory and pedagogical approaches
5. Create contexts pedagogy, new curriculum and assessment metrics for future
UNIT- I INTRODUCTION AND METHODOLOGY: 06 Hours Aims and rationale, Policy background, Conceptual framework and terminology Theories of
learning, Curriculum, Teacher education. Conceptual framework, Research questions.
Overview of methodology and Searching.
UNIT- II THEMATIC OVERVIEW: 03 Hours Pedagogical practices are being used by teachers in formal and informal classrooms in
developing countries. Curriculum, Teacher education
UNIT- III PEDAGOGICAL PRACTICES: 06 Hours Evidence on the effectiveness of pedagogical practices Methodology for the in depth stage:
quality assessment of included studies. How can teacher education (curriculum and
practicum) and the school curriculum and guidance materials best support effective
pedagogy? Theory of change. Strength and nature of the body of evidence for effective
pedagogical practices. Pedagogic theory and pedagogical approaches. Teachers‘ attitudes
and beliefs and Pedagogic strategies.
UNIT- IV PROFESSIONAL DEVELOPMENT: 06 Hours
Professional development: alignment with classroom practices and follow-up support Peer
Support Support from the head teacher and the community. Curriculum and assessment
Barriers to learning: limited resources and large class sizes
UNIT- V RESEARCH GAPS AND FUTURE DIRECTIONS: 03 Hours
Research design Contexts Pedagogy Teacher education Curriculum and assessment
Dissemination and research impact.
UNIT- VI NEW DEVELOPMENTS IN PEDAGOGY:
REFERENCES
1. Ackers J, Hardman F (2001) Classroom interaction in Kenyan primary schools,
Compare, 31 (2): 245-261.
2. Agrawal M (2004) Curricular reform in schools: The importance of evaluation,
Journal of Curriculum Studies, 36 (3): 361-379.
3. Akyeampong K (2003) Teacher training in Ghana - does it count? Multi-site teacher
education research project (MUSTER) country report 1. London: DFID.
4. Akyeampong K, Lussier K, Pryor J, Westbrook J (2013) Improving teaching and
learning of basic maths and reading in Africa: Does teacher preparation count?
International Journal Educational Development, 33 (3): 272–282.
CS61
5. Alexander RJ (2001) Culture and pedagogy: International comparisons in primary
education. Oxford and Boston: Blackwell.
6. Chavan M (2003) Read India: A mass scale, rapid, ‗learning to read‘ campaign
7. www.pratham.org/images/resource%20working%20paper%202.pdf.
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1: What pedagogical practices are being used by teachers in formal and informal
classrooms in developing countries?
CO2: What is the evidence on the effectiveness of these pedagogical practices, in
what conditions, and with what population of learners?
CO3: How can teacher education (curriculum and practicum) and the school
curriculum and guidance materials best support effective pedagogy
CO4: Assess pedagogic theory and pedagogical approaches
CO5: Design new curriculum and assessment metrics for future
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 CO2 3 CO3 3 CO4 3 CO5 3
1: Low 2: Medium 3:High
CS62
Course Code 18CS3E1A M. Tech (Computer Science and Engineering)
Category Engineering Science Courses (Theory)
Course title MACHINE LEARNING(Theory - Professional Elective)
Scheme and Credits No. of Hours/Week Semester – III
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
Data Mining (Preferable)
Course Objectives: The course will enable the students to:
1. Understand the concept of how to extract patterns.
2. Design and analyse various machine learning algorithms and techniques with a modern
outlook, focusing on recent advances.
3. Develop supervised and unsupervised learning paradigms of machine learning.
4. Assess Deep learning techniques and various feature extraction strategies.
5. Evaluate the machine learning algorithms.
UNIT I - SUPERVISED LEARNING (REGRESSION/CLASSIFICATION) 09 Hours
Basic methods: Distance-based methods, Nearest-Neighbours, Decision Trees, Naive Bayes
Linear models: Linear Regression, Logistic Regression, Generalized Linear Models, Support
Vector Machines, Nonlinearity and Kernel Methods, Beyond Binary Classification: Multi-Class
/ Structured Outputs, Ranking
UNIT II - UNSUPERVISED LEARNING 10 Hours
Clustering: K-means / Kernel K-means, Dimensionality Reduction: PCA and kernel PCA,
Matrix Factorization and Matrix Completion, Generative Models (mixture models and latent
factor models)
UNIT III - MACHINE LEARNING ALGORITHMS 09 Hours
Evaluating Machine Learning algorithms and Model Selection, Introduction to Statistical
Learning Theory, Ensemble Methods (Boosting, Bagging, Random Forests)
UNIT IV 10 Hours
Sparse Modeling and Estimation, Modeling Sequence/Time-Series Data, Deep Learning and
Feature Representation Learning
UNIT V 10 Hours
Scalable Machine Learning (Online and Distributed Learning) A selection from other advanced
topics, e.g., Semi-supervised Learning, Active Learning, Reinforcement Learning, Inference in
Graphical Models, Introduction to Bayesian Learning and Inference
UNIT VI
Recent trends in various learning techniques of machine learning and classification methods for
IOT applications. Various models for IOT applications
REFERENCES
1. Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012
2. Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning,
Springer 2009
3. Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2007
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1. Extract features that can be used for a particular machine learning approach in
various IOT applications.
CO2. Compare and contrast pros and cons of various machine learning techniques.
CS63
CO3. Get an insight of when to apply a particular machine learning approach.
CO4. Mathematically analyse various machine learning approaches and paradigms.
CO5. Design and formulate Supervised and Unsupervised learning paradigms of machine
learning
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not have
internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
Mapping of Course Outcomes (COS) to Program Outcomes
PO1 PO2 PO3
CO1 3 3
CO2 3 3
CO3 3 3
CO4 3 3
CO5 3 3
1. Low, 2. Medium, 3. High
CS64
Course Code 18CS3E1B M. Tech (Computer Science and Engineering)
Category Engineering Science Courses ( Integrated - Professional
Elective)
Course title BIG DATA ANALYTICS
Scheme and
Credits
No. of Hours/Week Semester – III
L T P S Credits
3 - 2 - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
Data Structures, Computer Architecture and Organization
Course Objectives: The course will enable the students to:
1. Understand big data for business intelligence.
2. Illustrate business case studies for big data analytics.
3. Discuss NoSQL big data management.
4. Demonstrate map-reduce analytics using Hadoop.
5. Compare Hadoop related tools such as HBase, Pig, Cassandra and Hive for big data
analytics.
UNIT I – INTRODUCTION TO BIG DATA 9 Hours Need for big data, convergence of key trends, unstructured data, industry examples of big
data, web analytics, big data and marketing, fraud and big data, risk and big data, credit
risk management, big data and algorithmic trading, big data and healthcare, big data in
medicine, advertising and big data, big data technologies, introduction to Hadoop, open
source technologies, cloud and big data, mobile business intelligence, Crowd sourcing
analytics, inter and trans firewall analytics.
UNIT II - INTRODUCTION TO NoSQL 10 Hours Aggregate data models, aggregates, key-value and document data models, relationships,
graph databases, schemaless databases, materialized views, distribution models, sharding,
master-slave replication, peer peer replication, sharding and replication, consistency,
relaxing consistency, version stamps, map-reduce, partitioning and combining, composing
map-reduce calculations.
UNIT III – HADOOP 10 Hours
Data format, analyzing data with Hadoop, scaling out, Hadoop streaming, Hadoop pipes,
design of Hadoop distributed file system (HDFS), HDFS concepts, Java interface, data
flow, Hadoop I/O, data integrity, compression, serialization, Avro, file-based data
structures
UNIT IV – MAPREDUCE 10 Hours MapReduce workflows, unit tests with MRUnit, test data and local tests, anatomy of
MapReduce job run, classic Map-reduce, YARN, failures in classic Map-reduce and
YARN, job scheduling, shuffle and sort, task execution, MapReduce types, input formats,
output formats.
UNIT V – Hbase 9 Hours
Hbase, data model and implementations, Hbase clients, Hbase examples, praxis.
Cassandra, Cassandra data model, Cassandra examples, Cassandra clients, Hadoop
integration, Pig, Grunt, pig data model, Pig Latin, developing and testing Pig Latin scripts.
Hive, data types and file formats, HiveQL data definition, HiveQL data manipulation,
HiveQL queries.
UNIT VI -
Recent advances in Data Analytics
CS65
UNIT –VII (Lab Programs)
1. (a) Perform setting up and Installing Hadoop in its two operating modes:
o Pseudo distributed,
o Fully distributed.
(b) Use web based tools to monitor your Hadoop setup.
2. (a) Implement the following file management tasks in Hadoop:
o Adding files and directories
o Retrieving files
o Deleting files
(b) Benchmark and stress test an Apache Hadoop cluster
3. Run a basic Word Count Map Reduce program to understand Map Reduce Paradigm.
(a) Find the number of occurrence of each word appearing in the input file(s)
(b) Performing a MapReduce Job for word search count (look for specific
keywords in a file)
4. Stop word elimination problem:
Input:
o A large textual file containing one sentence per line
o A small file containing a set of stop words (One stop word per line)
Output:
o A textual file containing the same sentences of the large input file without the
words appearing in the small file.
5. Write a Map Reduce program that mines weather data. Weather sensors collecting data
every hour at many locations across the globe gather large volume of log data, which is a
good candidate for analysis with MapReduce, since it is semi structured and record-
oriented.
Data available at: https://github.com/tomwhite/hadoopbook/tree/master/input/ncdc/all.
(a) Find average, max and min temperature for each year in NCDC data set?
(b) Filter the readings of a set based on value of the measurement, Output the line
of input files associated with a temperature value greater than 30.0 and store it
in a separate file.
6. Purchases.txt Dataset
(a) Instead of breaking the sales down by store, give us a sales breakdown by
product category across all of our stores
(b)What is the value of total sales for the following categories?
(i) Toys
(ii) Consumer Electronics
(c) Find the monetary value for the highest individual sale for each separate store
(d) What are the values for the following stores?
(i) Reno
(ii) Toledo
(iii)Chandler
(e) Find the total sales value across all the stores, and the total number of sales.
7. Install and Run Pig then write Pig Latin scripts to sort, group, join, project, and filter
your data.
8. Write a Pig Latin scripts for finding TF-IDF value for book dataset (A corpus of eBooks
available at: Project Gutenberg)
9. Install and Run Hive then use Hive to create, alter, and drop databases, tables, views,
functions, and indexes.
10. Install, Deploy & configure Apache Spark Cluster. Run apache spark applications using
Scala.
CS66
REFERENCES
1. Michael Minelli, Michelle Chambers, and Ambiga Dhiraj, "Big Data, Big
Analytics: Emerging Business Intelligence and Analytic Trends for Today's
Businesses", Wiley, 2013.
2. P. J. Sadalage and M. Fowler, "NoSQL Distilled: A Brief Guide to the Emerging
World of
Polyglot Persistence", Addison-Wesley Professional, 2012.
3. Tom White, "Hadoop: The Definitive Guide", Third Edition, O'Reilley, 2012.
4. Eric Sammer, "Hadoop Operations", O'Reilley, 2012.
5. E. Capriolo, D. Wampler, and J. Rutherglen, "Programming Hive", O'Reilley, 2012.
6. Lars George, "HBase: The Definitive Guide", O'Reilley, 2011.
7. Eben Hewitt, "Cassandra: The Definitive Guide", O'Reilley, 2010.
8. Alan Gates, "Programming Pig", O'Reilley, 2011.
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1. Describe big data and use cases from selected business domains.
CO2. Discuss the business case studies for big data analytics.
CO3. Explain NoSQL big data management.
CO4. Perform map-reduce analytics using Hadoop.
CO5. Use Hadoop related tools such as HBase, Cassandra, Pig, and Hive for big data
analytics.
SCHEME OF EXAMINATION:
CIE -
Practical
Conduction of experiments, performance of student in
every week lab and completion of lab record=50#
Total: Marks
50
Lab Test: Part-A =20, Part-B=20 and Vice-Voce=10
Marks(50##
)
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
# = CIE Marks for laboratory performance is to be evaluated for 50 marks and the
marks obtained shall be reduced for 25 marks
## = Lab test is to be conducted for 50 marks and the marks obtained shall be
reduced for 25 marks. Lab test shall be conducted by two examiners out of which
one examiner is the faculty taught the course. There is no SEE for the practical ‘s
portion of integrated course
CS67
Mapping of Course Outcomes (COS) to Program Outcomes
PO1 PO2 PO3
CO1 3 1
CO2 2 2
CO3 3 2
CO4 1 2
CO5 3
1. Low, 2. Medium, 3. High
CS68
Course Code 18CS3E1C M. Tech (Computer Science and Engineering)
Category Engineering Science Courses (Theory - Professional Elective)
Course title HIGH PERFORMANCE COMPUTING
Scheme and
Credits
No. of Hours/Week Semester – III
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
1. Computer Architecture
2. Operating Systems
COURSE OBJECTIVES
The course will enable the students to:
1. Understand the modern processors, their architectures and several case studies.
2. Understand the need of parallelism and types of parallelism.
3. Analyse shared and distributed based memory parallel programming using OpenMP
and MPI.
4. Evaluate hybrid parallel programming using MPI and OpenMPI.
5. Review of recent trends in efficiency MPI programming and scalable parallel
processing.
UNIT-I MODERN PROCESSORS 10 Hours
Stored-Program Computer Architecture, General-Purpose Cache-Based Microprocessor
Architecture, Memory, Multi-Core Processors, Multithreaded Processors, Vector Processors.
Basic Optimization Techniques For Serial Code: Scalar Profiling, Common Sense
Optimizations, Simple Measures, Large Impact, The Role of Compilers, C++ Optimizations.
Data Access Optimization: Balance Analysis and Light Speed Estimates, Storage Order, Case
Study: The Jacobi Algorithm, Case Study: Dense Matrix Transpose, Algorithm Classification
and Access Optimizations, Case Study: Sparse Matrix-Vector Multiply.
UNIT-II PARALLEL COMPUTERS 09 Hours
Taxonomy of Parallel Computing Paradigms, Shared-Memory Computers, Distributed-
Memory Computers, Hierarchical (Hybrid) Systems, Networks, Basics of Parallelization:
Why Parallelize? Data and Functional Parallelism, Parallel Scalability.
UNIT-III SHARED-MEMORY PARALLEL PROGRAMMING WITH OpenMP
09 Hours
Introduction to OpenMP, Case Study: OpenMP-Parallel Jacobi Algorithm. Efficient OpenMP
programming: Profiling OpenMP Programs Performance Pitfalls, Case Study: Parallel Sparse
Matrix-Vector Multiply.
UNIT-IV DISTRIBUTED-MEMORY PARALLEL PROGRAMMING WITH MPI
10 Hours
Message Passing, Introduction to MPI, Example: MPI Parallelization of a Jacobi Solver.
Efficient MPI Programming: MPI Performance Tools, Communication Parameters,
Synchronization, Serialization, Contention, Reducing Communication Overhead,
Understanding Intra-Node Point-To-Point Communication.
UNIT-V HYBRID PARALLELIZATION WITH MPI AND OpenMP 10 Hours
Basic MPI/OpenMP Programming Models, MPI Taxonomy of Thread Interoperability,
Hybrid Decomposition and Mapping, Potential Benefits and Drawbacks of Hybrid
Programming.
UNIT VI – Recent trends in efficient MPI programming and scalable parallel processing.
CS69
REFERENCES
1. Georg Hager and Gerhard Wellein, ―Introduction to High Performance Computing for
Scientists and Engineers‖, CRC Press, 2011.
2. Victor Eijkhout with Edmond Chow, Robert van de Geijn, ―Introduction to High
Performance Scientific Computing‖. II Edition, 2015.
3. Charles Severance Kevin Dowd, ―High Performance Computing‖, Oreilly Media, II
Edition, 1998
COURSE OUTCOMES
On completion of the course, the student would be able to:
CO1: Discuss various modern processes, along with their architectures.
CO2: Categorize and compare different types of parallelism.
CO3: Asses shared and distributed based memory parallel programming using OpenMPI and
MPI.
CO4: Investigate hybrid parallel programming using MPI and OpenMP
CO5: Design an efficient HPC system using MPI and OpenMP programming.
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not have
internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
Mapping of Course Outcomes (COS) to Program Outcomes
PO1 PO2 PO3
CO1 2
CO2 1 1
CO3 1 2
CO4 1
CO5 1 1 1
1. Low, 2. Medium, 3. High
CS70
Course Code 18CS3P1A M. Tech (Computer Science and Engineering)
Category Engineering Science Courses (Theory – Open Elective)
Course title ARITIFICIAL INTELLIGENCE
Scheme and
Credits
No. of Hours/Week Semester – III
L T P S Credits
4 0 0 0 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
This course will enable students to
1. Understand the various characteristics of Intelligent agents
2. Understand the different search strategies in AI
3. Learn to represent knowledge in solving AI problems
4. Analyse the different ways of designing software agents
5. Evaluate the various reasoning techniques for AI.
UNIT-I INTRODUCTION: 9 Hours Introduction Definition Future of Characteristics and Problem Solving Approach to Typical
AI problems. State Space Search and Heuristic Search Techniques Defining problems as
State Space search, Production systems and characteristics, Hill Climbing, Breadth first and
depth first search, Best first search.
UNIT-II KNOWLEDGE REPRESENTATION ISSUES: 9 Hours Representations and Mappings, Approaches to knowledge representation, Using Predicate
Logic and Representing Knowledge as Rules , Representing simple facts in logic,
Computable functions and predicates, Procedural vs Declarative knowledge, Logic
Programming, Forward vs backward reasoning.
UNIT-III SOFTWARE AGENTS: 10 Hours
Architecture for Intelligent Agents Agent communication Negotiation and Bargaining
Argumentation among Agents Trust and Reputation in Multi-agent systems.
UNIT-IV REASONING I: 10 Hours Symbolic Logic under Uncertainty, Non-monotonic Reasoning, Logics for non-monotonic
reasoning, Statistical Reasoning.
UNIT-V METHODS: 10 Hours
Probability and Bayes Theorem, Certainty factors, Probabilistic Graphical Models, Bayesian
Networks, Markov Networks, Fuzzy Logic.
UNIT-VI Recent advances and research being done in the topics mentioned above
units
REFERENCES:
1. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach", Prentice
Hall, Third Edition, 2009.
2. Rich and Knight, Artificial Intelligence, 2nd Edition, 2013
3. I. Bratko, "Prolog: Programming for Artificial Intelligence", Fourth edition,
Addison-Wesley Educational Publishers Inc., 2011.
4. M. Tim Jones, "Artificial Intelligence: A Systems Approach(Computer Science),
Jones and Bartlett Publishers, Inc.; First Edition, 2008
5. Nils J. Nilsson, "The Quest for Artificial Intelligence", Cambridge University
Press, 2009.
6. William F. Clocksin and Christopher S. Mellish," Programming Using
CS71
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1: Define and identify various AI concepts
CO2: illustrate different AI strategies
CO3: Sketch various knowledge representation for AI problems
CO4: Analyse agents usage for AI
CO5: Design AI inference techniques
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Total:
Marks 100 Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes
PO1 PO2 PO3
CO1 2 CO2 2 CO3 2 CO4 2 CO5 2 2
1: Low 2: Medium 3:High
CS72
Course Code 18CS3P1B M. Tech (Computer Science and Engineering)
Category Engineering Science Courses (Theory – Open Elective)
Course title BUSINESS ANALYTICS
Scheme and
Credits
No. of Hours/Week Semester – III
L T P S Credits
4 0 - - 4
CIE Marks:
50
SEE Marks: 50 Total Max. Marks:
100
Duration of SEE: 3 Hrs
Prerequisites (if any):
Course Objectives: The course will enable the students to:
1. Understand the role of business analytics within an organization.
2. Analyze data using statistical and data mining techniques.
3. Distinguish relationships between the underlying business processes of an
organization.
4. Gain an understanding of how managers use business analytics to formulate and solve
business problems and to support managerial decision making.
5. Discuss the uses of decision-making tools and Operations research techniques.
UNIT I – BUSINESS ANALYTICS 10 Hours Overview of Business analytics, Scope of Business analytics, Business Analytics Process,
Relationship of Business Analytics Process and organisation, competitive advantages of
Business Analytics. Statistical Tools: Statistical Notation, Descriptive Statistical
methods, Review of probability distribution and data modelling, sampling and estimation
methods overview –
UNIT II - TRENDINESS AND REGRESSION ANALYSIS: 9 Hours
Modelling Relationships and Trends in Data, simple Linear Regression. Important
Resources, Business Analytics Personnel, Data and models for Business analytics,
problem solving, Visualizing and Exploring Data, Business Analytics Technology
UNIT III - ORGANIZATION STRUCTURES OF BUSINESS ANALYTICS:
10 Hours Team management, Management Issues, Designing Information Policy, Outsourcing,
Ensuring Data Quality, Measuring contribution of Business analytics, Managing
Changes. Descriptive Analytics, predictive analytics, predicative Modelling, Predictive
analytics analysis, Data Mining, Data Mining Methodologies, Prescriptive analytics and
its step in the business analytics Process, Prescriptive Modelling, nonlinear Optimization.
UNIT IV – FORECASTING TECHNIQUES: 10 Hours
Qualitative and Judgmental Forecasting, Statistical Forecasting Models, Forecasting
Models for Stationary Time Series, Forecasting Models for Time Series with a Linear
Trend, Forecasting Time Series with Seasonality, Regression Forecasting with Casual
Variables, Selecting Appropriate Forecasting Models. Monte Carlo Simulation and Risk
Analysis: Monte Carle Simulation Using Analytic Solver Platform, New-Product
Development Model, Newsvendor Model, Overbooking Model, Cash Budget Model.
UNIT V – DECISION ANALYSIS 9 Hours Formulating Decision Problems, Decision Strategies with the without Outcome
Probabilities, Decision Trees, The Value of Information, Utility and Decision Making
UNIT VI -
Recent Trends in Embedded and collaborative business intelligence, Visual
data recovery, Data Storytelling and Data journalism.
CS73
REFERENCES
1. Business analytics Principles, Concepts, and Applications by Marc J. Schniederjans,
Dara G. Schniederjans, Christopher M. Starkey, Pearson FT Press, First edition,
2014
2. Business Analytics by James Evans, Pearson Education, First Edition, 2017.
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1. Develop the knowledge of data analytics.
CO2. Demonstrate the ability of think critically in making decisions based
on data and deep analytics
CO3. Discuss the uses of technical skills in predicative and prescriptive
modeling to support business decision-making
CO4. Demonstrate the ability to translate data into clear and actionable insights.
CO5. Evaluate and assess the forecasting techniques.
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not have
internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for
50 marks.
Mapping of Course Outcomes (COS) to Program Outcomes
PO1 PO2 PO3
CO1 3 3
CO2 3 3
CO3 3 3
CO4 3 3
CO5 3 3
1. Low, 2. Medium, 3. High
CS74
Course Code 18CS3P1C M. Tech (Computer Science and Engineering)
Category Engineering Science Courses ( Theory – Open Elective)
Course title MODELING AND SIMULATION
Scheme and
Credits
No. of Hours/Week Semester – III
L T P S Credits
3 1 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES:
The course will enable the students to:
1. Understand the system, specify systems using natural models of computation, modelling
techniques
2. Apply natural models of computation, modelling techniques to
understand behaviour of system , and analyse the simulation data
3. Analyse simulation data, simulation tools for simulation, Terminating Simulations –
Steady state simulations.
4. Evaluate the existing simulation models for verification, calibration and validation
5. Design validation, calibration model and decision support
UNIT – I INTRODUCTION TO SIMULATION 09 Hours
Introduction Simulation Terminologies- Application areas – Model Classification Types of
Simulation- Steps in a Simulation study- Concepts in Discrete Event Simulation Example.
UNIT-II MATHEMATICAL MODELS 10 Hours
Statistical Models - Concepts – Discrete Distribution- Continuous Distribution – Poisson
Process- Empirical Distributions- Queueing Models – Characteristics- Notation Queueing
Systems – Markovian Models- Properties of random numbers- Generation of Pseudo Random
numbers- Techniques for generating random numbers-Testing random number generators
Generating Random-Variates- Inverse Transform technique Acceptance- Rejection technique –
Composition & Convolution Method.
UNIT-III ANALYSIS OF SIMULATION DATA 10 Hours
Input Modelling - Data collection - Assessing sample independence – Hypothesizing
distribution family with data - Parameter Estimation - Goodness-of-fit tests – Selecting input
models in absence of data- Output analysis for a Single system – Terminating Simulations –
Steady state simulations.
UNIT -IV VERIFICATION AND VALIDATION 09 Hours
Building – Verification of Simulation Models – Calibration and Validation of Models –
Validation of Model Assumptions – Validating Input – Output Transformations
UNIT-V SIMULATION OF COMPUT ER SYSTEMS 10 Hours
Simulation Tools – Model Input – High level computer system simulation – CPU – Memory
Simulation – Comparison of systems via simulation – Simulation Programming techniques -
Development of Simulation models.
UNIT-VI Recent advances and research being done in the topics mentioned above units
REFERENCES
1. Jerry Banks and John Carson, ―Discrete Event System Simulation‖, Fourth Edition, PHI,
2005.
2. Geoffrey Gordon, ―System Simulation‖, Second Edition, PHI, 2006.
3. Frank L. Severance, ―System Modelling and Simulation‖, Wiley, 2001.
4. Averill M. Law and W. David Kelton, ―Simulation Modelling and Analysis, Third
Edition, McGraw Hill, 2006.
CS75
5. Jerry Banks, ―Handbook of Simulation: Principles, Methodology, Advances,
Applications and Practice‖, Wiley-Inter science, 1 edition, 1998.
COURSE OUTCOMES
On Completion of the course, the student will be able to:
CO1: Explain natural models of computation, modelling techniques
CO2: Determine suitable models of computation, modelling techniques to
understand behaviour of system.
CO3: Distinguish simulation models for verification, calibration and validation
CO4: Assess the performance of different simulation models, statistical models, queuing
Systems and Markovian Models for given problem
CO5: Design goodness-of-fit tests and input models in absence of data
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 20 marks Two Quizzes /
AAT = 10 marks
Total:50
marks Test II (Unit IV & V) – 20 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not have
internal choice. 20* 2 = 40 Marks
Total:100
marks Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
Mapping of Course Outcomes (COS) to Program Outcomes
PO1 PO2 PO3
CO1 2 CO2 3 CO3 3 CO4 3 CO5 3 2
1: Low 2: Medium 3:High
CS76
COURSE LEARNING OBJECTIVES:
The objectives of the SEMINAR-III is to prepare the students to learn to:
1.Carry out the literature review of general research area/current topic and analyse the same
effectively.
2.Prepare a technical report, reflecting his/her depth of understanding, on the selected area/topic
and prepare content rich presentation.
3.Acquire communication and time management skills for effective and impactful presentation.
Interact with peers to acquire the qualities of thoughtfulness, friendliness, adaptability,
responsiveness, and politeness in-group discussion. Overcome stage fear during the presentation.
GUIDE LINES
1. Seminar preparation and presentation is an individual student activity.
2. Topic may be of general/ specific interest to program of engineering or electives not offered
in the semester and to be selected in consultation with the faculty/Guide.
3. Select one pertinent research paper for the seminar presentation.
4. Prepare and submit a detailed technical report of the seminar topic.
COURSE OUTCOMES:
Students shall be able to:
1. Carry out the literature survey of topic of seminar.
2. Prepare a technical report on the selected area/topic.
3. Make an effective presentation with seamless flow of content within the time allocated.
Overcome inhibition in interacting with peers and hence develop the spirit of team work.
Overcome stage fear during the presentation.
SCHEME OF EXAMINATION
CIE – 50
marks
Phase -1 Marks =10 Seminar Report
Marks =20
Total:50
Marks Phase -2 Marks =20
Course Code 18CS3S01 M. Tech (Computer Science and Engineering)
Category Seminar Semester: III
Course title SEMINAR – III
Scheme and Credits
No. of Hours/Week
Total hours = 24 L T P S Credits
0 0 2 0 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any): NIL
CS77
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2 3 CO2 2 3 3 CO3 2
1. Low, 2. Medium, 3. High
Scheme of Continuous Internal Evaluation (CIE):
Evaluation would be carried out in TWO phases. The Seminar Evaluation Committee shall
comprise of Chairman of the Department, Faculty/Guide and one more faculty member nominated by
Chairman. The evaluation criteria shall be as per the rubrics given below:
Rubrics for Evaluation:
Topic - Technical Relevance, Sustainability and Societal Concerns : 15%
Review of Literature and Technical Content : 35%
Presentation Skills : 25%
Report : 25%
CS78
INTERNSHIP
COURSE LEARNING OBJECTIVES:
Objectives of the internship
1. Provide an opportunity to see how classroom and textbook learning applies to the real world,
and to expose the students to the relevant work experience.
2. Pay close attention to all the steps that go onto completing a job, thereby, help students to
become workforce ready before entering the job market as a graduate. Provide an opportunity
to select the topic of dissertation work by evaluating the requirement of organisation.
3. Prepare and present a technical report of internship.
GUIDELINES
1. Student has to approach the concerned heads of various Industries/organization, which are
related to the field of specialization of the M. Tech program.
2. If any student gets internship, he/she has to submit the internship offer letter duly signed by the
concerned authority of the company to the Chairperson of the Department.
3. The internship on full time basis will be after the examination of II semester and during III
semester for a period of 8 weeks without affects regular class work.
4. The progress has to be reported periodically to the faculty or to the Guide assigned by the
Chairperson as per the format acceptable to the respective industry /organizations and to the
Institution.
5. At the end of the internship the student has to prepare a detailed report and submit.
6. Students are advised to use ICT tools such as Skype to report their progress and submission of
periodic progress reports to the faculty in charge or guide.
7. Duly signed report from internal supervisor (faculty incharge or guide) and external supervisor
from the organization where internship is offered has to be submitted to the Chairperson of the
Department for his/her signature and further processing for evaluation.
The broad format of the internship final report shall contain Cover Page, Certificate from College,
Certificate from Industry / Organization of internship, Acknowledgement, Synopsis, Table of
Contents, chapters of Profile of the Organization - Organizational structure, Products, Services,
Business Partners, Financials, Manpower, Societal Concerns, Professional Practices, Activities of
the Department where internship is done, Tasks Performed and summary of the tasks performed.
specific technical and soft skills that student has acquired during internship, References &
Annexure.
Course Code 18CS3I01 M. Tech (Computer Science and Engineering)
Category Internship/ Mini Project Semester: III
Course title INTERNSHIP / MINI PROJECT
Scheme and Credits
No. of Hours/Week
Total hours = 80 L T P S Credits
--- --- 10 --- 5
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs for a
batch of 6 students
Prerequisites (if any): NIL
CS79
COURSE OUTCOMES:
The student will be able to:
1. Apply the gained experience along with the theoretical knowledge to solve the real world
problems what engineers ready do.
2. Get equipped with experience required before entering the job market. Explore the possibility of
formulating the dissertation problem.
3. Prepare a technical report and make a presentation of details of internship.
SCHEME OF EXAMINATION
CIE 1.Marks awarded by guide (Internal Examiner) = 50 marks
2.Marks awarded by the department internship monitoring committee = 50 marks
50*
Marks
SEE Presentation of internship in the presence of Guide (Internal Examiner) and external
examiner = 100 marks
50**
Marks
Note: *= CIE be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
**= SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 2
CO2 2 2
CO3 3
1. Low, 2. Medium, 3. High
Rubrics for CIE:
1. Topic of Internship = 10%
2. Objectives of Internship = 10%
3. Specific Skills Acquired = 20%
4. Document = 40%
5. Presentation = 20%
Rubrics for SEE:
1. Topic of Internship = 10%
2. Objectives of Internship = 10%
3. Specific Skills Acquired = 20%
4. Document = 40%
5. Presentation = 20%
CS80
MINI PROJECT
COURSE LEARNING OBJECTIVE:
1. Understand the method of applying engineering knowledge/use application software to solve
specific problems after carrying out literature survey.
2. Apply engineering and management principles while executing the project.
3. Demonstrate the skills for good technical report writing and presentation.
COURSE CONTENT/GUIDELINES
Student shall take up small problems in the field of domain of program as mini project. It can be
related to a solution to an engineering problem, verification and analysis of experimental data
available, conducting experiments on various engineering subjects, material characterisation, studying
a software tool for solution to an engineering problem, etc.
The mini project must be carried out preferably using the resources available in the department/college
and it can be of interdisciplinary also.
COURSE OUTCOMES:
The students shall be able to:
1. Conduct experiments / use the capabilities of relevant application software/ simulation tools
individually to generate data/ solve problems.
2. Assess the available engineering resources available in the institution.
3. Prepare and Present the technical document of mini project.
Rubrics for CI shall be done with weightage/distribution of marks as follows:
Note: * = CIE be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
Sl.
no
Particulars Weightage Marks Total marks
of CIE
1 Selection of the topic & formulation of objectives 10% 10
50*
2 Modelling and simulation/algorithm
development/experiment setup
25% 25
3 Conducting experiments/implementation/testing 25% 25
4 Demonstration & Presentation 15% 15
5 Report writing 25% 25
Total 100% 100
CS81
CIE 1.Marks awarded by guide (Internal Examiner) = 50 marks
2.Marks awarded by the department internship monitoring committee = 50 marks
50*
Marks
SEE Presentation of internship in the presence of Guide (Internal Examiner) and external
examiner = 100 marks
50**
Marks
Rubrics for SEE:
The SEE shall be done by two examiners out of which one examiner is the guide of mini project.
The following weightage would be given for the examination. Evaluation shall be done in batches, not
exceeding 6 students.
Sl.
no
Particulars Weightage Marks Total marks
of CIE
1 Brief write-up about the project 05% 05
50**
2 Presentation/demonstration of the project 20% 20
3 Methodology and Experimental Results and
Discussion
30% 30
4 Report 25% 25
5 Viva Voce 20% 20
Total 100% 100
Note: ** = SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 3
CO2 2 3
CO3 3
1. Low, 2. Medium, 3. High
CS82
COURSE LEARNING OBJECTIVES:
1. Choose a problem applying relevant knowledge and skills acquired during the course. Formulate
the specifications of the project work, identify the set of feasible solutions, prepare, and execute
project plan considering professional, cultural and societal factors. Identify the problem-solving
methodology using literature survey and present the same.
2. Develop experimental planning and select appropriate techniques and tools to conduct
experiments to Evaluate and critically examine the outcomes followed by concluding the results
and identifying relevant applications. Preparation of synopsis, preliminary report for approval of
topic selected along with literature survey, objectives and methodology.
3. Develop oral and written communication skills to effectively convey the technical content.
GUIDELINES
The Dissertation work will start in III semester and should be a problem with research potential
and should involve scientific research, design, generation/collection and analysis of data,
determining solution and must preferably bring out the individual contribution.
The Dissertation work will have to be done by only one student and the topic of dissertation
must be decided by the guide and the student. The dissertation work shall be carried out, on-
campus or in an industry or in an organisation with prior approval from the Chairperson of the
Department. The student has to be in regular contact with the guide atleast once in a week.
The report of Dissertation work phase I shall contain cover page, certificate from
College/Industry/Organisation, Acknowledgement, List of Figures and Tables Contents,
Nomenclature, Chapters of Introduction including motivation to choose topic, Literature survey,
Conclusion of literature survey, Objectives and Scope of Dissertation, Methodology to be
followed, Experimental requirements, References and Annexure.
The preliminary results (if available) of the problem of Dissertation work may also be
discussed in the report.
Course Code 18CS3D01 M. Tech (Computer Science and Engineering)
Category Dissertation Work Semester: III
Course title DISSERTATION WORK PHASE –I
Scheme and Credits
No. of Hours/Week
Total hours = 80 L T P S Credits
0 0 10 0 5
CIE Marks: 50 SEE Marks:50 Total Max. Marks: 100 Duration of SEE: 1Hour
Prerequisites (if any): NIL
CS83
COURSE OUTCOME:
The students will be able to:
1. Self learn various topics relevant to Dissertation work. Survey the literature such as books,
National/International reference journals, articles and contact resource persons for selected topics
of Dissertation.
2. Write and prepare a typical technical report.
3. Present and defend the contents of Dissertation work phase I in front of technically qualified
audience effectively.
SCHEME OF EXAMINATION
CIE 1.Marks awarded by guide (Internal Examiner) = 50 marks
2.Marks awarded by the department dissertation monitoring committee = 50 marks
50*
Marks
SEE Presentation of Dissertation work Phase-I in the presence of Guide (Internal
Examiner) and external examiner = 100 marks
50**
Marks
Note: *= CIE be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
**= SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
Rubrics for CIE: Weightage
1. Introduction and Justification of Topic = 10%
2. Literature Survey and Conclusion = 30%
3. Objectives and Scope of Dissertation Work = 30%
4. Methodology to be Adopted = 20%
5. Presentation of Contents of Dissertation Work Phase-I = 10%
Rubrics for SEE:
1. Introduction and Justification of topic = 10%
2. Literature Survey and Conclusion = 30%
3. Objectives and Scope of Dissertation Work = 30%
4. Methodology, Experimental/Software = 20%
5. Presentation of Dissertation Phase-I = 10%
Mapping of Course Outcomes (Cos) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 3
CO2 3 3
CO3 3 3
1. Low, 2. Medium, 3. High
CS84
COURSE LEARNING OBJECTIVES:
The objectives of the SEMINAR-IV is to prepare the students to learn to:
1.Carry out the literature review of general research area/current topic and analyse the same
effectively.
2.Prepare a technical report, reflecting his/her depth of understanding, on the selected area/topic
and prepare content rich presentation.
3.Acquire communication and time management skills for effective and impactful presentation.
Interact with peers to acquire the qualities of thoughtfulness, friendliness, adaptability,
responsiveness, and politeness in-group discussion. Overcome stage fear during the presentation.
GUIDE LINES
1.Seminar preparation and presentation is an individual student activity.
2.Topic may be of general/ specific interest to program of engineering or electives not offered in
the semester and to be selected in consultation with the faculty/Guide.
3.Select one pertinent research paper for the seminar presentation.
4.Prepare and submit a detailed technical report of the seminar topic.
COURSE OUTCOMES:
Students shall be able to:
1.Carry out the literature survey of topic of seminar.
2.Prepare a technical report on the selected area/topic.
3.Make an effective presentation with seamless flow of content within the time allocated. Overcome
inhibition in interacting with peers and hence develop the spirit of team work. Overcome stage
fear during the presentation.
SCHEME OF EXAMINATION
CIE – 50
marks
Phase -1 Marks =10 Seminar Report
Marks =20
Total:50
Marks Phase -2 Marks =20
Course Code 18CS4S01 M. Tech (Computer Science and Engineering)
Category Seminar Semester: IV
Course title SEMINAR – IV
Scheme and Credits
No. of Hours/Week
Total hours = 24 L T P S Credits
0 0 2 0 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any): NIL
CS85
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2 3 CO2 2 3 3 CO3 2
1. Low, 2. Medium, 3. High
Scheme of Continuous Internal Evaluation (CIE):
Evaluation would be carried out in TWO phases. The Seminar Evaluation Committee shall
comprise of Chairman of the Department, Faculty/Guide and one more faculty member nominated by
Chairman. The evaluation criteria shall be as per the rubrics given below:
Rubrics for Evaluation:
Topic - Technical Relevance, Sustainability and Societal Concerns : 15%
Review of Literature : 35%
Presentation Skills : 25%
Report : 25%
CS86
COURSE LEARNING OBJECTIVES:
1. Apply/Use different experimental techniques, equipments, software/ Computational/ Analytical
/Modelling and Simulation tools required for conducting tests and generate other relevant data.
Students will also be able to design and develop an experimental setup/test rig.
2. Analyse the results of the experiments conducted/models developed.
3. Create a detailed technical document as per format based on the outcome of dissertation work
phase I and II.
GUIDELINES
Dissertation work phase II is the continuation of project work started in III semester. The report of
Dissertation work that includes the details of Dissertation work phase I and phase II should be
presented in a standard format. The candidate shall prepare a detailed report of dissertation that
includes Cover Paper, Certificate from College/Industry/Organisation, Acknowledgement,
Abstract, Table of contents, List of Figures and Table, Nomenclature, Chapter of Introduction,
Literature survey, Conclusion of literature survey, Objectives and Scope of dissertation work,
Methodology, Experimentation, Results, Discussion, Conclusion, Scope for future work,
References, Annexure and full text of the publication (submitted or published).
COURSE OUTCOMES:
Students shall be able to:
1. Conduct experiments/ implement the capabilities of different Software /Computational /
Analytical/Modelling and simulation tools individually and generate data for validation of
hypothesis.
2. Investigate and assess the results obtained within the scope of experiments conducted followed by
conclusions.
3. Prepare a detailed technical document, Present and defend the contents of Dissertation work in
presence of technically qualified audience effectively.
Course Code 18CS4D01 M. Tech (Computer Science and Engineering)
Category Dissertation Work Semester: IV
Course title DISSERTATION WORK PHASE –II
Scheme and Credits
No. of Hours/Week
Total hours = 150 L T P S Credits
--- --- 30 --- 15
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100
Prerequisites (if any): NIL
CS87
SCHEME OF EXAMINATION
CIE
1. Marks awarded by guide = 50 marks
2. Marks awarded by the department dissertation monitoring committee
(Guide + Two faculty members )= 50 marks
100
marks
50*
marks
SEE
1. Dissertation evaluation by guide (Internal examiner) = 100 marks
2. Dissertation Evaluation by External Examiner = 100 marks
3. Viva- Voce examination by guide and external examiner who evaluated the
dissertation work =100 marks
300
marks
50**
marks
Note: * = CIE be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
** = SEE shall be conducted for 300 marks and the marks obtained shall be reduced for 50
marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 2 3
CO2 2 2 3
CO3 3 3 3
1. Low, 2. Medium, 3. High
Rubrics for CIE:
1. Presentation of Background of Dissertation Work = 10%
2. Literature survey, Problem Formulation and Objectives = 30%
3. Presentation of Methodology and Experimentation = 30%
4. Results and Discussion = 20%
5. Questions and Answers = 10%
Rubrics for SEE:
1. Originality = 05%
2. Literature Survey = 15%
3. Problem Formulation, Objectives and Scope of Work = 10%
4. Methodology, Experimentation/Theoretical Modelling = 10%
5. Results, Discussion and Conclusion = 20%
6. Questions and Answers = 20%
7. Submission/Publication of Technical Paper for Publication/ Presentation in
Journals/Conference = 20%
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 2 3
CO2 2 2 3
CO3 3 3 3
1. Low, 2. Medium, 3. High
BANGALORE UNIVERSITY
Department of Computer Science and Engineering
UNIVERSITY VISVESVARAYA COLLEGE OF ENGINEERING
K R Circle, Bengaluru-560 001.
Choice Based Credit System (CBCS)-2018
M. Tech in Computer Science and Engineering
Specialization: Information Technology
IT1
BANGALORE UNIVERSITY
VISION
―To strive for excellence in education for the realization of a vibrant and inclusive
society through knowledge creation and dissemination‖
MISSION
Impart quality education to meet national and global challenges
Blend theoretical knowledge with practical skills
Pursue academic excellence through high quality research and publications
Provide access to all sections of society to pursue higher education
Inculcate right values among students while encouraging competitiveness to
promote leadership qualities
Produce socially sensitive citizens
Hasten the process of creating a knowledge society
To contribute to nation building
IT2
Bangalore University UNIVERSITY VISVESVARAYA COLLEGE OF ENGINEERING
K R Circle, Bengaluru – 560 001.
University Visvesvaraya College of Engineering (UVCE) was started as a School of Mechanical
Engineering by Bharat Ratna Sir. M. Visvesvaraya in the year 1913 to meet the needs of the State for
skilled workers with S V Setty as its Superintendent. Later, it was converted to a full-fledged
Engineering College in the year 1917 under the name Government Engineering College and was
affiliated to the University of Mysore. It is the fifth Engineering College to be established in the country.
After the formation of Bangalore University in 1964, UVCE became one of the Constituent
Colleges of Bangalore University. This is one of the oldest Institutions in the country imparting
technical education leading to B.E., M.E, B.Arch., M.Sc. (Engineering), M.Arch. and Ph.D. degrees in
various disciplines of Engineering and Architecture. The Institution currently offers 7 Undergraduate
(B.E. / B.Arch.) Full-time, three Undergraduate (B.E.) Part-time and 24 Postgraduate (M.E. / M.Arch.)
Programmes.
VISION
The vision of UVCE is to strive for excellence in advancing engineering education through path
breaking innovations across the frontiers of human knowledge to realize a vibrant, inclusive and humane
society.
MISSION
The mission of UVCE is to prepare human resource and global leaders to achieve the above vision
through discovery, invention and develop friendly technologies to promote scientific temper for a
healthy society. UVCE shapes engineers to respond competently and confidently to the economic, social
and organizational challenges arising from globally advancing technical needs.
IT3
Bangalore University Bengaluru
Department of Computer Science and Engineering, UVCE, Bengaluru
M. Tech. DEGREE IN COMPUTER SCIENCE AND ENGINEERING under CBCS Scheme - 2K18
Specialization: Information Technology
Vision of the Department
Strive for Centre of Excellence in advancing Computer Science and Engineering education to produce
highly qualified human resources to meet local and global requirement.
Mission of the Department
CSM1. Implementing effectively, the outcome based education by imparting knowledge of basics and
advances in Computer Science and Engineering and other allied disciplines.
CSM2. Preparing and equipping human resources to become global leaders through innovation,
discovery, sustainable and environment friendly technology.
CSM3. Creatingconducive environment for effective teaching and learning process through
interdisciplinary research, online courses, interaction with institutions of higher learning and industries, R
and D laboratories of national importance, alumni, employers and other internal & external stake holders.
CSM4. Imbibing awareness of entrepreneurship, ethics, honesty, credibility, social and environmental
consciousness and providing opportunity to the faculty and technical staff for continuous academic
improvement and to equip them with then latest trends in Software Engineering and thereby inculcate the
habit of continuous learning in faculty, staff and students.
Program Outcomes:
Computer Science and Engineering Graduates will be able to:
CSPO1: An ability to independently carry out research/investigate and development work to solve
practical problems
CSPO2: An ability to write and present a substantial technical report/document
IT4
CSPO3: Students should be able to demonstrate a degree of mastery over the area as per the
specialization of the problem. The mastery should be at a level higher than the requirements in the
appropriate bachelor degree
Program Educational Objectives (PEO)
M. Tech (Information Technology)
After successful completion of the program, the graduates will be
ITPEO 1: Able to apply concepts of mathematical foundation and computing to Information
Technology
ITPEO 2: Able to design and develop interdisciplinary and innovative systems.
ITPEO 3: Able to inculcate effective communication skills, team work, ethics, leadership in
preparation for a successful career in industry and R&D organizations.
IT5
BANGLORE UNIVERSITY
SCHEME OF STUDIES AND EXAMINATION FOR 24MONTHS COURSE FOR THE AWARD OF
M. Tech. DEGREE IN INFORMATION TECHNOLOGY under CBCS Scheme – 2K18
Semester I Sl. No Course Type/ Course Name Teaching scheme Teaching Total CIE *SEE Credits
Course Code Hrs/Week DPT Hrs/week Marks Marks
L T P S
1 18CS1C01 Mathematical Foundations of Computer Science 3 1 0 0 CSE 4 50 50 4
2 18CS1C02 Advances in Computer Networks 4 0 0 0 CSE 4 50 50 4
3 18CS1C03 Advanced Database Management Systems 4 0 0 0 CSE 4 50 50 4
18CS1E1A Cloud Computing 4 0 0 0 CSE
4 18CS1E1B Mobile Computing 4 0 0 0 CSE 4 50 50 4
18CS1E1C Wireless Networks 4 0 0 0 CSE
18CS1E2A Soft Computing 3 0 2 0 CSE
5 18CS1E2B Advances in Storage Area Networks 4 0 0 0 CSE 4 50 50 4
18IT1E2C Web Engineering 4 0 0 0 CSE
6 18CS1L01 Network Programming Lab 0 0 4 0 CSE 4 50 50 2
7 18CS1M01 Research Methodology and IPR. 2 0 0 0 CSE 2 50 50 2
8 18IT1S01 Seminar -I 0 0 2 0 CSE 2 50 -- 1
9 18CS1M02 Audit Course-I (Technical Paper Writing) 2 0 0 0 English 2 50 -- 1
Total 30 450 350 26
*SEE shall be conducted for 100 marks and the marks obtained is to be reduced for 50 marks.
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Semester II Sl. No Course Type/ Course Name Teaching scheme Teaching Total CIE *SEE Credits
Course Code Hrs/Week DPT Hrs/week Marks Marks
L T P S
1 18CS2C01 Advanced Data Structures and Algorithms 4 0 0 0 CSE 4 50 50 4
2 18CS2C02 Advanced Operating Systems 4 0 0 0 CSE 4 50 50 4
3 18CS2C03 Advances in Digital Image Processing 4 0 0 0 CSE 4 50 50 4
4
18CS2E1A Data Warehousing and Mining 4 0 0 0 CSE
4
50
50
4
18CS2E1B Stochastic Process and Queuing Theory 4 0 0 0
18CS2E1C Internet of Things 3 0 2 0
5
18CS2E2A Network Security 4 0 0 0
CSE 4 50 50 4 18IT2E2B Cyber Security 4 0 0 0
18CS2E2C Web Security 4 0 0 0
6 18CS2L01 Advanced Data Structures and Algorithms Lab 0 0 4 0 CSE 4 50 50 2
7 18IT2S01 Seminar -II 0 0 2 0 CSE 2 50 -- 1
8 18CS2M01 Audit Course-II (Pedagogy Studies) 2 0 0 0 CSE 2 50 -- 1
Total 28 400 300 24
Semester III Sl. No Course Type/ Course Name Teaching scheme Teaching Total CIE *SEE Credits
Course Code Hrs/Week DPT Hrs/week Marks Marks
L T P S
1
2
18IT3E1A Social Network 4 0 0 0 CSE
CSE
CSE
4
4
50
50
50
50
4
4
18CS3E1B Big Data Analytics 3 0 2 0
18IT3E1C Information Retrieval Systems
4 0 0 0
Open Elective
3 18IT3S01 Seminar -III 0 0 2 0 CSE 2 50 1
4 18IT3I01 Internship / Mini Project 0 0 10 0 CSE 10 50 50 5
5 18IT3D01 Dissertation Phase -I 0 0 10 0 CSE 10 50 50 5
Total 30 250 200 19
IT7
Semester IV Sl. No Course Type/ Course Name Teaching scheme Teaching Total CIE *SEE Credits
Course Code Hrs/Week DPT Hrs/week Marks Marks
L T P S
1 18IT4S01 Seminar -IV 0 0 2 0 CSE 2 50 1
2 18IT4D01 Dissertation Phase -II 0 0 30 0 CSE 30 50 50 15
Total -- -- 32 -- 32 100 50 16
1 18ITMOOC MOOC Course 0 0 0 0 03
Grand Total of Credits 88
COURSE TYPE
CS: COMPUTER SCIENCE C: PROFESSIONAL CORE E: PROFESSIONAL ELECTIVE
P: OPEN ELECTIVE M: MANDATORY AUDIT L: LAB
S: SEMINAR I: INTERNSHIP/ MINI PROJECT D: DISSERTATION
L – Theory lecture, T – Tutorial, P – Lab work, S – Self-study:
Numbers under teaching scheme indicates contact clock hours.
IT8
Open Elective
Sl. No Course Type /
Course Code Course Name
Teaching Scheme (No. of hrs per week)
Teaching
Dept.
Total hrs
/ week
CIE
Marks
*See
Marks Credits
L T P S
1
18CS3P1A Artificial Intelligence
4 0 0 0 CSE 4 50 50 4 18CS3P1B Business Analytics
18CS3P1C Modeling and Simulation
2
18CV3P1A Significance of National Building Codes
4 0 0 0 Civil 4 50 50 4
18CV3P1B Water Laws, Rights and Administration
18CV3P1C Waste to Energy
18CV3P1D Remote Sensing and Geographic Information
System
3 18ME3P1A Composite and Smart Materials
4 0 0 0 Mech 4 50 50 4 18ME3P1B Industrial Safety
4
18EE3P1A Real Time Embedded Systems
4 0 0 0 EEE 4 50 50 4 18EE3P1B Robotics and Automation
18EE3P1C Solar and Wind Energy
5
18EC3P1A Reliability and Engineering
4 0 0 0 ECE 4 50 50 4 18EC3P1B M-Commerce and Applications
18EC3P1C Optimization Techniques
IT9
Course Code 18CS1C01 M. Tech (Information Technology)
Category Engineering Science Courses (Theory- Professional Core )
Course title MATHEMATICAL FOUNDATIONS OF COMPUTER SCIENCE
Scheme and
Credits
No. of Hours/Week Semester – I
L T P S Credits
3 1 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
1. Basics of probability
2. Basics of graph theory
COURSE OBJECTIVES
The course will enable the students to:
1. Understand the concepts of number theory and solve related problems.
2. Apply the concepts of stochastic process and queuing theory required to devise
analytical models for the real problems of computer science.
3. Analyze the various concepts of arranging, selecting and combining objects from a
set.
4. Understand the concept of advanced graph theory that can be used to model any
network, physical or conceptual.
UNIT -I NUMBER THEORY: 10 Hours
The Division algorithm, the Greatest Common Divisor, the Euclidean algorithm. The basic
properties of Congruencies, Binary and decimal representation of integer, linear congruence,
Chinese-Reminder Theorem, Fermat‘s Little theorem, The sum and number of Divisors, The
mobius inversion formula, The Greatest integer function (No theorem proofs).
UNIT -II PROBABILITY AND QUEUING THEORY: 10 Hours
Random Variables, Probability Distribution, Binomial Distribution, Poisson Distribution,
Geometric Distribution, Exponential Distribution, Normal Distribution, Uniform
Distribution. Two Dimensional Random Variables. Introduction to Stochastic Processes,
Markov process, Markov chain, one step and n-step Transition Probability, Chapman
Kolmogorov theorem (Statement only), Transition Probability Matrix, Classification of
States of a Markov chain. Introduction to Markovian queuing models, Single Server Model
with Infinite system capacity, Characteristics of the Model (M/M/1) : (∞/FIFO), Single
Server Model with Finite System Capacity, Characteristics of the Model (M/M/1) :
(K/FIFO).
UNIT -III COMBINATORICS: 10 Hours
Basics of Counting: Permutations, Permutations with Repetitions, Circular Permutations,
Restricted Permutations, Combinations: Restricted Combinations, Generating Functions of
Permutations and Combinations, Binomial and Multinomial Coefficients, Binomial and
Multinomial Theorems, The Principles of Inclusion Exclusion, Pigeonhole Principle and its
Application.
UNIT -IV RECURRENCE RELATIONS: 09 Hours Generating Functions, Function of Sequences, Partial Fractions, Calculating Coefficient of
Generating Functions, Recurrence Relations, Formulation as Recurrence Relations, Solving
Recurrence Relations by Substitution and Generating Functions, Method of Characteristic
Roots, Solving Inhomogeneous Recurrence Relations.
UNIT –V GRAPH THEORY: 09 Hours
Basic Concepts of Graphs, Sub graphs, Matrix Representation of Graphs: Adjacency
Matrices, Incidence Matrices, Isomorphic Graphs, Paths and Circuits, Eulerian and
Hamiltonian Graphs, Multi-graphs, Planar Graphs, Euler‗s Formula, Graph Colouring and
IT10
Covering, Chromatic Number, Spanning Trees, Algorithms for Spanning Trees (Concepts
and Problems Only, Theorems without Proofs).
UNIT-VI Recent advances and research being done in the topics mentioned above
units
REFERENCES
1. David M Burton, ―Elementary Number Theory‖, Allyn and Bacon, 1980.
2. K. S. Trivedi, ―Probability and Statistics with Reliability, Queuing for Computer
Science Applications‖, John Wiley and Sons, II Edition, 2008.
3. Gunter Bolch, Stefan Greiner, Hermann De Meer, K S Trivedi, ―Queuing Networks
and Markov Chains‖, John Wiley and Sons, II Edition, 2006.
4. Richard A Brualdi, Introductory Combinatorics 5th
Edition, Pearson 2009
5. J. A. Bondy and U. S. R. Murty, ―Graph Theory and Applications‖, Macmillan
Press, 1982.
COURSE OUTCOMES
On completion of the course, the student would be able to:
CO1. Solve problems related to number theory.
CO2: Design the analytical models using the concepts of probability and stochastic process.
CO3: Compare the various methods of counting using permutations and combinations.
CO4: Solve the problems of recurrence relations.
CO5: Apply the graph theory concepts in solving problems related to computer science.
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Total:
Marks 100 Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 1 CO2 2 CO3 1 1 CO4 1 CO5 2
1: Low 2: Medium 3:High
IT11
Course Code 18CS1C02 M. Tech (Information Technology)
Category Engineering Science Courses
Course title ADVANCES IN COMPUTER NETWORKS
Scheme and
Credits
No. of
Hours/Week
Semester – I
L T P SS Credits
4 0 - - 4
CIE Marks:
50
SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3
Hrs
Prerequisites (if any):
COURSE OBJECTIVE
The course will enable the student to:
1. Understand the requirement of various high speed networks
2. Learn the effect of congestion and its control.
3. Understand Network Traffic Management for reliable delivery.
4. Understand integrated and differentiated architecture and services.
5. Learn the effect of traffic in the networks on various QoS parameters
UNIT I- INTRODUCTION 9 Hours
OSI and TCP/ IP Reference Model, RS-232C, RS-449, Devices used in Physical Layers,
Framing, Flow and Error Control, Error Detection and Correction, Checksum, Sliding
Window Protocols-ARQ.
UNIT II- DATA LINK LAYER 10 Hours Multiple Access Control Protocols(MAC), ALOHA, CSMA/CD (802.3), Data Link
Protocol- HDLC,PPP, Wired LAN‘s : Fast Ethernet, Gigabit Ethernet, Fibre Channel,
Wireless LAN‘s(802.11), Broadband Wireless(802.16).
UNIT III- ROUTING AND CONGESTION CONTROL 10 Hours Design issues, Routing Algorithms, Distance Vector Routing, Link State Routing, Routing
in Mobile Host, Broadcast Routing, Reverse Path Forwarding, OSPF, IP Protocols (IPv4 -
ARP, RARP, IPv6), Queuing Analysis- Queuing Models – Single Server Queues –
Effects of Congestion – Congestion Control – Traffic Management – Congestion Control
in Packet Switching Networks.
UNIT IV – TRANSPORT LAYER AND INTEGRATED SERVICES 10 Hours
TCP Header, TCP Flow control – TCP Congestion Control – Retransmission – Timer
Management – Exponential RTO back-off – KARN‘s Algorithm – Window
management. Integrated Services Architecture – Approach, Components, Services-
Queuing Discipline, FQ, PS, BRFQ, GPS, WFQ – Random Early Detection,
Differentiated Services.
UNIT V - PROTOCOLS FOR QOS SUPPORT 09 Hours
RSVP – Goals & Characteristics, Data Flow, RSVP operations, Protocol
Mechanisms – Multiprotocol Label Switching – Operations, Label Stacking, Protocol
details – RTP – Protocol Architecture, Data Transfer Protocol, RTCP.
UNIT VI- To understand latest innovative networks such as Software Defined
Networks(SDN).
REFERENCES
1. Behrouz A Forouzan and Firouz Mosharraf, ―Computer Networks, A Top-Down
Approach‖, TMH, 2012.
2. Andrew S. Tanenbaum and David J. Wetherall, ―Computer Networks‖, Pearson Education, 5th
Edition,2011. 3. William Stallings, ―High Speed Networks and Internet‖, , Second Edition, 2012.
IT12
4. Prakash C Guptha, ―Data Communication and Computer Networks‖, PHI , 6th
printing 2012.
5. Larry L. Peterson and Bruce S Davis , ―Computer Network A System
Approach‖, Elsevier, 5th
edition 2010.
COURSE OUTCOMES
On Completion of the course, the student will be able to:
CO1: Apply the networking principles to manage the network traffic.
CO2: Control the various anomalies in the network to improve the QoS.
CO3: Study the relation and effect of one QoS parameter on the other.
CO4: Apply the efficient techniques to achieve effective and reliable communication.
CO5: Develop new protocols to mitigate emerging problems.
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100 Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2
CO3 3 2 2
CO4 3 2
CO5 2 2 2
1. Low, 2. Medium, 3. High
IT13
Course Code 18CS1C03 M. Tech (Information Technology)
Category Engineering Science Courses (Theory- Professional Core )
Course title ADVANCED DATABASE MANAGEMENT SYSTEMS
Scheme and Credits No. of Hours/Week Semester – I
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES:
The course will enable the students to:
1. Remembering the basics of database management systems.
2. Understanding the concepts of object relational databases and XML
3. Evaluate database security strategies.
4. Applying the concepts of Data Storage and Querying.
5. Understanding distributed, parallel databases and recent technologies
UNIT- I INTRODUCTION 09 Hours
Data models, schemas and instances, three schema architecture and data independence,
database languages and interfaces, database environment. ER model: entity types, entity sets,
attributes and keys, relationship types, relationship sets, roles and structural constraints, ER
Diagrams. SQL3 - Overview of the SQL Query Language, SQL Data Definition, Basic
Structure of SQL Queries, Additional Basic Operations, Set Operations, Null Values,
Aggregate Functions, Nested Subqueries.
UNIT-II OBJECT AND OBJECT RELATIONAL DATABASES 10 Hours Object oriented concepts, object identity, object structure and type constructors, encapsulation
of operations, methods and persistence, class hierarchies and inheritance, object model of
ODMG, object definition language, object query language.XML: Structured, Semi structured,
and Unstructured Data, Data Model, Documents, DTD, XML Schema, Storing and Extracting
XML Documents from Databases, XML Languages.
UNIT-III DATABASE SECURITY 09 Hours Issues, discretional access control and role base access control, SQL Injection, statistical
database security, public key infrastructure, privacy issues and preservation, Oracle Label-
Based Security
UNIT- IV INDEXING AND HASHING 10 Hours Basic Concepts, Ordered Indices, B + -Tree Index Files, B + -Tree Extensions, Multiple-Key
Access, Static Hashing, Dynamic Hashing, Comparison of Ordered Indexing and Hashing,
Bitmap Indices. Query Processing: Overview, Measures of Query Cost, Selection Operation,
Sorting, Join Operation, Evaluation of Expressions. Query Optimization: Overview,
Transformation of Relational Expressions, Estimating Statistics of Expression Results, Choice
of Evaluation Plans, Materialized Views.
UNIT-V PARALLEL AND DISTRIBUTED DATABASES 10 Hours Parallel Databases: Introduction, I/O Parallelism, Interquery Parallelism, Intraquery
Parallelism, Intraoperation Parallelism, Interoperation Parallelism, Query Optimization, Design
of Parallel Systems, Parallelism on Multicore Processors. Distributed Databases:
Homogeneous and Heterogeneous Databases, Distributed Data Storage, Distributed
Transactions, Commit Protocols, Concurrency Control in Distributed Databases, Availability,
Distributed Query Processing, Heterogeneous Distributed Databases, Cloud-Based Databases,
IT14
Directory Systems.
UNIT-VI RECENT TECHNOLOGIES
Latest technologies such as NoSQL, BigData, Multimedia Databases, Mobility and Personal
Databases
REFERENCES
1. Elmasri and Navathe, Fundamentals of Database Systems, 7th
edition, Pearson, 2016.
2. A. Silberschatz, H. F. Korth and S. Sudarshan, Database system concepts 6th ed. 2011
3. Raghu Ramakrishnan, Database Management System, McGraw Hill, 3rd
edition, 2003.
4. Ceri and Pelagatti, Distributed Databases: Principles and Systems, Tata McGraw Hill, 2008,
5. C.J.Date, A.Kannan and S.Swamynathan, An introduction to Database System, Pearson
Education, 8th
edition, 2009.
6. Dr. P.S. Deshpande, SQL and PL/SQL for Oracle log, Black Books Dreamtech Press.
COURSE OUTCOMES
Upon completion of the course, the students would be able to: CO1: State and identify the key concepts of database management systems
CO2: Design and implement object relational databases.
CO3: Determine the different strategies for database security and key issues.
CO4: Apply the concepts of query optimization and indexing.
CO5: Illustrate distributed and parallel database technologies.
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit VI(AAT)=15
marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks..
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 1
CO2 1
CO3 1
CO4 1 2
CO5 2
1. Low, 2. Medium, 3. High
IT15
Course Code 18CS1E1A M. Tech (Information Technology)
Category Engineering Science Courses (Theory- Professional Elective )
Course title CLOUD COMPUTING
Scheme and Credits No. of Hours/Week Semester – I
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
1. Operating systems
2. Basics of distributed computing
COURSE OBJECTIVES
The course will enable the students to:
1. Understand the various cloud service providers and cloud interoperability
2. Apply the cloud computing applications and paradigms
3. Analyse the concept of virtualization
4. Acquire the knowledge of the cloud resource management and scheduling mechanism
5. Learn various security issues in cloud computing
UNIT-I CLOUD INFRASTRUCTURE 09 Hours
Cloud computing at Amazon, Cloud computing-the Google perspective, Microsoft Windows
Azure and Online services, Open-Source Software Platforms for Private Clouds Cloud Storage
Diversity and Vendor Lock-in, Cloud Computing Interoperability: The Intercloud, Service- and
Compliance-Level Agreements, Responsibility Sharing Between User and Cloud Service
Provider, User Experience, Software Licensing.
UNIT- II CLOUD COMPUTING: APPLICATIONS AND PARADIGMS 09 Hours Challenges for Cloud Computing, Existing Cloud Applications and New Application
Opportunities Architectural Styles for Cloud Applications, Workflows: Coordination of Multiple
Activities, Coordination Based on a State Machine Model: The ZooKeeper, The MapReduce
Programming Model, A Case Study: The GrepTheWeb Application, High-Performance
Computing on a Cloud.
UNIT-III CLOUD VIRTUALIZATION 10 Hours Virtualization, Layering and Virtualization, Virtual Machine Monitors, Virtual Machines,
Performance and Security Isolation, Full Virtualization and Paravirtualization, Hardware Support
for Virtualization, Case Study: Xen, a VMM Based on Paravirtualization, Optimization of
Network Virtualization in Xen 2.0, vBlades: Paravirtualization Targeting an x86-64 Itanium
Processor, A Performance Comparison of Virtual Machines.
UNIT-IV CLOUD RESOURCE MANAGEMENT AND SCHEDULING 10 Hours Policies and Mechanisms for Resource Management, Applications of Control Theory to Task
Scheduling on a Cloud, Stability of a Two-Level Resource Allocation Architecture, Feedback
Control Based on Dynamic Thresholds, Coordination of Specialized Autonomic Performance
Managers, A Utility-Based Model for Cloud-Based Web Services, Resource Bundling:
Combinatorial Auctions for Cloud Resources, Scheduling Algorithms for Computing Clouds,
Fair Queuing, Start-Time Fair Queuing, Borrowed Virtual Time Cloud Scheduling Subject to
Deadlines, Scheduling MapReduce Applications Subject to Deadlines, Resource Management
and Dynamic Application Scaling.
UNIT-V CLOUD SECURITY 10 Hours Cloud Security Risks, Security: The Top Concern for Cloud Users, Privacy and Privacy Impact
Assessment, Trust Operating System Security, Virtual Machine Security, Security of
Virtualization, Security Risks Posed by Shared Images, Security Risks Posed by a Management
OS.
IT16
UNIT-VI Recent developments and current research in multi cloud, cloud security, mobile
cloud computing.
REFERENCES
1. Dan C Marinescu, ―Cloud Computing: Theory and Practice‖, Morgan
Kaufmann/Elsevier. 2013.
2. George Reese, ―Cloud Application Architectures: Building Applications and
Infrastructure in the Cloud‖, O‘Reilly, 2009.
3. Rajkumar Buyya, James Broberg and Andrzej M. Goscinski , ―Cloud Computing:
Principles and Paradigms‖, Wiley, 2011.
4. Kai Hwang, Geoffrey C Fox, Jack G Dongarra, ―Distributed and Cloud Computing: From
Parallel Processing to the Internet of Things‖, Morgan Kaufmann Publishers, 2012.
COURSE OUTCOMES
Upon completion of the course, the students would be able to:
CO1: Categorize the architectures, services and delivery models in cloud computing
CO2: Implement the concept of virtualization and its management in cloud computing
CO3: Design the extended functionalities of resource management and scheduling mechanisms
CO4: Analyse the security models in cloud environment
CO5: Investigate recent developments in multi cloud, cloud security and mobile cloud computing
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit VI(AAT)=15
marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2
CO2 2
CO3 1 2
CO4 2 1
CO5 2 2
2. Low, 2. Medium, 3. High
IT17
Course Code 18CS1E1B M. Tech (Information Technology)
Category Engineering Science Courses (Theory- Professional Elective )
Course title MOBILE COMPUTING
Scheme and Credits No. of Hours/Week Semester – I
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
1. Computer Networks
2. Database Management Systems
3. Operating Systems
COURSE OBJECTIVES:
The course will enable the students to:
1. Understand the GSM architecture, services and protocols.
2. Understand the wireless MAC, mobile IP and transport layer functions and protocols.
3. Analyse the concepts of mobile databases, data dissemination, broadcasting systems and data
synchronization.
4. Review various mobile technologies including WLAN, WiFi, WAP, Bluetooth, Zigbee.
5. Understand mobile application languages and mobile operating systems
UNIT- I MOBILE COMPUTING ARCHITECTURE AND GSM 09 Hours
Mobile Computing Architecture: Types of Networks, Architecture for Mobile Computing, 3-tier
Architecture, Design Considerations for Mobile Computing. GSM: Services and System Architectures,
Radio Interfaces, Protocols, Localization, Calling, Handover, General Packet Radio Service.
UNIT-II WIRELESS MAC, IP and TRANSPORT LAYER 10 Hours
Medium Access Control, Introduction to CDMA based Systems, IP and Mobile IP Network Layers,
Packet Delivery and Handover Management, Location Management, Registration, Tunnelling and
Encapsulation, Route Optimization, Dynamic Host Configuration Protocol. Indirect TCP, Snooping
TCP, Mobile TCP, Other Methods of TCP.
UNIT-III DATABASES, DATA DISSEMINATION AND BROADCASTING SYSTEMS 10
Hours
Database Hoarding Techniques, Data Caching, Client – Server Computing and Adaptation,
Transactional Models, Query Processing, Data Recovery Process, Issues relating to Quality of Service.
Communication Asymmetry, Classification of Data – Delivery Mechanisms, Data Dissemination
Broadcast Models, Selective Tuning and Indexing Techniques, Digital Audio Broadcasting, Digital
video Broadcasting.
UNIT-IV DATA SYNCHRONIZATION IN MOBILE COMPUTING SYSTEMS 09 Hours
Synchronization, Synchronization Protocols, SyncML – Synchronization Language for Mobile
Computing, Synchronized Multimedia Markup Language (SMIL). –
UNIT-V MOBILE DEVICES, SERVER AND MANAGEMENT AND MOBILE APPLICATION
LANGUAGES 10 Hours
Wireless LAN, Mobile Internet Connectivity and Personal Area Network, Mobile agent, Application
Server, Gateways, Portals, Service Discovery, Device Management, Mobile File Systems. Wireless
LAN (Wi-Fi) Architecture and Protocol Layers, WAP 1.1 and WAP 2.0 Architectures, Bluetooth –
enabled Devices Network, Zigbee. XML, JAVA, J2ME and JAVACARD, Mobile Operating Systems:
Introduction, PalmOS, Windows CE, Symbian OS, Linux for Mobile Devices.
UNIT-VI Recent trends in wireless and mobile network security, mobile cloud computing.
IT18
REFERENCES
1. Raj Kamal, ―Mobile Computing‖, Oxford University Press, 2007.
2. Ashok Talukder, Ms Roopa Yavagal, and Mr. Hasan Ahmed, ―Mobile Computing,
Technology, Applications and Service Creation‖, II Edition, Tata McGraw Hill, 2010.
3. Jochen Schiller, ―Mobile Communications‖, Addison-Wesley. II Edition, 2004.
4. Hansmann, Merk, Nicklous, Stober, ―Principles of Mobile Computing‖, Springer, II Edition,
2003.
COURSE OUTCOMES
Upon completion of the course, the student would be able to:
CO1: Demonstrate the knowledge of GSM architecture, services and protocols.
CO2: Simulate a typical GSM network and demonstrate the performance analysis.
CO3: Extending the functionalities of mobile IP and transport layer protocols.
CO4: Apply the mobile application languages to design mobile applications.
CO5: Investigate recent developments in wireless, mobile network security and mobile cloud
computing.
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit VI(AAT)=15
marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2
CO3 2
CO4 2 2
CO5 2 2
1. Low, 2. Medium, 3. High
IT19
Course Code 18CS1E1C M. Tech (Information Technology)
Category Engineering Science Courses (Theory- Professional Elective )
Course title WIRELESS NETWORKS
Scheme and
Credits
No. of Hours/Week Semester – I
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks:
50
Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
Computer Networks
COURSE OBJECTIVES:
The course will enable the students to:
1. Get familiar with the wireless market and the future needs and challenges.
2. Learn the key concepts of wireless networks, standards, technologies and their
basic operations
3. Know various generations of cellular networks and learn cellular architecture
4. Understand the key concept of sensor networks
5. Analyse security techniques and vulnerabilities
UNIT- I INTRODUCTION 09 Hours
Wireless Networking Trends, Key Wireless Physical Layer Concepts, Multiple Access
Technologies -CDMA, FDMA, TDMA, Spread Spectrum technologies, Frequency reuse,
Radio Propagation and Modelling, Challenges in Mobile Computing: Resource poorness,
Bandwidth, energy etc.
UNIT-II WIRELESS LOCAL AREA NETWORKS 10 Hours
IEEE 802.11 Wireless LANs Physical & MAC layer, 802.11 MAC Modes (DCF & PCF)
IEEE 802.11 standards, Architecture & protocols, Infrastructure vs. Adhoc Modes, Hidden
Node & Exposed Terminal Problem, Fading Effects in Indoor and outdoor WLANs,
WLAN Deployment issues.
UNIT- III WIRELESS CELLULAR NETWORKS 10 Hours
1G and 2G, 2.5G, 3G, and 4G, Mobile IPv4, Mobile IPv6, TCP over Wireless Networks,
Cellular architecture, Frequency reuse, Channel assignment strategies, Handoff strategies,
Interference and system capacity, Improving coverage and capacity in cellular systems
UNIT- IV WIRELESS SENSOR NETWORKS 10 Hours
Introduction, Application, Physical, MAC layer and Network Layer, Power Management,
Tiny OS Overview. Wireless Pans Bluetooth and Zigbee, Introduction to Wireless
Sensors networks, deployment, key design challenges, network deployment, Routing
protocols, routing challenges and design issues, routing strategies.
UNIT-V SECURITY 09 Hours
Security in wireless Networks, Vulnerabilities, Security techniques, Wi-Fi Security, DoS
in wireless communication.
UNIT-VI RECENT TRENDS Recent trends in Wireless networks, Vehicular Adhoc Networks.
IT20
REFERENCES
1. Schiller J., Mobile Communications, Addison Wesley 2000
2. Stallings W., Wireless Communications and Networks, Pearson Education 2005
3. Stojmenic Ivan, Handbook of Wireless Networks and Mobile Computing, John Wiley
and Sons Inc 2002
4. Yi Bing Lin and Imrich Chlamtac, Wireless and Mobile Network Architectures, John
Wiley and Sons Inc 2000
5. Pandya Raj, Mobile and Personal Communications Systems and Services, PHI 2000
6.Feng Zhao, leonidas Guibas, ―Wireless sensor Networks: An information processing
approach‖, Elsevier, 2004
COURSE OUTCOMES
Upon completion of the course, the students will be able to:
CO1: Demonstrate advanced knowledge of networking and wireless networking
CO2: Understand various types of wireless networks, standards, operations and use cases.
CO3: Be able to design and compare cellular based upon underlying propagation and
performance analysis.
CO4: Demonstrate knowledge of WPAN and sensor networks
CO5: Assess security measure and vulnerabilities.
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit VI(AAT)=15
marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for
50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 3
CO2 2 3
CO3 2 3
CO4 3 3
CO5 1 3
1. Low, 2. Medium, 3. High
IT21
Course Code 18CS1E2A M. Tech (Information Technology)
Category Engineering Science Courses (Integrated- Professional Elective)
Course title SOFT COMPUTNG
Scheme and
Credits
No. of Hours/Week Semester – I
L T P S Credits
3 0 2 0 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
1. Basic knowledge of mathematics
COURSE OBJECTIVES:
The course will enable the students to:
1. Describe soft computing concepts and techniques and foster their abilities in
designing appropriate technique for a given scenario.
2. Choose Neural network algorithms for real – world problems.
3. Analyse and compare the different Optimization techniques.
4. Develop the applications of Genetic Algorithms in Machine Learning.
5. Provide a hands-on experience on MATLAB to implement various strategies
UNIT-I INTRODUCTION TO SOFT COMPUTING AND NEURAL NETWORKS
09 Hours
Evolution of Computing: Soft Computing Constituents, Conventional AI to Computational
Intelligence: Machine Learning Basics, Hard-Margin and Soft-Margin SVMs- Concepts of
Kernel and Feature Spaces, Basics of Optimization and Quadratic programming,
Introduction to Steganography and Applications of SVMs to Steganalysis
UNIT-II NEURAL NETWORKS 10 Hours Introduction to
ANN, Architectures, Learning methods, Bayesian Networks, Back Propagation network,
Perceptrons, Hopfield Networks, Kohonen Self Organizing Feature Maps, Chaos Theory
UNIT-III OPTIMIZATION TECHNIQUES 09 Hours Introduction, Elitism based Ant Colony Optimization, Min-Max based Ant Colony
Optimization, Particle Swarm Optimization, Artificial Bee Colony Optimization, Multi-
Swarm Optimization, Cuckoo Search, Whole Optimization, Firefly algorithm, Bat
Algorithm, Introduction to missing data-Imputation techniques, Principal Component
Analysis, Gradient Descent
UNIT-IV GENETIC ALGORITHMS and FUZZY LOGIC 10 Hours
Introduction to Genetic Algorithms (GA), Applications of GA in Machine Learning:
Machine Learning Approach to Knowledge Acquisition. Fuzzy Logic: Fuzzy Sets,
Operations on Fuzzy Sets, Fuzzy Relations, Membership Functions: Fuzzy Rules and Fuzzy
Reasoning, Fuzzy Inference Systems, Fuzzy Expert Systems, Fuzzy Decision Making,
Defuzzification
UNIT-V Matlab Lib 10 Hours
Introduction to Matlab, Arrays and array operations, Functions and Files, Study of neural
network toolbox and fuzzy logic toolbox, Simple implementation of Artificial Neural
Network and Fuzzy Logic
UNIT-VI
Recent Trends in deep learning, various classifiers, neural networks and genetic algorithm.
Implementation of recently proposed soft computing techniques
UNIT-VII (Lab Programs)
1. a) Write a MATLAB Program for Hebb Net to classify two dimensional input
IT22
patterns in bipolar with given targets.
b) Generate XOR function and ANDNOT function using McCulloch-Pitts Neural
Network.
2. Classification of a 4-Class problem with a Perceptron using MATLAB.
3. Write a MATLAB program to apply Back Propagation network for pattern
recognition problem.
4. Develop a Kohonen Self Organizing feature map for image recognition problem.
5. Write a MATLAB program to implement Discrete Hopfield Network and test the
input pattern.
6. Write a MATLAB program for edge detection using Fuzzy logic.
7. Use a genetic algorithms approach for Travelling Salesman Problem.
8. Develop a simple Ant Colony Optimization problem with MATLAB to find the
optimum path.
9. Solve a feature selection problem using Artificial Bee Colony Optimization.
10. Implementation of minimum Spanning tree using Particle Swarm Optimization.
REFERENCES
1. S. N. Sivanandam and S. N. Deepa, ―Principles of Soft Computing‖, 2nd
Edition,
Wiley India, 2012.
2. Samir Roy, Udit Chakraborty, ―Introduction to Soft Computing- Neuro-Fuzzy and
Genetic Algorithms‖, First Edition, 2013.
3. David E Goldberg, ―Genetic Algorithms in Search Optimization and Machine
Learning‖, Addison Wesley, 1997.
4. MATLAB Toolkit Manual.
COURSE OUTCOMES
Upon completion of the course, the students would be able to:
CO1: Explain the concepts and techniques of soft computing and their roles in building
intelligent machines
CO2: Apply fuzzy logic and reasoning to handle uncertainty and solve various
engineering problems.
CO3: Differentiate the various Optimization techniques.
CO4: Implement and evaluate the genetic algorithms in Machine learning.
CO5: Evaluate and compare solutions by various soft computing approaches for a given
Problem.
SCHEME OF EXAMINATION:
CIE -
Practical
Conduction of experiments, performance of student in
every week lab and completion of lab record=50#
Total: Marks
50
Lab Test: Part-A =20, Part-B=20 and Vice-Voce=10
Marks(50##
)
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
IT23
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
# = CIE Marks for laboratory performance is to be evaluated for 50 marks and the
marks obtained shall be reduced for 25 marks
## = Lab test is to be conducted for 50 marks and the marks obtained shall be
reduced for 25 marks. Lab test shall be conducted by two examiners out of which
one examiner is the faculty taught the course. There is no SEE for the practical ‘s
portion of integrated course
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 3
CO2 3 3
CO3 3 3
CO4 3 3
CO5 3 3
1. Low, 2. Medium, 3. High
IT24
Course Code 18CS1E2B M. Tech (Information Technology)
Category Engineering Science Courses (Theory- Professional Elective )
Course title ADVANCES IN STORAGE AREA NETWORKS
Scheme and Credits No. of Hours/Week Semester – I
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
1.Computer Networks
2.Database Management Systems
3.Operating Systems
COURSE OBJECTIVES
This course will enable the students to
1. Understand storage centric and server centric systems
2. Apply various metrics used for designing storage area networks
3. Analysis RAID concepts
4. Evaluate data maintains at data centres with the concepts of backup
5. Create techniques for data storage management at data centres
UNIT -I INTRODUCTION: 10 Hours
Server Centric IT Architecture and its Limitations; Storage – Centric IT Architecture and its
advantages. Case study: Replacing a server with Storage Networks The Data Storage and Data
Access problem; The Battle for size and access. Intelligent Disk Subsystems: Architecture of
Intelligent Disk Subsystems; Hard disks and Internal 8 Hours I/O Channels; JBOD, Storage
virtualization using RAID and different RAID levels; Caching: Acceleration of Hard Disk
Access; Intelligent disk subsystems, Availability of disk subsystems.
UNIT -II I/O TECHNIQUES: 10 Hours
The Physical I/O path from the CPU to the Storage System; SCSI; Fibre Channel Protocol
Stack; Fibre Channel SAN; IP Storage. Network Attached Storage: The NAS Architecture, The
NAS hardware Architecture, The NAS Software Architecture, Network connectivity, NAS as a
storage system. File System and NAS: Local File Systems; Network file Systems and file
servers; Shared Disk file systems; Comparison of fibre Channel and NAS.
UNIT -III STORAGE VIRTUALIZATION: 10 Hours
Definition of Storage virtualization; Implementation Considerations; Storage virtualization on
Block or file level; Storage virtualization on various levels of the storage Network; Symmetric
and Asymmetric storage virtualization in the Network.
UNIT- IV SAN ARCHITECTURE AND HARDWARE DEVICES: 09
Hours
Overview, Creating a Network for storage; SAN Hardware devices; The fibre channel switch;
Host Bus Adaptors; Putting the storage in SAN; Fabric operation from a Hardware perspective.
Software Components of SAN: The switch‘s Operating system; Device Drivers; Supporting the
switch‘s components; Configuration options for SANs.
UNIT–V MANAGEMENT OF STORAGE NETWORK: 09
Hours
System Management, Requirement of management System, Support by Management System,
Management Interface, Standardized Mechanisms, Property Mechanisms, In-band Management,
IT25
Use of SNMP, CIM and WBEM, Storage.
UNIT-VI Recent advances and research being done in the topics mentioned above units
REFERENCES
1. Ulf Troppens, Rainer Erkens and Wolfgang Muller: Storage Networks Explained, Wiley
India 2013.
2. Robert Spalding: ―Storage Networks The Complete Reference‖, Tata McGraw-Hill, 2011.
3. Marc Farley: Storage Networking Fundamentals – An Introduction to Storage Devices,
Subsystems, Applications, Management, and File Systems, Cisco Press, 2005.
4. Richard Barker and Paul Massiglia: ―Storage Area Network Essentials A Complete Guide to
understanding and Implementing SANs‖, Wiley India, 2006.
COURSE OUTCOMES :
The students should be able to:
CO1: Distinguish storage centric and server centric systems
CO2: Determine the need for performance evaluation and the metrics used for it
CO3: Extrapolate RAID and different RAID levels
CO4: Validate data maintained at data centres
CO5: Develop techniques for storage management
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Total:
Marks 100
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 CO2 2 CO3 3 CO4 3 CO5 1 2
1: Low 2: Medium 3:High
IT26
Course Code 18IT1E2C M. Tech (Information Technology)
Category Engineering Science Courses (Integrated - Professional Elective )
Course title WEB ENGINEERING
Scheme and Credits No. of Hours/Week Semester – I
L T P S Credits
3 - 2 - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
Course Objectives: The course will enable the students to:
1. Understand the concepts of Web engineering and requirement engineering.
2. Apply the architecture and models for Web applications.
3. Verify and analyse the Web applications.
4. Provide the knowledge on CGI Programming to implement various Web applications.
5. Design Embedded Web applications using PHP.
UNIT I - INTRODUCTION TO WEB ENGINEERING AND REQUIREMENTS
ENGINEERING: 10 Hours The need for Web engineering, Categories of Web Applications, Characteristics of Web
Applications. Evolution of Web Engineering, Requirement Engineering and modeling in web
engineering: RE specifics in Web Engineering, principles, modeling requirements. Methods
and Tools for modeling in Web Engineering, Designing a Web application.
UNIT II - WEB APPLICATION ARCHITECTURES AND MODELING WEB
APPLICATIONS: 10 Hours Introduction- Categorizing Architectures, Specifics of Web Application Architectures,
Components of a Generic Web Application Architecture, Layered Architectures: 2-Layer and
N-Layer Architectures, Data-aspect Architectures, Database-centric Architectures,
Architectures for Web Document Management, Architectures for Multimedia Data. Web
application design, Model based web application development: OOHDM method, W2000
method
UNIT III - TESTING WEB APPLICATIONS: 09 Hours Introduction, Fundamentals, Test approaches, Test methods and techniques, Test driven
development, Test Automation, Test tools.
UNIT IV - CGI PROGRAMMING: 10 Hours Structural- Apache web server, Apache configuration, MySQL- introduction, Database
independent interface, Loading and Dumping a Database. CGI Programming: Dynamic-
Introduction CGI.pm, Information received by the CGI Program, Form widget Methods, CGI
security considerations.
UNIT V – EMBEDDED WEB APPLICATION 09 Hours Introduction, Security considerations, PHP-introduction, Embedding PHP into HTML,
Configuration, Quick examples, Built-in PHP functions.
UNIT VI
Recent Trends in Web engineering and Web application tools
UNIT VII (Lab Programs)
1. Write a Perl script to read in a string from the console and print:
(a) The length and reverse of the string
(b) The upper and lower case version of the string
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2. a) Write a Perl program to extract Log file information using regular expression.
b) Write a perl script to compute the nth
power of a given number.
3. a) Write a Perl program to display various Server Information like Server Name, Server
Software, Server protocol, CGI Revision etc.
b) Write a Perl program to accept UNIX command from a HTML form and to display
the output of the command executed.
4. a) Write a Perl Program to check whether the given number is Armstrong number or
not.
b) Write a Perl program to insert name and age information entered by the user into a
table created using MySQL and to display the current contents of this table.
5. Write a Perl program to accept the User Name and display a greeting message
randomly chosen from a list of 4 greeting messages.
6. Write a Perl program to keep track of the number of visitors visiting the web page and
to display this count of visitors, with proper headings.
7. Write a Perl program to display a digital clock which displays the current time of the
server.
8. Write a PHP program to store current date-time in a COOKIE and display the ‗Last
visited on‘ date-time on the web page upon reopening of the same page.
9. Write a PHP program to store page views count in SESSION, to increment the count on
each refresh, and to show the count on web page.
10. Using PHP and MySQL develop a program to accept book information viz. Accession
number, title, authors, edition and publisher from a web page and store the information
in a database and to search for a book with the title specified by the user and to display
the search results with proper headings.
REFERENCES
1. Web Engineering: The Discipline of Systematic Development of Web Applications by
Kappel et al., John Wiley, 2006
2. Web Engineering by Emilia Mendes and Nile Mosley, 1st Edition, Springer, 2006
3. Open Source Web Development with LAMP-using Linux, Apache, MySQL, perl and
PHP by James Lee and Brent Ware, Addison Wesley/Pearson Inc 2003.
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1. Discuss Web engineering and requirement engineering concepts.
CO2. Make use of the Architecture and various modeling techniques for web applications.
CO2. Discuss design issues involved in web application development.
CO3. Validate and use testing process specific to Web applications.
CO4. Develop the Web applications using CGI Programming.
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SCHEME OF EXAMINATION:
CIE -
Practical
Conduction of experiments, performance of student in
every week lab and completion of lab record=50#
Total: Marks
50
Lab Test: Part-A =20, Part-B=20 and Vice-Voce=10
Marks(50##
)
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
# = CIE Marks for laboratory performance is to be evaluated for 50 marks and the
marks obtained shall be reduced for 25 marks
## = Lab test is to be conducted for 50 marks and the marks obtained shall be
reduced for 25 marks. Lab test shall be conducted by two examiners out of which
one examiner is the faculty taught the course. There is no SEE for the practical ‘s
portion of integrated course
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 3
CO2 3 3
CO3 3 3
CO4 3 3
CO5 3 3
1. Low, 2. Medium, 3. High
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Course Code 18CS1L01 M. Tech (Information Technology)
Category Engineering Science Courses ( Practical )
Course title NETWORK PROGRAMMING LAB
Scheme and
Credits
No. of Hours/Week Semester – I
L T P S Credits
- - 3 - 2
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
1. Computer Networks
2. Programming in Java and C++
3. NS-3 simulator
COURSE OBJECTIVES
The course will enable the students to:
1. Understand the implementation of various network protocols.
2. Understand programming the network protocols using Java.
3. Analyse the programming environment of NS-3 simulator.
4. Evaluate typical wired/wireless network using the NS-3 simulator.
5. Create a typical GSM network using NS-3
PART – A
Write a Java Program to design a :
1. TCP iterative Client-Server application to reverse the given input sequence.
2. TCP concurrent Client-Server application to reverse the given input sequence.
3. TCP Client-Server application to transfer a file.
4. UDP Client-Server application to transfer a file.
5. ARP/RARP protocol.
6. DHCP protocol.
7. Distance Vector Routing protocol.
8. Dijkstra‘s shortest path routing protocol.
PART – B
1. Write a C++ program to connect two nodes on NS-3 (for practise only).
2. Write a C++ program to connect three nodes considering one as a central node on
NS-3 (for practise only).
3. Write a C++ program to implement a star topology on NS-3.
4. Write a C++ program to implement a bus topology on NS-3.
5. Write a C++ program showing the connection of two nodes and four routers such that
the extreme nodes act as client and server on NS-3.
6. Implement and study the performance of a typical GSM network on NS-3 (using
MAC layer).
COURSE OUTCOMES
On completion of the course, the student would be able to:
CO1: Design programs for any type of TCP and UDP based client-server applications using
Java and analysed
CO2: Implement a typical wired network using Java.
CO3: Extend the functionalities of a routing protocol using Java.
CO4: Implement and analyse the performance of a wireless/mobile network on NS-3.
CO5: Design a typical GSM network on NS-3.
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SCHEME OF EXAMINATION The student has to write and implement two programs selecting ONE from each part
Continuous Internal
Evaluation (CIE) (Laboratory
– 50 Marks)
Marks Semester End Evaluation (SEE)
(Laboratory – 100 Marks) Marks
Performance of the Student in
the laboratory every week
20 Write up 10
Test at the end of the semester 20 Experiment-1 (Part - A) – 35 Marks
Experiment-2 (Part - B) – 35 Marks
70
Viva Voce 10 Viva Voce 20
Total 100
Total (CIE) 50 Total (SEE) 50*
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 1
CO2 2
CO3 2
CO4 2 2 3
CO5 2 2
1. Low, 2. Medium, 3. High
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Course Code 18CS1M01 M. Tech (Information Technology)
Category Engineering Science Courses ( Mandatory Audit)
Course title RESEARCH METHODOLOGY AND IPR
Scheme and
Credits
No. of Hours/Week Semester – I
L T P S Credits
2 0 0 0 2
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3
Hrs
Prerequisites (if any):
COURSE OBJECTIVES
This course will enable students to
1. Understand the formulation of research problem, scope and objectives of research
problem
2. Use methods for effective technical writing skills
3. Analyse Approaches of investigation of solutions for research problem
4. Evaluate the format of research proposal , intellectual property and patent
5. Create patent, research paper
UNIT -I RESEARCH PROBLEM: 03 Hours Meaning of research problem, Sources of research problem, Criteria Characteristics of a good
research problem, Errors in selecting a research problem, Scope and objectives of research
problem. Approaches of investigation of solutions for research problem, data collection,
analysis, interpretation, Necessary instrumentations
UNIT- II RESEARCH REQUIREMENTS: 03 Hours
Effective literature studies approaches, analysis Plagiarism, Research ethics,
UNIT- III EFFECTIVE TECHNICAL WRITING: 06 Hours Effective technical writing, how to write report, Paper Developing a Research Proposal, Format of research
proposal, a presentation and assessment by a review committee
UNIT- IV NATURE OF INTELLECTUAL PROPERTY: 06 Hours Patents, Designs, Trade and Copyright. Process of Patenting and Development: technological research,
innovation, patenting, development. International Scenario: International cooperation on Intellectual Property.
Procedure for grants of patents, Patenting under PCT.
UNIT- V PATENT RIGHTS: 06 Hours Scope of Patent Rights. Licensing and transfer of technology. Patent information and databases. Geographical
Indications.
UNIT- VI NEW DEVELOPMENTS IN IPR: Administration of Patent System. New developments in IPR; IPR of Biological Systems, Computer Software
etc. Traditional knowledge Case Studies, IPR and IITs.
REFERENCES
1. Stuart Melville and Wayne Goddard, ―Research methodology: an introduction for
science & engineering students‘‖
2. Wayne Goddard and Stuart Melville, ―Research Methodology: An Introduction‖
3. Ranjit Kumar, 2nd Edition, ―Research Methodology: A Step by Step Guide for
beginners‖ Halbert, ―Resisting Intellectual Property‖, Taylor & Francis Ltd ,2007.
4. Mayall, ―Industrial Design‖, McGraw Hill, 1992.
5. Niebel, ―Product Design‖, McGraw Hill, 1974.
6. Asimov, ―Introduction to Design‖, Prentice Hall, 1962.
7. Robert P. Merges, Peter S. Menell, Mark A. Lemley, ― Intellectual Property in New
Technological Age‖, 2016.
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8. T. Ramappa, ―Intellectual Property Rights Under WTO‖, S. Chand, 2008
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1: Understand research problem formulation. Analyze research related information and
follow research ethics
CO2: Understanding that when IPR would take such important place in growth of
individuals and nation, it is needless to emphasis the need of information about
Intellectual Property Right to be promoted among students in general & engineering
in particular.
CO3: Understand that IPR protection provides an incentive to inventors for further research
work and investment in R & D, which leads to creation of new and better products,
and in turn brings about, economic growth and social benefits.
CO4: Analyze research related information
CO5: Follow research ethics
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 03 hours shall not have
internal choice
20*2=40
Marks
Total:
Marks 100
Unit which have 06 hours shall have internal
choice
20*3=60
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 CO2 3 CO3 3 CO4 CO5 3 3
1: Low 2: Medium 3:High
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Course Code 18IT1S01 M. Tech (Information Technology)
Category Seminar Semester- I
Course title SEMINAR – I
Scheme and Credits
No. of Hours/Week
Total hours = 24 L T P S Credits
0 0 2 0 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any): NIL
COURSE LEARNING OBJECTIVES:
The objectives of the SEMINAR-I is to prepare the students to learn to:
1. Carry out the literature review of general research area/current topic and analyse the same
effectively.
2. Prepare a technical report, reflecting his/her depth of understanding, on the selected
area/topic and prepare content rich presentation.
3. Acquire communication and time management skills for effective and impactful presentation.
Interact with peers to acquire the qualities of thoughtfulness, friendliness, adaptability,
responsiveness, and politeness in-group discussion. Overcome stage fear during the
presentation.
GUIDE LINES
1. Seminar preparation and presentation is an individual student activity.
2. Topic may be of general/ specific interest to program of engineering or electives not offered in
the semester and to be selected in consultation with the faculty/Guide.
3. Select one pertinent research paper for the seminar presentation.
4. Prepare and submit a detailed technical report of the seminar topic.
COURSE OUTCOMES:
Students shall be able to:
1. Carry out the literature survey of topic of seminar.
2. Prepare a technical report on the selected area/topic.
3. Make an effective presentation with seamless flow of content within the time allocated.
Overcome inhibition in interacting with peers and hence develop the spirit of team work.
Overcome stage fear during the presentation.
SCHEME OF EXAMINATION
CIE – 50
marks
Phase -1 Marks =10 Seminar Report
Marks =20
Total:50
Marks Phase -2 Marks =20
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Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2 3 CO2 2 3 3 CO3 2
1. Low, 2. Medium, 3. High
Scheme of Continuous Internal Evaluation (CIE):
Evaluation would be carried out in TWO phases. The Seminar Evaluation Committee shall
comprise of Chairman of the Department, Faculty/Guide and one more faculty member nominated by
Chairman. The evaluation criteria shall be as per the rubrics given below:
Rubrics for Evaluation:
Topic - Technical Relevance, Sustainability and Societal Concerns : 15%
Review of Literature and Technical Content : 35%
Presentation Skills : 25%
Report : 25%
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Course Code 18CS1M02 M. Tech (Information Technology)
Category Engineering Science Courses ( Mandatory Audit )
Course title AUDIT COURSE-I ( TECHNICAL PAPER WRITING )
Scheme and
Credits
No. of Hours/Week Semester – I
L T P S Credits
2 0 0 0 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any):
COURSE OBJECTIVES
This course will enable students to
1. Understand the planning section of research paper and preparation of paper writing
2. Apply key skill while writing research paper and know about what to write in each
section
3. Analyse literature, methods,
4. Evaluate research paper, paraphrasing paper
5. Create good research paper
UNIT-I PLANNING AND PREPARATION: 06 Hours Planning and Preparation, Word Order, Breaking up long sentences, Structuring Paragraphs
and Sentences, Being Concise and Removing Redundancy, Avoiding Ambiguity and
Vagueness
UNIT- II CLARIFYING: 03 Hours Clarifying Who Did What, Highlighting Your Findings, Hedging and Criticising,
Paraphrasing and Plagiarism, Sections of a Paper, Abstracts. Introduction
UNIT- III REVIEW OF THE LITERATURE: 06 Hours Review of the Literature, Methods, Results, Discussion, Conclusions, The Final Check.
UNIT- IV KEY SKILLS: 06 Hours Key skills are needed when writing a Title, key skills are needed when writing an Abstract,
key skills are needed when writing an Introduction, skills needed when writing a Review of
the Literature,
UNIT- V METHODS: 03 Hours
skills are needed when writing the Methods, skills needed when writing the Results, skills are
needed when writing the Discussion, skills are needed when writing the Conclusions.
UNIT- VI NEW DEVELOPMENTS IN RESEARCH PAPER WRITING: useful phrases, how to ensure paper is as good as it could possibly be the first- time
submission
REFERENCES
1. Goldbort R (2006) Writing for Science, Yale University Press (available on Google
Books)
2. Day R (2006) How to Write and Publish a Scientific Paper, Cambridge University
Press
3. Highman N (1998), Handbook of Writing for the Mathematical Sciences, SIAM.
Highman‘sbook.
4. Adrian Wallwork, English for Writing Research Papers, Springer New York
Dordrecht Heidelberg London, 2011
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COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1: List of section of research paper and preparation of paper writing
CO2: Determine key skill while writing research paper
CO3: Analyse literature, methods
CO4: Assess research paper, do paraphrasing paper
CO5: Formulate research paper and results of simulation
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 CO2 3 CO3 3 CO4 3 CO5 3
1: Low 2: Medium 3:High
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Course Code 18CS2C01 M. Tech (Information Technology)
Category Engineering Science Courses (Theory- Professional Core )
Course title ADVANCED DATA STRUCTURES AND ALGORITHMS
Scheme and
Credits
No. of Hours/Week Semester – II
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVE
The course will enable the student to:
1. Learn various data structures and its usage in designing algorithms.
2. Understand to the advanced methods of designing and analysing algorithms.
3. Learn various string matching and graph algorithms.
4. Acquire the knowledge of polynomial, non polynomial and approximation problems.
5. Understand the recent developments in the area of algorithmic design.
UNIT-1 REVIEW OF ANALYSIS TECHNIQUES 09 Hours
Growth of Functions: Asymptotic notations; Standard notations and common functions;
Recurrences -The substitution method, recursion-tree method, the master method,
Probabilistic Analysis and Randomized Algorithms.
UNIT- II BASIC DATA STRUCTURES 09 Hours Stacks and Queues, Vectors, Lists, and Sequences, Trees, Priority Queues and Heaps,
Dictionaries and Hash Tables, Search Trees and Skip lists- Ordered Dictionaries and
Binary Search Trees, AVL Trees, Bounded-Depth Search Trees, Splay Trees, Skip Lists.
UNIT -III DYNAMIC PROGRAMMING 10 Hours
Matrix-Chain multiplication, Elements of dynamic programming, longest common
subsequences. Graph algorithms: Bellman - Ford Algorithm; Single source shortest paths
in a DAG; Johnson‘s Algorithm for sparse graphs; Flow networks and Ford-Fulkerson
method. .
UNIT- IV TRIES AND STRING MACHING ALGORITHMS 10 Hours
Quad trees and K-d trees. String-Matching Algorithms: Naïve string Matching; Rabin -
Karp algorithm; String matching with finite automata; KnuthMorris-Pratt algorithm.
UNIT- V NP-COMPLETENESS 10 Hours
: Polynomial time, Polynomial time verification, NP-Completeness and reducibility, NP-
Complete problems. Approximation Algorithms: vertex cover problem, the set – covering
problem, randomization and linear programming, the subset – sum problem.
UNIT VI
Recent Trends in problem solving paradigms applying recently proposed data
structures
REFERENCES
1. Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein,‖
Introduction to Algorithms‖, Third Edition, Prentice-Hall, 2011.
2. M T Goodrich, Roberto Tamassia, ―Algorithm Design‖, John Wiley, 2002.
3. Mark Allen Weiss, ―Data Structures and Algorithm Analysis in C++‖, 4th
Edition,
Pearson, 2014.
4. Alfred V. Aho, John E. Hopcroft, Jeffrey D. Ullman, ―Data Structures and
Algorithms‖, Pearson Education, Reprint 2006.
5. Ellis Horowitz, Sartaj Sahni, Dinesh Mehta, ―Fundamentals of Data Structures in C‖,
Silicon Pr, 2007.
6. Aho, Hopcroft and Ullman, Data structures and algorithms, 1st edition, Pearson
IT38
Education, India, 2002, ISBN: 8177588265, 978817758826
COURSE OUTCOMES
On completion of the course, the student will be able to:
CO1: Develop and analyze algorithms for red-black trees, B-trees and Splay trees and for
text processing applications.
CO2: Identify suitable data structures and develop algorithms for solving a particular set of
problems
CO3: Analyze the complexity/ performance of different algorithms.
CO4: Categorize the different problems in various classes according to their complexity.
CO5: Use appropriate data structure and algorithms in real time applications.
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for
50 marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2 2
CO3 2 2
CO4 2
CO5 2 2
1. Low, 2. Medium, 3. High
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Course Code 18CS2C02 M. Tech (Information Technology)
Category Engineering Science Courses (Theory- Professional Core )
Course title ADVANCED OPERATING SYSTEMS
Scheme and
Credits
No. of Hours/Week Semester – II
L T P S Credits
4 - - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
The course will enable the students to:
1. Understand the Design Approaches and Issues related to Advanced Operating Systems.
2. Apply the Knowledge on Distributed Operating Systems Concepts to develop Clocks,
Mutual Exclusion Algorithms.
3. Analyse the Distributed Deadlock Detection Algorithms and Agreement Protocols.
4. Evaluate the Algorithms for Distributed Scheduling, Examine the Commit Protocols
and review Concurrency Control Algorithms.
5. Create Advanced Operating Systems Applications with recent technologies
UNIT- I INTRODUCTION: 09 Hours
Concept of Batch system, Multiprogramming, Time Sharing, Parallel, Distributed and Real-
time System, Process Management: Concept of Process, Synchronization, CPU Scheduling,
IPC, Deadlock: Concept of Deadlock Prevention, Avoidance, Detection and its Recovery.
Memory Management: Contiguous allocation, Paging and Segmentation. Virtual memory:
Demand Paging, Page Replacement Algorithms. Distributed OS- Design Approaches and
Issues in DOS. Message Passing Model and RPC.
UNIT -II CLOCKS AND DISTRIBUTED MUTUAL EXCLUSION: 10 Hours
Concept of Lamport‘s Logical Clock and Vector Clocks, Termination Detection. A simple
solution to distributed mutual exclusion, Non Token based algorithms: Lamport‘s algorithm,
Ricart Agarwala‘s algorithm, Maekawa‘s algorithm, Token based algorithms: Suzuki Kasami‘s
broadcast algorithm, Raymond‘s tree based algorithm.
UNIT -III DISTRIBUTED DEADLOCK DETECTION: 10 Hours
Deadlock Handling, Strategies in Distributed Systems, Issues in Deadlock Detection And
Resolution, Control Organization for Distributed Deadlock Detection, Centralized Deadlock
Detection Algorithm: Ho-Ramamoorthy‘s Algorithm, Distributed Deadlock Detection
Algorithms: A Path- Pushing Algorithm and Edge Chasing Algorithm, Hierarchical Deadlock
Detection Algorithms: Menasce- Muntz Algorithm, Ho-Ramamoorthy‘s Algorithm.
Agreement Protocols: Byzantine Agreement Problem, Solution to The Byzantine Agreement
Problem- Lamport -Shostak- Pease Algorithm, Dolev et al.‘s Algorithm
UNIT –IV DISTRIBUTED SCHEDULING AND FAULT TOLERANCE: 10 Hours Issues in Load Distribution, Components of a Load Distributing Algorithms, Load Distributing
Algorithms, Performance Comparison, Selecting Suitable Load Sharing Algorithms,
Requirements of Load Sharing Policies. Commit Protocols, Nonblocking Commit Protocols,
Voting Protocols, Dynamic Voting Protocols, The Majority Based Dynamic Voting Protocol,
Dynamic Vote Reassignment Protocols.
UNIT -V DATABASE OS & CONCURRENCY CONTROL 09 Hours
Requirement of Database OS, A Concurrency Control Model of a Database System, The
Problem of Concurrency Control, Serializability Theory, Concurrency Control Algorithms,
Basic Synchronization Primitives, Lock Based Algorithms, Timestamp Based Algorithms,
Optimistic Algorithms, Concurrency Control Algorithms for Data Replication.
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UNIT-VI Recent advances and research being done in the topics mentioned above units
REFERENCES
1. Mukesh Singhal and Niranjan G Shivaratri, Advanced Concepts in Operating Systems, Tata
Mcgraw Hill, 2002.
2. A Silberchatz and Peter Baer Galvin, Operating System Concepts, 10th Edition, John Wiley
and Sons, 2018.
3. William Stallings, Operating System: Internals and Design Principles, 9th Edition, Prentice
Hall India, 2017.
4. Andrew S Tennenbaum and Albert S Woodhull, Operating System Design and
Implementation, 3rd Edition, Pearson Education Inc., 2006.
5. Ceri S and Pelagorthi S, Distributed Databases: Principles and Systems, 1984, McGraw Hill.
COURSE OUTCOMES
On completion of the course, the student would be able to:
CO1: Explain the Fundamentals of Advanced Operating Systems, Design issues.
CO2: Determine the various Clock Synchronization Principles and Implement Mutual
Exclusion Algorithms.
CO3: Differentiate the various Distributed Deadlock Detection Algorithms and Analyze the
Agreement Protocols.
CO4: Select the appropriate Distributed Scheduling Algorithms, Commit Protocols and
Concurrency Control Algorithms.
CO5: Integrate the Concepts of Advanced Operating Systems with recent trends and
technologies to Design and Develop Applications.
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Total:
Marks 100
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs) PO1 PO2 PO3
CO1 1 - CO2 1 2 CO3 1 2 CO4 1 3 CO5 3 2 2
1: Low 2: Medium 3:High
IT41
Course code 18CS2C03 M. Tech (Information Technology)
Category Engineering Science Courses (Theory- Professional Core )
Course title ADVANCES IN DIGITAL IMAGE PROCESSING
Scheme and Credits No. of Hours/Week Semester – II
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES:
The course will enable the students to:
1. Learn Digital Image Fundamentals and mathematical transforms necessary for image
processing
2. Apply image enhancement techniques in Spatial and Frequency Domains
3. Investigate the Image Restoration/Degradation Process
4. Demonstrate the image segmentation and representation techniques.
5. Be Familiar With Image Compression Techniques.
UNIT-I DIGITAL IMAGE FUNDAMENTALS & IMAGE TRANSFORMS 10 Hours Digital Image Fundamentals, Components of an Image Processing System, Sampling and
Quantization, Relationship between Pixels
Image Transforms Discrete Fourier Transform, Discrete Cosine Transform, Hadamard
Transform - Haar Transform - Slant Transform - KL Transform -Properties And Examples.
UNIT-II IMAGE ENHANCEMENT IN THE SPATIAL DOMAIN 10 Hours
Gray level transformations, histogram processing, Enhancement using Arithmetic/logical
operations, Basics of spatial filtering, smoothening and sharpening spatial filters.
Image Enhancement in the Frequency Domain: Filtering in Frequency Domain,
smoothening and sharpening frequency domain filters.
UNIT-III IMAGE RESTORATION 09 Hours
Degradation Model, Noise Models, Restoration in Presence of Noise Only- Spatial Filtering,
Periodic Noise Reduction by Frequency Domain Filtering, Estimation of Degradation
Function, Inverse Filtering.
UNIT-IV IMAGE SEGMENTATION AND REPRESENTATION 09 Hours Detection of Discontinuities, Edge Linking And Boundary Detection, Thresholding, Region
Oriented Segmentation.
Representation, Boundary Descriptors and Regional Descriptors
UNIT-VIMAGE COMPRESSION 10 Hours
Fundamentals, Image Compression Models, Error Free Compression, Lossy Compression,
Image Compression Standards
UNIT-VI APPLICATIONS
Character Recognition, Fingerprint Recognition, Remote Sensing. Applications using different
Imaging modalities such as acoustic Imaging, Medical imaging, electron microscopy etc.
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REFERENCES
1. Digital Image Processing – Rafael C. Gonzalez, Richard E. Woods, 3rd Edition,
Pearson, 2008
2. Digital Image Processing- S Jayaraman, S Esakkirajan, T Veerakumar- TMH, 2015.
3. Digital Image Processing and Analysis-Human and Computer Vision Application with
using CVIP Tools – Scotte Umbaugh, 2nd Ed, CRC Press, 2011
4. Digital Image Processing using MATLAB — Rafael C. Gonzalez, Richard E Woods
and Steven L. Eddings, 2nd Edition, TMH, 2010.
5. Fundamentals of Digital Image Processing — A.K.Jain, PHI, 2015
COURSE OUTCOMES
Upon Completion of the course, the student would be able to:
CO1: Discuss Digital Image Fundamentals.
CO2:Apply Image Enhancement techniques in spatial and frequency domain.
CO3:Distinquish image Restoration and Degradation processes.
CO4: Design image analysis techniques in the form of image segmentation and to evaluate the
Methodologies for segmentation.
CO5: Use Image Compression Techniques.
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit VI(AAT)=15
marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 1
CO2 3 1
CO3 1 3
CO4 3 3
CO5 3 3
1. Low, 2. Medium, 3. High
IT43
Course Code 18CS2E1A M. Tech ( Computer Science and Engineering)
Category Engineering Science Courses(Theory- Professional Elective )
Course title DATA WAREHOUSING AND MINING
Scheme and
Credits
No. of Hours/Week Semester – II
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
Course Objectives: The course will enable the students to:
1. Understand the principles of Data warehousing and data mining.
2. Perform classification and prediction of data.
3. Examine the types of data in cluster analysis with various clustering methods.
4. Illustrate the concepts of mining object, spatial, multimedia, text and web data.
5. Build a data warehouse and mapping the data warehouse to a multiprocessor
architecture.
UNIT I - INTRODUCTION TO DATA MINING: 9
Hours Data Mining Functionalities, Data Pre-processing, Data Cleaning, Data Integration and
Transformation, Data Reduction, Data Discretization and Concept Hierarchy Generation.
Association Rule Mining: Efficient and Scalable Frequent Item set Mining Methods,
Mining Various Kinds of Association Rules, Association Mining to Correlation Analysis,
Constraint-Based Association Mining, Handling categorical, Continuous Attributes,
Concept hierarchy, Sequential and Sub graph Patterns.
UNIT II - CLASSIFICATION AND PREDICTION: 10
Hours
Issues Regarding Classification and Prediction, Classification by Decision Tree
Introduction, Bayesian Classification, Rule Based Classification, Classification by Back
propagation, Support Vector Machines, Associative Classification, Lazy Learners, Other
Classification Methods, Prediction, Accuracy and Error Measures, Evaluating the
Accuracy of a Classifier or Predictor, Ensemble Methods, Model Section.
UNIT III - CLUSTER ANALYSIS: 10
Hours Types of Data in Cluster Analysis, A Categorization of Major Clustering Methods,
Partitioning Methods, Hierarchical methods, Density-Based Methods, Grid-Based
Methods, Model-Based Clustering Methods, Clustering High-Dimensional Data,
Constraint-Based Cluster Analysis, Outlier Analysis, Quality and validity of Cluster
Analysis.
UNIT IV - MINING OBJECT, SPATIAL, MULTIMEDIA, TEXT AND WEB DATA:
9 Hours
Multidimensional Analysis and Descriptive Mining of Complex Data Objects, Spatial
Data Mining, Multimedia Data Mining, Text Mining, Mining the World Wide Web,
Stream Data Mining, Social Network Analysis.
UNIT V – DATA WAREHOUSING AND BUSINESS ANALYSIS: 10
Hours
Data warehousing Components, Building a Data warehouse, Mapping the Data Warehouse
IT44
to a Multiprocessor Architecture, DBMS Schemas for Decision Support, Data Extraction,
Cleanup, and Transformation Tools, Metadata, reporting, Query tools and Applications,
Online Analytical Processing (OLAP), OLAP and Multidimensional Data Analysis.
UNIT VI - Recent Trends in Distributed warehousing and Data Mining, Class Imbalance
Problem, Graph mining, Social Network Analysis.
REFERENCES
1. Jiawei Han and Micheline Kamber ―Data Mining Concepts and Techniques‖,
Second Edition, Elsevier, 2011.
2. Vipin Kumar, Introduction to Data Mining - Pang-Ning Tan, Michael Steinbach,
Addison Wesley, 2006.
3. G Dong and J Pei, Sequence Data Mining, Springer, 2007.
4. Alex Berson and Stephen J. Smith ―Data Warehousing, Data Mining & OLAP‖, Tata
McGraw – Hill Edition, Tenth Reprint 2007.
5. K.P. Soman, Shyam Diwakar and V. Ajay ―Insight into Data Mining Theory and
Practice‖, Easter Economy Edition, Prentice Hall of India, 2006.
G. K. Gupta ―Introduction to Data Mining with Case Studies‖, Easter Economy Edition,
Prentice Hall of India, 2006.
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1. Demonstrate the concept of data mining principles, data warehousing Architecture
and its
Implementation
CO2. Apply the association rules, design and deploy appropriate classification techniques
for
mining the data
CO3. Cluster the high dimensional data for better organization of the data
CO4. Describe stream mining, Time-Series and sequence data in high dimensional system
CO5. Acquire the concept of Mining Object, Spatial, Multimedia, Text, and Web Data
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for
50 marks.
IT45
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3
CO2 2
CO3 3
CO4 2
CO5 3
1. Low, 2. Medium, 3. High
IT46
Course Code 18CS2E1B M. Tech (Information Technology)
Category Engineering Science Courses Theory- Professional l Elective )
Course title STOCHASTIC PROCESS AND QUEUING THEORY
Scheme and Credits No. of Hours/Week Semester – II
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any)
1. Probability Theory
COURSE OBJECTIVES:
The course will enable the students to:
1. Understand the concepts of stochastic processes, and Markov chains.
2. Understand Markov processes with discrete and continuous state spaces.
3. Understand the concepts of queuing theory and different queues.
4. Understand open and closed queuing networks.
5. Analyse single and multi-server queuing models.
UNIT-I INTRODUCTION TO STOCHASTIC PROCESSES AND MARKOV CHAINS 09 Hours Introduction, Specifications, Classification of Stochastic Processes, Stationary Process, Poisson Processes,
Renewal Processes, Markov Chains: Transition Probabilities, Classification of States and Chains, Reducible
Chains, Statistical Inference of Markov Chains, Markov Chains with Continuous State Space, Non-
homogenous Chains.
UNIT-II MARKOV PROCESSES WITH DISCRETE AND CONTINUOUS STATE SPACE 09 Hours
Poisson Process and its Related Distributions, Generalization of Poisson Processes, Birth and Death Process,
Markov Process with Discrete State Space (Continuous Time Markov Chains), Brownian Motion, Wiener
Process, Differential Equations for Wiener Process, Kolmogorav Equations, First Passage Time Distribution
for Wiener Process.
UNIT-III QUEUING THEORY AND MARKOVIAN QUEUING MODELS 10 Hours
Introduction, Characteristics Notations, Birth and Death Processes, Single-Server Queues (M|M|1), Multi-
Server Queues (M|M|c), Choosing the Number of Servers, Queues with Truncation (M|M|c|K), Erlang‘s Loss
Formula (M|M|c|c), Queues with Unlimited Service, Finite Source Queues, State-Dependent Service, Queues
with Impatience, Transient Behaviour, Busy-Period Analysis, Bulk Input and Bulk Service.
UNIT-IV NETWORKS, SERIES, AND CYCLIC QUEUES 10 Hours
Series Queues, Open Jackson Networks, Closed Jackson Networks, Cyclic Queues, Extensions of Jackson
Networks, Non-Jackson Networks.
UNIT-V GENERAL ARRIVAL OR SERVICE PATTERNS 10 Hours General Service, Single Server (M|G|1), General Service, Multi-server (M|G|c|∙, M|G|∞), General Input
(G|M|1, G|M|c).
UNIT-VI Performance analysis of data networks.
REFERENCES
1. Jyothiprasad Medhi, ―Stochastic Processes‖, New Age International Publishers, II Edition, 2002.
2. Kishore S. Trivedi, ―Probability and Statistics with Reliability, Queuing and Computer Science
Applications‖, John Wiley and Sons, II Edition, 2008.
3. Donald Gross, John F. Shortle, James M. Thomson, and Carl M. Harris, ―Fundamentals of Queuing
Theory‖, John Wiley and Sons, IV Edition, 2008.
4. Oliver Knill, ―Probability Theory and Stochastic Processes with Applications‖, Overseas Press, 2009.
IT47
COURSE OUTCOMES
On completion of the course, the student would be able to:
CO1: Solve problems on stochastic process and Markov chains.
CO2: Analyse Markov Process for Discrete and Continuous State Spaces.
CO3: Model the Behaviour of Various Computer Networks and Distributed Systems using Queuing Models.
CO4: Analyse the Arrival and Service Patterns of any System and Solve Problems in Computer Networks
and Distributed Systems.
CO5:Investigate the performance analysis of data networks
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit VI(AAT)=15
marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 1
CO2 2
CO3 2
CO4 2
CO5 1
1. Low, 2. Medium, 3. High
IT48
Course Code 18CS2E1C M. Tech (Information Technology)
Category Engineering Science Courses ( Integrated-- Professional
Elective ) )
Course title INTERNET OF THINGS
Scheme and
Credits
No. of Hours/Week Semester – II
L T P S Credits
3 0 2 - 4
CIE Marks:
50
SEE Marks: 50 Total Max. Marks:
100
Duration of SEE: 3 Hrs
Prerequisites (if any):
1. Computer Networks
COURSE OBJECTIVES
The course will enable the students to:
1. Understand the IoT architecture and its enabling technologies.
2. Realize the various applications of IoT, understand the IoT system management
using NETCONF-YANG.
3. Understand the design of IoT, Python programming language, packages for IoT
and Raspberry Pi.
4. Create the various IoT protocols and their support in the implementation of
services.
5. Create a typical IoT input using the standard IT protocols.
UNIT I – INTRODUCTION TO INTERNET OF THINGS (IoT) 09 Hours
Definition and Characteristics of IoT, Physical Design of IoT, Logical Design of IoT, IoT
Enabling Technologies, IoT Levels and Deployment Templates.
UNIT II – DOMAIN SPECIFIC IoT, M2M and IoT System Management 09 Hours
Home Automation, Cities, Environment, Energy, Retail, Logistics, Agriculture, Industry,
Health and Lifestyle, M2M, Difference between IoT and M2M, SDN and NFV for IoT,
Need for IoT Systems Management, Simple Network Management Protocol, Network
Operator Requirements, IoT System Management with NETCONF-YANG.
UNIT III – DEVELOPING IoT USING PYTHON 10 Hours
IoT Design Methodology, IoT Systems – Logical Design using Python, Python Data
Types and Data Structures, Control Flow, Functions, Modules, Packages, File Handling,
Data/Time Operations. Classes, Python Packages for IoT: JSON, XML, HTTPLib and
URLLib, SMTPLib.
UNIT IV – IoT DEVICES AND PROTOCOLS 09 Hours
Basic Building Blocks of an IoT Device, Raspberry Pi, Programming Raspberry Pi using
Python, Basics of IoT Protocols: HTTP, UPnP, MQTT, CoAP and XMPP.
UNIT V – IoT PROTOCOLS 10 Hours
HTTP: Adding HTTP Support to Sensor, Adding HTTP Support to Actuator, Adding
HTTP Support to Controller. UPnP Protocol: Creating a Device Description Document,
Creating a Service Description Document, Providing a Web Interface, Creating an UPnP
Interface, Implementing the Still Image Service using Camera. CoAP Protocol: Making
HTTP Binary, Adding CoAP to Sensor, Adding CoAP to Actuator. MQTT Protocol:
Adding MQTT Support to Sensor, Adding MQTT Support to Actuator, Adding MQTT
Support to Controller. XMPP Protocol: Adding XMPP Support to a Thing, Adding
XMPP Support to Actuator, Adding XMPP Support to Camera, Adding XMPP Support
to Controller, Connecting All Together.
UNIT VI – Recent Trends in Industrial Internet of Things and Social Internet of Things.
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UNIT VII- ( Lab Programs)
1. Study and Install Python in Eclipse and WAP for data types in python.
2. Write a Program for arithmetic operation in Python.
3. Write a Program for looping statement in Python.
4. Study and Install IDE of Arduino and different types of Arduino.
5. Write program using Arduino IDE for Blink LED.
6. Write Program for RGB LED using Arduino.
7. Study the Temperature sensor and Write Program foe monitor temperature using
Arduino.
8. Study and Implement RFID, NFC using Arduino.
9. Study and implement MQTT protocol using Arduino.
10. Study and Configure Raspberry Pi.
11. WAP for LED blink using Raspberry Pi.
12. Study and Implement Zigbee Protocol using Arduino / Raspberry Pi.
REFERENCES
1. Arshdeep Bahga and Vijay Madisetti, ―Internet of Things: A Hands-on
Approach‖, University Press, 2015.
2. Peter Waher, ―Learning Internet of Things‖, PACKT Publishing, 2015.
3. Adrian McEwen and Hakim Cassimally, ―Designing Internet of Things‖, John
Wiley and Sons, 2014.
COURSE OUTCOMES
On completion of the course, the student would be able to:
CO1: Demonstrate the knowledge of IoT architecture and design.
CO2: Manage the IoT system with NETCONF-YANG.
CO3: Program the Raspberry Pi using Python.
CO4: Develop an IoT application using the IoT protocol.
CO5: Investigate the standard IoT protocol.
SCHEME OF EXAMINATION:
CIE -
Practical
Conduction of experiments, performance of student in
every week lab and completion of lab record=50#
Total: Marks
50
Lab Test: Part-A =20, Part-B=20 and Vice-Voce=10
Marks(50##
)
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
IT50
for 50 marks
# = CIE Marks for laboratory performance is to be evaluated for 50 marks and
the marks obtained shall be reduced for 25 marks
## = Lab test is to be conducted for 50 marks and the marks obtained shall be
reduced for 25 marks. Lab test shall be conducted by two examiners out of which
one examiner is the faculty taught the course. There is no SEE for the practical ‘s
portion of integrated course
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 1 2 CO2 1 CO3 3 CO4 1 CO5 2
1. Low, 2. Medium, 3. High
IT51
Course Code 18CS2E2A M. Tech (Information Technology)
Category Engineering Science Courses (Theory- Professional Elective )
Course title NETWORK SECURITY
Scheme and
Credits
No. of Hours/Week Semester – II
L T P S Credits
4 0 - - 4
CIE Marks:
50
SEE Marks: 50 Total Max. Marks:
100
Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
The course will enable the students to:
1: Learn the basics of security and various types of security issues.
2: Understand cryptography techniques available and various security attacks.
3: Explore network security and how they are implemented in real world.
4: Analyze various issues of wireless security techniques.
5: Effectively design secured wireless sensor network
UNIT I- INTRODUCTION TO SECURITY 09 Hours
Need for security, Security approaches, Principles of security, Types of attacks.
Encryption Techniques: Plaintext, Cipher text, Substitution & Transposition techniques,
Encryption & Decryption, Types of attacks, Key range & Size. Symmetric &
Asymmetric Key Cryptography: Algorithm types & Modes, DES, AES, RSA, ECC;
UNIT II- SECURED HASH ALGORITHMS 09 Hours
Message Digest, Key- Distribution Algorithms, Digital signatures, User Authentication
Mechanisms, Key Management, Certificates, Kerberos.
UNIT III - DISTRIBUTED SYSTEM SECURITY 10 Hours Firewalls, Proxy-Servers, Network intrusion detection. Transport security: Mechanisms
of TLS, SSL, IPSec. Network -level solutions, Secure socket layer, IP Security, DoS
Counter measures, DNS Solutions.
UNIT IV - WIRELESS SECURITY 10 Hours
Security in wireless Networks Vulnerabilities, Security techniques, Wi-Fi Security, DoS
in wireless communication.
UNIT V - WIRELESS SENSOR NETWORKS SECURITY 10 Hours
Security in Wireless Sensor Networks, Possible attacks, countermeasures, SPINS, Static
and dynamic key Management
UNIT VI Recent trends in IOT security, IDS – 04 Hours
REFERENCES
1. W. Stallings. Cryptography and Network Security. Prentice Hall, 7th
Edition - 2017
2. W. R. Cheswick and S. M. Bellovin. Firewalls and Internet Security. Addison Wesley,
2007.
3. B. Schneier. Applied Cryptography. Wiley, 2006.
4. Stallings W., Wireless Communications and Networks, Pearson Education 2005
5. KazemSohraby, Daniel Minoli and TaiebZnati, ―wireless sensor networks -
Technology,
Protocols, and Applications‖, Wiley Interscience 2007
6. Takahiro Hara,Vladimir I. Zadorozhny, and Erik Buchmann, ―Wireless Sensor
IT52
NetworkTechnologies for the Information Explosion Era‖, springer 2010
COURSE OUTCOMES
On completion of the course, the student would be able to:
CO1:analized various security issues related to computer networks
CO2: Implements various networks security algorithms
CO3: Design and implements various security algorithms for distributed environments
CO4: Analyses security issues and apply the relevant algorithms to mitigate the same
CO5: Analyses various security attracts
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for
50 marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2 CO2 2 CO3 2 2 CO4 3 2 CO5 2 2
1. Low, 2. Medium, 3. High
IT53
Course Code 18IT2E2B M. Tech (Information Technology)
Category Engineering Science Courses (Theory- Professional Elective )
Course title CYBER SECURITY
Scheme and
Credits
No. of Hours/Week Semester – II
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
The course will enable the students to:
1. Memorise basis of security concepts and security techniques.
2. Understand the cybercrime and law.
3. Identify and determine the motive and remedial measures for cybercrime, detection
and handling.
4. Analyse areas affected by cybercrime and identify Legal Perspectives in cyber
security.
5. Effectively design a secure cyber system.
UNIT I - INTRODUCTION TO SECURITY 09 Hours Introduction to Security: Need for security, Security approaches, Principles of security,
Types of attacks. Encryption Techniques: Plaintext, Cipher text, Substitution &
Transposition techniques, Encryption & Decryption, Types of attacks, Key range & Size.
Symmetric & Asymmetric Key Cryptography: DES,RSA
UNIT II- INTRODUCTION TO CYBERCRIME 09 Hours Cybercrime: Definition and Origins of the Word, Cybercrime and Information Security,
Cybercriminals, Classifications of Cybercrimes, Cybercrime: The Legal Perspectives,
Cybercrimes: An Indian Perspective, Cybercrime and the Indian ITA 2000, A Global
Perspective on Cybercrimes, Cybercrime Era: Survival Mantra for the Netizens.
Cyberoffenses: Criminals Plan: Attacks, Social Engineering, Cyberstalking, Cybercafe and
Cybercrimes, Botnets: The Fuel for Cybercrime, Attack Vector, Cloud Computing.
UNIT III CYBERCRIME: MOBILE AND WIRELESS DEVICES 10 Hours Introduction, Proliferation of Mobile and Wireless Devices, Trends in Mobility, Credit
Card Frauds in Mobile and Wireless Computing Era, Security Challenges Posed by Mobile
Devices, Registry Settings for Mobile Devices, Authentication Service Security, Attacks
on Mobile/Cell Phones, Mobile Devices: Security Implications for organizations,
Organizational Measures for Handling Mobile, Organizational Security Policies and
Measures in Mobile Computing Era, Laptops.
UNIT IV- TOOLS AND METHODS USED IN CYBERCRIME 10 Hours Introduction, Proxy Servers and Anonymizers, Phishing, Password Cracking, Keyloggers
and Spywares, Virus and Worms, Trojan Horses and Backdoors, Steganography, DoS and
DDoS Attacks, SQL Injection, Buffer Overflow, Attacks on Wireless Networks. Phishing
and Identity Theft : Introduction, Phishing, Identity Theft (ID Theft).
UNIT V- INTRODUCTION TO SECURITY POLICIES AND CYBER LAWS
10 Hours
Need for An Information Security Policy, Information Security Standards – ISO,
Introducing Various Security Policies and Their Review Process, Introduction to Indian
Cyber Law, Objective and Scope of the it Act, 2000, Intellectual Property Issues, Overview
IT54
of Intellectual - Property - Related Legislation in India, Patent, Copyright, Law Related to
Semiconductor Layout and Design, Software License.
UNIT VI - Recent developments in Security Policies and Cyber Laws
REFERENCES
1. W. Stallings. Cryptography and Network Security. Prentice Hall, 7th
Edition - 2017
2. Sunit Belapure and Nina Godbole, ―Cyber Security: Understanding Cyber Crimes,
Computer Forensics And Legal Perspectives‖, Wiley India Pvt Ltd, ISBN: 978-81-
265-21791, 2013.
3. Dr. Surya PrakashTripathi, RitendraGoyal, Praveen Kumar Shukla, KLSI.
―Introduction to information security and cyber laws‖. Dreamtech Press. ISBN:
9789351194736, 2015.
4. Thomas J. Mowbray, ―Cybersecurity: Managing Systems, Conducting Testing, and
Investigating Intrusions‖, Copyright © 2014 by John Wiley & Sons, Inc, ISBN: 978
-1-11884965 -1
5. I. A. Dhotre , ―Cyber Forensics , Technical Publications; 1st Edition edition (2016),
ISBN- 13:978-9333211475
COURSE OUTCOMES At the end of the course, the students will be able to:
CO1: Interpret the basic concepts of cyber security, cyber law and their roles.
CO2: Articulate evidence collection and legal challenges
CO3: Discuss tools support for detection of various attacks.
CO4: Analyse various cyber risks.
CO5: Validate different cyber techniques in cyber system.
SCHEME OF EXAMINATION
CIE –
50
mark
s
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:
50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
mark
s
Answer FIVE full questions
Units which have 09 Hours shall not have
internal choice. 20* 2 = 40 Marks Total:
100
marks Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for
50 marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 CO2 2 CO3 2 CO4 2 CO5 2
1. Low, 2. Medium, 3. High
IT55
Course Code 18CS2E2C M. Tech (Information Technology)
Category Engineering Science Courses(Theory- Professional Elective )
Course title WEB SECURITY
Scheme and
Credits
No. of Hours/Week Semester – II
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
The course will enable the students to:
1. Understand web application‘s vulnerability and malicious attacks.
2. Understand basic web technologies used for web application development.
3. Analyse basic concepts of Mapping the application
4. Illustrate different attacking illustrations.
5. Emphasis various basic concepts of Attacking Data Stores.
.
UNIT I: WEB APPLICATION SECURITY 09 Hours
The Evolution of Web Applications, Common Web Application Functions, Benefits of
Web Applications, Web Application Security.
Core Defense Mechanisms: Handling User Access Authentication, Session Management,
Access Control, Handling User Input, Varieties of Input Approaches to Input Handling,
Boundary Validation.
Multistep Validation and Canonicalization: Handling Attackers, Handling Errors,
Maintaining Audit Logs, Alerting Administrators, Reacting to Attacks.
UNIT II: WEB APPLICATION TECHNOLOGIES 09 Hours The HTTP Protocol, HTTP Requests, HTTP Responses, HTTP Methods, URLs, REST,
HTTP Headers, Cookies, Status Codes, HTTPS, HTTP Proxies, HTTP Authentication,
Web Functionality, Server-Side Functionality, Client-Side Functionality, State and
Sessions, Encoding Schemes, URL Encoding, Unicode Encoding, HTML Encoding,
Base64 Encoding, Hex Encoding, Remoting and Serialization Frameworks.
UNIT III: MAPPING THE APPLICATION 10 Hours Enumerating Content and Functionality, Web Spidering, User-Directed Spidering,
Discovering Hidden Content, Application Pages Versus Functional Paths, Discovering
Hidden Parameters, Analyzing the Application, Identifying Entry Points for User Input,
Identifying Server-Side Technologies, Identifying Server-Side Functionality, Mapping the
Attack Surface.
UNIT IV: ATTACKING AUTHENTICATION 10 Hours
Authentication Technologies, Design Flaws in Authentication Mechanisms, Bad
Passwords, Brute-Forcible Login, Verbose Failure Messages, Vulnerable Transmission of
Credentials, Password Change, Functionality, Forgotten Password Functionality, User
Impersonation, Functionality Incomplete, Validation of Credentials, Nonunique
Usernames, Predictable Usernames, Predictable Initial Passwords, Insecure Distribution of
Credentials. Attacking Access Controls.
UNIT V - ATTACKING DATA STORES 10 Hours
Injecting into Interpreted Contexts, Bypassing a Login, Injecting into SQL, Exploiting a
Basic Vulnerability Injecting into Different Statement Types, Finding SQL Injection Bugs,
Fingerprinting the Database, The UNION Operator, Extracting Useful Data, Extracting
IT56
Data with UNION, Bypassing Filters, Second-Order SQL Injection, Advanced Exploitation
Beyond SQL Injection: Escalating the Database Attack, Using SQL Exploitation Tools,
SQL Syntax and Error Reference, Preventing SQL Injection.
UNIT VI
Recent trends in Web Applications and its Security
REFERENCES
1. Defydd Stuttard, Marcus Pinto , The Web Application Hacker's Handbook: Finding And
Exploiting Security, Wiley Publishing, Second Edition.
2.Andres Andreu, Professional Pen Testing for Web application, Wrox Press.
3. Carlos Serrao, Vicente Aguilera, Fabio Cerullo, ―Web Application Security‖ Springer;
1st Edition
4. Joel Scambray, Vincent Liu, Caleb Sima ,―Hacking exposed‖, McGraw-Hill; 3rd
Edition, (October, 2010).
5. OReilly Web Security Privacy and Commerce 2nd Edition 2011.
6. Software Security Theory Programming and Practice, Richard sinn, Cengage Learning.
COURSE OUTCOMES
On completion of the course, the student would be able to:
CO1:Achieve Knowledge of web application‘s vulnerability and malicious attacks.
CO2:Understand the basic web technologies used for web application development
CO3:Understands the basic concepts of Mapping the application.
CO4:Able to illustrate different attacking illustrations
C05:Investigate technique of attacking Data Stores
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for
50 marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 1 3
CO2 2 1 3
CO3 1 3
CO4 3 1 3
CO5 1 3
1. Low, 2. Medium, 3. High
IT57
Course Code 18CS2L01 M. Tech (Information Technology)
Category Engineering Science Courses ( Practical )
Course title ADVANCED DATS STRUCTURES AND ALGORITHMS LAB
Scheme and Credits No. of Hours/Week Semester – II
L T P S Credits
0 0 4 0 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
1. Data structures and Algorithm
2. Java Programming
Course Objectives: The course will enable the students to:
1. Acquire the knowledge of using advanced data structures
2. Acquire the knowledge of sorting and balancing the tree structure
3. Understand the usage of graph structures and string matching.
4. Understand the implementation of various string matching algorithms.
5. learn to solve the various NP complete problems
Each student has to work individually on assigned lab exercises. Lab sessions could be
scheduled as one contiguous four-hour session per week. It is recommended that all
implementations are carried out in Java. Exercises should be designed to cover the following
topics:
1. Doubly Circular Linked List
2. AVL Tree
3. Efficiency of Heap Sort & Quick Sort
4. Travelling Salesman Problem (Dynamic Programming)
5. N Queens Problem (Backtracking/ Branch & Bound)
6. Bellman-Ford algorithm
7. Shortest paths in a DAG
8. Ford-Fulkerson algorithm
9. Robin-Karp algorithm
10. Knuth-Morris-Pratt algorithms
11. String matching with Finite Automata
12. Vertex Cover problem
13. The Set Covering problem
14. The Subset-Sum problem
15. Maximum Bipartite algorithm
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1: Design and implement basic and advanced data structures extensively.
CO2: Design and apply graph structures for various applications.
CO3: Design and develop efficient algorithms with minimum complexity using design
techniques.
CO4: Design and develop advanced string matching and NP Complete problems.
CO5: Achieve proficiency in Java programming.
SCHEME OF EXAMINATION The student has to write and implement two programs selecting ONE from each part
Continuous Internal Marks Semester End Evaluation (SEE) Marks
IT58
Evaluation (CIE) (Laboratory
– 50 Marks)
(Laboratory – 100 Marks)
Performance of the Student in
the laboratory every week
20 Write up 10
Test at the end of the semester 20 Experiment-1 (Part - A) – 35 Marks
Experiment-2 (Part - B) – 35 Marks
70
Viva Voce 10 Viva Voce 20
Total 100
Total (CIE) 50 Total (SEE) 50*
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2 CO2 2 CO3 2 CO4 2 CO5 2
1. Low, 2. Medium, 3. High
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COURSE LEARNING OBJECTIVES:
The objectives of the SEMINAR-II is to prepare the students to learn to:
1.Carry out the literature review of general research area/current topic and analyse the same
effectively.
2.Prepare a technical report, reflecting his/her depth of understanding, on the selected area/topic
and prepare content rich presentation.
3.Acquire communication and time management skills for effective and impactful presentation.
Interact with peers to acquire the qualities of thoughtfulness, friendliness, adaptability,
responsiveness, and politeness in-group discussion. Overcome stage fear during the presentation.
GUIDE LINES
1.Seminar preparation and presentation is an individual student activity.
2.Topic may be of general/ specific interest to program of engineering or electives not offered in
the semester and to be selected in consultation with the faculty/Guide.
3.Select one pertinent research paper for the seminar presentation.
4.Prepare and submit a detailed technical report of the seminar topic.
COURSE OUTCOMES:
Students shall be able to:
1.Carry out the literature survey of topic of seminar.
2.Prepare a technical report on the selected area/topic.
3.Make an effective presentation with seamless flow of content within the time allocated. Overcome
inhibition in interacting with peers and hence develop the spirit of team work. Overcome stage fear
during the presentation.
SCHEME OF EXAMINATION
CIE – 50
marks
Phase -1 Marks =10 Seminar Report
Marks =20
Total:50
Marks Phase -2 Marks =20
Course Code 18IT2S01 M. Tech (Information Technology)
Category Seminar Semester: II
Course title SEMINAR - II
Scheme and Credits
No. of Hours/Week
Total hours = 24 L T P S Credits
0 0 2 0 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any): NIL
IT60
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2 3 CO2 2 3 3 CO3 2
1. Low, 2. Medium, 3. High
Scheme of Continuous Internal Evaluation (CIE):
Evaluation would be carried out in TWO phases. The Seminar Evaluation Committee shall
comprise of Chairman of the Department, Faculty/Guide and one more faculty member nominated by
Chairman. The evaluation criteria shall be as per the rubrics given below:
Rubrics for Evaluation:
Topic - Technical Relevance, Sustainability and Societal Concerns : 15%
Review of literature and Technical Content : 35%
Presentation Skills : 25%
Report : 25%
IT61
Course Code 18CS2M01 M. Tech (Information Technology)
Category Engineering Science Courses ( Mandatory Audit )
Course title PEDAGOGY STUDIES
Scheme and Credits No. of Hours/Week Semester – II
L T P S Credits
2 0 0 0 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any):
COURSE OBJECTIVES
SThis course will enable students to
1. Understand the Thematic Overview and Pedagogical practices
2. Apply professional classroom practices , curriculum and assessment
3. Analyse methodology for quality assessment of school curriculum teacher
4. Evaluate pedagogic theory and pedagogical approaches
5. Create contexts pedagogy, new curriculum and assessment metrics for future
UNIT- I INTRODUCTION AND METHODOLOGY: 06 Hours Aims and rationale, Policy background, Conceptual framework and terminology Theories of
learning, Curriculum, Teacher education. Conceptual framework, Research questions.
Overview of methodology and Searching.
UNIT- II THEMATIC OVERVIEW: 03 Hours Pedagogical practices are being used by teachers in formal and informal classrooms in
developing countries. Curriculum, Teacher education
UNIT- III PEDAGOGICAL PRACTICES: 06 Hours Evidence on the effectiveness of pedagogical practices Methodology for the in depth stage:
quality assessment of included studies. How can teacher education (curriculum and
practicum) and the school curriculum and guidance materials best support effective
pedagogy? Theory of change. Strength and nature of the body of evidence for effective
pedagogical practices. Pedagogic theory and pedagogical approaches. Teachers‘ attitudes
and beliefs and Pedagogic strategies.
UNIT- IV PROFESSIONAL DEVELOPMENT: 06 Hours Professional development: alignment with classroom practices and follow-up support Peer
support Support from the head teacher and the community. Curriculum and assessment
Barriers to learning: limited resources and large class sizes
UNIT- V RESEARCH GAPS AND FUTURE DIRECTIONS: 03 Hours Research design Contexts Pedagogy Teacher education Curriculum and assessment
Dissemination and research impact.
UNIT- VI NEW DEVELOPMENTS IN PEDAGOGY:
REFERENCES
1. Ackers J, Hardman F (2001) Classroom interaction in Kenyan primary schools,
Compare, 31 (2): 245-261.
2. Agrawal M (2004) Curricular reform in schools: The importance of evaluation,
Journal of Curriculum Studies, 36 (3): 361-379.
3. Akyeampong K (2003) Teacher training in Ghana - does it count? Multi-site teacher
education research project (MUSTER) country report 1. London: DFID.
4. Akyeampong K, Lussier K, Pryor J, Westbrook J (2013) Improving teaching and
learning of basic maths and reading in Africa: Does teacher preparation count?
International Journal Educational Development, 33 (3): 272–282.
IT62
5. Alexander RJ (2001) Culture and pedagogy: International comparisons in primary
education. Oxford and Boston: Blackwell.
6. Chavan M (2003) Read India: A mass scale, rapid, ‗learning to read‘ campaign
7. www.pratham.org/images/resource%20working%20paper%202.pdf.
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1: What pedagogical practices are being used by teachers in formal and informal
classrooms in developing countries?
CO2: What is the evidence on the effectiveness of these pedagogical practices, in
what conditions, and with what population of learners?
CO3: How can teacher education (curriculum and practicum) and the school
curriculum and guidance materials best support effective pedagogy
CO4: Assess pedagogic theory and pedagogical approaches
CO5: Design new curriculum and assessment metrics for future
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 CO2 3 CO3 3 CO4 3 CO5 3
1: Low 2: Medium 3:High
IT63
Course Code 18IT3E1A M. Tech (Information Technology)
Category Engineering Science Courses (Theory- Professional Elective )
Course title SOCIAL NETWORK
Scheme and
Credits
No. of Hours/Week Semester – III
L T P S Credits
4 - - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES This course will enable students to
1. Understand the concept of semantic web and related applications.
2. Construct social network using various representation
3. Understand social web and related communities
4. Build sentiment analysis of social
UNIT-I INTRODUCTION: 9 Hours Introduction to Web - Limitations of current Web – Development of Semantic Web –
Emergence of the Social Web, Evolution in Social Networks , Statistical Properties of
Social Networks -Network analysis - Development of Social Network Analysis - Key
concepts and measures in network analysis - Discussion networks - Blogs and online
communities - Web-based networks
UNIT- II MODELING AND VISUALIZATION: 10 Hours Visualizing Online Social Networks - A Taxonomy of Visualizations - Graph
Representation -Centrality- Clustering - Node-Edge Diagrams - Visualizing Social
Networks with Matrix Based Representations- Node-Link Diagrams - Hybrid
Representations - Modelling and aggregating social network data – Random Walks and
their Applications - Ontological representation of social individuals and relationships
UNIT- III SOCIAL NETWORK ANALYSIS TECHNIQUES: 10 Hours Framework - Tracing Smoothly Evolving Communities - Models and Algorithms for
Social Influence Analysis - Influence Related Statistics - Social Similarity and Influence -
Influence Maximization in Viral Marketing - Algorithms and Systems for Expert Location
in Social Networks - Expert Location without Graph Constraints - with Score Propagation
– Expert Team Formation - Link Prediction in Social Networks -Feature based Link
Prediction - Bayesian Probabilistic Models - Probabilistic Relational Models
UNIT -IV MINING COMMUNITIES: 9 Hours
Aggregating and reasoning with social network data, Advanced Representations -
Extracting evolution of Web Community from a Series of Web Archive - Detecting
Communities in Social Networks - Evaluating Communities – Core Methods for
Community Detection & Mining - Applications of Community Mining Algorithms - Node
Classification in Social Networks.
UNIT- V TEXT AND OPINION MINING: 10 Hours Text Mining in Social Networks -Opinion extraction – Sentiment classification and
clustering - Temporal sentiment analysis - Irony detection in opinion mining - Wish
analysis - Product review mining – Review Classification – Tracking sentiments towards
topics over time
UNIT-VI Recent advances and research being done in the topics mentioned above
units
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REFERENCES
1. Charu C. Aggarwal, ―Social Network Data Analytics‖, Springer; 2011
2. Peter Mika, ―Social Networks and the Semantic Web‖, Springer, 1st edition, 2007.
3. Borko Furht, ―Handbook of Social Network Technologies and Applications‖,
Springer, 1st edition, 2010.
4. Guandong Xu , Yanchun Zhang and Lin Li, ―Web Mining and Social Networking –
Techniques and applications‖, Springer, 1st edition, 2011.
5. Giles, Mark Smith, John Yen, ―Advances in Social Network Mining and Analysis‖,
Springer, 2010.
6. Ajith Abraham, Aboul Ella Hassanien, Václav Snášel, ―Computational Social
Network Analysis: Trends, Tools and Research Advances‖, Springer, 2009.
7. Toby Segaran, ―Programming Collective Intelligence‖, O‘Reilly, 2012
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1 Develop semantic web related applications.
CO2: Represent knowledge using ontology
CO3: Analysis of models in social network.
CO4: Predict social web and related communities.
CO5: Visualize and sentiment analysis of social networks
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Total:
Marks 100
Unit which have 10 hours shall t have internal
choice
20*3=60
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes
PO1 PO2 PO3
CO1 2 3 CO2 2 CO3 1 3 CO4 1 3 CO5 1 1 3
1: Low 2: Medium 3:High
IT65
Course Code 18CS3E1B M. Tech (Information Technology)
Category Engineering Science Courses ( Integrated - Professional
Elective)
Course title BIG DATA ANALYTICS
Scheme and
Credits
No. of Hours/Week Semester – III
L T P S Credits
3 - 2 - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
Data Structures, Computer Architecture and Organization
Course Objectives: The course will enable the students to:
1. Understand big data for business intelligence.
2. Illustrate business case studies for big data analytics.
3. Discuss NoSQL big data management.
4. Demonstrate map-reduce analytics using Hadoop.
5. Compare Hadoop related tools such as HBase, Pig, Cassandra and Hive for big data
analytics.
UNIT I – INTRODUCTION TO BIG DATA 9 Hours Need for big data, convergence of key trends, unstructured data, industry examples of big
data, web analytics, big data and marketing, fraud and big data, risk and big data, credit
risk management, big data and algorithmic trading, big data and healthcare, big data in
medicine, advertising and big data, big data technologies, introduction to Hadoop, open
source technologies, cloud and big data, mobile business intelligence, Crowd sourcing
analytics, inter and trans firewall analytics.
UNIT II - INTRODUCTION TO NoSQL 10 Hours Aggregate data models, aggregates, key-value and document data models, relationships,
graph databases, schemaless databases, materialized views, distribution models, sharding,
master-slave replication, peer peer replication, sharding and replication, consistency,
relaxing consistency, version stamps, map-reduce, partitioning and combining, composing
map-reduce calculations.
UNIT III – HADOOP 10 Hours
Data format, analyzing data with Hadoop, scaling out, Hadoop streaming, Hadoop pipes,
design of Hadoop distributed file system (HDFS), HDFS concepts, Java interface, data
flow, Hadoop I/O, data integrity, compression, serialization, Avro, file-based data
structures
UNIT IV – MAPREDUCE 10 Hours MapReduce workflows, unit tests with MRUnit, test data and local tests, anatomy of
MapReduce job run, classic Map-reduce, YARN, failures in classic Map-reduce and
YARN, job scheduling, shuffle and sort, task execution, MapReduce types, input formats,
output formats.
UNIT V – Hbase 9 Hours
Hbase, data model and implementations, Hbase clients, Hbase examples, praxis.
Cassandra, Cassandra data model, Cassandra examples, Cassandra clients, Hadoop
integration, Pig, Grunt, pig data model, Pig Latin, developing and testing Pig Latin scripts.
Hive, data types and file formats, HiveQL data definition, HiveQL data manipulation,
HiveQL queries.
UNIT VI -
Recent advances in Data Analytics
IT66
UNIT –VII (Lab Programs)
1. (a) Perform setting up and Installing Hadoop in its two operating modes:
o Pseudo distributed,
o Fully distributed.
(b) Use web based tools to monitor your Hadoop setup.
2. (a) Implement the following file management tasks in Hadoop:
o Adding files and directories
o Retrieving files
o Deleting files
(b) Benchmark and stress test an Apache Hadoop cluster
3. Run a basic Word Count Map Reduce program to understand Map Reduce Paradigm.
(a) Find the number of occurrence of each word appearing in the input
file(s)
(b) Performing a MapReduce Job for word search count (look for specific
keywords in a file)
4. Stop word elimination problem:
Input:
o A large textual file containing one sentence per line
o A small file containing a set of stop words (One stop word per line)
Output:
o A textual file containing the same sentences of the large input file without the
words appearing in the small file.
5. Write a Map Reduce program that mines weather data. Weather sensors collecting data
every hour at many locations across the globe gather large volume of log data, which is a
good candidate for analysis with MapReduce, since it is semi structured and record
oriented.
Data available at https://github.com/tomwhite/hadoopbook/tree/master/input/ncdc/all.
o Find average, max and min temperature for each year in NCDC data set?
o Filter the readings of a set based on value of the measurement, Output the line
of input files associated with a temperature value greater than 30.0 and store it
in a separate file.
6. Purchases.txt Dataset
(a) Instead of breaking the sales down by store, give us a sales breakdown by
product category across all of our stores
(b) What is the value of total sales for the following categories?
o Toys.
o Consumer Electronics
(c) Find the monetary value for the highest individual sale for each separate
store
(d) What are the values for the following stores?
Reno
Toledo
Chandler
(e) Find the total sales value across all the stores, and the total number of
sales.
7. Install and Run Pig then write Pig Latin scripts to sort, group, join, project, and filter
your data.
8. Write a Pig Latin scripts for finding TF-IDF value for book dataset (A corpus of eBooks
IT67
available at: Project Gutenberg)
9. Install and Run Hive then use Hive to create, alter, and drop databases, tables, views,
functions, and indexes.
10. Install, Deploy & configure Apache Spark Cluster. Run apache spark applications using
Scala.
REFERENCES
1. Michael Minelli, Michelle Chambers, and Ambiga Dhiraj, "Big Data, Big
Analytics: Emerging Business Intelligence and Analytic Trends for Today's
Businesses", Wiley, 2013.
2. P. J. Sadalage and M. Fowler, "NoSQL Distilled: A Brief Guide to the Emerging
World of
Polyglot Persistence", Addison-Wesley Professional, 2012.
3. Tom White, "Hadoop: The Definitive Guide", Third Edition, O'Reilley, 2012.
4. Eric Sammer, "Hadoop Operations", O'Reilley, 2012.
5. E. Capriolo, D. Wampler, and J. Rutherglen, "Programming Hive", O'Reilley, 2012.
6. Lars George, "HBase: The Definitive Guide", O'Reilley, 2011.
7. Eben Hewitt, "Cassandra: The Definitive Guide", O'Reilley, 2010.
8. Alan Gates, "Programming Pig", O'Reilley, 2011.
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1. Describe big data and use cases from selected business domains.
CO2. Discuss the business case studies for big data analytics.
CO3. Explain NoSQL big data management.
CO4. Perform map-reduce analytics using Hadoop.
CO5. Use Hadoop related tools such as HBase, Cassandra, Pig, and Hive for big data
analytics.
SCHEME OF EXAMINATION:
CIE -
Practical
Conduction of experiments, performance of student in
every week lab and completion of lab record=50#
Total: Marks
50
Lab Test: Part-A =20, Part-B=20 and Vice-Voce=10
Marks(50##
)
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
# = CIE Marks for laboratory performance is to be evaluated for 50 marks and the
marks obtained shall be reduced for 25 marks
## = Lab test is to be conducted for 50 marks and the marks obtained shall be
reduced for 25 marks. Lab test shall be conducted by two examiners out of which
one examiner is the faculty taught the course. There is no SEE for the practical ‘s
portion of integrated course
IT68
Mapping of Course Outcomes (COS) to Program Outcomes
PO1 PO2 PO3
CO1 3 1
CO2 2
CO3 3 2
CO4 1 2
CO5 3
1. Low, 2. Medium, 3. High
IT69
Course Code 18IT3E1C M. Tech (Information Technology)
Category Engineering Science Courses (Theory- Professional Elective )
Course title INFORMATION RETRIEVAL SYSTEMS
Scheme and
Credits
No. of Hours/Week Semester – III
L T P S Credits
4 0 - - 4
CIE Marks:
50
SEE Marks: 50 Total Max. Marks:
100
Duration of SEE: 3 Hrs
Prerequisites (if any):
Course Objectives
This course will enable students to
1. Understand the taxonomy and models of Information retrieval system.
2. Discuss the retrieval evaluation methods.
3. Acquire learning techniques for text classification and clustering.
4. Design the search engine
5. Experiment web content structure searching in search engine.
UNIT I-INTRODUCTION 10 Hours Motivation, Basic concepts, Past, present, and future, The retrieval process. Modelling:
Introduction, A taxonomy of information retrieval models, Retrieval: Adhoc and filtering,
A formal characterization of IR models, Classic information retrieval, Alternative set
theoretic models, Alternative algebraic models, Alternative probabilistic models,
Structured text retrieval models, Models for browsing.
UNIT II- RETRIEVAL EVALUATION 10 Hours Introduction, Retrieval performance evaluation, Reference collections. Query
Languages: Introduction, keyword-based querying, Pattern matching, Structural
queries, Query protocols. Query Operations: Introduction, User relevance feedback,
Automatic local analysis, Automatic global analysis.
UNIT III - TEXT AND MULTIMEDIA LANGUAGES AND PROPERTIES
09 Hours Introduction, Metadata, Text, Markup languages, Multimedia. Text Operations:
Introduction, Document pre-processing, Document clustering, Text compression,
Comparing text compression techniques
UNIT IV – USER INTERFACES AND VISUALIZATION 10 Hours Introduction, Human-Computer interaction, The information access process, Starting
pints, Query specification, Context, Using relevance judgments, Interface support for
the search process. Searching the Web: Introduction, Challenges, Characterizing the
web, Search engines, Browsing, Meta searchers, Finding the needle in the haystack,
Searching using hyperlinks.
UNIT V - INDEXING AND SEARCHING 09 Hours Introduction; Inverted Files; Other indices for text;Boolean queries; Sequential searching;
Pattern matching; Structural queries;Compression. Parallel and Distributed IR:
Introduction, Parallel IR, Distributed IR.
UNIT VI -
Recent trends in information retrieval systems
IT70
REFERENCES
1. Ricardo Baeza-Yates, Berthier Ribeiro-Neto: Modern Information Retrieval, Pearson,
1999.
2. David A. Grossman, Ophir Frieder: Information Retrieval Algorithms and Heuristics,
2nd
Edition, Springer, 2004
COURSE OUTCOMES
Upon completion of this course, the students should be able to: CO1: Summerize taxonomy and models of information retrieval system.
CO2: Design the various components of an information retrieval system
CO3: Design text classification and clustering applying machine learning technique.
CO4: Demonstrate the functions of search engine.
CO5: Analyse web content structure for efficient information retrieval.
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for
50 marks.
Mapping of Course Outcomes (COS) to Program Outcomes
PO1 PO2 PO3
CO1 2
CO2 3
CO3 2
CO4 3
CO5 2 3
1. Low, 2. Medium, 3. High
IT71
Course Code 18CS3P1A M. Tech (Information Technology)
Category Engineering Science Courses (Theory- Open Elective )
Course title ARITIFICIAL INTELLIGENCE
Scheme and
Credits
No. of Hours/Week Semester – III
L T P S Credits
4 0 0 0 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
This course will enable students to
1. Understand the various characteristics of Intelligent agents
2. Understand the different search strategies in AI
3. Learn to represent knowledge in solving AI problems
4. Analyse the different ways of designing software agents
5. Evaluate the various reasoning techniques for AI.
UNIT-I INTRODUCTION: 9 Hours Introduction Definition Future of Characteristics and Problem Solving Approach to Typical
AI problems. State Space Search and Heuristic Search Techniques Defining problems as
State Space search, Production systems and characteristics, Hill Climbing, Breadth first and
depth first search, Best first search.
UNIT-II KNOWLEDGE REPRESENTATION ISSUES: 9 Hours Representations and Mappings, Approaches to knowledge representation, Using Predicate
Logic and Representing Knowledge as Rules , Representing simple facts in logic,
Computable functions and predicates, Procedural vs Declarative knowledge, Logic
Programming, Forward vs backward reasoning.
UNIT-III SOFTWARE AGENTS: 10 Hours
Architecture for Intelligent Agents Agent communication Negotiation and Bargaining
Argumentation among Agents Trust and Reputation in Multi-agent systems.
UNIT-IV REASONING I: 10 Hours Symbolic Logic under Uncertainty , Non-monotonic Reasoning, Logics for non-monotonic
reasoning, Statistical Reasoning.
UNIT-V METHODS: 10 Hours
Probability and Bayes Theorem, Certainty factors, Probabilistic Graphical Models, Bayesian
Networks, Markov Networks, Fuzzy Logic.
UNIT-VI Recent advances and research being done in the topics mentioned above
units
REFERENCES:
1. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach", Prentice
Hall, Third Edition, 2009.
2. Rich and Knight, Artificial Intelligence, 2nd Edition, 2013
3. I. Bratko, "Prolog: Programming for Artificial Intelligence", Fourth edition,
Addison-Wesley Educational Publishers Inc., 2011.
4. M. Tim Jones, "Artificial Intelligence: A Systems Approach(Computer Science),
Jones and Bartlett Publishers, Inc.; First Edition, 2008
5. Nils J. Nilsson, "The Quest for Artificial Intelligence", Cambridge University
Press, 2009.
6. William F. Clocksin and Christopher S. Mellish," Programming Using
IT72
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1: Define and identify various AI concepts
CO2: illustrate different AI strategies
CO3: Sketch various knowledge representation for AI problems
CO4: Analyse agents usage for AI
CO5: Design AI inference techniques
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Total:
Marks 100
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes
PO1 PO2 PO3
CO1 2 CO2 2 CO3 2 CO4 2 CO5 2 2
1: Low 2: Medium 3:High
IT73
Course Code 18CS3P1B M. Tech (Information Technology)
Category Engineering Science Courses (Theory-Open Elective )
Course title BUSINESS ANALYTICS
Scheme and
Credits
No. of Hours/Week Semester – III
L T P S Credits
4 0 - - 4
CIE Marks:
50
SEE Marks: 50 Total Max. Marks:
100
Duration of SEE: 3 Hrs
Prerequisites (if any):
Course Objectives: The course will enable the students to:
1. Understand the role of business analytics within an organization.
2. Analyze data using statistical and data mining techniques.
3. Distinguish relationships between the underlying business processes of an
organization.
4. Gain an understanding of how managers use business analytics to formulate and solve
business problems and to support managerial decision making.
5. Discuss the uses of decision-making tools and Operations research techniques.
UNIT I – BUSINESS ANALYTICS 10 Hours Overview of Business analytics, Scope of Business analytics, Business Analytics Process,
Relationship of Business Analytics Process and organisation, competitive advantages of
Business Analytics. Statistical Tools: Statistical Notation, Descriptive Statistical
methods, Review of probability distribution and data modelling, sampling and estimation
methods overview –
UNIT II - TRENDINESS AND REGRESSION ANALYSIS: 9 Hours
Modelling Relationships and Trends in Data, simple Linear Regression. Important
Resources, Business Analytics Personnel, Data and models for Business analytics,
problem solving, Visualizing and Exploring Data, Business Analytics Technology
UNIT III - ORGANIZATION STRUCTURES OF BUSINESS ANALYTICS:
10 Hours Team management, Management Issues, Designing Information Policy, Outsourcing,
Ensuring Data Quality, Measuring contribution of Business analytics, Managing
Changes. Descriptive Analytics, predictive analytics, predicative Modelling, Predictive
analytics analysis, Data Mining, Data Mining Methodologies, Prescriptive analytics and
its step in the business analytics Process, Prescriptive Modelling, nonlinear Optimization.
UNIT IV – FORECASTING TECHNIQUES: 10 Hours
Qualitative and Judgmental Forecasting, Statistical Forecasting Models, Forecasting
Models for Stationary Time Series, Forecasting Models for Time Series with a Linear
Trend, Forecasting Time Series with Seasonality, Regression Forecasting with Casual
Variables, Selecting Appropriate Forecasting Models. Monte Carlo Simulation and Risk
Analysis: Monte Carle Simulation Using Analytic Solver Platform, New-Product
Development Model, Newsvendor Model, Overbooking Model, Cash Budget Model.
UNIT V – DECISION ANALYSIS 9 Hours Formulating Decision Problems, Decision Strategies with the without Outcome
Probabilities, Decision Trees, The Value of Information, Utility and Decision Making
UNIT VI -
Recent Trends in Embedded and collaborative business intelligence, Visual
data recovery, Data Storytelling and Data journalism.
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REFERENCES
1. Business analytics Principles, Concepts, and Applications by Marc J. Schniederjans,
Dara G. Schniederjans, Christopher M. Starkey, Pearson FT Press, First edition,
2014
2. Business Analytics by James Evans, Pearson Education, First Edition, 2017.
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1. Develop the knowledge of data analytics.
CO2. Demonstrate the ability of think critically in making decisions based
on data and deep analytics
CO3. Discuss the uses of technical skills in predicative and prescriptive
modeling to support business decision-making
CO4. Demonstrate the ability to translate data into clear and actionable insights.
CO5. Evaluate and assess the forecasting techniques.
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for
50 marks.
Mapping of Course Outcomes (COS) to Program Outcomes
PO1 PO2 PO3
CO1 3 3
CO2 3 3
CO3 3 3
CO4 3 3
CO5 3 3
1. Low, 2. Medium, 3. High
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Course Code 18CS3P1C M. Tech (Information Technology)
Category Engineering Science Courses (Theory-Open Elective)
Course title MODELING AND SIMULATION
Scheme and
Credits
No. of Hours/Week Semester – III
L T P S Credits
3 1 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES:
The course will enable the students to:
6. Understand the system, specify systems using natural models of computation, modelling
techniques
7. Apply natural models of computation, modelling techniques to
understand behaviour of system , and analyse the simulation data
8. Analyse simulation data, simulation tools for simulation, Terminating Simulations –
Steady state simulations.
9. Evaluate the existing simulation models for verification, calibration and validation
10. Design validation, calibration model and decision support
UNIT – I INTRODUCTION TO SIMULATION 09 Hours
Introduction Simulation Terminologies- Application areas – Model Classification Types of
Simulation- Steps in a Simulation study- Concepts in Discrete Event Simulation Example.
UNIT-II MATHEMATICAL MODELS 10 Hours
Statistical Models - Concepts – Discrete Distribution- Continuous Distribution – Poisson
Process- Empirical Distributions- Queueing Models – Characteristics- Notation Queueing
Systems – Markovian Models- Properties of random numbers- Generation of Pseudo Random
numbers- Techniques for generating random numbers-Testing random number generators
Generating Random-Variates- Inverse Transform technique Acceptance- Rejection technique –
Composition & Convolution Method.
UNIT-III ANALYSIS OF SIMULATION DATA 10 Hours
Input Modelling - Data collection - Assessing sample independence – Hypothesizing
distribution family with data - Parameter Estimation - Goodness-of-fit tests – Selecting input
models in absence of data- Output analysis for a Single system – Terminating Simulations –
Steady state simulations.
UNIT -IV VERIFICATION AND VALIDATION 09 Hours
Building – Verification of Simulation Models – Calibration and Validation of Models –
Validation of Model Assumptions – Validating Input – Output Transformations
UNIT-V SIMULATION OF COMPUT ER SYSTEMS 10 Hours
Simulation Tools – Model Input – High level computer system simulation – CPU – Memory
Simulation – Comparison of systems via simulation – Simulation Programming techniques -
Development of Simulation models.
UNIT-VI Recent advances and research being done in the topics mentioned above units
REFERENCES
1. Jerry Banks and John Carson, ―Discrete Event System Simulation‖, Fourth Edition, PHI,
2005.
2. Geoffrey Gordon, ―System Simulation‖, Second Edition, PHI, 2006.
3. Frank L. Severance, ―System Modelling and Simulation‖, Wiley, 2001.
4. Averill M. Law and W. David Kelton, ―Simulation Modelling and Analysis, Third
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Edition, McGraw Hill, 2006.
5. Jerry Banks, ―Handbook of Simulation: Principles, Methodology, Advances,
Applications and Practice‖, Wiley-Inter science, 1 edition, 1998.
COURSE OUTCOMES
On Completion of the course, the student will be able to:
CO1: Explain natural models of computation, modelling techniques
CO2: Determine suitable models of computation, modelling techniques to
understand behaviour of system.
CO3: Distinguish simulation models for verification, calibration and validation
CO4: Assess the performance of different simulation models, statistical models, queuing
Systems and Markovian Models for given problem
CO5: Design goodness-of-fit tests and input models in absence of data
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 20 marks Two Quizzes / AAT
= 10 marks
Total:50
marks Test II (Unit IV & V) – 20 marks
SEE
– 100
marks
Answer FIVE full questions Total:100 marks
Units which have 09 Hours shall not have
internal choice. 20* 2 = 40 Marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
Mapping of Course Outcomes (COS) to Program Outcomes
PO1 PO2 PO3
CO1 2 CO2 3 CO3 3 CO4 3 CO5 3 2
1: Low 2: Medium 3:High
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COURSE LEARNING OBJECTIVES:
The objectives of the SEMINAR-III is to prepare the students to learn to:
1.Carry out the literature review of general research area/current topic and analyse the same
effectively.
2.Prepare a technical report, reflecting his/her depth of understanding, on the selected area/topic
and prepare content rich presentation.
3.Acquire communication and time management skills for effective and impactful presentation.
Interact with peers to acquire the qualities of thoughtfulness, friendliness, adaptability,
responsiveness, and politeness in-group discussion. Overcome stage fear during the presentation.
GUIDE LINES
1.Seminar preparation and presentation is an individual student activity.
2.Topic may be of general/ specific interest to program of engineering or electives not offered in
the semester and to be selected in consultation with the faculty/Guide.
3.Select one pertinent research paper for the seminar presentation.
4.Prepare and submit a detailed technical report of the seminar topic.
COURSE OUTCOMES:
Students shall be able to:
1. Carry out the literature survey of topic of seminar.
2. Prepare a technical report on the selected area/topic.
3. Make an effective presentation with seamless flow of content within the time allocated.
Overcome inhibition in interacting with peers and hence develop the spirit of team work.
Overcome stage fear during the presentation.
SCHEME OF EXAMINATION
CIE – 50
marks
Phase -1 Marks =10 Seminar Report
Marks =20
Total:50
Marks Phase -2 Marks =20
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
Course Code 18IT3S01 M. Tech (Information Technology)
Category Seminar Semester: III
Course title SEMINAR - III
Scheme and Credits
No. of Hours/Week
Total hours = 24 L T P S Credits
0 0 2 0 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any): NIL
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PO1 PO2 PO3
CO1 2 2 3 CO2 2 3 3 CO3 2
1. Low, 2. Medium, 3. High
Scheme of Continuous Internal Evaluation (CIE):
Evaluation would be carried out in TWO phases. The Seminar Evaluation Committee shall
comprise of Chairman of the Department, Faculty/Guide and one more faculty member nominated by
Chairman. The evaluation criteria shall be as per the rubrics given below:
Rubrics for Evaluation:
Topic - Technical Relevance, Sustainability and Societal Concerns : 15%
Review of literature and Technical Content : 35%
Presentation Skills : 25%
Report : 25%
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INTERNSHIP
COURSE LEARNING OBJECTIVES:
Objectives of the internship
1. Provide an opportunity to see how classroom and textbook learning applies to the real world,
and to expose the students to the relevant work experience.
2. Pay close attention to all the steps that go onto completing a job, thereby, help students to
become workforce ready before entering the job market as a graduate. Provide an opportunity
to select the topic of dissertation work by evaluating the requirement of organisation.
3. Prepare and present a technical report of internship.
GUIDELINES
1. Student has to approach the concerned heads of various Industries/organization, which are
related to the field of specialization of the M. Tech program.
2. If any student gets internship, he/she has to submit the internship offer letter duly signed by the
concerned authority of the company to the Chairperson of the Department.
3. The internship on full time basis will be after the examination of II semester and during III
semester for a period of 8 weeks without affects regular class work.
4. The progress has to be reported periodically to the faculty or to the Guide assigned by the
Chairperson as per the format acceptable to the respective industry /organizations and to the
Institution.
5. At the end of the internship the student has to prepare a detailed report and submit.
6. Students are advised to use ICT tools such as Skype to report their progress and submission of
periodic progress reports to the faculty in charge or guide.
7. Duly signed report from internal supervisor (faculty incharge or guide) and external supervisor
from the organization where internship is offered has to be submitted to the Chairperson of the
Department for his/her signature and further processing for evaluation.
The broad format of the internship final report shall contain Cover Page, Certificate from College,
Certificate from Industry / Organization of internship, Acknowledgement, Synopsis, Table of
Contents, chapters of Profile of the Organization - Organizational structure, Products, Services,
Business Partners, Financials, Manpower, Societal Concerns, Professional Practices, Activities of
the Department where internship is done, Tasks Performed and summary of the tasks
performed. specific technical and soft skills that student has acquired during internship,
References & Annexure.
Course Code 18IT3I01 M. Tech (Information Technology)
Category Internship/ Mini Project Semester: III
Course title INTERNSHIP / MINI PROJECT
Scheme and Credits
No. of Hours/Week
Total hours = 80 L T P S Credits
--- --- 10 --- 5
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs for a
batch 6 students
Prerequisites (if any): NIL
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COURSE OUTCOMES:
The student will be able to:
1. Apply the gained experience along with the theoretical knowledge to solve the real world
problems what engineers ready do.
2. Get equipped with experience required before entering the job market. Explore the possibility of
formulating the dissertation problem.
3. Prepare a technical report and make a presentation of details of internship.
SCHEME OF EXAMINATION
CIE 1.Marks awarded by guide (Internal Examiner) = 50 marks
2.Marks awarded by the department internship monitoring committee = 50 Marks
50*
Marks
SEE Presentation of internship in the presence of Guide (Internal Examiner) and external
examiner =100 Marks
50**
Marks
Note: *= CIE be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
**= SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 2
CO2 2 2
CO3 3
1. Low, 2. Medium, 3. High
Rubrics for CIE:
1. Topic of internship = 10%
2. Objectives of internship = 10%
3. Specific skills acquired = 20%
4. Document = 40%
5. Presentation = 20%
Rubrics for SEE:
1. Topic of internship = 10%
2. Objectives of internship = 10%
3. Specific skills acquired = 20%
4. Document = 40%
5. presentation = 20%
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MINI PROJECT
COURSE LEARNING OBJECTIVE:
1. Understand the method of applying engineering knowledge/use application software to solve
specific
problems after carrying out literature survey.
2. Apply engineering and management principles while executing the project.
3. Demonstrate the skills for good technical report writing and presentation.
COURSE CONTENT/GUIDELINES
Student shall take up small problems in the field of domain of program as mini project. It can be
related to a solution to an engineering problem, verification and analysis of experimental data
available, conducting experiments on various engineering subjects, material characterisation, studying
a software tool for solution to an engineering problem, etc.
The mini project must be carried out preferably using the resources available in the department/college
and it can be of interdisciplinary also.
COURSE OUTCOMES:
The students shall be able to:
1. Conduct experiments / use the capabilities of relevant application software/ simulation tools
individually to generate data/ solve problems.
2. Assess the available engineering resources available in the institution.
3. Prepare and Present the technical document of mini project.
Rubrics for CIE shall be done with weightage/distribution of marks as follows:
Note: * = CIE be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
Sl.
no
Particulars Weightage Marks Total marks
of CIE
1 Selection of the topic & formulation of objectives 10% 10
50*
2 Modelling and simulation/algorithm
development/experiment setup
25% 25
3 Conducting experiments/implementation/testing 25% 25
4 Demonstration & Presentation 15% 15
5 Report writing 25% 25
Total 100% 100
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CIE 1.Marks awarded by guide (Internal Examiner) = 50 marks
2.Marks awarded by the department internship monitoring committee = 50 Marks
50*
Marks
SEE Presentation of internship in the presence of Guide (Internal Examiner) and external
examiner =100 Marks
50**
Marks
Rubrics for SEE:
The SEE shall be done by two examiners out of which one examiner is the guide of mini project.
The following weightage would be given for the examination. Evaluation shall be done in batches, not
exceeding 6 students.
Sl.
no
Particulars Weightage Marks Total marks
of CIE
1 Brief write-up about the project 05% 05
50**
2 Presentation/demonstration of the project 20% 20
3 Methodology and Experimental Results &
Discussion
30% 30
4 Report 25% 25
5 Viva Voce 20% 20
Total 100% 100
Note: ** = SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 3
CO2 2 3
CO3 3
1. Low, 2. Medium, 3. High
IT83
COURSE LEARNING OBJECTIVES:
1. Choose a problem applying relevant knowledge and skills acquired during the course. Formulate
the specifications of the project work, identify the set of feasible solutions, prepare, and execute
project plan considering professional, cultural and societal factors. Identify the problem-solving
methodology using literature survey and present the same.
2. Develop experimental planning and select appropriate techniques and tools to conduct
experiments to Evaluate and critically examine the outcomes followed by concluding the results
and identifying relevant applications. Preparation of synopsis, preliminary report for approval of
topic selected along with literature survey, objectives and methodology.
3. Develop oral and written communication skills to effectively convey the technical content.
GUIDELINES
The Dissertation work will start in III semester and should be a problem with research potential
and should involve scientific research, design, generation/collection and analysis of data,
determining solution and must preferably bring out the individual contribution.
The Dissertation work will have to be done by only one student and the topic of dissertation
must be decided by the guide and the student. The dissertation work shall be carried out, on-
campus or in an industry or in an organisation with prior approval from the Chairperson of the
Department. The student has to be in regular contact with the guide atleast once in a week.
The report of Dissertation work phase I shall contain cover page, certificate from
College/Industry/Organisation, Acknowledgement, List of Figures and Tables Contents,
Nomenclature, Chapters of Introduction including motivation to choose topic, Literature survey,
Conclusion of literature survey, Objectives and Scope of Dissertation, Methodology to be
followed, Experimental requirements, References and Annexure.
The preliminary results (if available) of the problem of Dissertation work may also be
discussed in the report.
Course Code 18IT3D01 M. Tech (Information Technology)
Category Dissertation Work Semester: III
Course title DISSERTATION WORK PHASE -I
Scheme and Credits
No. of Hours/Week
Total hours = 80 L T P S Credits
0 0 10 0 5
CIE Marks: 50 SEE Marks:50 Total Max. Marks: 100 Duration of SEE: 1Hour
Prerequisites (if any): NIL
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COURSE OUTCOME:
The students will be able to:
1. Self learn various topics relevant to Dissertation work. Survey the literature such as books,
National/International reference journals, articles and contact resource persons for selected topics
of Dissertation.
2. Write and prepare a typical technical report.
3. Present and defend the contents of Dissertation work phase I in front of technically qualified
audience effectively.
SCHEME OF EXAMINATION
CIE 1.Marks awarded by guide (Internal examiner) = 50 Marks
2.Marks awarded by the department dissertation monitoring committee = 50 marks
50*
Marks
SEE Presentation of Dissertation work Phase-I in the presence of Guide (Internal
examiner) and external examiner =100 Marks
50**
Marks
Note: *= CIE be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
**= SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
Rubrics for CIE: Weightage
1. Introduction and Justification of topic = 10%
2. Literature survey and Conclusion = 30%
3. Objectives and Scope of Dissertation work = 30%
4. Methodology to be adopted = 20%
5. Presentation of contents of Dissertation work Phase-I = 10%
Rubrics for SEE:
1. Introduction and Justification of topic = 10%
2. Literature survey and Conclusion = 30%
3. Objectives and Scope of Dissertation work = 30%
4. Methodology, Experimental /Software = 20%
5. Presentation of Dissertation Phase-I = 10%
Mapping of Course Outcomes (Cos) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 3
CO2 3 3
CO3 3 3
1. Low, 2.Medium, 3. High
IT85
COURSE LEARNING OBJECTIVES:
The objectives of the SEMINAR-IV is to prepare the students to learn to:
1.Carry out the literature review of general research area/current topic and analyse the same
effectively.
2.Prepare a technical report, reflecting his/her depth of understanding, on the selected area/topic
and prepare content rich presentation.
3.Acquire communication and time management skills for effective and impactful presentation.
Interact with peers to acquire the qualities of thoughtfulness, friendliness, adaptability,
responsiveness, and politeness in-group discussion. Overcome stage fear during the presentation.
GUIDE LINES
1.Seminar preparation and presentation is an individual student activity.
2.Topic may be of general/ specific interest to program of engineering or electives not offered in
the semester and to be selected in consultation with the faculty/Guide.
3.Select one pertinent research paper for the seminar presentation.
4.Prepare and submit a detailed technical report of the seminar topic.
COURSE OUTCOMES:
Students shall be able to:
1.Carry out the literature survey of topic of seminar.
2.Prepare a technical report on the selected area/topic.
3.Make an effective presentation with seamless flow of content within the time allocated. Overcome
inhibition in interacting with peers and hence develop the spirit of team work. Overcome stage fear
during the presentation.
SCHEME OF EXAMINATION
CIE – 50
marks
Phase -1 Marks =10 Seminar Report
Marks =20
Total:50
Marks Phase -2 Marks =20
Course Code 18IT4S01 M. Tech (Information Technology)
Category Seminar Semester: IV
Course title SEMINAR - IV
Scheme and Credits
No. of Hours/Week
Total hours = 24 L T P S Credits
0 0 2 0 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any): NIL
IT86
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2 3 CO2 2 3 3 CO3 2
1. Low, 2. Medium, 3. High
Scheme of Continuous Internal Evaluation (CIE):
Evaluation would be carried out in TWO phases. The Seminar Evaluation Committee shall
comprise of Chairman of the Department, Faculty/Guide and one more faculty member nominated by
Chairman. The evaluation criteria shall be as per the rubrics given below:
Rubrics for Evaluation:
Topic - Technical Relevance, Sustainability and Societal Concerns : 15%
Review of literature : 35%
Presentation Skills : 25%
Report : 25%
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COURSE LEARNING OBJECTIVES:
1. Apply/Use different experimental techniques, equipments, software/ Computational/ Analytical
/Modelling and Simulation tools required for conducting tests and generate other relevant data.
Students will also be able to design and develop an experimental setup/test rig.
2. Analyse the results of the experiments conducted/models developed.
3. Create a detailed technical document as per format based on the outcome of dissertation work
phase I and II.
GUIDELINES
Dissertation work phase II is the continuation of project work started in III semester. The report of
Dissertation work that includes the details of Dissertation work phase I and phase II should be
presented in a standard format. The candidate shall prepare a detailed report of dissertation that
includes Cover Paper, Certificate from College/Industry/Organisation, Acknowledgement,
Abstract, Table of contents, List of Figures and Table, Nomenclature, Chapter of Introduction,
Literature survey, Conclusion of literature survey, Objectives and Scope of dissertation work,
Methodology, Experimentation, Results, Discussion, Conclusion, Scope for future work,
References, Annexure and full text of the publication (submitted or published).
COURSE OUTCOMES:
Students shall be able to:
1. Conduct experiments/ implement the capabilities of different Software /Computational /
Analytical/Modelling and simulation tools individually and generate data for validation of
hypothesis.
2. Investigate and assess the results obtained within the scope of experiments conducted followed
by conclusions.
Course Code 18IT4D01 M. Tech (Information Technology)
Category Dissertation Work Semester: IV
Course title DISSERTATION WORK PHASE -II
Scheme and Credits
No. of Hours/Week
Total hours = 150 L T P S Credits
--- --- 30 --- 15
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100
Prerequisites (if any): NIL
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3. Prepare a detailed technical document, Present and defend the contents of Dissertation work
in presence of technically qualified audience effectively.
SCHEME OF EXAMINATION
CIE
1. Marks awarded by guide = 50 marks
2. Marks awarded by the department dissertation monitoring committee
(Guide + Two faculty members )= 50 marks
100
marks
50*
marks
SEE
1. Dissertation evaluation by guide (Internal examiner) = 100 marks
2. Dissertation evaluation by External examiner =100 marks
3. Viva- Voce examination by guide and external examiner who evaluated the
dissertation work =100 marks
300
marks
50**
marks
Note: * = CIE be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
** = SEE shall be conducted for 300 marks and the marks obtained shall be reduced for 50
marks.
Rubrics for CIE:
1.Presentation of background of dissertation work = 10%
2.Literature survey, Problem formulation and Objectives = 30%
3.Presentation of methodology and Experimentation = 30%
4.Results and Discussion = 20%
5.Questions and Answers = 10%
Rubrics for SEE:
1. Originality = 05%
2. Literature survey = 15%
3. Problem formulation, Objectives and Scope of Work = 10%
4. Methodology, Experimentation/Theoretical modelling = 10%
5. Results, Discussion and Conclusion = 20%
6. Questions and Answers = 20%
7. Submission/Publication of technical paper for Publication/ Presentation in Journals/Conference
= 20%
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 2 3
CO2 2 2 3
CO3 3 3 3
1. Low, 2. Medium, 3. High
CN1
BANGALORE UNIVERSITY
Department of Computer Science and Engineering
UNIVERSITY VISVESVARAYA COLLEGE OF ENGINEERING
K R Circle, Bengaluru-560 001.
Choice Based Credit System (CBCS)-2018
M.Tech in Computer Science and Engineering
Specialization: Computer Networking
CN1
BANGALORE UNIVERSITY
VISION
“To strive for excellence in education for the realization of a vibrant and
inclusive society through knowledge creation and dissemination”
MISSION
Impart quality education to meet national and global challenges
Blend theoretical knowledge with practical skills
Pursue academic excellence through high quality research and
publications
Provide access to all sections of society to pursue higher education
Inculcate right values among students while encouraging
competitiveness to promote leadership qualities
Produce socially sensitive citizens
Hasten the process of creating a knowledge society
To contribute to nation building
CN2
Bangalore University
UNIVERSITY VISVESVARAYA COLLEGE OF ENGINEERING
K R Circle, Bengaluru – 560 001.
University Visvesvaraya College of Engineering (UVCE) was started as a School of
Mechanical Engineering by Bharat Ratna Sir. M. Visvesvaraya in the year 1913 to meet the
needs of the State for skilled workers with S V Setty as its Superintendent. Later, it was
converted to a full-fledged Engineering College in the year 1917 under the name Government
Engineering College and was affiliated to the University of Mysore. It is the fifth Engineering
College to be established in the country.
After the formation of Bangalore University in 1964, UVCE became one of the
Constituent Colleges of Bangalore University. This is one of the oldest Institutions in the
country imparting technical education leading to B.E., M.E, B.Arch., M.Sc. (Engineering),
M.Arch. and Ph.D. degrees in various disciplines of Engineering and Architecture. The
Institution currently offers 7 Undergraduate (B.E. / B.Arch.) Full-time, three Undergraduate
(B.E.) Part-time and 24 Postgraduate (M.E. / M.Arch.) Programmes.
VISION
The vision of UVCE is to strive for excellence in advancing engineering education through
path breaking innovations across the frontiers of human knowledge to realize a vibrant,
inclusive and humane society.
MISSION
The mission of UVCE is to prepare human resource and global leaders to achieve the above
vision through discovery, invention and develop friendly technologies to promote scientific
temper for a healthy society. UVCE shapes engineers to respond competently and confidently
to the economic, social and organizational challenges arising from globally advancing
technical needs.
CN3
Bangalore University Bengaluru
Department of Computer Science and Engineering, UVCE, Bengaluru
M. Tech. DEGREE IN COMPUTER SCIENCE AND ENGINEERING under CBCS Scheme -
2K18
Specialization: Computer Networking
Vision of the Department
Strive for Centre of Excellence in advancing Computer Science and Engineering education to
produce highly qualified human resources to meet local and global requirement.
Mission of the Department
CSM1. Implementing effectively, the outcome based education by imparting knowledge of
basics and advances in Computer Science and Engineering and other allied disciplines.
CSM2. Preparing and equipping human resources to become global leaders through
innovation, discovery, sustainable and environment friendly technology.
CSM3. Creatingconducive environment for effective teaching and learning process through
interdisciplinary research, online courses, interaction with institutions of higher learning and
industries, R and D laboratories of national importance, alumni, employers and other internal &
external stake holders.
CSM4. Imbibing awareness of entrepreneurship, ethics, honesty, credibility, social and
environmental consciousness and providing opportunity to the faculty and technical staff for
continuous academic improvement and to equip them with then latest trends in Software
Engineering and thereby inculcate the habit of continuous learning in faculty, staff and
students.
CN4
Program Outcomes
Computer Networking Graduates will be able to:
CNPO1: An ability to independently carry out research/investigate and development work to
solve practical problems
CNPO2: An ability to write and present a substantial technical report/document
CNPO3: Students should be able to demonstrate a degree of mastery over the area as per the
specialization of the problem. The mastery should be at a level higher than the
requirements in the appropriate bachelor degree
Program Educational Objectives (PEO):
The postgraduates of M.Tech in Computer Networking will provide the knowledge and skill
to:
CNPEO1: Possess strong fundamentals of computer networks, develop analytical and
computational skills to solve real time hardware / software problems and apply
innovative technical techniques to develop solutions in an ever changing world.
CNPEO2: Develop ability to establish peer-recognized expertise in the field of Computer
Networking and excel in research by articulating this expertise in formulating and
solving new problems using mathematical foundations, algorithmic principles and
computer networking concepts.
CNPEO3: Demonstrate leadership capabilities to communicate, collaborate, inspire, innovate
and be the leaders in the field of Computer Networking.
CN5
BANGLORE UNIVERSITY
SCHEME OF STUDIES AND EXAMINATION FOR 24MONTHS COURSE FOR THE AWARD OF
M. Tech. DEGREE IN COMPUTER SCIENCE AND ENGINEERING (COMPUTER NETWORKING) under CBCS
Scheme – 2K18
Semester I
Sl. No Course Type /
Course Code Course Name
Teaching scheme
Hrs/Week Teaching
DPT
Total
Hrs/week
CIE
Marks
*SEE
Marks Credits
L T P S
1 18CS1C01 Mathematical Foundations of Computer Science 3 1 0 0 CSE 4 50 50 4
2 18CS1C02 Advances in Computer Networks 4 0 0 0 CSE 4 50 50 4
3 18CN1C03 TCP / IP 4 0 0 0 CSE 4 50 50 4
4
18CS1E1A Cloud Computing
4 0 0 0
CSE
CSE
CSE
4 50 50 4 18CS1E1B Mobile Computing
18CS1E1C Wireless Networks
5
18CS1E2A Soft Computing 3 0 2 0 CSE
CSE
CSE
4 50 50 4 18CS1E2B Advances in Storage Area Networks 4 0 0 0
18CN1E2C Distributed Database Systems 4 0 0 0
6 18CS1L01 Network Programming Lab 0 0 4 0 CSE 4 50 50 2
7 18CS1M01 Research Methodology and IPR. 2 0 0 0 CSE 2 50 50 2
8 18CN1S01 Seminar - I 0 0 2 0 CSE 2 50 -- 1
9 18CS1M02 Audit Course - I (Technical Paper Writing) 2 0 0 0 English 2 50 -- 1
Total 30 450 350 26
CN6
Semester II
Sl. No Course Type /
Course Code Course Name
Teaching scheme
Hrs/Week Teaching
DPT
Total
Hrs/week
CIE
Marks
*SEE
Marks Credits
L T P S
1 18CS2C01 Advanced Data Structures and Algorithms 4 0 0 0 CSE 4 50 50 4
2 18CS2C02 Advanced Operating Systems 4 0 0 0 CSE 4 50 50 4
3 18CN2C03 Internet of Things 3 0 2 0 CSE 4 50 50 4
4
18CS2E1A Data Warehousing and Mining
4 0 0 0
CSE
CSE
CSE
4 50 50 4 18CS2E1B Stochastic Process and Queuing Theory
18CN2E1C Optimization Techniques
5
18CS2E2A Network Security
4 0 0 0
CSE
CSE
CSE
4 50 50 4 18IT2E2B Cyber Security
18CS2E2C Web Security
6 18CS2L01 Advanced Data Structures and Algorithms Lab 0 0 4 0 CSE 4 50 50 2
7 18CN2S01 Seminar - II 0 0 2 0 CSE 2 50 -- 1
8 18CS2M01 Audit Course - II (Pedagogy Studies) 2 0 0 0 CSE 2 50 -- 1
Total 28 400 300 24
CN7
Semester III
Sl. No Course Type /
Course Code Course Name
Teaching scheme
Hrs/Week Teaching
DPT
Total
Hrs/week
CIE
Marks
*SEE
Marks Credits
L T P S
1
18CS3E1A Machine Learning 4 0 0 0
CSE 4 50 50 4 18CS3E1B Big Data Analytics 3 0 2 0
18CS3E1C High Performance Computing 4 0 0 0
2 Open Elective 4 0 0 0 CSE 4 50 50 4
3 18CN3S01 Seminar - III 0 0 2 0 CSE 2 50 1
4 18CN3I01 Internship / Mini Project 0 0 10 0 CSE 10 50 50 5
5 18CN3D01 Dissertation Phase - I 0 0 10 0 CSE 10 50 50 5
Total 30 250 200 19
Open Elective
Sl. No Course Type /
Course Code Course Name
Teaching Scheme (No. of hrs per week)
Teaching
Dept.
Total hrs
/ week
CIE
Marks
*See
Marks Credits
L T P S
1
18CS3P1A Artificial Intelligence
4 0 0 0 CSE 4 50 50 4 18CS3P1B Business Analytics
18CS3P1C Modeling and Simulation
2
18CV3P1A Significance of National Building Codes
4 0 0 0 Civil 4 50 50 4 18CV3P1B Water Laws, Rights and Administration
18CV3P1C Waste to Energy
18CV3P1D Remote Sensing and Geographic Information System
3 18ME3P1A Composite and Smart Materials
4 0 0 0 Mech 4 50 50 4 18ME3P1B Industrial Safety
4
18EE3P1A Real Time Embedded Systems
4 0 0 0 EEE 4 50 50 4 18EE3P1B Robotics and Automation
18EE3P1C Solar and Wind Energy
5
18EC3P1A Reliability and Engineering
4 0 0 0 ECE 4 50 50 4 18EC3P1B M-Commerce and Applications
18EC3P1C Optimization Techniques
CN8
COURSE TYPE
CS: COMPUTER SCIENCE AND ENGINEERING CN: COMPUTER NETWORK C: PROFESSIONAL CORE
E: PROFESSIONAL ELECTIVE P: OPEN ELECTIVE M: MANDATORY AUDIT
L: LABORATORY S: SEMINAR I: INTERNSHIP / MINI PROJECT
D: DISSERTATION
L – Theory lecture, T – Tutorial, P – Lab work, S – Self study:
Numbers under teaching scheme indicates contact clock hours.
NOTE:
1) In any course (Program Core or Program Elective), if self study of 4 hrs per week for students is allocated, then the teaching scheme of
such courses will be 3-0-0-4 and the total credits will be 4.
2) * = SEE shall be conducted for 100 marks and the marks obtained shall be reduced to 50 marks.
3) # = The CIE test of the lab component of integrated course shall be conducted with the external examiner for 50 marks and shall be
reduced to 25 marks.
Semester IV
Sl. No Course Type /
Course Code Course Name
Teaching scheme
Hrs/Week Teaching
DPT
Total
Hrs/week
CIE
Marks
SEE
Marks Credits
L T P S
1 18CN4S01 Seminar - IV 0 0 2 0 CSE 2 50 1
2 18CN4D01 Dissertation Phase - II 0 0 30 0 CSE 30 50 50 15
Total 32 100 50 16
1 18CSMOOC MOOC Course 0 0 0 0 03
Grand Total of Credits 88
CN8
I Semester
CN9
Course Code 18CS1C01 M. Tech (Computer Networking)
Category Professional Core
Course title MATHEMATICAL FOUNDATIONS OF COMPUTER SCIENCE
Scheme and
Credits
No. of Hours/Week Semester – I
L T P S Credits
3 1 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
1. Basics of probability
2. Basics of graph theory
COURSE OBJECTIVES
The course will enable the students to:
1. Understand the concepts of number theory and solve related problems.
2. Apply the concepts of stochastic process and queuing theory required to devise
analytical models for the real problems of computer science.
3. Analyze the various concepts of arranging, selecting and combining objects from a
set.
4. Understand the concept of advanced graph theory that can be used to model any
network, physical or conceptual.
UNIT -I NUMBER THEORY: 10 Hours
The Division algorithm, the Greatest Common Divisor, the Euclidean algorithm. The basic
properties of Congruencies, Binary and decimal representation of integer, linear congruence,
Chinese-Reminder Theorem, Fermat‟s Little theorem, The sum and number of Divisors, The
mobius inversion formula, The Greatest integer function (No theorem proofs).
UNIT -II PROBABILITY AND QUEUING THEORY: 10 Hours
Random Variables, Probability Distribution, Binomial Distribution, Poisson Distribution,
Geometric Distribution, Exponential Distribution, Normal Distribution, Uniform
Distribution. Two Dimensional Random Variables. Introduction to Stochastic Processes,
Markov process, Markov chain, one step and n-step Transition Probability, Chapman
Kolmogorov theorem (Statement only), Transition Probability Matrix, Classification of
States of a Markov chain. Introduction to Markovian queuing models, Single Server Model
with Infinite system capacity, Characteristics of the Model (M/M/1) : (∞/FIFO), Single
Server Model with Finite System Capacity, Characteristics of the Model (M/M/1) :
(K/FIFO).
UNIT -III COMBINATORICS: 10 Hours
Basics of Counting: Permutations, Permutations with Repetitions, Circular Permutations,
Restricted Permutations, Combinations: Restricted Combinations, Generating Functions of
Permutations and Combinations, Binomial and Multinomial Coefficients, Binomial and
Multinomial Theorems, The Principles of Inclusion Exclusion, Pigeonhole Principle and its
Application.
UNIT -IV RECURRENCE RELATIONS: 09 Hours
Generating Functions, Function of Sequences, Partial Fractions, Calculating Coefficient of
Generating Functions, Recurrence Relations, Formulation as Recurrence Relations, Solving
Recurrence Relations by Substitution and Generating Functions, Method of Characteristic
CN10
Roots, Solving Inhomogeneous Recurrence Relations.
UNIT –V GRAPH THEORY: 09 Hours
Basic Concepts of Graphs, Sub graphs, Matrix Representation of Graphs: Adjacency
Matrices, Incidence Matrices, Isomorphic Graphs, Paths and Circuits, Eulerian and
Hamiltonian Graphs, Multi-graphs, Planar Graphs, Euler„s Formula, Graph Colouring and
Covering, Chromatic Number, Spanning Trees, Algorithms for Spanning Trees (Concepts
and Problems Only, Theorems without Proofs).
UNIT-VI Recent advances and research being done in the topics mentioned above
units
REFERENCES
1. David M Burton, “Elementary Number Theory”, Allyn and Bacon, 1980.
2. K. S. Trivedi, “Probability and Statistics with Reliability, Queuing for Computer
Science Applications”, John Wiley and Sons, II Edition, 2008.
3. Gunter Bolch, Stefan Greiner, Hermann De Meer, K S Trivedi, “Queuing Networks
and Markov Chains”, John Wiley and Sons, II Edition, 2006.
4. Richard A Brualdi, Introductory Combinatorics 5th
Edition, Pearson 2009
5. J. A. Bondy and U. S. R. Murty, “Graph Theory and Applications”, Macmillan
Press, 1982.
COURSE OUTCOMES
On completion of the course, the student would be able to:
CO1. Solve problems related to number theory.
CO2: Design the analytical models using the concepts of probability and stochastic process.
CO3: Compare the various methods of counting using permutations and combinations.
CO4: Solve the problems of recurrence relations.
CO5: Apply the graph theory concepts in solving problems related to computer science.
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100 Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 1
CO2 2
CO3 1 1
CO4 1
CO5 2
1: Low 2: Medium 3:High
CN11
Course Code 18CS1C02 M. Tech (Computer Networking)
Category Engineering Science Courses
Course title ADVANCES IN COMPUTER NETWORKS
Scheme and
Credits
No. of
Hours/Week
Semester – I
L T P SS Credits
4 0 - - 4
CIE Marks:
50
SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3
Hrs
Prerequisites (if any):
COURSE OBJECTIVE
The course will enable the student to:
1. Understand the requirement of various high speed networks
2. Learn the effect of congestion and its control.
3. Understand Network Traffic Management for reliable delivery.
4. Understand integrated and differentiated architecture and services.
5. Learn the effect of traffic in the networks on various QoS parameters
UNIT I- INTRODUCTION 9 Hours
OSI and TCP/ IP Reference Model, RS-232C, RS-449, Devices used in Physical Layers,
Framing, Flow and Error Control, Error Detection and Correction, Checksum, Sliding
Window Protocols-ARQ.
UNIT II- DATA LINK LAYER 10 Hours
Multiple Access Control Protocols(MAC), ALOHA, CSMA/CD (802.3), Data Link
Protocol- HDLC,PPP, Wired LAN‟s : Fast Ethernet, Gigabit Ethernet, Fibre Channel,
Wireless LAN‟s(802.11), Broadband Wireless(802.16).
UNIT III- ROUTING AND CONGESTION CONTROL 10 Hours
Design issues, Routing Algorithms, Distance Vector Routing, Link State Routing, Routing
in Mobile Host, Broadcast Routing, Reverse Path Forwarding, OSPF, IP Protocols (IPv4 -
ARP, RARP, IPv6), Queuing Analysis- Queuing Models – Single Server Queues –
Effects of Congestion – Congestion Control – Traffic Management – Congestion Control
in Packet Switching Networks.
UNIT IV – TRANSPORT LAYER AND INTEGRATED SERVICES 10 Hours
TCP Header, TCP Flow control – TCP Congestion Control – Retransmission – Timer
Management – Exponential RTO back-off – KARN‟s Algorithm – Window
management. Integrated Services Architecture – Approach, Components, Services-
Queuing Discipline, FQ, PS, BRFQ, GPS, WFQ – Random Early Detection,
Differentiated Services.
UNIT V - PROTOCOLS FOR QOS SUPPORT 09 Hours
RSVP – Goals & Characteristics, Data Flow, RSVP operations, Protocol
Mechanisms – Multiprotocol Label Switching – Operations, Label Stacking, Protocol
details – RTP – Protocol Architecture, Data Transfer Protocol, RTCP.
UNIT VI- To understand latest innovative networks such as Software Defined
Networks(SDN).
CN12
REFERENCES
1. Behrouz A Forouzan and Firouz Mosharraf, “Computer Networks, A Top-Down
Approach”, TMH, 2012.
2. Andrew S. Tanenbaum and David J. Wetherall, “Computer Networks”, Pearson
Education, 5th Edition,2011.
3. William Stallings, “High Speed Networks and Internet”, , Second Edition, 2012.
4. Prakash C Guptha, “Data Communication and Computer Networks”, PHI , 6th
printing 2012.
5. Larry L. Peterson and Bruce S Davis , “Computer Network A System
Approach”, Elsevier, 5th
edition 2010.
COURSE OUTCOMES
On Completion of the course, the student will be able to:
CO1: Apply the networking principles to manage the network traffic.
CO2: Control the various anomalies in the network to improve the QoS.
CO3: Study the relation and effect of one QoS parameter on the other.
CO4: Apply the efficient techniques to achieve effective and reliable communication.
CO5: Develop new protocols to mitigate emerging problems.
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100 Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COs) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2
CO3 3 2 2
CO4 3 2
CO5 2 2 2
1:Low, 2:Medium, 3:High
CN13
Course Code 18CN1C03 M. Tech (Computer Networking)
Category Professional Core
Course title TCP/IP PROTOCOLS
Scheme and Credits No. of Hours/Week Semester – I
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
1. Basics of Computer Networks
2. Layered Architecture
Course Objectives:
The course will enable the students to:
1. Acquire the concept of packet switching and addressing.
2. Understand the IP packet format, operations and various IP network protocols.
3. Acquire the knowledge of transport layer services and TCP protocols.
4. Learn other important network protocols, future needs and challenges.
5. Review the recent development in wireless network and sensor network.
UNIT I - OSI MODEL AND TCP/IP PROTOCOL SUITE 09 hours
The OSI Model, TCP/IP Protocol Suite, Architecture, Addressing, Wired Local Area
Networks, Wireless LANS, Point to Point WANS, Switched WANS, Connecting Device.
UNIT II - NETWORK LAYER 09 hours
Switching: Packet Switching, Network: Network Layer Services, Network Layer Issues,
Classful Addressing, Classless Addressing, Special Addresses, NAT Delivery, Forwarding
Structure of a Router.-– 08 Hours
UNIT III - INTERNET PROTOCOL 10 hours
Datagram, fragmentation, options, checksum, IP package, Address Mapping, ARP Protocol,
ARP Package, RARP, ICMP Protocol, Messages, Debugging Tools, ICMP Packages.
UNIT IV - TCP PROTOCOL 10 hours
Transport Layer Services, TCP Protocols, TCP Connection, State Transition Diagrams,
Windows in TCP, flow, congestion and error control, TCP package and operation.
UNIT V - OTHER IMPORTANT PROTOCOLS 10 hours
DHCP, DNS, TELNET, FTP, SMTP, POP, IPv6
UNIT VI
Recent developments in wireless networks, sensor networks and Internet of things
REFERENCES
1. Behrouz A. Forouzan, “TCP/IP Protocol Suite”, IV Edition, McGraw Hill, 2010.
2. Kevin R Fall and W. Richard Stevens, “TCP/IP Illustrated, Volume 1 The Protocols”,
II Edition, Addison-Wesley Professional Computing Series.
3. Douglas E Comer, “Internetworking with TCP/IP: Principles, Protocols and
Architectures, IV Edition, Prentice Hall, 1995.
4. Charles M Kozierok, “The TCP/IP Guide: A Comprehensive, Illustrated, Internet
Protocols Reference”, No Starch Press, 2005.
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1. Distinguish the OSI and TCP/IP Protocol Stack
CO2: Demonstrate various network and transport layer services
CO3: Implement the IP protocols
CO4: Implement TCP protocols
CO5: Ivestigate TCP/IP in wireless and sensor network TCP/IP.
CN14
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE –
100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2
CO3 1
CO4 1
CO5 1 2
1. Low, 2. Medium, 3. High
CN15
Course Code 18CS1E1A M. Tech (Computer Networking)
Category Professional Elective
Course title CLOUD COMPUTING
Scheme and Credits No. of Hours/Week Semester – I
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
1. Operating systems
2. Basics of distributed computing
COURSE OBJECTIVES
The course will enable the students to:
1. Understand the various cloud service providers and cloud interoperability
2. Apply the cloud computing applications and paradigms
3. Analyse the concept of virtualization
4. Acquire the knowledge the cloud resource management and scheduling mechanism
5. Learn various security issues in cloud computing.
UNIT-I CLOUD INFRASTRUCTURE 09 Hours
Cloud computing at Amazon, Cloud computing-the Google perspective, Microsoft Windows
Azure and Online services, Open-Source Software Platforms for Private Clouds Cloud Storage
Diversity and Vendor Lock-in, Cloud Computing Interoperability: The Intercloud, Service- and
Compliance-Level Agreements, Responsibility Sharing Between User and Cloud Service
Provider, User Experience, Software Licensing.
UNIT- II CLOUD COMPUTING: APPLICATIONS AND PARADIGMS 09 Hours
Challenges for Cloud Computing, Existing Cloud Applications and New Application
Opportunities Architectural Styles for Cloud Applications, Workflows: Coordination of Multiple
Activities, Coordination Based on a State Machine Model: The ZooKeeper, The MapReduce
Programming Model, A Case Study: The GrepTheWeb Application, High-Performance
Computing on a Cloud.
UNIT-III CLOUD VIRTUALIZATION 10 Hours
Virtualization, Layering and Virtualization, Virtual Machine Monitors, Virtual Machines,
Performance and Security Isolation, Full Virtualization and Paravirtualization, Hardware Support
for Virtualization, Case Study: Xen, a VMM Based on Paravirtualization, Optimization of
Network Virtualization in Xen 2.0, vBlades: Paravirtualization Targeting an x86-64 Itanium
Processor, A Performance Comparison of Virtual Machines.
UNIT-IV CLOUD RESOURCE MANAGEMENT AND SCHEDULING 10 Hours
Policies and Mechanisms for Resource Management, Applications of Control Theory to Task
Scheduling on a Cloud, Stability of a Two-Level Resource Allocation Architecture, Feedback
Control Based on Dynamic Thresholds, Coordination of Specialized Autonomic Performance
Managers, A Utility-Based Model for Cloud-Based Web Services, Resource Bundling:
Combinatorial Auctions for Cloud Resources, Scheduling Algorithms for Computing Clouds,
Fair Queuing, Start-Time Fair Queuing, Borrowed Virtual Time Cloud Scheduling Subject to
Deadlines, Scheduling MapReduce Applications Subject to Deadlines, Resource Management
and Dynamic Application Scaling.
CN16
UNIT-V CLOUD SECURITY 10 Hours
Cloud Security Risks, Security: The Top Concern for Cloud Users, Privacy and Privacy Impact
Assessment, Trust Operating System Security, Virtual Machine Security, Security of
Virtualization, Security Risks Posed by Shared Images, Security Risks Posed by a Management
OS.
UNIT-VI Recent developments and current research in multi cloud, cloud security, mobile
cloud computing.
REFERENCES
1. Dan C Marinescu, “Cloud Computing: Theory and Practice”, Morgan
Kaufmann/Elsevier. 2013.
2. George Reese, “Cloud Application Architectures: Building Applications and
Infrastructure in the Cloud”, O‟Reilly, 2009.
3. Rajkumar Buyya, James Broberg and Andrzej M. Goscinski , “Cloud Computing:
Principles and Paradigms”, Wiley, 2011.
4. Kai Hwang, Geoffrey C Fox, Jack G Dongarra, “Distributed and Cloud Computing: From
Parallel Processing to the Internet of Things”, Morgan Kaufmann Publishers, 2012.
COURSE OUTCOMES
Upon completion of the course, the students would be able to:
CO1: Categorize the architectures, services and delivery models in cloud computing
CO2: Implement the concept of virtualization and its management in cloud computing
CO3: Design the extended functionalities of resource management and scheduling mechanisms
CO4: Analyse the security models in cloud environment
CO5: Investigate recent developments in multi cloud, cloud security and mobile cloud computing
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit VI(AAT)=15
marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks
Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2
CO2 2
CO3 1 2
CO4 2 1
CO5 2 2
1. Low, 2. Medium, 3. High
CN17
Course Code 18CS1E1B M. Tech (Computer Networking)
Category Professional Elective
Course title MOBILE COMPUTING
Scheme and Credits No. of Hours/Week Semester – I
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
1. Computer Networks
2. Database Management Systems
3. Operating Systems
COURSE OBJECTIVES:
The course will enable the students to:
1. Understand the GSM architecture, services and protocols.
2. Understand the wireless MAC, mobile IP and transport layer functions and protocols.
3. Analyse the concepts of mobile databases, data dissemination, broadcasting systems and data
synchronization.
4. Review various mobile technologies including WLAN, WiFi, WAP, Bluetooth, Zigbee.
5. Understand mobile application languages and mobile operating systems
UNIT- I MOBILE COMPUTING ARCHITECTURE AND GSM 09 Hours
Mobile Computing Architecture: Types of Networks, Architecture for Mobile Computing, 3-tier
Architecture, Design Considerations for Mobile Computing. GSM: Services and System Architectures,
Radio Interfaces, Protocols, Localization, Calling, Handover, General Packet Radio Service.
UNIT-II WIRELESS MAC, IP and TRANSPORT LAYER 10 Hours
Medium Access Control, Introduction to CDMA based Systems, IP and Mobile IP Network Layers,
Packet Delivery and Handover Management, Location Management, Registration, Tunnelling and
Encapsulation, Route Optimization, Dynamic Host Configuration Protocol. Indirect TCP, Snooping
TCP, Mobile TCP, Other Methods of TCP.
UNIT-III DATABASES, DATA DISSEMINATION AND BROADCASTING SYSTEMS 10
Hours
Database Hoarding Techniques, Data Caching, Client – Server Computing and Adaptation,
Transactional Models, Query Processing, Data Recovery Process, Issues relating to Quality of Service.
Communication Asymmetry, Classification of Data – Delivery Mechanisms, Data Dissemination
Broadcast Models, Selective Tuning and Indexing Techniques, Digital Audio Broadcasting, Digital
video Broadcasting.
UNIT-IV DATA SYNCHRONIZATION IN MOBILE COMPUTING SYSTEMS 09 Hours
Synchronization, Synchronization Protocols, SyncML – Synchronization Language for Mobile
Computing, Synchronized Multimedia Markup Language (SMIL). –
UNIT-V MOBILE DEVICES, SERVER AND MANAGEMENT AND MOBILE APPLICATION
LANGUAGES 10 Hours
Wireless LAN, Mobile Internet Connectivity and Personal Area Network, Mobile agent, Application
Server, Gateways, Portals, Service Discovery, Device Management, Mobile File Systems. Wireless
LAN (Wi-Fi) Architecture and Protocol Layers, WAP 1.1 and WAP 2.0 Architectures, Bluetooth –
enabled Devices Network, Zigbee. XML, JAVA, J2ME and JAVACARD, Mobile Operating Systems:
Introduction, PalmOS, Windows CE, Symbian OS, Linux for Mobile Devices.
CN18
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2
CO3 2
CO4 2 2
CO5 2 2
1. Low, 2. Medium, 3. High
UNIT-VI
Recent trends in wireless and mobile network security, mobile cloud computing.
REFERENCES
1. Raj Kamal, “Mobile Computing”, Oxford University Press, 2007.
2. Ashok Talukder, Ms Roopa Yavagal, and Mr. Hasan Ahmed, “Mobile Computing,
Technology, Applications and Service Creation”, II Edition, Tata McGraw Hill, 2010.
3. Jochen Schiller, “Mobile Communications”, Addison-Wesley. II Edition, 2004.
4. Hansmann, Merk, Nicklous, Stober, “Principles of Mobile Computing”, Springer, II Edition,
2003.
COURSE OUTCOMES
Upon completion of the course, the student would be able to:
CO1: Demonstrate the knowledge of GSM architecture, services and protocols.
CO2: Simulate a typical GSM network and demonstrate the performance analysis.
CO3: Extending the functionalities of mobile IP and transport layer protocols.
CO4: Apply the mobile application languages to design mobile applications.
CO5: Investigate recent developments in wireless, mobile network security and mobile cloud
computing.
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit VI(AAT)=15
marks
Total:50
marks Test II (Unit IV & V) – 15 marks
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks
Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
CN19
Course Code 18CS1E1C M. Tech (Computer Networking)
Category Professional Elective
Course title WIRELESS NETWORKS
Scheme and
Credits
No. of Hours/Week Semester – I
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks:
50
Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
Computer Networks
COURSE OBJECTIVES:
The course will enable the students to:
1. Get familiar with the wireless market and the future needs and challenges.
2. Learn the key concepts of wireless networks, standards, technologies and their
basic operations
3. Know various generations of cellular networks and learn cellular architecture
4. Understand the key concept of sensor networks
5. Analyse security techniques and vulnerabilities
UNIT- I INTRODUCTION 09 Hours
Wireless Networking Trends, Key Wireless Physical Layer Concepts, Multiple Access
Technologies -CDMA, FDMA, TDMA, Spread Spectrum technologies, Frequency reuse,
Radio Propagation and Modelling, Challenges in Mobile Computing: Resource poorness,
Bandwidth, energy etc.
UNIT-II WIRELESS LOCAL AREA NETWORKS 10 Hours
IEEE 802.11 Wireless LANs Physical & MAC layer, 802.11 MAC Modes (DCF & PCF)
IEEE 802.11 standards, Architecture & protocols, Infrastructure vs. Adhoc Modes, Hidden
Node & Exposed Terminal Problem, Fading Effects in Indoor and outdoor WLANs,
WLAN Deployment issues.
UNIT- III WIRELESS CELLULAR NETWORKS 10 Hours
1G and 2G, 2.5G, 3G, and 4G, Mobile IPv4, Mobile IPv6, TCP over Wireless Networks,
Cellular architecture, Frequency reuse, Channel assignment strategies, Handoff strategies,
Interference and system capacity, Improving coverage and capacity in cellular systems
UNIT- IV WIRELESS SENSOR NETWORKS 10 Hours
Introduction, Application, Physical, MAC layer and Network Layer, Power Management,
Tiny OS Overview. Wireless Pans Bluetooth and Zigbee, Introduction to Wireless
Sensors networks, deployment, key design challenges, network deployment, Routing
protocols, routing challenges and design issues, routing strategies.
UNIT-V SECURITY 09 Hours
Security in wireless Networks, Vulnerabilities, Security techniques, Wi-Fi Security, DoS
in wireless communication.
UNIT-VI RECENT TRENDS
Recent trends in Wireless networks, Vehicular Adhoc Networks.
CN20
REFERENCES
1. Schiller J., Mobile Communications, Addison Wesley 2000
2. Stallings W., Wireless Communications and Networks, Pearson Education 2005
3. Stojmenic Ivan, Handbook of Wireless Networks and Mobile Computing, John Wiley
and Sons Inc 2002
4. Yi Bing Lin and Imrich Chlamtac, Wireless and Mobile Network Architectures, John
Wiley and Sons Inc 2000
5. Pandya Raj, Mobile and Personal Communications Systems and Services, PHI 2000
6.Feng Zhao, leonidas Guibas, “Wireless sensor Networks: An information processing
approach”, Elsevier, 2004
COURSE OUTCOMES
Upon completion of the course, the students will be able to:
CO1: Demonstrate advanced knowledge of networking and wireless networking
CO2: Understand various types of wireless networks, standards, operations and use cases.
CO3: Be able to design and compare cellular based upon underlying propagation and
performance analysis.
CO4: Demonstrate knowledge of WPAN and sensor networks
CO5: Assess security measure and vulnerabilities.
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit VI(AAT)=15
marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks
Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for
50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 3
CO2 2 3
CO3 2 3
CO4 3 3
CO5 1 3
1. Low, 2. Medium, 3. High
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Course Code 18CS1E2A M. Tech (Computer Networking)
Category Professional Elective - Integrated
Course title SOFT COMPUTNG
Scheme and
Credits
No. of Hours/Week Semester – I
L T P S Credits
3 0 2 0 4
CIE Marks:
50
SEE Marks: 50 Total Max. Marks:
100
Duration of SEE: 3 Hrs
Prerequisites (if any):
1. Basic knowledge of mathematics
COURSE OBJECTIVES:
The course will enable the students to:
1. Describe soft computing concepts and techniques and foster their abilities in
designing appropriate technique for a given scenario.
2. Choose Neural network algorithms for real – world problems.
3. Analyse and compare the different Optimization techniques.
4. Develop the applications of Genetic Algorithms in Machine Learning.
5. Provide a hands-on experience on MATLAB to implement various strategies
UNIT-I INTRODUCTION TO SOFT COMPUTING AND NEURAL NETWORKS
09 Hours
Evolution of Computing: Soft Computing Constituents, Conventional AI to
Computational Intelligence: Machine Learning Basics, Hard-Margin and Soft-Margin
SVMs- Concepts of Kernel and Feature Spaces, Basics of Optimization and Quadratic
programming, Introduction to Steganography and Applications of SVMs to Steganalysis
UNIT-II NEURAL NETWORKS 10 Hours
Introduction to ANN, Architectures, Learning methods, Bayesian Networks, Back
Propagation network, Perceptrons, Hopfield Networks, Kohonen Self Organizing Feature
Maps, Chaos Theory
UNIT-III OPTIMIZATION TECHNIQUES 09 Hours Introduction, Elitism based Ant Colony Optimization, Min-Max based Ant Colony
Optimization, Particle Swarm Optimization, Artificial Bee Colony Optimization, Multi-
Swarm Optimization, Cuckoo Search, Whole Optimization, Firefly algorithm, Bat
Algorithm, Introduction to missing data-Imputation techniques, Principal Component
Analysis, Gradient Descent
UNIT-IV GENETIC ALGORITHMS and FUZZY LOGIC 10 Hours
Introduction to Genetic Algorithms (GA), Applications of GA in Machine Learning:
Machine Learning Approach to Knowledge Acquisition. Fuzzy Logic: Fuzzy Sets,
Operations on Fuzzy Sets, Fuzzy Relations, Membership Functions: Fuzzy Rules and
Fuzzy Reasoning, Fuzzy Inference Systems, Fuzzy Expert Systems, Fuzzy Decision
Making, Defuzzification
UNIT-V Matlab Lib 10 Hours
Introduction to Matlab, Arrays and array operations, Functions and Files, Study of neural
network toolbox and fuzzy logic toolbox, Simple implementation of Artificial Neural
Network and Fuzzy Logic
UNIT-VI
Recent Trends in deep learning, various classifiers, networks and genetic algorithm.
Implementation of recently proposed soft computing techniques
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UNIT –VII (Lab Programs)
1. a) Write a MATLAB Program for Hebb Net to classify two dimensional input
patterns in bipolar with given targets.
b) Generate XOR function and ANDNOT function using McCulloch-Pitts Neural
Network.
2. Classification of a 4-Class problem with a Perceptron using MATLAB.
3. Write a MATLAB program to apply Back Propagation network for pattern
recognition problem.
4. Develop a Kohonen Self Organizing feature map for image recognition problem.
5. Write a MATLAB program to implement Discrete Hopfield Network and test the
input pattern.
6. Write a MATLAB program for edge detection using Fuzzy logic.
7. Use a genetic algorithms approach for Travelling Salesman Problem.
8. Develop a simple Ant Colony Optimization problem with MATLAB to find the
optimum path.
9. Solve a feature selection problem using Artificial Bee Colony Optimization.
10. Implementation of minimum Spanning tree using Particle Swarm Optimization.
REFERENCES
1. S. N. Sivanandam and S. N. Deepa, “Principles of Soft Computing”, 2nd
Edition,
Wiley India, 2012.
2. Samir Roy, Udit Chakraborty, “Introduction to Soft Computing- Neuro-Fuzzy and
Genetic Algorithms”, First Edition, 2013.
3. David E Goldberg, “Genetic Algorithms in Search Optimization and Machine
Learning”, Addison Wesley, 1997.
4. MATLAB Toolkit Manual.
COURSE OUTCOMES
Upon completion of the course, the students would be able to:
CO1: Explain the concepts and techniques of soft computing and their roles in building
intelligent machines
CO2: Apply fuzzy logic and reasoning to handle uncertainty and solve various
engineering problems.
CO3: Differentiate the various Optimization techniques.
CO4: Implement and evaluate the genetic algorithms in Machine learning.
CO5: Evaluate and compare solutions by various soft computing approaches for a given
Problem.
SCHEME OF EXAMINATION:
CIE -
Practical
Conduction of experiments, performance of student in
every week lab and completion of lab record=50#
Total: Marks
50
Lab Test: Part-A =20, Part-B=20 and Vice-Voce=10
Marks(50##
)
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
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SEE-100
Marks
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
# = CIE Marks for laboratory performance is to be evaluated for 50 marks and
the marks obtained shall be reduced for 25 marks
## = Lab test is to be conducted for 50 marks and the marks obtained shall be
reduced for 25 marks. Lab test shall be conducted by two examiners out of which
one examiner is the faculty taught the course. There is no SEE for the practical ‘s
portion of integrated course
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 3
CO2 3 3
CO3 3 3
CO4 3 3
CO5 3 3
1. Low, 2. Medium, 3. High
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Course Code 18SE1E2B M. Tech (Computer Networking)
Category Professional Elective
Course title ADVANCES IN STORAGE AREA NETWORKS
Scheme and Credits No. of Hours/Week Semester – I
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
1.Computer Networks
2.Database Management Systems
3.Operating Systems
COURSE OBJECTIVES
This course will enable the students to
1. Understand storage centric and server centric systems
2. Apply various metrics used for designing storage area networks
3. Analysis RAID concepts
4. Evaluate data maintains at data centres with the concepts of backup
5. Create techniques for data storage management at data centres
UNIT -I INTRODUCTION: 10 Hours
Server Centric IT Architecture and its Limitations; Storage – Centric IT Architecture and its
advantages. Case study: Replacing a server with Storage Networks The Data Storage and Data
Access problem; The Battle for size and access. Intelligent Disk Subsystems: Architecture of
Intelligent Disk Subsystems; Hard disks and Internal 8 Hours I/O Channels; JBOD, Storage
virtualization using RAID and different RAID levels; Caching: Acceleration of Hard Disk
Access; Intelligent disk subsystems, Availability of disk subsystems.
UNIT -II I/O TECHNIQUES: 10 Hours The Physical
I/O path from the CPU to the Storage System; SCSI; Fibre Channel Protocol Stack; Fibre
Channel SAN; IP Storage. Network Attached Storage: The NAS Architecture, The NAS
hardware Architecture, The NAS Software Architecture, Network connectivity, NAS as a
storage system. File System and NAS: Local File Systems; Network file Systems and file
servers; Shared Disk file systems; Comparison of fibre Channel and NAS.
UNIT -III STORAGE VIRTUALIZATION: 10 Hours Definition of
Storage virtualization; Implementation Considerations; Storage virtualization on Block or file
level; Storage virtualization on various levels of the storage Network; Symmetric and
Asymmetric storage virtualization in the Network.
UNIT- IV SAN ARCHITECTURE AND HARDWARE DEVICES: 9 Hours
Overview, Creating a Network for storage; SAN Hardware devices; The fibre channel switch;
Host Bus Adaptors; Putting the storage in SAN; Fabric operation from a Hardware perspective.
Software Components of SAN: The switch‟s Operating system; Device Drivers; Supporting the
switch‟s components; Configuration options for SANs.
UNIT–V MANAGEMENT OF STORAGE NETWORK: 9 Hours
System Management, Requirement of management System, Support by Management System,
Management Interface, Standardized Mechanisms, Property Mechanisms, In-band Management,
Use of SNMP, CIM and WBEM, Storage.
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UNIT-VI Recent advances and research being done in the topics mentioned above units
REFERENCES
1. Ulf Troppens, Rainer Erkens and Wolfgang Muller: Storage Networks Explained, Wiley
India 2013.
2. Robert Spalding: “Storage Networks The Complete Reference”, Tata McGraw-Hill, 2011.
3. Marc Farley: Storage Networking Fundamentals – An Introduction to Storage Devices,
Subsystems, Applications, Management, and File Systems, Cisco Press, 2005.
4. Richard Barker and Paul Massiglia: “Storage Area Network Essentials A Complete Guide to
understanding and Implementing SANs”, Wiley India, 2006.
COURSE OUTCOMES :
The students should be able to:
CO1: Distinguish storage centric and server centric systems
CO2: Determine the need for performance evaluation and the metrics used for it
CO3: Extrapolate RAID and different RAID levels
CO4: Validate data maintained at data centres
CO5: Develop techniques for storage management
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*2=40
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2
CO3 3
CO4 3
CO5 1 2
1: Low 2: Medium 3:High
CN26
Course Code 18CN1E2C M. Tech (Computer Networking)
Category Professional Elective
Course title DISTRIBUTED DATABASE SYSTEMS
Scheme and Credits No. of Hours/Week Semester – I
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
Course Objectives
This course will enable students to
1. Understand the fundamental concepts and issues of managing large volume of shared data in a
parallel and distributed environment.
2. Acquire the knowledge of distributed and parallel database concepts.
3. Handle and mange queries in a distributed environment.
4. Learn the various causes for failure and measures to achieve reliability.
5. Acquire the knowledge of the pros and cons of various database systems.
UNIT I- INTRODUCTION 09 Hours
Distributed data processing; What is a DDBS; Advantages and disadvantages of DDBS; Problem
areas; Overview of database and computer network concepts, Distributed Database Management
System Architecture: Transparencies in a distributed DBMS; Distributed DBMS architecture;
Global directory issues.
UNIT II- DISTRIBUTED DATABASE DESIGN 10 Hours
Alternative design strategies; Distributed design issues; Fragmentation; Data allocation, Semantics
Data Control View Management; Data security; Semantic Integrity Control, Query Processing
Issues: Objectives of query processing; Characterization of query processors; Layers of query
processing; Query decomposition; Localization of distributed data.
UNIT III - DISTRIBUTED QUERY OPTIMIZATION 10 Hours
Factors governing query optimization; Centralized query optimization; Ordering of fragment
queries; Distributed query optimization algorithms, Transaction Management: The transaction
concept; Goals of transaction management; Characteristics of transactions; Taxonomy of
transaction models, Concurrency Control: Concurrency control in centralized database systems;
Concurrency control in DDBSs; Distributed concurrency control algorithms; Deadlock
management.
UNIT IV - RELIABILITY 09 Hours
Reliability Concepts and Measures: System, State, and Failure, Reliability and Availability, Mean
Time between Failures/Mean Time to Repair, Failures in Distributed DBMS: Transaction Failures,
Site (System) Failures, Media Failures, Communication Failures, Local Reliability Protocols:
Architectural Considerations, Recovery Information Execution of LRM Commands,
Checkpointing, Handling Media Failures, Distributed Reliability Protocols: Components of
Distributed Reliability Protocols, Two-Phase Commit Protocol, Variations of 2PC, Dealing with
Site Failures : Termination and Recovery Protocols for 2PC, Three-Phase Commit Protocol.
UNIT V - PARALLEL DATABASE SYSTEMS 10 Hours
Parallel Database System Architectures: Objectives, Functional Architecture, Parallel DBMS
Architectures, Parallel Data Placement, Parallel Query Processing: Query Parallelism, Parallel
Algorithms for Data Processing, Parallel Query Optimization, Load Balancing: Parallel Execution
Problems, Intra-Operator Load Balancing, Inter-Operator Load Balancing, Intra-Query Load
Balancing, Database Clusters: Database Cluster Architecture, Replication, Load Balancing, Query
Processing , Fault-tolerance.
CN27
UNIT VI - Recent trends in Mobile Databases, Distributed Object Management, Multi-databases
REFERENCES
1. Principles of Distributed Database Systems, M. Tamer Ozsu Patrick Valduriez, 3rd
Edition,
Springer, 2011.
2. Distributed Databases principles and systems, Stefano Ceri, Giuseppe Pelagatti, Tata
McGraw Hill, Indian Edition, 2017.
3. Database System Concepts, Henry Korth, Abraham Silberschatz, S. Sudarshan, 5th
Edition,
2012.
4. Distributed Database Systems, D. Bell and J. Grimson, Addison-Wesley, 1992.
COURSE OUTCOMES
Upon completion of this course, the students should be able to:
CO1: Design and Analyse Queries in Distributed Database Systems.
CO2: Efficiently retrieve information from database and references:
CO3: Balance the load effectively among parallel and distributed environment.
CO4: Effectively adopt reliability and recovery techniques in real time applications.
CO5: Apply and design suitable methods to achieve reliability in various stages of distributed and
parallel database.
SCHEME OF EXAMINATION
CIE – 50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE – 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2
CO3 2 2
CO4 2 2
CO5 3 2 2
1. Low, 2. Medium, 3. High
CN28
Course Code 18CS1L01 M. Tech (Computer Networking)
Category Laboratory
Course title NETWORK PROGRAMMING LAB
Scheme and
Credits
No. of Hours/Week Semester – I
L T P S Credits
- - 4 - 2
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
1. Computer Networks
2. Programming in Java and C++
3. NS-3 simulator
COURSE OBJECTIVES
The course will enable the students to:
1. Understand the implementation of various network protocols.
2. Understand programming the network protocols using Java.
3. Analyse the programming environment of NS-3 simulator.
4. Evaluate typical wired/wireless network using the NS-3 simulator.
5. Create a typical GSM network using NS-3
PART – A
Write a Java Program to design a :
1. TCP iterative Client-Server application to reverse the given input sequence.
2. TCP concurrent Client-Server application to reverse the given input sequence.
3. TCP Client-Server application to transfer a file.
4. UDP Client-Server application to transfer a file.
5. ARP/RARP protocol.
6. DHCP protocol.
7. Distance Vector Routing protocol.
8. Dijkstra‟s shortest path routing protocol.
PART – B
1. Write a C++ program to connect two nodes on NS-3 (for practise only).
2. Write a C++ program to connect three nodes considering one as a central node on
NS-3 (for practise only).
3. Write a C++ program to implement a star topology on NS-3.
4. Write a C++ program to implement a bus topology on NS-3.
5. Write a C++ program showing the connection of two nodes and four routers such that
the extreme nodes act as client and server on NS-3.
6. Implement and study the performance of a typical GSM network on NS-3 (using
MAC layer).
COURSE OUTCOMES
On completion of the course, the student would be able to:
CO1: Design programs for any type of TCP and UDP based client-server applications using
Java.
CO2: Implement and analyze a typical wired network using Java.
CO3: Extend the functionalities of a routing protocol using Java.
CO4: Implement and analyse the performance of a wireless/mobile network on NS-3.
CO5: Design a typical GSM network on NS-3.
CN29
SCHEME OF EXAMINATION
The student has to write and implement two programs selecting ONE from each part
Continuous Internal
Evaluation (CIE) (Laboratory
– 50 Marks)
Marks Semester End Evaluation (SEE)
(Laboratory – 100 Marks) Marks
Performance of the Student in
the laboratory every week
20 Write up 10
Test at the end of the semester 20 Experiment-1 (Part - A) – 35 Marks
Experiment-2 (Part - B) – 35 Marks
70
Viva Voce 10 Viva Voce 20
Total 100
Total (CIE) 50 Total (SEE) 50*
Note. * = SEE shall be conducted for 100 marks for practical and the marks obtained shall be
reduced for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 1
CO2 2
CO3 2
CO4 2 2 3
CO5 2 2
1. Low, 2. Medium, 3. High
CN30
Course Code 18CS1M01 M. Tech (Computer Networking)
Category Mandatory Audit
Course title RESEARCH METHODOLOGY AND IPR
Scheme and Credits No. of Hours/Week Semester – I
L T P SS Credits
2 0 - - 2
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
This course will enable students to
1. Understand the formulation of research problem, scope and objectives of research problem
2. Use methods for effective technical writing skills
3. Analyse Approaches of investigation of solutions for research problem
4. Evaluate the format of research proposal , intellectual property and patent
5. Create patent, research paper
UNIT -I RESEARCH PROBLEM: 3 Hours
Meaning of research problem, Sources of research problem, Criteria Characteristics of a good
research problem, Errors in selecting a research problem, Scope and objectives of research problem.
Approaches of investigation of solutions for research problem, data collection, analysis,
interpretation, Necessary instrumentations
UNIT- II RESEARCH REQUIREMENTS: 3 Hours
Effective literature studies approaches, analysis Plagiarism, Research ethics,
UNIT- III EFFECTIVE TECHNICAL WRITING: 6 Hours
Effective technical writing, how to write report, Paper Developing a Research Proposal, Format of research
proposal, a presentation and assessment by a review committee
UNIT- IV NATURE OF INTELLECTUAL PROPERTY: 6 Hours
Patents, Designs, Trade and Copyright. Process of Patenting and Development: technological research,
innovation, patenting, development. International Scenario: International cooperation on Intellectual Property.
Procedure for grants of patents, Patenting under PCT.
UNIT- V PATENT RIGHTS: 6 Hours
Scope of Patent Rights. Licensing and transfer of technology. Patent information and databases. Geographical
Indications.
UNIT- VI NEW DEVELOPMENTS IN IPR:
Administration of Patent System. New developments in IPR; IPR of Biological Systems, Computer Software
etc. Traditional knowledge Case Studies, IPR and IITs.
REFERENCES
1. Stuart Melville and Wayne Goddard, “Research methodology: an introduction for science &
engineering students‟”
2. Wayne Goddard and Stuart Melville, “Research Methodology: An Introduction”
3. Ranjit Kumar, 2nd Edition, “Research Methodology: A Step by Step Guide for beginners”
Halbert, “Resisting Intellectual Property”, Taylor & Francis Ltd ,2007.
4. Mayall, “Industrial Design”, McGraw Hill, 1992.
5. Niebel, “Product Design”, McGraw Hill, 1974.
6. Asimov, “Introduction to Design”, Prentice Hall, 1962.
CN31
7. Robert P. Merges, Peter S. Menell, Mark A. Lemley, “ Intellectual Property in New
Technological Age”, 2016.
8. T. Ramappa, “Intellectual Property Rights Under WTO”, S. Chand, 2008
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1: Understand research problem formulation. Analyze research related information and
follow research ethics
CO2: Understanding that when IPR would take such important place in growth of
individuals and nation, it is needless to emphasis the need of information about
Intellectual Property Right to be promoted among students in general & engineering
in particular.
CO3: Understand that IPR protection provides an incentive to inventors for further research
work and investment in R & D, which leads to creation of new and better products,
and in turn brings about, economic growth and social benefits.
CO4: Analyze research related information
CO5: Follow research ethics
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 6 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 3 hours shall not have internal
choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3
CO2 3
CO3 3
CO4
CO5 3 3
1: Low 2: Medium 3:High
CN32
COURSE LEARNING OBJECTIVES:
The objectives of the SEMINAR-I is to prepare the students to learn to:
1. Carry out the literature review of general research area/current topic and analyse the
same effectively.
2. Prepare a technical report, reflecting his/her depth of understanding, on the selected
area/topic and prepare content rich presentation.
3. Acquire communication and time management skills for effective and impactful
presentation. Interact with peers to acquire the qualities of thoughtfulness, friendliness,
adaptability, responsiveness, and politeness in-group discussion. Overcome stage fear
during the presentation.
GUIDE LINES
1. Seminar preparation and presentation is an individual student activity.
2. Topic may be of general/ specific interest to program of engineering or electives not
offered in the semester and to be selected in consultation with the faculty/Guide.
3. Select one pertinent research paper for the seminar presentation.
4. Prepare and submit a detailed technical report of the seminar topic.
COURSE OUTCOMES:
Students shall be able to:
1. Carry out the literature survey of topic of seminar.
2. Prepare a technical report on the selected area/topic.
3. Make an effective presentation with seamless flow of content within the time allocated.
Overcome inhibition in interacting with peers and hence develop the spirit of team
work. Overcome stage fear during the presentation.
SCHEME OF EXAMINATION
CIE – 50
marks
Phase -1 Marks =10 Seminar Report
Marks =20
Total:50
Marks Phase -2 Marks =20
Course Code 18CN1S01 M. Tech (Computer Networking)
Category Seminar Semester: I
Course title SEMINAR - I
Scheme and Credits
No. of Hours/Week
Total hours = 24 L T P S Credits
0 0 2 0 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any): NIL
CN33
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2 3
CO2 2 3 3
CO3 2
1. Low, 2. Medium, 3. High
Scheme of Continuous Internal Evaluation (CIE):
Evaluation would be carried out in TWO phases. The Seminar Evaluation Committee
shall comprise of Chairman of the Department, Faculty/Guide and one more faculty member
nominated by Chairman. The evaluation criteria shall be as per the rubrics given below:
Rubrics for Evaluation:
Topic - Technical Relevance, Sustainability and Societal Concerns : 15%
Review of literature and technical content : 35%
Presentation Skills : 25%
Report : 25%
CN34
Course Code 18CS1M02 M. Tech (Computer Networking)
Category Audit Course-I
Course title TECHNICAL PAPER WRITING
Scheme and Credits No. of Hours/Week Semester – I
L T P SS Credits
2 0 - - 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any):
COURSE OBJECTIVES
This course will enable students to
1. Understand the planning section of research paper and preparation of paper writing
2. Apply key skill while writing research paper and know about what to write in each section
3. Analyse literature, methods,
4. Evaluate research paper, paraphrasing paper
5. Create good research paper
UNIT-I PLANNING AND PREPARATION: 6 Hours
Planning and Preparation, Word Order, Breaking up long sentences, Structuring Paragraphs and
Sentences, Being Concise and Removing Redundancy, Avoiding Ambiguity and Vagueness
UNIT- II CLARIFYING: 3 Hours
Clarifying Who Did What, Highlighting Your Findings, Hedging and Criticising, Paraphrasing and
Plagiarism, Sections of a Paper, Abstracts. Introduction
UNIT- III REVIEW OF THE LITERATURE: 6 Hours
Review of the Literature, Methods, Results, Discussion, Conclusions, The Final Check.
UNIT- IV KEY SKILLS: 6 Hours
Key skills are needed when writing a Title, key skills are needed when writing an Abstract, key skills
are needed when writing an Introduction, skills needed when writing a Review of the Literature,
UNIT- V METHODS: 3 Hours
skills are needed when writing the Methods, skills needed when writing the Results, skills are needed
when writing the Discussion, skills are needed when writing the Conclusions.
UNIT- VI NEW DEVELOPMENTS IN RESEARCH PAPER WRITING:
useful phrases, how to ensure paper is as good as it could possibly be the first- time submission
REFERENCES
1. Goldbort R (2006) Writing for Science, Yale University Press (available on Google Books)
2. Day R (2006) How to Write and Publish a Scientific Paper, Cambridge University Press
3. Highman N (1998), Handbook of Writing for the Mathematical Sciences, SIAM.
Highman‟sbook.
4. Adrian Wallwork, English for Writing Research Papers, Springer New York Dordrecht
Heidelberg London, 2011
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1: List of section of research paper and preparation of paper writing
CO2: Determine key skill while writing research paper
CO3: Analyse literature, methods
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CO4: Assess research paper, do paraphrasing paper
CO5: Formulate research paper and results of simulation
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=20 Marks
Total: Marks
50
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3
CO2 3
CO3 3
CO4 3
CO5 3
1: Low 2: Medium 3:High
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Semester II
CN35
Course Code 18CS2C01 M. Tech (Computer Networking)
Category Professional Core
Course title ADVANCED DATA STRUCTURES AND ALGORITHMS
Scheme and
Credits
No. of Hours/Week Semester – II
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVE
The course will enable the student to:
1. Learn various data structures and its usage in designing algorithms.
2. Understand to the advanced methods of designing and analysing algorithms.
3. Learn various string matching and graph algorithms.
4. Acquire the knowledge of polynomial, non polynomial and approximation problems.
5. Understand the recent developments in the area of algorithmic design.
UNIT-1 REVIEW OF ANALYSIS TECHNIQUES 09 Hours
Growth of Functions: Asymptotic notations; Standard notations and common functions;
Recurrences -The substitution method, recursion-tree method, the master method,
Probabilistic Analysis and Randomized Algorithms.
UNIT- II BASIC DATA STRUCTURES 09 Hours Stacks and Queues, Vectors, Lists, and Sequences, Trees, Priority Queues and Heaps,
Dictionaries and Hash Tables, Search Trees and Skip lists- Ordered Dictionaries and
Binary Search Trees, AVL Trees, Bounded-Depth Search Trees, Splay Trees, Skip Lists.
UNIT -III DYNAMIC PROGRAMMING 10 Hours
Matrix-Chain multiplication, Elements of dynamic programming, longest common
subsequences. Graph algorithms: Bellman - Ford Algorithm; Single source shortest paths
in a DAG; Johnson‟s Algorithm for sparse graphs; Flow networks and Ford-Fulkerson
method. .
UNIT- IV TRIES AND STRING MACHING ALGORITHMS 10 Hours
Quad trees and K-d trees. String-Matching Algorithms: Naïve string Matching; Rabin -
Karp algorithm; String matching with finite automata; KnuthMorris-Pratt algorithm.
UNIT- V NP-COMPLETENESS 10 Hours
: Polynomial time, Polynomial time verification, NP-Completeness and reducibility, NP-
Complete problems. Approximation Algorithms: vertex cover problem, the set – covering
problem, randomization and linear programming, the subset – sum problem.
UNIT VI
Recent Trends in problem solving paradigms applying recently proposed data
structures
REFERENCES
1. Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein,”
Introduction to Algorithms”, Third Edition, Prentice-Hall, 2011.
2. M T Goodrich, Roberto Tamassia, “Algorithm Design”, John Wiley, 2002.
3. Mark Allen Weiss, “Data Structures and Algorithm Analysis in C++”, 4th
Edition,
Pearson, 2014.
4. Alfred V. Aho, John E. Hopcroft, Jeffrey D. Ullman, ―Data Structures and
Algorithms‖, Pearson Education, Reprint 2006.
5. Ellis Horowitz, Sartaj Sahni, Dinesh Mehta, “Fundamentals of Data Structures in C”,
Silicon Pr, 2007.
6. Aho, Hopcroft and Ullman, Data structures and algorithms, 1st edition, Pearson
Education, India, 2002, ISBN: 8177588265, 978817758826
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COURSE OUTCOMES
On completion of the course, the student will be able to:
CO1: Develop and analyze algorithms for red-black trees, B-trees and Splay trees and for
text processing applications.
CO2: Identify suitable data structures and develop algorithms for solving a particular set of
problems
CO3: Analyze the complexity/ performance of different algorithms.
CO4: Categorize the different problems in various classes according to their complexity.
CO5: Use appropriate data structure and algorithms in real time applications.
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for
50 marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2 2
CO3 2 2
CO4 2
CO5 2 2
1. Low, 2. Medium, 3. High
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Course Code 18CS2C02 M. Tech (Computer Networking)
Category Professional Core
Course title ADVANCED OPERATING SYSTEMS
Scheme and
Credits
No. of Hours/Week Semester – II
L T P S Credits
4 - - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
The course will enable the students to:
1. Understand the Design Approaches and Issues related to Advanced Operating Systems.
2. Apply the Knowledge on Distributed Operating Systems Concepts to develop Clocks,
Mutual Exclusion Algorithms.
3. Analyze the Distributed Deadlock Detection Algorithms and Agreement Protocols.
4. Evaluate the Algorithms for Distributed Scheduling, Examine the Commit Protocols
and review Concurrency Control Algorithms.
5. Create Advanced Operating Systems Applications with recent technologies
UNIT- I INTRODUCTION: 09 Hours
Concept of Batch system, Multiprogramming, Time Sharing, Parallel, Distributed and Real-
time System, Process Management: Concept of Process, Synchronization, CPU Scheduling,
IPC, Deadlock: Concept of Deadlock Prevention, Avoidance, Detection and its Recovery.
Memory Management: Contiguous allocation, Paging and Segmentation. Virtual memory:
Demand Paging, Page Replacement Algorithms. Distributed OS- Design Approaches and
Issues in DOS. Message Passing Model and RPC.
UNIT -II CLOCKS AND DISTRIBUTED MUTUAL EXCLUSION: 10 Hours
Concept of Lamport‟s Logical Clock and Vector Clocks, Termination Detection. A simple
solution to distributed mutual exclusion, Non Token based algorithms: Lamport‟s algorithm,
Ricart Agarwala‟s algorithm, Maekawa‟s algorithm, Token based algorithms: Suzuki Kasami‟s
broadcast algorithm, Raymond‟s tree based algorithm.
UNIT -III DISTRIBUTED DEADLOCK DETECTION: 10 Hours
Deadlock Handling, Strategies in Distributed Systems, Issues in Deadlock Detection And
Resolution, Control Organization for Distributed Deadlock Detection, Centralized Deadlock
Detection Algorithm: Ho-Ramamoorthy‟s Algorithm, Distributed Deadlock Detection
Algorithms: A Path- Pushing Algorithm and Edge Chasing Algorithm, Hierarchical Deadlock
Detection Algorithms: Menasce- Muntz Algorithm, Ho-Ramamoorthy‟s Algorithm.
Agreement Protocols: Byzantine Agreement Problem, Solution to The Byzantine Agreement
Problem- Lamport -Shostak- Pease Algorithm, Dolev et al.‟s Algorithm
UNIT –IV DISTRIBUTED SCHEDULING AND FAULT TOLERANCE: 10 Hours
Issues in Load Distribution, Components of a Load Distributing Algorithms, Load Distributing
Algorithms, Performance Comparison, Selecting Suitable Load Sharing Algorithms,
Requirements of Load Sharing Policies. Commit Protocols, Nonblocking Commit Protocols,
Voting Protocols, Dynamic Voting Protocols, The Majority Based Dynamic Voting Protocol,
Dynamic Vote Reassignment Protocols.
UNIT -V DATABASE OS & CONCURRENCY CONTROL 09 Hours
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Requirement of Database OS, A Concurrency Control Model of a Database System, The
Problem of Concurrency Control, Serializability Theory, Concurrency Control Algorithms,
Basic Synchronization Primitives, Lock Based Algorithms, Timestamp Based Algorithms,
Optimistic Algorithms, Concurrency Control Algorithms for Data Replication.
UNIT-VI Recent advances and research being done in the topics mentioned above units
REFERENCES
1. Mukesh Singhal and Niranjan G Shivaratri, Advanced Concepts in Operating Systems, Tata
Mcgraw Hill, 2002.
2. A Silberchatz and Peter Baer Galvin, Operating System Concepts, 10th Edition, John Wiley
and Sons, 2018.
3. William Stallings, Operating System: Internals and Design Principles, 9th Edition, Prentice
Hall India, 2017.
4. Andrew S Tennenbaum and Albert S Woodhull, Operating System Design and
Implementation, 3rd Edition, Pearson Education Inc., 2006.
5. Ceri S and Pelagorthi S, Distributed Databases: Principles and Systems, 1984, McGraw Hill.
COURSE OUTCOMES
On completion of the course, the student would be able to:
CO1: Explain the Fundamentals of Advanced Operating Systems, Design issues.
CO2: Determine the various Clock Synchronization Principles and Implement Mutual
Exclusion Algorithms.
CO3: Differentiate the various Distributed Deadlock Detection Algorithms and Analyze the
Agreement Protocols.
CO4: Select the appropriate Distributed Scheduling Algorithms, Commit Protocols and
Concurrency Control Algorithms.
CO5: Integrate the Concepts of Advanced Operating Systems with recent trends and
technologies to Design and Develop Applications.
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*2=40
Marks
Total:
Marks 100 Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 1 -
CO2 1 2
CO3 1 2
CO4 1 3
CO5 3 2 2
1: Low 2: Medium 3:High
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Course Code 18CN2C03 M. Tech (Computer Networking)
Category Professional Core - Integrated
Course title INTERNET OF THINGS
Scheme and
Credits
No. of Hours/Week Semester – II
L T P S Credits
3 0 2 - 4
CIE Marks:
50
SEE Marks: 50 Total Max. Marks:
100
Duration of SEE: 3 Hrs
Prerequisites (if any):
1. Computer Networks
COURSE OBJECTIVES
The course will enable the students to:
1. Understand the IoT architecture and its enabling technologies.
2. Realize the various applications of IoT, understand the IoT system management
using NETCONF-YANG.
3. Understand the design of IoT, Python programming language, packages for IoT
and Raspberry Pi.
4. Create the various IoT protocols and their support in the implementation of
services.
5. Create a typical IoT input using the standard IT protocols.
UNIT I – INTRODUCTION TO INTERNET OF THINGS (IoT) 09 Hours
Definition and Characteristics of IoT, Physical Design of IoT, Logical Design of IoT, IoT
Enabling Technologies, IoT Levels and Deployment Templates.
UNIT II – DOMAIN SPECIFIC IoT, M2M and IoT System Management 09 Hours
Home Automation, Cities, Environment, Energy, Retail, Logistics, Agriculture, Industry,
Health and Lifestyle, M2M, Difference between IoT and M2M, SDN and NFV for IoT,
Need for IoT Systems Management, Simple Network Management Protocol, Network
Operator Requirements, IoT System Management with NETCONF-YANG.
UNIT III – DEVELOPING IoT USING PYTHON 10 Hours
IoT Design Methodology, IoT Systems – Logical Design using Python, Python Data
Types and Data Structures, Control Flow, Functions, Modules, Packages, File Handling,
Data/Time Operations. Classes, Python Packages for IoT: JSON, XML, HTTPLib and
URLLib, SMTPLib.
UNIT IV – IoT DEVICES AND PROTOCOLS 09 Hours
Basic Building Blocks of an IoT Device, Raspberry Pi, Programming Raspberry Pi using
Python, Basics of IoT Protocols: HTTP, UPnP, MQTT, CoAP and XMPP.
UNIT V – IoT PROTOCOLS 10 Hours
HTTP: Adding HTTP Support to Sensor, Adding HTTP Support to Actuator, Adding
HTTP Support to Controller. UPnP Protocol: Creating a Device Description Document,
Creating a Service Description Document, Providing a Web Interface, Creating an UPnP
Interface, Implementing the Still Image Service using Camera. CoAP Protocol: Making
HTTP Binary, Adding CoAP to Sensor, Adding CoAP to Actuator. MQTT Protocol:
Adding MQTT Support to Sensor, Adding MQTT Support to Actuator, Adding MQTT
Support to Controller. XMPP Protocol: Adding XMPP Support to a Thing, Adding
XMPP Support to Actuator, Adding XMPP Support to Camera, Adding XMPP Support
to Controller, Connecting All Together.
UNIT VI – Recent Trends in Industrial Internet of Things and Social Internet of Things.
UNIT VII (Lab Programs)
1. Study and Install Python in Eclipse and WAP for data types in python.
2. Write a Program for arithmetic operation in Python.
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3. Write a Program for looping statement in Python.
4. Study and Install IDE of Arduino and different types of Arduino.
5. Write program using Arduino IDE for Blink LED.
6. Write Program for RGB LED using Arduino.
7. Study the Temperature sensor and Write Program foe monitor temperature using
Arduino.
8. Study and Implement RFID, NFC using Arduino.
9. Study and implement MQTT protocol using Arduino.
10. Study and Configure Raspberry Pi.
11. WAP for LED blink using Raspberry Pi. 12. Study and Implement Zigbee Protocol using Arduino / Raspberry Pi.
REFERENCES
1. Arshdeep Bahga and Vijay Madisetti, “Internet of Things: A Hands-on
Approach”, University Press, 2015.
2. Peter Waher, “Learning Internet of Things”, PACKT Publishing, 2015.
3. Adrian McEwen and Hakim Cassimally, “Designing Internet of Things”, John
Wiley and Sons, 2014.
COURSE OUTCOMES
On completion of the course, the student would be able to:
CO1: Demonstrate the knowledge of IoT architecture and design.
CO2: Manage the IoT system with NETCONF-YANG.
CO3: Program the Raspberry Pi using Python.
CO4: Develop an IoT application using the IoT protocol.
CO5: Investigate the standard IoT protocol.
SCHEME OF EXAMINATION
CIE -
Practical
Conduction of experiments, performance of student in
every week lab and completion of lab record=50#
Total: Marks
50
Lab Test: Part-A =20, Part-B=20 and Vice-Voce=10
Marks(50##
)
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
# = CIE Marks for laboratory performance is to be evaluated for 50 marks and
the marks obtained shall be reduced for 25 marks
## = Lab test is to be conducted for 50 marks and the marks obtained shall be
reduced for 25 marks. Lab test shall be conducted by two examiners out of which
one examiner is the faculty taught the course. There is no SEE for the practical ‘s
portion of integrated course
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Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 1 2
CO2 1
CO3 3
CO4 1
CO5 2
1. Low, 2. Medium, 3. High
CN42
Course Code 18CS2E1A M. Tech (Computer Networking)
Category Professional Elective
Course title DATA WAREHOUSING AND MINING
Scheme and
Credits
No. of Hours/Week Semester – II
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
Course Objectives: The course will enable the students to:
1. Understand the principles of Data warehousing and data mining.
2. Perform classification and prediction of data.
3. Examine the types of data in cluster analysis with various clustering methods.
4. Illustrate the concepts of mining object, spatial, multimedia, text and web data.
5. Build a data warehouse and mapping the data warehouse to a multiprocessor
architecture.
UNIT I - INTRODUCTION TO DATA MINING: 9 Hours
Data Mining Functionalities, Data Pre-processing, Data Cleaning, Data Integration and
Transformation, Data Reduction, Data Discretization and Concept Hierarchy Generation.
Association Rule Mining: Efficient and Scalable Frequent Item set Mining Methods, Mining
Various Kinds of Association Rules, Association Mining to Correlation Analysis, Constraint-Based
Association Mining, Handling categorical, Continuous Attributes, Concept hierarchy, Sequential
and Sub graph Patterns.
UNIT II - CLASSIFICATION AND PREDICTION: 10 Hours
Issues Regarding Classification and Prediction, Classification by Decision Tree Introduction,
Bayesian Classification, Rule Based Classification, Classification by Back propagation, Support
Vector Machines, Associative Classification, Lazy Learners, Other Classification Methods,
Prediction, Accuracy and Error Measures, Evaluating the Accuracy of a Classifier or Predictor,
Ensemble Methods, Model Section.
UNIT III - CLUSTER ANALYSIS: 10 Hours
Types of Data in Cluster Analysis, A Categorization of Major Clustering Methods, Partitioning
Methods, Hierarchical methods, Density-Based Methods, Grid-Based Methods, Model-Based
Clustering Methods, Clustering High-Dimensional Data, Constraint-Based Cluster Analysis,
Outlier Analysis, Quality and validity of Cluster Analysis.
UNIT IV - MINING OBJECT, SPATIAL, MULTIMEDIA, TEXT AND WEB DATA: 9
Hours
Multidimensional Analysis and Descriptive Mining of Complex Data Objects, Spatial Data
Mining, Multimedia Data Mining, Text Mining, Mining the World Wide Web, Stream Data
Mining, Social Network Analysis.
UNIT V – DATA WAREHOUSING AND BUSINESS ANALYSIS: 10 Hours
Data warehousing Components, Building a Data warehouse, Mapping the Data Warehouse to a
Multiprocessor Architecture, DBMS Schemas for Decision Support, Data Extraction, Cleanup, and
Transformation Tools, Metadata, reporting, Query tools and Applications, Online Analytical
Processing (OLAP), OLAP and Multidimensional Data Analysis.
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UNIT VI - Recent Trends in Distributed warehousing and Data Mining, Class Imbalance Problem,
Graph mining, Social Network Analysis.
REFERENCES REFERENCES
1. Jiawei Han and Micheline Kamber “Data Mining Concepts and Techniques”, Second Edition,
Elsevier, 2011.
2. Vipin Kumar, Introduction to Data Mining - Pang-Ning Tan, Michael Steinbach, Addison Wesley, 2006.
3. G Dong and J Pei, Sequence Data Mining, Springer, 2007.
4. Alex Berson and Stephen J. Smith “Data Warehousing, Data Mining & OLAP”, Tata McGraw – Hill
Edition, Tenth Reprint 2007.
5. K.P. Soman, Shyam Diwakar and V. Ajay “Insight into Data Mining Theory and Practice”, Easter Economy
Edition, Prentice Hall of India, 2006.
G. K. Gupta “Introduction to Data Mining with Case Studies”, Easter Economy Edition, Prentice Hall of India,
2006. COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1. Demonstrate the concept of data mining principles, data warehousing Architecture and its
Implementation
CO2. Apply the association rules, design and deploy appropriate classification techniques for
mining the data
CO3. Cluster the high dimensional data for better organization of the data
CO4. Describe stream mining, Time-Series and sequence data in high dimensional system
CO5. Acquire the concept of Mining Object, Spatial, Multimedia, Text, and Web Data
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for
50 marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3
CO2 2
CO3 3
CO4 2
CO5 3
1. Low, 2. Medium, 3. High
CN44
Course Code 18CS2E1B M. Tech (Computer Networking)
Category Professional Elective
Course title STOCHASTIC PROCESS AND QUEUING THEORY
Scheme and Credits No. of Hours/Week Semester – II
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any)
1. Probability Theory
COURSE OBJECTIVES:
The course will enable the students to:
1. Understand the concepts of stochastic processes, and Markov chains.
2. Understand Markov processes with discrete and continuous state spaces.
3. Understand the concepts of queuing theory and different queues.
4. Understand open and closed queuing networks.
5. Analyse single and multi-server queuing models.
UNIT-I INTRODUCTION TO STOCHASTIC PROCESSES AND MARKOV CHAINS 09 Hours
Introduction, Specifications, Classification of Stochastic Processes, Stationary Process, Poisson Processes,
Renewal Processes, Markov Chains: Transition Probabilities, Classification of States and Chains, Reducible
Chains, Statistical Inference of Markov Chains, Markov Chains with Continuous State Space, Non-
homogenous Chains.
UNIT-II MARKOV PROCESSES WITH DISCRETE AND CONTINUOUS STATE SPACE 09 Hours
Poisson Process and its Related Distributions, Generalization of Poisson Processes, Birth and Death Process,
Markov Process with Discrete State Space (Continuous Time Markov Chains), Brownian Motion, Wiener
Process, Differential Equations for Wiener Process, Kolmogorav Equations, First Passage Time Distribution
for Wiener Process.
UNIT-III QUEUING THEORY AND MARKOVIAN QUEUING MODELS 10 Hours
Introduction, Characteristics Notations, Birth and Death Processes, Single-Server Queues (M|M|1), Multi-
Server Queues (M|M|c), Choosing the Number of Servers, Queues with Truncation (M|M|c|K), Erlang‟s Loss
Formula (M|M|c|c), Queues with Unlimited Service, Finite Source Queues, State-Dependent Service, Queues
with Impatience, Transient Behaviour, Busy-Period Analysis, Bulk Input and Bulk Service.
UNIT-IV NETWORKS, SERIES, AND CYCLIC QUEUES 10 Hours
Series Queues, Open Jackson Networks, Closed Jackson Networks, Cyclic Queues, Extensions of Jackson
Networks, Non-Jackson Networks.
UNIT-V GENERAL ARRIVAL OR SERVICE PATTERNS 10 Hours
General Service, Single Server (M|G|1), General Service, Multi-server (M|G|c|∙, M|G|∞), General Input
(G|M|1, G|M|c).
UNIT-VI Performance analysis of data networks.
REFERENCES
1. Jyothiprasad Medhi, “Stochastic Processes”, New Age International Publishers, II Edition, 2002.
2. Kishore S. Trivedi, “Probability and Statistics with Reliability, Queuing and Computer Science
Applications”, John Wiley and Sons, II Edition, 2008.
3. Donald Gross, John F. Shortle, James M. Thomson, and Carl M. Harris, “Fundamentals of Queuing
Theory”, John Wiley and Sons, IV Edition, 2008.
4. Oliver Knill, “Probability Theory and Stochastic Processes with Applications”, Overseas Press, 2009.
CN45
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 1
CO2 2
CO3 2
CO4 2
CO5 1
1. Low, 2. Medium, 3. High
COURSE OUTCOMES
On completion of the course, the student would be able to:
CO1: Solve problems on stochastic process and Markov chains.
CO2: Analyse Markov Process for Discrete and Continuous State Spaces.
CO3: Model the Behaviour of Various Computer Networks and Distributed Systems using Queuing Models.
CO4: Analyse the Arrival and Service Patterns of any System and Solve Problems in Computer Networks
and Distributed Systems.
CO5:Investigate the performance analysis of data networks
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit VI(AAT)=15
marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks
Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
CN46
Course Code 18CN2E1C M. Tech (Computer Networking)
Category Professional Elective
Course title OPIMIZATION TECHNIQUES
Scheme and
Credits
No. of Hours/Week Semester – II
L T P S Credits
4 0 - - 4
CIE Marks:
50
SEE Marks: 50 Total Max. Marks:
100
Duration of SEE: 3 Hrs
Prerequisites (if any):
1. Basics of Operations Research
COURSE OBJECTIVES
The course will enable the students to:
1. Understand the mathematical formulation of real world problems.
2. Understand linear programming and its associated methods.
3. Understand non-linear programming and its associated methods.
4. Understand unconstrained optimization techniques and apply them in solving
engineering problems.
5. 5. Review of recent developments in geometric and dynamic programming
UNIT I – CLASSICAL OPTIMIZATION TECHNIQUES 10 Hours
Engineering Applications of Optimization, Statement of an Optimization Problem,
Classification of Optimization Problems, Single-Variable Optimization, Multivariable
Optimization with No Constraint, Multivariable Optimization with Equality Constraints,
Solution by Direct Substitution, Constrained Variation and Lagrange Multipliers,
Multivariable Optimization with Inequality Constraints: Kuhn–Tucker Conditions,
Constraint Qualification, Convex Programming Problem.
UNIT II – LINEAR PROGRAMMING 09 Hours
Simplex Method, Revised Simplex Method, Duality in Linear Programming,
Decomposition Principle, Sensitivity or Post-optimality Analysis. (only concepts, no
problems).
UNIT III – NON-LINEAR PROGRAMMING 09 Hours
Elimination Methods: Unrestricted Search, Search with Fixed Step Size, Search with
Accelerated Step Size, Exhaustive Search, Dichotomous Search, Interval Halving
Method, Fibonacci Method, Golden Section Method. Interpolation Methods: Quadratic
Interpolation Method, Cubic Interpolation Method, Direct Root Methods, Newton
Method, Quasi-Newton Method, Secant Method.
UNIT IV – UNCONSTRAINED OPTIMIZATION TECHNIQUES – I 10 Hours
Classification of Unconstrained Minimization Methods, General Approach, Rate of
Convergence, Scaling of Design Variables, Direct Search Methods: Random Search
Methods, Random Jumping Methods, Random Walk Method, Random Walk Method
with Direct Exploitation, Grid Search Method, Univariate Method, Powell‟s Method
UNIT V – UNCONSTRAINED OPTIMIZATION TECHNIQUES – II 10 Hours
Indirect Search Methods: Gradient of a Function, Evaluation of the Gradient, Rate of
Change of a Function along a Direction, Steepest Descent (Cauchy) Method, Conjugate
Gradient (Fletcher–Reeves) Method, Development of the Fletcher–Reeves Method,
Fletcher–Reeves Method, Newton‟s Method, Marquardt Method, Quasi-Newton
Methods, Rank 1 and Rank 2 Updates, Davidon–Fletcher–Powell Method, Broyden–
Fletcher–Goldfarb–Shanno Method, Test Functions
UNIT VI – Geometric Programming, Dynamic Programming.
REFERENCES
1. Singiresu S. Rao, “Engineering Optimization: Theory and Practice”, John Wiley
CN47
and Sons, IV Edition, 2009.
2. Edwin K P Chong and Stanislaw H Zak, “An Introduction to Optimization”, John
Wiley and Sons, IV Edition, 2010.
3. John K. Karlof, “Integer Programming: Theory and Practice”, CRC Press.
COURSE OUTCOMES
On completion of the course, the student would be able to:
CO1: Formulate and use optimization techniques, model the real world problems and
simulate it.
CO2: Apply the concepts of linear and non-linear programming to engineering problems
and carry out sensitivity analysis.
CO3: Apply the concept of optimality criteria for various types of optimization problems.
CO4: Solve various constrained and unconstrained problems in single variable as well as
multivariable.
CO5: Categorise the problems related to dynamic and geometric programming.
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for
50 marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2
CO3 2
CO4 2
CO5 1
1. Low, 2. Medium, 3. High
CN48
Course Code 18CS2E2A M. Tech (Computer Networking)
Category Professional Elective
Course title NETWORK SECURITY
Scheme and
Credits
No. of Hours/Week Semester – II
L T P S Credits
4 0 - - 4
CIE Marks:
50
SEE Marks: 50 Total Max. Marks:
100
Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
The course will enable the students to:
1: Learn the basics of security and various types of security issues.
2: Understand cryptography techniques available and various security attacks.
3: Explore network security and how they are implemented in real world.
4: Analyze various issues of wireless security techniques.
5: Effectively design secured wireless sensor network
UNIT I- INTRODUCTION TO SECURITY 09 Hours
Need for security, Security approaches, Principles of security, Types of attacks.
Encryption Techniques: Plaintext, Cipher text, Substitution & Transposition techniques,
Encryption & Decryption, Types of attacks, Key range & Size. Symmetric &
Asymmetric Key Cryptography: Algorithm types & Modes, DES, AES, RSA, ECC;
UNIT II- SECURED HASH ALGORITHMS 09 Hours
Message Digest, Key- Distribution Algorithms, Digital signatures, User Authentication
Mechanisms, Key Management, Certificates, Kerberos.
UNIT III - DISTRIBUTED SYSTEM SECURITY 10 Hours Firewalls, Proxy-Servers, Network intrusion detection. Transport security: Mechanisms
of TLS, SSL, IPSec. Network -level solutions, Secure socket layer, IP Security, DoS
Counter measures, DNS Solutions.
UNIT IV - WIRELESS SECURITY 10 Hours
Security in wireless Networks Vulnerabilities, Security techniques, Wi-Fi Security, DoS
in wireless communication.
UNIT V - WIRELESS SENSOR NETWORKS SECURITY 10 Hours
Security in Wireless Sensor Networks, Possible attacks, countermeasures, SPINS, Static
and dynamic key Management
UNIT VI Recent trends in IOT security, IDS – 04 Hours
REFERENCES
1. W. Stallings. Cryptography and Network Security. Prentice Hall, 7th
Edition - 2017
2. W. R. Cheswick and S. M. Bellovin. Firewalls and Internet Security. Addison Wesley,
2007.
3. B. Schneier. Applied Cryptography. Wiley, 2006.
4. Stallings W., Wireless Communications and Networks, Pearson Education 2005
5. KazemSohraby, Daniel Minoli and TaiebZnati, “wireless sensor networks -
Technology,
Protocols, and Applications”, Wiley Interscience 2007
6. Takahiro Hara,Vladimir I. Zadorozhny, and Erik Buchmann, “Wireless Sensor
NetworkTechnologies for the Information Explosion Era”, springer 2010
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COURSE OUTCOMES
On completion of the course, the student would be able to:
CO1: Analyze various security issues related to computer networks.
CO2: Implement various network security algorithms.
CO3: Design, Implement various security algorithms for distributed environment.
CO4: Analyze the security issues and apply the relevant algorithm to mitigate the same.
CO5: Analyze various security attacks in WSN.
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for
50 marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2
CO2 2
CO3 2 2
CO4 3 2
CO5 2 2
1. Low, 2. Medium, 3. High
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Course Code 18IT2E2B M. Tech (Computer Networking)
Category Professional Elective
Course title CYBER SECURITY
Scheme and
Credits
No. of Hours/Week Semester – II
L T P S Credits
4 0 - - 4
CIE Marks:
50
SEE Marks: 50 Total Max. Marks:
100
Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
The course will enable the students to:
1. Memorise basis of security concepts and security techniques.
2. Understand the cybercrime and law.
3. Identify and determine the motive and remedial measures for cybercrime, detection
and handling.
4. Analyze areas affected by cybercrime and identify Legal Perspectives in cyber
security.
5. Effectively design a secure cyber system.
UNIT I - INTRODUCTION TO SECURITY 09 Hours Introduction to Security: Need for security, Security approaches, Principles of security,
Types of attacks. Encryption Techniques: Plaintext, Cipher text, Substitution &
Transposition techniques, Encryption & Decryption, Types of attacks, Key range & Size.
Symmetric & Asymmetric Key Cryptography: DES,RSA
UNIT II- INTRODUCTION TO CYBERCRIME 09 Hours
Cybercrime: Definition and Origins of the Word, Cybercrime and Information Security,
Cybercriminals, Classifications of Cybercrimes, Cybercrime: The Legal Perspectives,
Cybercrimes: An Indian Perspective, Cybercrime and the Indian ITA 2000, A Global
Perspective on Cybercrimes, Cybercrime Era: Survival Mantra for the Netizens.
Cyberoffenses: Criminals Plan: Attacks, Social Engineering, Cyberstalking, Cybercafe
and Cybercrimes, Botnets: The Fuel for Cybercrime, Attack Vector, Cloud Computing.
UNIT III CYBERCRIME: MOBILE AND WIRELESS DEVICES 10 Hours
Introduction, Proliferation of Mobile and Wireless Devices, Trends in Mobility, Credit
Card Frauds in Mobile and Wireless Computing Era, Security Challenges Posed by
Mobile Devices, Registry Settings for Mobile Devices, Authentication Service Security,
Attacks on Mobile/Cell Phones, Mobile Devices: Security Implications for organizations,
Organizational Measures for Handling Mobile, Organizational Security Policies and
Measures in Mobile Computing Era, Laptops.
UNIT IV- TOOLS AND METHODS USED IN CYBERCRIME 10 Hours
Introduction, Proxy Servers and Anonymizers, Phishing, Password Cracking, Keyloggers
and Spywares, Virus and Worms, Trojan Horses and Backdoors, Steganography, DoS
and DDoS Attacks, SQL Injection, Buffer Overflow, Attacks on Wireless Networks.
Phishing and Identity Theft : Introduction, Phishing, Identity Theft (ID Theft).
UNIT V- INTRODUCTION TO SECURITY POLICIES AND CYBER LAWS
10 Hours
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Need for An Information Security Policy, Information Security Standards – ISO,
Introducing Various Security Policies and Their Review Process, Introduction to Indian
Cyber Law, Objective and Scope of the it Act, 2000, Intellectual Property Issues,
Overview of Intellectual - Property - Related Legislation in India, Patent, Copyright, Law
Related to Semiconductor Layout and Design, Software License.
UNIT VI - Recent developments in Security Policies and Cyber Laws
REFERENCES
1. W. Stallings. Cryptography and Network Security. Prentice Hall, 7th
Edition -
2017
2. Sunit Belapure and Nina Godbole, “Cyber Security: Understanding Cyber
Crimes, Computer Forensics And Legal Perspectives”, Wiley India Pvt Ltd,
ISBN: 978-81-265-21791, 2013.
3. Dr. Surya PrakashTripathi, RitendraGoyal, Praveen Kumar Shukla, KLSI.
“Introduction to information security and cyber laws”. Dreamtech Press. ISBN:
9789351194736, 2015.
4. Thomas J. Mowbray, “Cybersecurity: Managing Systems, Conducting Testing,
and Investigating Intrusions”, Copyright © 2014 by John Wiley & Sons, Inc,
ISBN: 978 -1-11884965 -1
5. I. A. Dhotre , “Cyber Forensics , Technical Publications; 1st Edition edition
(2016), ISBN- 13:978-9333211475
COURSE OUTCOMES At the end of the course, the students will be able to:
CO1: Interpret the basic concepts of cyber security, cyber law and their roles.
CO2: Articulate evidence collection and legal challenges
CO3: Discuss tools support for detection of various attacks.
CO4: Analyse various cyber risks.
CO5: Validate different cyber techniques in cyber system.
SCHEME OF EXAMINATION
CIE –
50
mark
s
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:
50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
mark
s
Answer FIVE full questions
Units which have 09 Hours shall not have
internal choice. 20* 2 = 40 Marks Total:
100
marks Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for
50 marks.
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Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3
CO2 2
CO3 2
CO4 2
CO5 2
1. Low, 2. Medium, 3. High
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Course Code 18CS2E2C M. Tech (Computer Networking)
Category Professional Elective
Course title WEB SECURITY
Scheme and
Credits
No. of Hours/Week Semester – II
L T P S Credits
4 0 - - 4
CIE Marks:
50
SEE Marks: 50 Total Max. Marks:
100
Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
The course will enable the students to:
1. Understand web application‟s vulnerability and malicious attacks.
2. Understand basic web technologies used for web application development.
3. Analyse basic concepts of Mapping the application
4. Illustrate different attacking illustrations.
5. Emphasis various basic concepts of Attacking Data Stores. .
UNIT I: WEB APPLICATION SECURITY 09 Hours
The Evolution of Web Applications, Common Web Application Functions, Benefits of
Web Applications, Web Application Security.
Core Defense Mechanisms: Handling User Access Authentication, Session
Management, Access Control, Handling User Input, Varieties of Input Approaches to
Input Handling, Boundary Validation.
Multistep Validation and Canonicalization: Handling Attackers, Handling Errors,
Maintaining Audit Logs, Alerting Administrators, Reacting to Attacks.
UNIT II: WEB APPLICATION TECHNOLOGIES 09 Hours
The HTTP Protocol, HTTP Requests, HTTP Responses, HTTP Methods, URLs, REST,
HTTP Headers, Cookies, Status Codes, HTTPS, HTTP Proxies, HTTP Authentication,
Web Functionality, Server-Side Functionality, Client-Side Functionality, State and
Sessions, Encoding Schemes, URL Encoding, Unicode Encoding, HTML Encoding,
Base64 Encoding, Hex Encoding, Remoting and Serialization Frameworks.
UNIT III: MAPPING THE APPLICATION 10 Hours
Enumerating Content and Functionality, Web Spidering, User-Directed Spidering,
Discovering Hidden Content, Application Pages Versus Functional Paths, Discovering
Hidden Parameters, Analyzing the Application, Identifying Entry Points for User Input,
Identifying Server-Side Technologies, Identifying Server-Side Functionality, Mapping
the Attack Surface.
UNIT IV: ATTACKING AUTHENTICATION 10 Hours
Authentication Technologies, Design Flaws in Authentication Mechanisms, Bad
Passwords, Brute-Forcible Login, Verbose Failure Messages, Vulnerable Transmission of
Credentials, Password Change, Functionality, Forgotten Password Functionality, User
Impersonation, Functionality Incomplete, Validation of Credentials, Nonunique
Usernames, Predictable Usernames, Predictable Initial Passwords, Insecure Distribution
of Credentials. Attacking Access Controls.
UNIT V - ATTACKING DATA STORES 10 Hours
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Injecting into Interpreted Contexts, Bypassing a Login, Injecting into SQL, Exploiting a
Basic Vulnerability Injecting into Different Statement Types, Finding SQL Injection
Bugs, Fingerprinting the Database, The UNION Operator, Extracting Useful Data,
Extracting Data with UNION, Bypassing Filters, Second-Order SQL Injection, Advanced
Exploitation Beyond SQL Injection: Escalating the Database Attack, Using SQL
Exploitation Tools, SQL Syntax and Error Reference, Preventing SQL Injection.
UNIT VI Recent trends in Web Applications and its Security
REFERENCES
1. Defydd Stuttard, Marcus Pinto , The Web Application Hacker's Handbook: Finding
And Exploiting Security, Wiley Publishing, Second Edition.
2.Andres Andreu, Professional Pen Testing for Web application, Wrox Press.
3. Carlos Serrao, Vicente Aguilera, Fabio Cerullo, “Web Application Security” Springer;
1st Edition
4. Joel Scambray, Vincent Liu, Caleb Sima ,“Hacking exposed”, McGraw-Hill; 3rd
Edition, (October, 2010).
5. OReilly Web Security Privacy and Commerce 2nd Edition 2011.
6. Software Security Theory Programming and Practice, Richard sinn, Cengage Learning.
COURSE OUTCOMES
On completion of the course, the student would be able to:
CO1:Achieve Knowledge of web application‟s vulnerability and malicious attacks.
CO2:Understand the basic web technologies used for web application development
CO3:Understands the basic concepts of Mapping the application.
CO4:Able to illustrate different attacking illustrations
C05:Investigate technique of attacking Data Stores
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for
50 marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 1 3
CO2 2 1 3
CO3 1 3
CO4 3 1 3
CO5 1 3
1. Low, 2. Medium, 3. High
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Course Code 18CS2L01 M. Tech (Computer Networking)
Category Laboratory
Course title ADVANCED DATA STRUCTURES AND ALGORITHMS
LAB
Scheme and
Credits
No. of Hours/Week Semester – II
L T P S Credits
0 0 4 0 4
CIE Marks:
50
SEE Marks: 50 Total Max. Marks:
100
Duration of SEE: 3 Hrs
Prerequisites (if any):
1. Data structures and Algorithm
2. Java Programming
Course Objectives: The course will enable the students to:
1. Acquire the knowledge of using advanced data structures
2. Acquire the knowledge of sorting and balancing the tree structure
3. Understand the usage of graph structures and string matching.
4. Understand the implementation of various string matching algorithms.
5. learn to solve the various NP complete problems
Each student has to work individually on assigned lab exercises. Lab sessions could be
scheduled as one contiguous four-hour session per week. It is recommended that all
implementations are carried out in Java. Exercises should be designed to cover the
following topics:
1. Doubly Circular Linked List
2. AVL Tree
3. Efficiency of Heap Sort & Quick Sort
4. Travelling Salesman Problem (Dynamic Programming)
5. N Queens Problem (Backtracking/ Branch & Bound)
6. Bellman-Ford algorithm
7. Shortest paths in a DAG
8. Ford-Fulkerson algorithm
9. Robin-Karp algorithm
10. Knuth-Morris-Pratt algorithms
11. String matching with Finite Automata
12. Vertex Cover problem
13. The Set Covering problem
14. The Subset-Sum problem
15. Maximum Bipartite algorithm
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1: Design and implement basic and advanced data structures extensively.
CO2: Design and apply graph structures for various applications.
CO3: Design and develop efficient algorithms with minimum complexity using design
techniques.
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CO4: Design and develop advanced string matching and NP Complete problems.
CO5: Achieve proficiency in Java programming.
Continuous Internal
Evaluation (CIE) (Lab – 50
Marks)
Marks Semester End Evaluation (SEE)
(Lab – 100 Marks) Marks
Performance of the Student in
the Lab every week
20 Write up 10
Test at the end of the semester 20 Experiment 70
Viva Voce 10 Viva Voce 20
Total 100
Total (CIE) 50 Total (SEE) 50*
Note. * = SEE shall be conducted for 100 marks for practical and the marks obtained shall be
reduced for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2
CO2 2
CO3 2
CO4 2
CO5 2
1. Low, 2. Medium, 3. High
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Course Code 18CS2M01 M. Tech (Computer Science and Engineering)
Category Audit Course-2
Course title PEDAGOGY STUDIES
Scheme and Credits No. of Hours/Week Semester – II
L T P SS Credits
2 0 - - 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any):
COURSE OBJECTIVES
SThis course will enable students to
1. Understand the Thematic Overview and Pedagogical practices
2. Apply professional classroom practices , curriculum and assessment
3. Analyse methodology for quality assessment of school curriculum teacher
4. Evaluate pedagogic theory and pedagogical approaches
5. Create contexts pedagogy, new curriculum and assessment metrics for future
UNIT- I INTRODUCTION AND METHODOLOGY: 6 Hours
Aims and rationale, Policy background, Conceptual framework and terminology Theories of
learning, Curriculum, Teacher education. Conceptual framework, Research questions. Overview of
methodology and Searching.
UNIT- II THEMATIC OVERVIEW: 3 Hours
Pedagogical practices are being used by teachers in formal and informal classrooms in developing
countries. Curriculum, Teacher education
UNIT- III PEDAGOGICAL PRACTICES: 6 Hours
Evidence on the effectiveness of pedagogical practices Methodology for the in depth stage: quality
assessment of included studies. How can teacher education (curriculum and practicum) and the
school curriculum and guidance materials best support effective pedagogy? Theory of change.
Strength and nature of the body of evidence for effective pedagogical practices. Pedagogic theory
and pedagogical approaches. Teachers‟ attitudes and beliefs and Pedagogic strategies.
UNIT- IV PROFESSIONAL DEVELOPMENT: 6 Hours
Professional development: alignment with classroom practices and follow-up support Peer support
Support from the head teacher and the community. Curriculum and assessment Barriers to learning:
limited resources and large class sizes
UNIT- V RESEARCH GAPS AND FUTURE DIRECTIONS: 3 Hours
Research design Contexts Pedagogy Teacher education Curriculum and assessment Dissemination
and research impact.
UNIT- VI NEW DEVELOPMENTS IN PEDAGOGY:
REFERENCES
1. Ackers J, Hardman F (2001) Classroom interaction in Kenyan primary schools, Compare, 31
(2): 245-261.
2. Agrawal M (2004) Curricular reform in schools: The importance of evaluation, Journal of
Curriculum Studies, 36 (3): 361-379.
3. Akyeampong K (2003) Teacher training in Ghana - does it count? Multi-site teacher
education research project (MUSTER) country report 1. London: DFID.
CN58
4. Akyeampong K, Lussier K, Pryor J, Westbrook J (2013) Improving teaching and learning of
basic maths and reading in Africa: Does teacher preparation count? International Journal
Educational Development, 33 (3): 272–282.
5. Alexander RJ (2001) Culture and pedagogy: International comparisons in primary education.
Oxford and Boston: Blackwell.
6. Chavan M (2003) Read India: A mass scale, rapid, „learning to read‟ campaign
7. www.pratham.org/images/resource%20working%20paper%202.pdf.
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1: What pedagogical practices are being used by teachers in formal and informal
classrooms in developing countries?
CO2: What is the evidence on the effectiveness of these pedagogical practices, in what
conditions, and with what population of learners?
CO3: How can teacher education (curriculum and practicum) and the school curriculum and
guidance materials best support effective pedagogy
CO4: Assess pedagogic theory and pedagogical approaches
CO5: Design new curriculum and assessment metrics for future
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3
CO2 3
CO3 3
CO4 3
CO5 3
1: Low 2: Medium 3:High
CN59
COURSE LEARNING OBJECTIVES:
The objectives of the SEMINAR-II is to prepare the students to learn to:
1. Carry out the literature review of general research area/current topic and analyse the
same effectively.
2. Prepare a technical report, reflecting his/her depth of understanding, on the selected
area/topic and prepare content rich presentation.
3. Acquire communication and time management skills for effective and impactful
presentation. Interact with peers to acquire the qualities of thoughtfulness, friendliness,
adaptability, responsiveness, and politeness in-group discussion. Overcome stage fear
during the presentation.
GUIDE LINES
1. Seminar preparation and presentation is an individual student activity.
2. Topic may be of general/ specific interest to program of engineering or electives not
offered in the semester and to be selected in consultation with the faculty/Guide.
3. Select one pertinent research paper for the seminar presentation.
4. Prepare and submit a detailed technical report of the seminar topic.
COURSE OUTCOMES:
Students shall be able to:
1. Carry out the literature survey of topic of seminar.
2. Prepare a technical report on the selected area/topic.
3. Make an effective presentation with seamless flow of content within the time allocated.
Overcome inhibition in interacting with peers and hence develop the spirit of team
work. Overcome stage fear during the presentation.
SCHEME OF EXAMINATION
CIE – 50
marks
Phase -1 Marks =10 Seminar Report
Marks =20
Total:50
Marks Phase -2 Marks =20
Course Code 18CN2S01 M. Tech (Computer Networking)
Category Seminar Semester: II
Course title SEMINAR - II
Scheme and Credits
No. of Hours/Week
Total hours = 24 L T P S Credits
0 0 2 0 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any): NIL
CN60
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2 3
CO2 2 3 3
CO3 2
1. Low, 2. Medium, 3. High
Scheme of Continuous Internal Evaluation (CIE):
Evaluation would be carried out in TWO phases. The Seminar Evaluation Committee
shall comprise of Chairman of the Department, Faculty/Guide and one more faculty member
nominated by Chairman. The evaluation criteria shall be as per the rubrics given below:
Rubrics for Evaluation:
Topic - Technical Relevance, Sustainability and Societal Concerns : 15%
Review of literature and technical content : 35%
Presentation Skills : 25%
Report : 25%
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Semester III
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Course Code 18CS3E1A M. Tech (Computer Networking)
Category Professional Elective
Course title MACHINE LEARNING
Scheme and Credits No. of Hours/Week Semester – III
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
Data Mining (Preferrable)
Course Objectives: The course will enable the students to:
1. Understand the concept of how to extract patterns.
2. Design and analyse various machine learning algorithms and techniques with a modern
outlook, focusing on recent advances.
3. Develop supervised and unsupervised learning paradigms of machine learning.
4. Assess Deep learning techniques and various feature extraction strategies.
5. Evaluate the machine learning algorithms.
UNIT I - SUPERVISED LEARNING (REGRESSION/CLASSIFICATION) 09 Hours
Basic methods: Distance-based methods, Nearest-Neighbours, Decision Trees, Naive Bayes
Linear models: Linear Regression, Logistic Regression, Generalized Linear Models, Support
Vector Machines, Nonlinearity and Kernel Methods, Beyond Binary Classification: Multi-Class
/ Structured Outputs, Ranking
UNIT II - UNSUPERVISED LEARNING 10 Hours
Clustering: K-means / Kernel K-means, Dimensionality Reduction: PCA and kernel PCA,
Matrix Factorization and Matrix Completion, Generative Models (mixture models and latent
factor models)
UNIT III - MACHINE LEARNING ALGORITHMS 09 Hours
Evaluating Machine Learning algorithms and Model Selection, Introduction to Statistical
Learning Theory, Ensemble Methods (Boosting, Bagging, Random Forests)
UNIT IV 10 Hours
Sparse Modeling and Estimation, Modeling Sequence/Time-Series Data, Deep
Learning and Feature Representation Learning
UNIT V 10 Hours
Scalable Machine Learning (Online and Distributed Learning) A selection from other advanced
topics, e.g., Semi-supervised Learning, Active Learning, Reinforcement Learning, Inference in
Graphical Models, Introduction to Bayesian Learning and Inference
UNIT VI
Recent trends in various learning techniques of machine learning and classification methods for
IOT applications. Various models for IOT applications
REFERENCES
1. Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012
2. Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning,
Springer 2009
3. Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2007
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1. Extract features that can be used for a particular machine learning approach in
various IOT applications.
CO2. Compare and contrast pros and cons of various machine learning techniques.
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CO3. Get an insight of when to apply a particular machine learning approach.
CO4. Mathematically analyse various machine learning approaches and paradigms.
CO5. Design and formulate Supervised and Unsupervised learning paradigms of machine
learning
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 3
CO2 3 3
CO3 3 3
CO4 3 3
CO5 3 3
1. Low, 2. Medium, 3. High
CN63
Course Code 18CS3E1B M. Tech (Computer Networking)
Category Professional Elective - Integrated
Course title BIG DATA ANALYTICS
Scheme and
Credits
No. of Hours/Week Semester – III
L T P S Credits
3 - 2 - 4
CIE Marks:
50
SEE Marks: 50 Total Max. Marks:
100
Duration of SEE: 3 Hrs
Prerequisites (if any):
Data Structures, Computer Architecture and Organization
Course Objectives: The course will enable the students to:
1. Understand big data for business intelligence.
2. Illustrate business case studies for big data analytics.
3. Discuss NoSQL big data management.
4. Demonstrate map-reduce analytics using Hadoop.
5. Compare Hadoop related tools such as HBase, Pig, Cassandra and Hive for big data
analytics.
UNIT I – INTRODUCTION TO BIG DATA 9 Hours Need for big data, convergence of key trends, unstructured data, industry examples of big
data, web analytics, big data and marketing, fraud and big data, risk and big data, credit risk
management, big data and algorithmic trading, big data and healthcare, big data in medicine,
advertising and big data, big data technologies, introduction to Hadoop, open source
technologies, cloud and big data, mobile business intelligence, Crowd sourcing analytics,
inter and trans firewall analytics.
UNIT II - INTRODUCTION TO NoSQL 10 Hours Aggregate data models, aggregates, key-value and document data models, relationships,
graph databases, schemaless databases, materialized views, distribution models, sharding,
master-slave replication, peer peer replication, sharding and replication, consistency,
relaxing consistency, version stamps, map-reduce, partitioning and combining, composing
map-reduce calculations.
UNIT III – HADOOP 10 Hours
Data format, analyzing data with Hadoop, scaling out, Hadoop streaming, Hadoop pipes,
design of Hadoop distributed file system (HDFS), HDFS concepts, Java interface, data flow,
Hadoop I/O, data integrity, compression, serialization, Avro, file-based data structures
UNIT IV – MAPREDUCE 10 Hours MapReduce workflows, unit tests with MRUnit, test data and local tests, anatomy of
MapReduce job run, classic Map-reduce, YARN, failures in classic Map-reduce and YARN,
job scheduling, shuffle and sort, task execution, MapReduce types, input formats, output
formats.
UNIT V – Hbase 9 Hours
Hbase, data model and implementations, Hbase clients, Hbase examples, praxis. Cassandra,
Cassandra data model, Cassandra examples, Cassandra clients, Hadoop integration, Pig,
Grunt, pig data model, Pig Latin, developing and testing Pig Latin scripts. Hive, data types
and file formats, HiveQL data definition, HiveQL data manipulation, HiveQL queries.
UNIT VI -
Recent advances in Big data analytics
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UNIT - VII (Lab Programs)
1. (a) Perform setting up and Installing Hadoop in its two operating modes:
o Pseudo distributed,
o Fully distributed.
(b) Use web based tools to monitor your Hadoop setup.
2. (a) Implement the following file management tasks in Hadoop:
o Adding files and directories
o Retrieving files
o Deleting files
(b) Benchmark and stress test an Apache Hadoop cluster
3. Run a basic Word Count Map Reduce program to understand Map Reduce Paradigm.
(a) Find the number of occurrence of each word appearing in the input file(s)
(b) Performing a MapReduce Job for word search count (look for specific keywords in a
file)
4. Stop word elimination problem:
Input:
o A large textual file containing one sentence per line
o A small file containing a set of stop words (One stop word per line)
Output:
o A textual file containing the same sentences of the large input file without the
words appearing in the small file.
5. Write a Map Reduce program that mines weather data. Weather sensors collecting data
every hour at many locations across the globe gather large volume of log data, which is a
good candidate for analysis with MapReduce, since it is semi structured and record-oriented.
Data available at: https://github.com/tomwhite/hadoopbook/tree/master/input/ncdc/all.
(a) Find average, max and min temperature for each year in NCDC data set?
(b) Filter the readings of a set based on value of the measurement, Output the line of
input files associated with a temperature value greater than 30.0 and store it in a
separate file.
6. Purchases.txt Dataset
(a) Instead of breaking the sales down by store, give us a sales breakdown by
product category across all of our stores
(b) What is the value of total sales for the following categories?
Toys
Consumer Electronics
(c) Find the monetary value for the highest individual sale for each separate store
(d) What are the values for the following stores?
Reno
Toledo
Chandler
(e) Find the total sales value across all the stores, and the total number of sales.
7. Install and Run Pig then write Pig Latin scripts to sort, group, join, project, and filter your
data.
8. Write a Pig Latin scripts for finding TF-IDF value for book dataset (A corpus of eBooks
available at: Project Gutenberg)
9. Install and Run Hive then use Hive to create, alter, and drop databases, tables, views,
functions, and indexes.
10. Install, Deploy & configure Apache Spark Cluster. Run apache spark applications using
Scala.
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REFERENCES
1. Michael Minelli, Michelle Chambers, and Ambiga Dhiraj, "Big Data, Big Analytics:
Emerging Business Intelligence and Analytic Trends for Today's Businesses", Wiley,
2013.
2. P. J. Sadalage and M. Fowler, "NoSQL Distilled: A Brief Guide to the Emerging
World of Polyglot Persistence", Addison-Wesley Professional, 2012.
3. Tom White, "Hadoop: The Definitive Guide", Third Edition, O'Reilley, 2012.
4. Eric Sammer, "Hadoop Operations", O'Reilley, 2012.
5. E. Capriolo, D. Wampler, and J. Rutherglen, "Programming Hive", O'Reilley, 2012.
6. Lars George, "HBase: The Definitive Guide", O'Reilley, 2011.
7. Eben Hewitt, "Cassandra: The Definitive Guide", O'Reilley, 2010.
8. Alan Gates, "Programming Pig", O'Reilley, 2011.
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1. Describe big data and use cases from selected business domains.
CO2. Discuss the business case studies for big data analytics.
CO3. Explain NoSQL big data management.
CO4. Perform map-reduce analytics using Hadoop.
CO5. Use Hadoop related tools such as HBase, Cassandra, Pig, and Hive for big data
analytics.
SCHEME OF EXAMINATION
CIE -
Practical
Conduction of experiments, performance of student in
every week lab and completion of lab record=50#
Total: Marks
50
Lab Test: Part-A =20, Part-B=20 and Vice-Voce=10
Marks(50##
)
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
# = CIE Marks for laboratory performance is to be evaluated for 50 marks and the
marks obtained shall be reduced for 25 marks
## = Lab test is to be conducted for 50 marks and the marks obtained shall be
reduced for 25 marks. Lab test shall be conducted by two examiners out of which
one examiner is the faculty taught the course. There is no SEE for the practical ‘s
portion of integrated course
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Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 3
CO2 3 3
CO3 3 3
CO4 3 3
CO5 3 3
1. Low, 2. Medium, 3. High
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Course Code 18CS2E2B M. Tech (Computer Networking)
Category Professional Elective
Course title HIGH PERFORMANCE COMPUTING
Scheme and
Credits
No. of Hours/Week Semester – III
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
1. Computer Architecture
2. Operating Systems
COURSE OBJECTIVES
The course will enable the students to:
1. Understand the modern processors, their architectures and several case studies.
2. Understand the need of parallelism and types of parallelism.
3. Analyse shared and distributed based memory parallel programming using OpenMP
and MPI.
4. Evaluate hybrid parallel programming using MPI and OpenMPI.
5. Review of recent trends in efficiency MPI programming and scalable parallel
processing.
UNIT-I MODERN PROCESSORS 10 Hours
Stored-Program Computer Architecture, General-Purpose Cache-Based Microprocessor
Architecture, Memory, Multi-Core Processors, Multithreaded Processors, Vector Processors.
Basic Optimization Techniques For Serial Code: Scalar Profiling, Common Sense
Optimizations, Simple Measures, Large Impact, The Role of Compilers, C++ Optimizations.
Data Access Optimization: Balance Analysis and Light Speed Estimates, Storage Order, Case
Study: The Jacobi Algorithm, Case Study: Dense Matrix Transpose, Algorithm Classification
and Access Optimizations, Case Study: Sparse Matrix-Vector Multiply.
UNIT-II PARALLEL COMPUTERS 09 Hours
Taxonomy of Parallel Computing Paradigms, Shared-Memory Computers, Distributed-
Memory Computers, Hierarchical (Hybrid) Systems, Networks, Basics of Parallelization:
Why Parallelize? Data and Functional Parallelism, Parallel Scalability.
UNIT-III SHARED-MEMORY PARALLEL PROGRAMMING WITH OpenMP
09 Hours
Introduction to OpenMP, Case Study: OpenMP-Parallel Jacobi Algorithm. Efficient OpenMP
programming: Profiling OpenMP Programs Performance Pitfalls, Case Study: Parallel Sparse
Matrix-Vector Multiply.
UNIT-IV DISTRIBUTED-MEMORY PARALLEL PROGRAMMING WITH MPI
10 Hours
Message Passing, Introduction to MPI, Example: MPI Parallelization of a Jacobi Solver.
Efficient MPI Programming: MPI Performance Tools, Communication Parameters,
Synchronization, Serialization, Contention, Reducing Communication Overhead,
Understanding Intra-Node Point-To-Point Communication. – 12 Hours
UNIT-V HYBRID PARALLELIZATION WITH MPI AND OpenMP 10 Hours
Basic MPI/OpenMP Programming Models, MPI Taxonomy of Thread Interoperability,
Hybrid Decomposition and Mapping, Potential Benefits and Drawbacks of Hybrid
Programming.
UNIT VI – Recent trends in efficient MPI programming and scalable parallel processing.
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REFERENCES
1. Georg Hager and Gerhard Wellein, “Introduction to High Performance Computing for
Scientists and Engineers”, CRC Press, 2011.
2. Victor Eijkhout with Edmond Chow, Robert van de Geijn, “Introduction to High
Performance Scientific Computing”. II Edition, 2015.
3. Charles Severance Kevin Dowd, “High Performance Computing”, Oreilly Media, II
Edition, 1998
COURSE OUTCOMES
On completion of the course, the student would be able to:
CO1: Discuss various modern processes, along with their architectures.
CO2: Categorize and compare different types of parallelism.
CO3: Asses shared and distributed based memory parallel programming using OpenMPI and
MPI.
CO4: Investigate hybrid parallel programming using MPI and OpenMP
CO5: Design an efficient Hpc system using MPI and OpenMP programming.
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
Mapping of Course Outcomes (COS) to Program Outcomes
PO1 PO2 PO3
CO1 2
CO2 1 1
CO3 1 2
CO4 1
CO5 1 1 1
1. Low, 2. Medium, 3. High
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Course Code 18CS3P1A M. Tech (Computer Networking)
Category Open Elective
Course title ARITIFICIAL INTELLIGENCE
Scheme and
Credits
No. of Hours/Week Semester – III
L T P S Credits
4 - - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
This course will enable students to
1. Understand the various characteristics of Intelligent agents
2. Understand the different search strategies in AI
3. Learn to represent knowledge in solving AI problems
4. Analyse the different ways of designing software agents
5. Evaluate the various reasoning techniques for AI.
UNIT-I INTRODUCTION: 9 Hours
Introduction Definition Future of Artificial Intelligence Characteristics and Problem Solving
Approach to Typical AI problems. State Space Search and Heuristic Search Techniques
Defining problems as State Space search, Production systems and characteristics, Hill
Climbing, Breadth first and depth first search, Best first search.
UNIT-II KNOWLEDGE REPRESENTATION ISSUES: 9 Hours
Representations and Mappings, Approaches to knowledge representation, Using Predicate
Logic and Representing Knowledge as Rules , Representing simple facts in logic,
Computable functions and predicates, Procedural vs Declarative knowledge, Logic
Programming, Forward vs backward reasoning.
UNIT-III SOFTWARE AGENTS: 10 Hours
Architecture for Intelligent Agents Agent communication Negotiation and Bargaining
Argumentation among Agents Trust and Reputation in Multi-agent systems.
UNIT-IV REASONING I: 10 Hours
Symbolic Logic under Uncertainty , Non-monotonic Reasoning, Logics for non-monotonic
reasoning, Statistical Reasoning.
UNIT-V METHODS: 10 Hours
Probability and Bayes Theorem, Certainty factors, Probabilistic Graphical Models, Bayesian
Networks, Markov Networks, Fuzzy Logic.
UNIT-VI Recent advances and research being done in the topics mentioned above
units
REFERENCES:
1. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach", Prentice
Hall, Third Edition, 2009.
2. Rich and Knight, Artificial Intelligence, 2nd Edition, 2013
3. I. Bratko, "Prolog: Programming for Artificial Intelligence", Fourth edition,
Addison-Wesley Educational Publishers Inc., 2011.
4. M. Tim Jones, "Artificial Intelligence: A Systems Approach(Computer Science),
Jones and Bartlett Publishers, Inc.; First Edition, 2008
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5. Nils J. Nilsson, "The Quest for Artificial Intelligence", Cambridge University
Press, 2009.
6. William F. Clocksin and Christopher S. Mellish," Programming Using
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1: Define and identify various AI concepts
CO2: illustrate different AI strategies
CO3: Sketch various knowledge representation for AI problems
CO4: Analyse agents usage for AI
CO5: Design AI inference techniques
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*2=40
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2
CO3 2
CO4 2
CO5 2 2
1: Low 2: Medium 3:High
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Course Code 18CS3P1B M. Tech (Computer Networking)
Category Open Elective
Course title BUSINESS ANALYTICS
Scheme and
Credits
No. of Hours/Week Semester – III
L T P S Credits
4 - - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
The course will enable the students to:
1. Understand the role of business analytics within an organization.
2. Analyze data using statistical and data mining techniques.
3. Distinguish relationships between the underlying business processes of an
organization.
2. Gain an understanding of how managers use business analytics to formulate and
solve business problems and to support managerial decision making.
3. Discuss the uses of decision-making tools and Operations research techniques.
UNIT -I BUSINESS ANALYTICS: 10 Hours
Overview of Business analytics, Scope of Business analytics, Business Analytics Process,
Relationship of Business Analytics Process and organisation, competitive advantages of
Business Analytics. Statistical Tools: Statistical Notation, Descriptive Statistical methods,
Review of probability distribution and data modelling, sampling and estimation methods
overview
UNIT -II TRENDINESS AND REGRESSION ANALYSIS: 9 Hours
Modelling Relationships and Trends in Data, simple Linear Regression. Important
Resources, Business Analytics Personnel, Data and models for Business analytics, problem
solving, Visualizing and Exploring Data, Business Analytics Technology
UNIT -III ORGANIZATION STRUCTURES OF BUSINESS ANALYTICS:
10 Hours
Team management, Management Issues, Designing Information Policy, Outsourcing,
Ensuring Data Quality, Measuring contribution of Business analytics, Managing Changes.
Descriptive Analytics, predictive analytics, predicative Modelling, Predictive analytics
analysis, Data Mining, Data Mining Methodologies, Prescriptive analytics and its step in
the business analytics Process, Prescriptive Modelling, nonlinear Optimization
UNIT -IV FORECASTING TECHNIQUES: 10 Hours
Qualitative and Judgmental Forecasting, Statistical Forecasting Models, Forecasting
Models for Stationary Time Series, Forecasting Models for Time Series with a Linear
Trend, Forecasting Time Series with Seasonality, Regression Forecasting with Casual
Variables, Selecting Appropriate Forecasting Models. Monte Carlo Simulation and Risk
Analysis: Monte Carle Simulation Using Analytic Solver Platform, New-Product
Development Model, Newsvendor Model, Overbooking Model, Cash Budget Model
UNIT- V DECISION ANALYSIS: 9 Hours
Formulating Decision Problems, Decision Strategies with the without Outcome
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Probabilities, Decision Trees, The Value of Information, Utility and Decision Making
UNIT-VI Recent advances and research being done in the topics mentioned above
units
REFERENCES
1. Business analytics Principles, Concepts, and Applications by Marc J. Schniederjans,
Dara G. Schniederjans, Christopher M. Starkey, Pearson FT Press
2. Business Analytics by James Evans, persons Education
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1. Develop the knowledge of data analytics.
CO2. Demonstrate the ability of think critically in making decisions based
on data and deep analytics
CO3. Discuss the uses of technical skills in predicative and prescriptive
modeling to support business decision-making
CO4. Demonstrate the ability to translate data into clear and actionable insights.
CO5. Evaluate and assess the forecasting techniques.
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*2=40
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 3
CO2 3 3
CO3 3 3
CO4 3 3
CO5 3 3
1: Low 2: Medium 3:High
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Course Code 18CS3P1C M. Tech (Computer Networking)
Category Open Elective
Course title SYSTEM SIMULATION AND MODELING
Scheme and
Credits
No. of Hours/Week Semester – III
L T P S Credits
3 1 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES:
The course will enable the students to:
1. Understand the system, specify systems using natural models of computation, modelling
techniques
2. Apply natural models of computation, modelling techniques to
understand behaviour of system , and analyse the simulation data
3. Analyse simulation data, simulation tools for simulation, Terminating Simulations –
Steady state simulations.
4. Evaluate the existing simulation models for verification, calibration and validation
5. Design validation, calibration model and decision support
UNIT – I INTRODUCTION TO SIMULATION 09 Hours
Introduction Simulation Terminologies- Application areas – Model Classification Types of
Simulation- Steps in a Simulation study- Concepts in Discrete Event Simulation Example.
UNIT-II MATHEMATICAL MODELS 10 Hours
Statistical Models - Concepts – Discrete Distribution- Continuous Distribution – Poisson
Process- Empirical Distributions- Queueing Models – Characteristics- Notation Queueing
Systems – Markovian Models- Properties of random numbers- Generation of Pseudo Random
numbers- Techniques for generating random numbers-Testing random number generators
Generating Random-Variates- Inverse Transform technique Acceptance- Rejection technique –
Composition & Convolution Method.
UNIT-III ANALYSIS OF SIMULATION DATA 10 Hours
Input Modelling - Data collection - Assessing sample independence – Hypothesizing
distribution family with data - Parameter Estimation - Goodness-of-fit tests – Selecting input
models in absence of data- Output analysis for a Single system – Terminating Simulations –
Steady state simulations.
UNIT -IV VERIFICATION AND VALIDATION 09 Hours
Building – Verification of Simulation Models – Calibration and Validation of Models –
Validation of Model Assumptions – Validating Input – Output Transformations
UNIT-V SIMULATION OF COMPUT ER SYSTEMS 10 Hours
Simulation Tools – Model Input – High level computer system simulation – CPU – Memory
Simulation – Comparison of systems via simulation – Simulation Programming techniques -
Development of Simulation models.
UNIT-VI Recent advances and research being done in the topics mentioned above units
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REFERENCES
1. Jerry Banks and John Carson, “Discrete Event System Simulation”, Fourth Edition, PHI,
2005.
2. Geoffrey Gordon, “System Simulation”, Second Edition, PHI, 2006.
3. Frank L. Severance, “System Modelling and Simulation”, Wiley, 2001.
4. Averill M. Law and W. David Kelton, “Simulation Modelling and Analysis, Third
Edition, McGraw Hill, 2006.
5. Jerry Banks, “Handbook of Simulation: Principles, Methodology, Advances,
Applications and Practice”, Wiley-Inter science, 1 edition, 1998.
COURSE OUTCOMES
On Completion of the course, the student will be able to:
CO1: Explain natural models of computation, modelling techniques
CO2: Determine suitable models of computation, modelling techniques to
understand behaviour of system.
CO3: Distinguish simulation models for verification, calibration and validation
CO4: Assess the performance of different simulation models, statistical models, queuing
Systems and Markovian Models for given problem
CO5: Design goodness-of-fit tests and input models in absence of data
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 20 marks Two Quizzes / AAT
= 10 marks
Total:50
marks Test II (Unit IV & V) – 20 marks
SEE
– 100
marks
Answer FIVE full questions Total:100 marks
Units which have 09 Hours shall not have
internal choice. 20* 2 = 40 Marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 3
CO3 3
CO4 3
CO5 3 2
1: Low 2: Medium 3:High
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COURSE LEARNING OBJECTIVES:
The objectives of the SEMINAR-III is to prepare the students to learn to:
1. Carry out the literature review of general research area/current topic and analyse the
same effectively.
2. Prepare a technical report, reflecting his/her depth of understanding, on the selected
area/topic and prepare content rich presentation.
3. Acquire communication and time management skills for effective and impactful
presentation. Interact with peers to acquire the qualities of thoughtfulness, friendliness,
adaptability, responsiveness, and politeness in-group discussion. Overcome stage fear
during the presentation.
GUIDE LINES
1. Seminar preparation and presentation is an individual student activity.
2. Topic may be of general/ specific interest to program of engineering or electives not
offered in the semester and to be selected in consultation with the faculty/Guide.
3. Select one pertinent research paper for the seminar presentation.
4. Prepare and submit a detailed technical report of the seminar topic.
COURSE OUTCOMES:
Students shall be able to:
1. Carry out the literature survey of topic of seminar.
2. Prepare a technical report on the selected area/topic.
3. Make an effective presentation with seamless flow of content within the time allocated.
Overcome inhibition in interacting with peers and hence develop the spirit of team
work. Overcome stage fear during the presentation.
SCHEME OF EXAMINATION
CIE – 50
marks
Phase -1 Marks =10 Seminar Report
Marks =20
Total:50
Marks Phase -2 Marks =20
Course Code 18CN3S01 M. Tech (Computer Networking)
Category Seminar Semester: III
Course title SEMINAR - III
Scheme and Credits
No. of Hours/Week
Total hours = 24 L T P S Credits
0 0 2 0 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any): NIL
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Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2 3
CO2 2 3 3
CO3 2
1. Low, 2. Medium, 3. High
Scheme of Continuous Internal Evaluation (CIE):
Evaluation would be carried out in TWO phases. The Seminar Evaluation Committee
shall comprise of Chairman of the Department, Faculty/Guide and one more faculty member
nominated by Chairman. The evaluation criteria shall be as per the rubrics given below:
Rubrics for Evaluation:
Topic - Technical Relevance, Sustainability and Societal Concerns : 15%
Review of literature and technical content : 35%
Presentation Skills : 25%
Report : 25%
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INTERNSHIP
COURSE LEARNING OBJECTIVES:
Objectives of the internship
1. Provide an opportunity to see how classroom and textbook learning applies to the real
world, and to expose the students to the relevant work experience.
2. Pay close attention to all the steps that go onto completing a job, thereby, help students
to become workforce ready before entering the job market as a graduate. Provide an
opportunity to select the topic of dissertation work by evaluating the requirement of
organisation.
3. Prepare and present a technical report of internship.
GUIDELINES
1. Student has to approach the concerned heads of various Industries/organization, which
are related to the field of specialization of the M. Tech program.
2. If any student gets internship, he/she has to submit the internship offer letter duly signed
by the concerned authority of the company to the Chairperson of the Department.
3. The internship on full time basis will be after the examination of II semester and during
III semester for a period of 8 weeks without affects regular class work.
4. The progress has to be reported periodically to the faculty or to the Guide assigned by
the Chairperson as per the format acceptable to the respective industry /organizations
and to the Institution.
5. At the end of the internship the student has to prepare a detailed report and submit.
6. Students are advised to use ICT tools such as Skype to report their progress and
submission of periodic progress reports to the faculty in charge or guide.
7. Duly signed report from internal supervisor (faculty incharge or guide) and external
supervisor from the organization where internship is offered has to be submitted to the
Chairperson of the Department for his/her signature and further processing for
evaluation.
The broad format of the internship final report shall contain Cover Page, Certificate from
College, Certificate from Industry / Organization of internship, Acknowledgement,
Synopsis, Table of Contents, chapters of Profile of the Organization - Organizational
structure, Products, Services, Business Partners, Financials, Manpower, Societal Concerns,
Professional Practices, Activities of the Department where internship is done, Tasks
Performed and summary of the tasks performed. specific technical and soft skills that
student has acquired during internship, References & Annexure.
Course Code 18CN3I01 M. Tech (Computer Networking)
Category Internship / Mini Project Semester: III
Course title INTERNSHIP / MINI PROJECT
Scheme and Credits
No. of Hours/Week
Total hours = 80 L T P S Credits
--- --- 10 --- 5
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs for a
batch of 6 students
Prerequisites (if any): NIL
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COURSE OUTCOMES:
The student will be able to:
1. Apply the gained experience along with the theoretical knowledge to solve the real world
problems what
engineers ready do.
2. Get equipped with experience required before entering the job market. Explore the
possibility of formulating the dissertation problem.
3. Prepare a technical report and make a presentation of details of internship.
SCHEME OF EXAMINATION
CIE 1.Marks awarded by guide (Internal examiner) = 50 marks
2.Marks awarded by the department internship monitoring committee = 50 marks
50*
Marks
SEE Presentation of internship in the presence of Guide (Internal examiner) and external
examiner = 100 marks
50**
Marks
Note: *= CIE be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
**= SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 2
CO2 3 2
CO3 3
1. Low, 2. Medium, 3. High
Rubrics for CIE:
1. Topic of internship = 10%
2. Objectives of internship = 10%
3. Specific skills acquired = 20%
4. Document = 40%
5. presentation = 20%
Rubrics for SEE:
1. Topic of internship = 10%
2. Objectives of internship = 10%
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3. Specific skills acquired = 20%
4. Document = 40%
5. presentation = 20%
MINI PROJECT
COURSE LEARNING OBJECTIVE:
1. Understand the method of applying engineering knowledge/use application software to solve
specific problems after carrying out literature survey.
2. Apply engineering and management principles while executing the project.
3. Demonstrate the skills for good technical report writing and presentation.
COURSE CONTENT/GUIDELINES
Student shall take up small problems in the field of domain of program as mini project. It can be
related to a solution to an engineering problem, verification and analysis of experimental data
available, conducting experiments on various engineering subjects, material characterisation,
studying a software tool for solution to an engineering problem, etc.
The mini project must be carried out preferably using the resources available in the
department/college and it can be of interdisciplinary also.
COURSE OUTCOMES:
The students shall be able to:
1. Conduct experiments / use the capabilities of relevant application software/ simulation tools
individually to generate data/ solve problems.
2. Assess the available engineering resources available in the institution.
3. Prepare and Present the technical document of mini project.
SCHEME OF EXAMINATION
CIE
1.Marks awarded by guide (Internal examiner) = 50 marks
2.Marks awarded by the department internship/mini project monitoring committee
= 50 marks
50*
Marks
SEE Presentation of internship in the presence of Guide (Internal examiner) and external
examiner = 100 marks
50**
Marks
Note: *= CIE be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
**= SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
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Rubrics for CIE
Note: * = CIE be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
Rubrics for SEE:
The SEE shall be done by two examiners out of which one examiner is the guide of mini
project. The following weightage would be given for the examination. Evaluation shall be done
in batches, not exceeding 6 students.
Sl.
no
Particulars Weightage Marks Total
marks of
SEE
1 Brief write-up about the project 05% 05
50**
2 Presentation/demonstration of the project 20% 20
3 Methodology and Experimental Results &
Discussion
35% 30
4 Report 25% 25
5 Viva Voce 20% 20
Total 100% 100
Note: ** = SEE shall be conducted for 100 marks and the marks obtained shall be reduced for
50 marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 3
CO2 2 3
CO3 3
1. Low, 2. Medium, 3. High
Sl.
no
Particulars Weightage Marks Total
marks of
CIE
1 Selection of the topic & formulation of objectives 10% 10
50*
2 Modelling and simulation/algorithm
development/experiment setup
25% 25
3 Conducting experiments/implementation/testing 25% 25
4 Demonstration & Presentation 15% 15
5 Report writing 25% 25
Total 100% 100
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COURSE LEARNING OBJECTIVES:
1. Choose a problem applying relevant knowledge and skills acquired during the course. Formulate the
specifications of the project work, identify the set of feasible solutions, prepare, and execute project plan
considering professional, cultural and societal factors. Identify the problem-solving methodology using
literature survey and present the same.
2. Develop experimental planning and select appropriate techniques and tools to conduct experiments to
Evaluate and critically examine the outcomes followed by concluding the results and identifying
relevant applications. Preparation of synopsis, preliminary report for approval of topic selected along
with literature survey, objectives and methodology.
3. Develop oral and written communication skills to effectively convey the technical content.
GUIDELINES
The Dissertation work will start in III semester and should be a problem with research potential and
should involve scientific research, design, generation/collection and analysis of data, determining
solution and must preferably bring out the individual contribution.
The Dissertation work will have to be done by only one student and the topic of dissertation must be
decided by the guide and the student. The dissertation work shall be carried out, on-campus or in an
industry or in an organisation with prior approval from the Chairperson of the Department. The student
has to be in regular contact with the guide atleast once in a week.
The report of Dissertation work phase I shall contain cover page, certificate from
College/Industry/Organisation, Acknowledgement, List of Figures and Tables Contents, Nomenclature,
Chapters of Introduction including motivation to choose topic, Literature survey, Conclusion of
literature survey, Objectives and Scope of Dissertation, Methodology to be followed, Experimental
requirements, References and Annexure.
The preliminary results (if available) of the problem of Dissertation work may also be discussed in
the report.
COURSE OUTCOME:
The students will be able to:
1. Self learn various topics relevant to Dissertation work. Survey the literature such as books,
National/International reference journals, articles and contact resource persons for selected topics of
Dissertation.
2. Write and prepare a typical technical report.
3. Present and defend the contents of Dissertation work phase I in front of technically qualified audience
effectively.
Course Code 18CN3D01 M. Tech (Computer Networking)
Category Dissertation Work Semester: III
Course title DISSERTATION WORK PHASE -I
Scheme and Credits
No. of Hours/Week
Total hours = 80 L T P S Credits
0 0 10 0 5
CIE Marks: 50 SEE Marks:50 Total Max. Marks: 100 Duration of SEE: 1Hour
Prerequisites (if any): NIL
CN82
SCHEME OF EXAMINATION
CIE 1.Marks awarded by guide (Internal examiner) = 50 marks
2.Marks awarded by the department dissertation monitoring committee = 50 marks
50*
Marks
SEE Presentation of Dissertation work Phase-I in the presence of Guide (Internal
examiner) and external examiner
50**
Marks
Note: *= CIE be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
**= SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
Rubrics for CIE: Weightage
1. Introduction and Justification of topic = 10%
2. Literature survey and Conclusion = 30%
3. Objectives and Scope of Dissertation work = 30%
4. Methodology to be adopted = 20%
5. Presentation of contents of Dissertation work Phase-I = 10%
Rubrics for SEE:
1. Introduction and Justification of topic = 10%
2. Literature survey and Conclusion = 30%
3. Objectives and Scope of Dissertation work = 30%
4. Methodology, Experimental /Software = 20%
5. Presentation of Dissertation Phase-I = 10%
Mapping of Course Outcomes (Cos) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 3
CO2 3 3
CO3 3 3
1. Low, 2.Medium, 3. High
CN82
Semester IV
CN83
COURSE LEARNING OBJECTIVES:
The objectives of the SEMINAR-IV is to prepare the students to learn to:
1. Carry out the literature review of general research area/current topic and analyse the same
effectively.
2. Prepare a technical report, reflecting his/her depth of understanding, on the selected area/topic and
prepare content rich presentation.
3. Acquire communication and time management skills for effective and impactful presentation.
Interact with peers to acquire the qualities of thoughtfulness, friendliness, adaptability,
responsiveness, and politeness in-group discussion. Overcome stage fear during the presentation.
GUIDE LINES
1. Seminar preparation and presentation is an individual student activity.
2. Topic may be of general/ specific interest to program of engineering or electives not offered in the
semester and to be selected in consultation with the faculty/Guide.
3. Select one pertinent research paper for the seminar presentation.
4. Prepare and submit a detailed technical report of the seminar topic.
COURSE OUTCOMES:
Students shall be able to:
1. Carry out the literature survey of topic of seminar.
2. Prepare a technical report on the selected area/topic.
3. Make an effective presentation with seamless flow of content within the time allocated. Overcome
inhibition in interacting with peers and hence develop the spirit of team work. Overcome stage fear
during the presentation.
SCHEME OF EXAMINATION
CIE – 50
marks
Phase -1 Marks =10 Seminar Report
Marks =20
Total:50
Marks Phase -2 Marks =20
Course Code 18CN4S01 M. Tech (Computer Networking)
Category Seminar Semester: IV
Course title SEMINAR - IV
Scheme and Credits
No. of Hours/Week
Total hours = 24 L T P S Credits
0 0 2 0 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any): NIL
CN84
Scheme of Continuous Internal Evaluation (CIE):
Evaluation would be carried out in TWO phases. The Seminar Evaluation Committee shall comprise
of Chairman of the Department, Faculty/Guide and one more faculty member nominated by Chairman. The
evaluation criteria shall be as per the rubrics given below:
Rubrics for Evaluation:
Topic - Technical Relevance, Sustainability and Societal Concerns : 15%
Review of literature and technical content : 35%
Presentation Skills : 25%
Report : 25%
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2 3
CO2 2 3 3
CO3 2
1. Low, 2. Medium, 3. High
CN85
COURSE LEARNING OBJECTIVES:
1. Apply/Use different experimental techniques, equipments, software/ Computational/
Analytical /Modelling and Simulation tools required for conducting tests and generate other
relevant data. Students will also be able to design and develop an experimental setup/test rig.
2. Analyse the results of the experiments conducted/models developed.
3. Create a detailed technical document as per format based on the outcome of dissertation
work phase I and II.
GUIDELINES
Dissertation work phase II is the continuation of project work started in III semester. The
report of Dissertation work that includes the details of Dissertation work phase I and
phase II should be presented in a standard format. The candidate shall prepare a detailed
report of dissertation that includes Cover Paper, Certificate from
College/Industry/Organisation, Acknowledgement, Abstract, Table of contents, List of
Figures and Table, Nomenclature, Chapter of Introduction, Literature survey, Conclusion
of literature survey, Objectives and Scope of dissertation work, Methodology,
Experimentation, Results, Discussion, Conclusion, Scope for future work, References,
Annexure and full text of the publication (submitted or published)
COURSE OUTCOMES:
Students shall be able to:
1. Conduct experiments/ implement the capabilities of different Software /Computational /
Analytical/Modelling and simulation tools individually and generate data for validation
of hypothesis.
2. Investigate and assess the results obtained within the scope of experiments conducted
followed by conclusions.
3. Prepare a detailed technical document, Present and defend the contents of Dissertation
work in presence of technically qualified audience effectively.
Course Code 18CN4D01 M. Tech (Computer Networking)
Category Dissertation Work Semester: IV
Course title DISSERTATION WORK PHASE -II
Scheme and Credits
No. of Hours/Week
Total hours = 150 L T P S Credits
--- --- 30 --- 15
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100
Prerequisites (if any): NIL
CN86
SCHEME OF EXAMINATION
CIE
1. Marks awarded by guide = 50 marks
2. Marks awarded by the department dissertation monitoring committee
(Guide + Two faculty members )= 50 marks
100
marks
50*
marks
SEE
1. Dissertation evaluation by guide (Internal examiner) = 100 marks
2. Dissertation evaluation by external examiner = 100 marks
3. Viva- Voce examination by guide and external examiner who evaluated the
dissertation work =100 marks
300
marks
50**
marks
Note: * = CIE be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
** = SEE shall be conducted for 300 marks and the marks obtained shall be reduced for
50 marks.
Rubrics for CIE:
1. Presentation of background of dissertation work = 10%
2. Literature survey, Problem formulation and Objectives = 30%
3. Presentation of methodology and experimentation = 30%
4. Results and Discussion = 20%
5. Questions and Answers = 10%
Rubrics for SEE:
1. Originality = 05%
2. Literature survey = 15%
3. Problem formulation, Objectives and Scope of Work = 10%
4. Methodology, Experimentation/Theoretical modelling = 10%
5. Results, Discussion and Conclusion = 20%
6. Questions and Answers = 20%
7. Acceptance/Publication of technical paper in Journals/Conference = 20%
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 2 3
CO2 2 2 3
CO3 3 3 3
1. Low, 2. Medium, 3. High
BANGALORE UNIVERSITY
Department of Computer Science and Engineering
UNIVERSITY VISVESVARAYA COLLEGE OF ENGINEERING
K R Circle, Bengaluru-560 001.
Choice Based Credit System (CBCS)-2018
M.Tech in Computer Science and Engineering
Specialization: Bioinformatics
BI-1
BANGALORE UNIVERSITY
VISION
“To strive for excellence in education for the realization of a vibrant and inclusive
society through knowledge creation and dissemination”
MISSION
· Impart quality education to meet national and global challenges
· Blend theoretical knowledge with practical skills
· Pursue academic excellence through high quality research and publications
· Provide access to all sections of society to pursue higher education
· Inculcate right values among students while encouraging competitiveness to
promote leadership qualities
· Produce socially sensitive citizens
· Hasten the process of creating a knowledge society
· To contribute to nation building
BI-2
Bangalore University
UNIVERSITY VISVESVARAYA COLLEGE OF ENGINEERING
K R Circle, Bengaluru – 560 001.
University Visvesvaraya College of Engineering (UVCE) was started as a School of Mechanical
Engineering by Bharat Ratna Sir. M. Visvesvaraya in the year 1913 to meet the needs of the State for
skilled workers with S V Setty as its Superintendent. Later, it was converted to a full-fledged
Engineering College in the year 1917 under the name Government Engineering College and was
affiliated to the University of Mysore. It is the fifth Engineering College to be established in the country.
After the formation of Bangalore University in 1964, UVCE became one of the Constituent
Colleges of Bangalore University. This is one of the oldest Institutions in the country imparting
technical education leading to B.E., M.E, B.Arch., M.Sc. (Engineering), M.Arch. and Ph.D. degrees in
various disciplines of Engineering and Architecture. The Institution currently offers 7 Undergraduate
(B.E. / B.Arch.) Full-time, three Undergraduate (B.E.) Part-time and 24 Postgraduate (M.E. / M.Arch.)
Programmes.
VISION
The vision of UVCE is to strive for excellence in advancing engineering education through path
breaking innovations across the frontiers of human knowledge to realize a vibrant, inclusive and humane
society.
MISSION
The mission of UVCE is to prepare human resource and global leaders to achieve the above vision
through discovery, invention and develop friendly technologies to promote scientific temper for a
healthy society. UVCE shapes engineers to respond competently and confidently to the economic, social
and organizational challenges arising from globally advancing technical needs.
BI-3
Bangalore University Bengaluru
Department of Computer Science and Engineering, UVCE, Bengaluru M. Tech. DEGREE IN COMPUTER SCIENCE AND ENGINEERING under CBCS Scheme - 2K18
Specialization: Bioinformatics
Vision of the Department
Strive for Centre of Excellence in advancing Computer Science and Engineering education to produce
highly qualified human resources to meet local and global requirement.
Mission of the Department
Mission of the Department
CSEM1. Impart quality education and promote scientific temper
CSEM2. Blend theoretical knowledge with practical skills.
CSEM3. Inculcate right values in students.
CSEM4. Providing access to all sections of the society to purse higher education.
CSEM5. Pursue academic excellence through quality teaching, research and publishing
CSEM6: Promote leadership qualities among students
CSEM7: Hasten the process of creating a knowledge society
CSEM8: Produce socially sensitive citizens
Program Outcomes (PO)
BIPO1: An ability to independently carry out research /investigation and development work to
solve practical problems
BIPO2: An ability to write and present a substantial technical report/document
BIPO3: Students should be able to demonstrate a degree of mastery over the area as per the
specialization of the program. The mastery should be at a level higher than the
requirements in the appropriate bachelor program
BI-4
Program Educational Objectives (PEO)
The post graduates of M.Tech in Bioinformatics will be provided the knowledge and skill to:
Program Educational Objectives:
M. Tech (Bioinformatics)
BIPE01 Be Bioinformatics engineers with a strong fundamental in engineering principles to
design new drugs or find evolutionary patterns that exist among species through
simulation
BIPE02 An ability to define, assess, tailor bioinformatics processes and methodologies for
development of industrial applications and entrepreneurship skills
BIPE03 To be acquainted with various aspects of current research trends, and modern
technology for lifelong learning.
BI-5
BANGALORE UNIVERSITY
SCHEME OF STUDIES AND EXAMINATION FOR 24 MONTHS COURSE FOR THE AWARD OF
M. Tech. DEGREE IN COMPUTER SCIENCE AND ENGINEERING (Specialization: BIOINFORMATICS) under CBCS Scheme – 2K18
Semester I Sl. No Course Type/ Course Name Teaching scheme Teaching Total CIE *SEE Credits
Course Code Hrs/Week DPT Hrs/week Marks Marks
L T P S
1 18CS1C01 Mathematical Foundations of Computer Science 3 1 0 0 CSE 4 50 50 4
2 18 1C02 4 0 0 0 CSE 4 50 50 4
3 18BI1C03 Introduction to Bioinformatics 4 0 0 0 CSE 4 50 50 4
4
18BI1E1A Bio-molecular Structure Interaction and Dynamics 4 0 0 0
CSE 4 50 50 418BI1E1B Genomics and Proteomics 4 0 0 0
18BI1E1C Programming in Bioinformatics 4 0 0 0
5
18BI1E2A Advanced Biochemistry and Immunology 4 0 0 0
CSE4 50 50 4
18BI1E2B Metabolic Engineering 4 0 0 0
18BI1E2C Biostatics and Applications 4 0 0 0
6 18BI1L01 Advanced Bioinformatics Laboratory 0 0 4 0 CSE 4 50 50 2
7 18CS1M01 Research Methodology & IPR. 2 0 0 0 CSE 2 50 50 2
8 18SE1S01 Seminar -I 0 0 2 0 CSE 2 50 -- 1
9 18CS1M02 Audit Course-I (Technical Paper Writing) 2 0 0 0 English 2 50 -- 1
Total 30 450 350 26
*=SEE shall be conducted for 100 marks and the marks obtained is to be reduced for 50 marks.
BI-6
Semester II Sl. No Course Type/ Course Name Teaching scheme Teaching Total CIE *SEE Credits
Course Code Hrs/Week DPT Hrs/week Marks Marks
L T P S
1 18CS2C01 Advanced Data Structures and Algorithms 4 0 0 0 CSE 4 50 50 4
2 18CS2C02 Advanced Operating Systems 4 0 0 0 CSE 4 50 50 4
3 18BI2C03 Structure Bioinformatics 3 0 2 0 CSE 4 50 50 4
4
18BI2E1A Enzyme Kinetics 4 0 0 0CSE
4 50 50 418BI2E1B Next Generation Sequencing 4 0 0 0
18BI2E1C Microarray Bioinformatics 4 0 0 0
5
18BI2E2A Molecular Mechanics and Simulation 4 0 0 0CSE
4 50 50 418BI2E2B System Biology 4 0 0 0
18BI2E2C Python for Bioinformatics
6 18CS2L01 Advanced Data Structures and Algorithms Laboratory 0 0 4 0 CSE 4 50 50 2
7 18BI2S01 Seminar -II 0 0 2 0 CSE 2 50 -- 1
8 18CS2M01 Audit Course-II (Pedagogy Studies) 2 0 0 0 CSE 2 50 -- 1
Total 28 400 300 24
Semester III
Sl. No Cours Type/ Course Name Teaching scheme Teaching Total CIE *SEE Credits
Course Code Hrs/Week DPT Hrs/week Marks Marks
L T P S
1 18BI3E1A Protein And Insilico Drug Design
18BI3E1B Recombinant DNA Technology 4 0 0 0 CSE 4 50 50 4
18BI3E1C Genetic Engineering and Biotechnology
2
Open Elective 4 0 0 0 CSE 4 50 50 4
3 18BI3S01 Seminar -III 0 0 2 0 CSE 2 50 --- 1
4 18BI3I01 Internship / Mini Project 0 0 10 0 CSE 10 50 50 5
5 18BI3D01 Dissertation Phase -I 0 0 10 0 CSE 10 50 50 5
Total 30 250 200 19
BI-7
Semester IV Sl. No Course Type/ Course Name Teaching scheme Teaching Total CIE *SEE Credits
Course Code Hrs/Week DPT Hrs/week Marks Marks
L T P S
1 18BI4S01 Seminar -IV 0 0 2 0 CSE 2 50 --- 1
2 18BI4D01 Dissertation Phase -II - - 30 - CSE 30 50 50 15
Total 32 100 50 16
1 18BIMOOC MOOC Course - - - - 03
Grand Total of Credits 88
COURSE TYPE
BI: BIOINFORMATICSCS: COMPUTER SCIENCE AND ENGG C: PROFESSIONAL CORE E: PROFESSIONAL ELECTIVE
P: OPEN ELECTIVE M: MANDATORY AUDIT L: LABORATORY
S: SEMINAR I: INTERNSHIP/ MINI PROJECT D: DISSERTATION
L – Theory lecture, T – Tutorial, P – Lab work, S – Self study:
Numbers under teaching scheme indicates contact clock hours.
Note:
1. In Any curse(Program core or Program Elective), if self-study of 4 hours per week per students is allocated, then teaching scheme of such course will be 3-0-0-4
and the total credits will be 4.
2. *=SEE shall be conducted for 100 marks and the marks obtained is to be reduced for 50 marks.
3. #= the CIE test of the lab component of integrated course shall be conducted with the external examiners for 50 marks and shall be reduced to 25 marks
BI-8
BANGALORE UNIVERSITY
SCHEME OF STUDIES AND EXAMINATION FOR 24 MONTHS COURSE FOR THE AWARD OF
M. Tech. DEGREE IN COMPUTER SCIENCE AND ENGINEERING (Specialization: Bioinformatics) under CBCS Scheme – 2K18
Open Elective for M. Tech CBCS Scheme
Semester III Sl. No Course Type/ Course Name Teaching scheme Teaching Total CIE *SEE Credits
Course Code Hrs/Week DPT Hrs/week Marks Marks
L T P S
1
18CS3P1A Artificial Intelligence
4 0 0 0 CSE 4 50 50 418CS3P1B Business Analytics
18CS3P1C Modelling and Simulation
2
18CV3P1A Significance of National Building Codes
4 0 0 0 Civil 4 50 50 418CV3P1B Water Laws, Rights and Administration
18CV3P1C Waste to Energy
18CV3P1D Remote Sensing and Geographic information System
318 3P1A Composite and Smart Materials 4 0 0 0 Mech 4 50 50 4
18 3P1B Industrial Safety
4
18EE3P1A Real Time Embedded Systems
4 0 0 0 EEE4 50 50 4
18EE3P1B Robotics and Automation
18EE3P1C Solar and Wind Energy
5
18EC3P1A Reliability and Engineering
4 0 0 0 ECE 4 50 50 418EC3P1B M-Commerce and Applications
18EC3P1C Optimization Techniques
BI-9
Course Code 18CS1C01 M.Tech(Bioinformatics)
Category Theory-Professional Core
Course Title MATHEMATICAL FOUNDATIONS OF COMPUTER SCIENCE
Scheme and Credits No. of Hours/Week Semester-I
L T P SS Credits
3 1 4
CIE Marks:50 SEE Marks:50 Total Marks:50 Duration of SEE:03 Hours
Prerequisite(if any):
1. Basics of probability
2. Basics of graph theory
COURSE OBJECTIVES:
The course will enable the students to:
1. Understand the concepts of number theory and solve related problems.
2. Apply the concepts of stochastic process and queuing theory required to devise analytical
models for the real problems of computer science.
3. Analyze the various concepts of arranging, selecting and combining objects from a set.
4. Understand the concept of advanced graph theory that can be used to model any network,
physical or conceptual.
UNIT -I MATHEMATICAL LOGIC AND NUMBER THEORY: 10 Hours
The Division algorithm, the Greatest Common Divisor, the Euclidean algorithm. The basic properties of
Congruencies, Binary and decimal representation of integer, linear congruence, Chinese-Reminder
Theorem, Fermat’s Little theorem, The sum and number of Divisors, The mobius inversion formula, The
Greatest integer function (No theorem proofs).
UNIT -II PROBABILITY AND QUEUING THEORY: 10 Hours
Random Variables, Probability Distribution, Binomial Distribution, Poisson Distribution, Geometric
Distribution, Exponential Distribution, Normal Distribution, Uniform Distribution. Two Dimensional
Random Variables. Introduction to Stochastic Processes, Markov process, Markov chain, one step and
n-step Transition Probability, Chapman Kolmogorov theorem (Statement only), Transition Probability
Matrix, Classification of States of a Markov chain. Introduction to Markovian queuing models, Single
Server Model with Infinite system capacity, Characteristics of the Model (M/M/1) : (∞/FIFO), Single
Server Model with Finite System Capacity, Characteristics of the Model (M/M/1) : (K/FIFO).
UNIT -III COMBINATORICS: 10 Hours
Basics of Counting: Permutations, Permutations with Repetitions, Circular Permutations, Restricted
Permutations, Combinations: Restricted Combinations, Generating Functions of Permutations and
Combinations, Binomial and Multinomial Coefficients, Binomial and Multinomial Theorems, The
Principles of Inclusion Exclusion, Pigeonhole Principle and its Application
UNIT -IV RECURRENCE RELATIONS: 09 Hours Generating Functions, Function of Sequences, Partial Fractions, Calculating Coefficient of Generating
Functions, Recurrence Relations, and Formulation as Recurrence Relations, Solving Recurrence
Relations by Substitution and Generating Functions, Method of Characteristic Roots, Solving
Inhomogeneous Recurrence Relations.
UNIT –V GRAPH THEORY: 09 Hours
Basic Concepts of Graphs, Sub graphs, Matrix Representation of Graphs: Adjacency Matrices, Incidence
Matrices, Isomorphic Graphs, Paths and Circuits, Eulerian and Hamiltonian Graphs, Multi-graphs, Planar
Graphs, Euler‘s Formula, Graph Colouring and Covering, Chromatic Number, Spanning Trees,
Algorithms for Spanning Trees (Concepts and Problems Only, Theorems without Proofs).
UNIT -VI Recent advances and research being done in the topics mentioned above units
REFERENCES
1. David M Burton, “Elementary Number Theory”, Allyn and Bacon, 1980.
2. K. S. Trivedi, “Probability and Statistics with Reliability, Queuing for Computer Science
Applications”, John Wiley and Sons, II Edition, 2008.
3. Gunter Bolch, Stefan Greiner, Hermann De Meer, K S Trivedi, “Queuing Networks and Markov
Chains”, John Wiley and Sons, II Edition, 2006.
4. Richard A Brualdi, Introductory Combinatorics 5th Edition, Pearson 2009
5. J. A. Bondy and U. S. R. Murty, “Graph Theory and Applications”, Macmillan Press, 1982.
BI-10
COURSE OUTCOMES:
On completion of the course, the student would be able to:
CO1. Solve problems related to number theory.
CO2: Design the analytical models using the concepts of probability and stochastic process.
CO3: Compare the various methods of counting using permutations and combinations.
CO4: Solve the problems of recurrence relations.
CO5: Apply the graph theory concepts in solving problems related to computer science.
Scheme of Examination
CIE -50
Marks
Test I (Unit I,II, & III)-15 Marks
Test II (Unit IV & V) -15 Marks
Quiz/ AAT = 5 Marks
Unit- VI(AAT) =15 Marks
Total: Marks 50
SEE-100
Marks
Answer Five Full Questions
Unit which have 10 Hours shall have internal
choice
20*3=60
Marks Total : Marks
100 Unit which have 09 Hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced for 50 marks
Mapping of course Outcomes(COs) to Program Outcomes(POs)
PO1 PO2 PO3
CO1 3
CO2 3
CO3 3
CO4 3
CO5 3 2
1. LOW, 2. MEDIUM, 3.HIGH
BI-11
Course Code 18CS1C02 M.Tech (Bioinformatics)
Category Theory-Professional Core
Course title ADVANCES IN COMPUTER NETWORKS
Scheme and
Credits
No. of
Hours/Week
Semester – I
L T P SS Credits
4 0 - - 4
CIE Marks:
50
SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3
Hrs
Prerequisites (if any):
COURSE OBJECTIVE
The course will enable the student to:
1. Understand the requirement of various high speed networks
2. Learn the effect of congestion and its control.
3. Understand Network Traffic Management for reliable delivery.
4. Understand integrated and differentiated architecture and services.
5. Learn the effect of traffic in the networks on various QoS parameters
UNIT I- INTRODUCTION 9 Hours
OSI and TCP/ IP Reference Model, RS-232C, RS-449, Devices used in Physical Layers,
Framing, Flow and Error Control, Error Detection and Correction, Checksum, Sliding
Window Protocols-ARQ.
UNIT II- DATA LINK LAYER 10 Hours Multiple Access Control Protocols(MAC), ALOHA, CSMA/CD (802.3), Data Link
Protocol- HDLC,PPP, Wired LAN’s : Fast Ethernet, Gigabit Ethernet, Fibre Channel,
Wireless LAN’s(802.11), Broadband Wireless(802.16).
UNIT III- ROUTING AND CONGESTION CONTROL 10 Hours Design issues, Routing Algorithms, Distance Vector Routing, Link State Routing, Routing
in Mobile Host, Broadcast Routing, Reverse Path Forwarding, OSPF, IP Protocols (IPv4 -
ARP, RARP, IPv6), Queuing Analysis- Queuing Models – Single Server Queues –
Effects of Congestion – Congestion Control – Traffic Management – Congestion Control
in Packet Switching Networks.
UNIT IV – TRANSPORT LAYER AND INTEGRATED SERVICES 10 Hours
TCP Header, TCP Flow control – TCP Congestion Control – Retransmission – Timer
Management – Exponential RTO back-off – KARN’s Algorithm – Window
management. Integrated Services Architecture – Approach, Components, Services-
Queuing Discipline, FQ, PS, BRFQ, GPS, WFQ – Random Early Detection,
Differentiated Services.
UNIT V - PROTOCOLS FOR QOS SUPPORT 09 Hours
RSVP – Goals & Characteristics, Data Flow, RSVP operations, Protocol
Mechanisms – Multiprotocol Label Switching – Operations, Label Stacking, Protocol
details – RTP – Protocol Architecture, Data Transfer Protocol, RTCP.
UNIT VI- To understand latest innovative networks such as Software Defined
Networks(SDN).
REFERENCES
1. Behrouz A Forouzan and Firouz Mosharraf, “Computer Networks, A Top-Down
Approach”, TMH, 2012.
2. Andrew S. Tanenbaum and David J. Wetherall, “Computer Networks”, Pearson
Education, 5th Edition,2011.
3. William Stallings, “High Speed Networks and Internet”, , Second Edition, 2012.
BI-12
4. Prakash C Guptha, “Data Communication and Computer Networks”, PHI , 6th
printing 2012.
5. Larry L. Peterson and Bruce S Davis , “Computer Network A System
Approach”, Elsevier, 5th
edition 2010.
COURSE OUTCOMES
On Completion of the course, the student will be able to:
CO1: Apply the networking principles to manage the network traffic.
CO2: Control the various anomalies in the network to improve the QoS.
CO3: Study the relation and effect of one QoS parameter on the other.
CO4: Apply the efficient techniques to achieve effective and reliable communication.
CO5: Develop new protocols to mitigate emerging problems.
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100 Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COs) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2
CO3 3 2 2
CO4 3 2
CO5 2 2 2
1:Low, 2:Medium, 3:High
BI-13
Course Code 18BI1C03 M.Tech (Bioinformatics)
Category Theory-Professional Core
Course Title INTRODUCTION TO BIOLOGY AND BIOINFORMATICS
Scheme and Credits No. of Hours/Week Semester-I
L T P SS Credits
4 0 4
CIE Marks:50 SEE Marks:50 Total Marks:50 Duration of SEE:03 Hours
Prerequisite(if any):
COURSE OBJECTIVES:
The course will enable the student to:
1. Learn basic concepts of bioinformatics and the importance of sequence databases such as
Nucleic acid sequence databases
2. Apply the sequence analysis and pair wise sequence alignment methods
3. Analyze Sequence databases :PubMed, BioMed Central, Public Library of Sciences (PloS),
CiteXplore
4. Evaluate significance of biological database in bioinformatics, Cell cycle and Divisions and
Genetics
5. Create and protein sequences and analysis nucleic acid
UNIT I- INTRODUCTION TO BIOLOGY: 10 Hours
Differences in the basic structure and composition of prokaryotic cells and eukaryotic animal and plant
cells; structure and function of eukaryotic (plant and animal) cell organelles. Diversity in the size and
shape of cells depending on functions within different tissues; variations in the number and structure of
organelles depending on the type of cells (e.g., rich smooth endoplasmic reticulum in lipid secreting
cells). Mendelian laws of inheritance, examples of multiple alleles governing one phenotype;
overview of cytogenetics and genetic linkage; brief overview of molecular genetics, overview of
Central Dogma of molecular biology; epistasis, models for dominance, co-dominance and
pseudo-dominance, epigenetics; major human genetic disorders.
UNIT II- BIOINFORMATICS RESOURCES: 09 Hours
Aim and branches of Bioinformatics, Application of Bioinformatics, Role of internet and www in
bioinformatics. Bioinformatics Resources: NCBI, EBI, ExPASy, RCSB, DDBJ: The knowledge of
databases and bioinformatics tools available at these resources, organization of databases: data contents,
purpose and utility. Open access bibliographic resources and literature databases: PubMed, BioMed
Central, Public Library of Sciences (PloS), CiteXplore.
UNIT III- SEQUENCE DATABASES : 10 Hours
Sequence databases: Nucleic acid sequence databases: GenBank, EMBL, DDBJ; Protein sequence
databases: Uniprot-KB: SWISS-PROT, TrEMBL, UniParc; Structure Databases: PDB, NDB, PubChem,
ChemBank. Sequence file formats: Various file formats for bio-molecular sequences: GenBank, FASTA,
GCG, MSF etc. Protein and nucleic acid properties: Proteomics tools at the ExPASy server, GCG utilities
and EMBOSS, Computation of various parameters
UNIT IV- SEQUENCE ANALYSIS: 09 Hours
Sequence Analysis: Basic concepts of sequence similarity, identity and homology, definitions of
homologues, orthologues, paralogues and xenologues Scoring matrices: basic concept of a scoring
matrix, Matrices for nucleic acid and proteins sequences, PAM and BLOSUM series, matrix derivation
methods and principles..
UNIT V- SEQUENCE ALIGNMENT: 10 Hours Sequence alignment: Measurement of sequence similarity; Similarity and homology. Pairwise sequence
alignment: Basic concepts of sequence alignment, Needleman and Wunsch, Smith and Waterman
algorithms for pairwise alignments, gap penalties, use of pairwise alignments for analysis of Nucleic acid
and protein sequences and interpretation of results.
UNIT VI- Recent advances and research being done in the topics mentioned above units
BI-14
REFERENCES BOOKS:
1. Bioinformatics: Sequence and Genome Analysis Mount D., Cold Spring Harbor Laboratory Press,
New York. 2004.
2. Bioinformatics- a Practical Guide to the Analysis of Genes and Proteins by Baxevanis, A.D. and
Francis Ouellellette, B.F., Wiley India Pvt Ltd. 2009.
3. Orengo CA, Jones DT, Thornton, JM (Eds.), “Bioinformatics - Genes, Proteins and Computers”,
Bios Scientific Publishers Ltd., 2003..
COURSE OUTCOMES:
At the end of the course, the students will be able to:
CO1. Understand differences in the basic structure and composition of prokaryotic cells and
eukaryotic animal and plant cells
CO2: Determine protein and nucleic acid properties using sequence database
CO3: Compare sequence alignments methods
CO4: Validate the significance of biological database in bioinformatics
CO5: Development of Solutions that use of pairwise alignments for analysis of Nucleic acid and
protein sequences
Scheme of Examination
CIE -50
Marks
Test I (Unit I,II, & III)-15 Marks
Test II (Unit IV & V) -15 Marks
Quiz/ AAT = 5 Marks
Unit- VI(AAT) =15 Marks
Total: Marks 50
SEE-100
Marks
Answer Five Full Questions
Unit which have 10 Hours shall have internal
choice
20*3=60
Marks Total : Marks
100 Unit which have 09 Hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced for 50 marks
Mapping of course Outcomes(COs) to Program Outcomes(POs)
PO1 PO2 PO3
CO1 3
CO2 3
CO3 3
CO4 3
CO5 3 2
1. LOW, 2. MEDIUM, 3.HIGH
BI-15
BI-16
Course Code 18BI1E1A M.Tech(Bioinformatics)
Category Theory-Professional Elective
Course Title BIOMOLECULAR STRUCTURE INTERACTION AND DYNAMICS
Scheme and Credits No. of Hours/Week Semester-I
L T P SS Credits
4 0 4
CIE Marks:50 SEE Marks:50 Total Marks:50 Duration of SEE:03 Hours
Prerequisite(if any):
COURSE OBJECTIVES:
The course will enable the student to:
1. Learn basic concepts of structural features of proteins,
2. Understand molecular modeling and conversion of 2D Structural data into 3D form.
3. Acquire the knowledge of membrane proteins and simulation methods for membranes
UNIT I-BIOMOLECULAR STRUCTURE AND MODELING: 09 Hours Historical Perspective, Introduction to Molecular Modeling, Roots of Molecular modeling in Molecular
mechanics. Introduction to X-Ray crystallography and NMR spectroscopy. Introduction to PDB and 3D
Structure data, Structure of PDB and other 3D Structure record. Protein Structure Hierarchy: Structure
Hierarchy. Helices – Classic α-Helix and π Helices, Left-Handed α-Helix and Collagen Helix. β-Sheets
- Turns and Loops. Supersecondary and Tertiary structure. Complex 3D Networks. Classes in Protein
Architecture – Folds, α-Class, Bundles,Folded leaves, Hairpin arrays. β-Class folds, Anti-parallel β
domains, parallel and Antiparallel Combinations. α/β and α+β-Class, α/β Barrels, Open twisted α/β folds,
Leucine-rich α/β folds.α+β folds. Quaternary structure. Discussions with case studies.
UNIT II- FORCE FIELDS: 09 Hours Formulation of the Model and Energy, Quantifying Characteristic Motions, Complex Biomolecular
Spectra, Spectra as force constant sources, In-Plane and Out-of-Plane Bending. Bond Length Potentials
- Harmonic term, Morse term, Cubic and Quadratic terms. Bond Angle Potentials - Harmonic and
Trigonometric terms, Cross bond stretch / Angle bend terms. Torsional potentials - Origin of rotational
barriers, Fourier terms, Torsional parameter Assignment, Improper torsion, Cross dihedral/Bond angle,
Dihedral terms. Van der Waals potentials. Rapidly decaying potential. Parameter fitting from experiment.
Two parameter calculation protocols. Coulomb potential - Coulomb’s Law. Slowly decaying potential,
Dielectric function and Partial charges. Discussions with case studies.
UNIT III- MOLECULAR MODELING: 10 Hours Modelling basics, Generation of 3D Coordinates Crystal data, Fragment libraries, and conversion of 2D
Structural data into 3D form. Force fields, and Geometry optimization. Energy minimizing procedures –
Use of Charges, Solvent effects and Quantum Mechanical methods. Computational tools for Molecular
modeling. Methods of Conformational analysis – Systematic search procedures, Monte Carlo and
molecular dynamics methods. Determining features of proteins – Interaction potential, Molecular
electrostatic potential, molecular interaction fields, Properties on molecular surface and Pharmacophore
identification.
UNIT IV- 3D QSAR METHODS: 10 Hours
Comparative protein modeling – Conformational properties of protein structure, Types of secondary
structural elements, Homologous proteins. Procedures for sequence Alignments, Determination and
generation of structurally conserved regions, Construction ofstructurally variable regions, Side-Chain
modeling, Secondary structure prediction, Threading methods. Optimization and Validation of Protein
Models with suitable case studies. Computation of the Free Energy: Free energy calculations in
Biological Systems – Drug design, Signal transduction, Peptide folding, Membrane protein association,
Numerical methods for calculating the potential of mean force, Replica-Exchange-Based Free-Energy
Methods
UNIT V- MEMBRANE PROTEIN SIMULATIONS: 10 Marks
Membrane proteins and their importance, Membrane protein environments in vivo and in vitro. Modeling
a complex environment – Simulation methods for membranes, Membrane protein systems, Complex
solvents, Detergent micelles, Lipid bilayers, SelfAssembly and Complex systems. Modeling and
Simulation of Allosteric regulation in enzymes – Discussions with case studies. Electrostatics and
Enhanced Solvation Models: Implicit solvent electrostatics in Biomolecular Simulation, New distributed
BI-17
multipole methods. Quantum mechanical principles and applications to force field development with case
studies.
UNIT VI- Recent advances and research being done in the topics mentioned above units
REFERENCES
1. Molecular Modeling by Hans-Dieter Höltje, Wolfgang Sippl, Didier Rognan, GerdFolkers,
2008.
2. Modeling of Bimolecular Structures and Mechanisms by Alberte Pullman, Joshua Jortner,
1995.
3. Mathematical Approaches to Biomolecular Structure and Dynamics by Jill P. Mesirov, Klaus
Schulten, De Witt L. Sumners, 1996.
4. Foundations of Molecular Modeling and Simulation by Peter T. Cummings, Phillip R.
Westmorland, Brice Carnahan, Published by American Institute of Chemical Engineers, 2001.
New Algorithms for Macromolecular Simulation by Timothy J. Barth, Michael Griebel, David
E.Keyes, Risto M. Nieminen, Dirk Roose, Tamar Schlick, Published by Springer, 2006.
COURSE OUTCOMES:
At the end of the course, the students will be able to:
CO1: Learn about structural features of proteins.
CO2: Gain insights into the various tools used for modelling of small molecules, lipids and proteins.
CO3: Modelling and Simulation of Allosteric regulation in enzyme
CO4: Prove the membrane protein regulation through modeling and simulation
CO5: Investigate different 3D modelling along with uses cases
Scheme of Examination
CIE -50
Marks
Test I (Unit I,II, & III)-15 Marks
Test II (Unit IV & V) -15 Marks
Quiz/ AAT = 5 Marks
Unit- VI(AAT) =15 Marks
Total: Marks 50
SEE-100
Marks
Answer Five Full Questions
Unit which have 10 Hours shall have internal
choice
20*3=60
Marks Total : Marks
100 Unit which have 09 Hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced for 50 marks
Mapping of course Outcomes(Cos) to Program Outcomes(Pos)
PO1 PO2 PO3
CO1 3
CO2 3
CO3 3
CO4 3
CO5 3 2
1. LOW, 2. MEDIUM, 3.HIGH
BI-18
Course Code 18BI1E1B M.Tech(Bioinformatics)
Category Theory-Professional Elective
Course Title GENOMICS, PROTEOMICS
Scheme and Credits No. of Hours/Week Semester-I
L T P SS Credits
4 0 4
CIE Marks:50 SEE Marks:50 Total Marks:50 Duration of SEE:03 Hours
Prerequisite(if any):
COURSE OBJECTIVES:
The course will enable the student to:
1. Have a basic understanding of the organization of prokaryotic and eukaryotic genomes,
databases and sequencing techniques
2. Acquire molecular insight into the tools and techniques used in genome analysis.
3. Get an overview of the different protein purification and sequencing techniques
4. Get insight into the techniques involved in the identification and characterization of all the
proteins synthesized in a cell or a tissue.
UNIT I- INTRODUCTION TO GENOMICS: 09 Hours
Genome evolution and organization in prokaryotes and eukaryotes, Genome mapping: Genetic and
physical mapping. Molecular markers and protein markers, Genome sequencing, basics, strategies and
methodology. Comparative and Functional genomics; Model systems- Arabidopsis, Human, Drophila
and E coli. Serial analysis of gene expression (SAGE) and targeting induced local lesions in genome
(TILLING).Biological databases; Primary and secondary for nucleic acid and proteins, structural
databases, metabolic pathways and specialized databases. Genome Wide Association Studies (GWAS)
UNIT II- TOOLS FOR GENOMICS: 10 Hours
Computational analysis of sequences- finding genes and regulatory regions; Gene annotation; Similarity
searches; Pairwise and multiple alignments; Alignment statistics; Prediction of gene function using
homology, context, structures. Expression sequence tags (ESTs),
Microarrays technology- Principles and applications, FISH, transcriptome analysis and SNPs
determination. Allele mining and single nucteotide polymorphisms (SNPs).Transcriptomics; Cancer
Genomics, Epigenomics, Chemical Genomics; Metabolomics, Nutrigenomics, interactomics,
Metagenomics. Personal Genomics; Social, Legal and Ethical Implications of Human Genome Research.
UNIT III: INTRODUCTION AND SCOPE OF PROTEOMICS 9 Hours Introduction and scope of proteomics, Protein separation techniques: Ion exchange, Size exclusion and
affinity chromatographic techniques, Poly acrylamide gel electrophoresis, isoelectric focusing, two
dimensional poly acrylamide gel electrophoresis, Mass spectrometry based techniques for protein
identification.
UNIT IV- PROTEIN SEQUENCING : 10 Hours
Edman degradation, mass fingerprinting, protein synthesis and post translational modifications.
Identification of phosphorylated proteins, characterization of multi-protein complexes, protein – protein
interactions and quantitative proteomics- Characterization of interaction clusters using two-hybrid
systems. Protein arrays-definition, applications- diagnostics, expression profiling, Functional
proteomics, Protein structure analysis, Protein databases, Clinical and biomedical applications of
proteomics..
UNIT V-MICROARRAY TECHNIQUES: 10 Hours
Importance and applications of microarray techniques in biotechnology, Types – Single and multiple
approaches. Challenges of microarray technology. Microarray Probe preparation, hybridization, Image
processing, Transformation of expression ratio and data normalization. Low and high level information
Analysis – Data Preprocess of Chemical compounds Microarray, Biomolecular microarray – Protein and
proteomics Microarray, DNA Microarray, MicroRNA Population, Cellular and tissue microarray.
Microarray Database for Serial Analysis of Gene Expression, Gene Expression Omnibus of NCBI, Array
Express of EMBL and Antibodies Arrays on Miniature Western Blots methods.
UNIT VI- Recent advances and research being done in the topics mentioned above units
BI-19
REFERENCES
1. Sandor Suhai, Genomics and Proteomics: Functional and Computational aspects, Kluwer academic
publishers, 2007, ISBN: 9780306468230.
2. Liebler,D.C. Introduction to Proteomics: Tools for the New Biology, Humana Press,
2002. ISBN-13: 978-0896039926.
3. R.M. Twyman, Principles of Proteomics, garland Science/BIOS Scientific publishers, 2004, ISBN-
10: 1-85996-273-4.
4. Steven Russell, Lisa A. Meadows and Roslin R. Russell. Microarray Technology in Practice: 2nd
Edition, Academic Press, 2013. ISBN 13: 978-0-12-372516-5.
COURSE OUTCOMES:
At the end of the course, the students will be able to:
CO1: Explain the construction concepts of various genome maps and large scale sequencing
CO2: Apply diagnostic tools for plant, animal and human diseases
CO3: Analyse proteomics to solve complex biological problems regardless of types of organism.
CO4: Develop the basic concepts of microarrays and analyse the differential gene expression.
CO5: Sketch an execution flow of micro-array experiment to determine gene expression
Scheme of Examination
CIE -50
Marks
Test I (Unit I,II, & III)-15 Marks
Test II (Unit IV & V) -15 Marks
Quiz/ AAT = 5 Marks
Unit- VI(AAT) =15 Marks
Total: Marks 50
SEE-100
Marks
Answer Five Full Questions
Unit which have 10 Hours shall have internal
choice
20*3=60
Marks Total : Marks
100 Unit which have 09 Hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced for 50 marks
Mapping of course Outcomes(Cos) to Program Outcomes(Pos)
PO1 PO2 PO3
CO1 3
CO2 3
CO3 3 -
CO4 3
CO5 3 3
1. LOW, 2. MEDIUM, 3.HIGH
BI-20
Course Code 18BI1E1C M.Tech(Bioinformatics)
Category Theory-Professional Elective
Course Title PROGRAMMING IN BIOINFORMATICS
Scheme and Credits No. of Hours/Week Semester-I
L T P SS Credits
4 0 4
CIE Marks:50 SEE Marks:50 Total Marks:50 Duration of SEE:03 Hours
Prerequisite(if any):
COURSE OBJECTIVES:
The course will enable the student to:
1. Learn about various algorithms that are used in developing software.
2. Learning various software used in modern biology
3. Mathematical operations using R Language
UNIT I- INTRODUCTION TO BIOPERL AND BIOPERL OBJECTS: 09 Hours
Basics of Perl. Introduction to BioPerl and BioPerl Objects – Brief descriptions (Seq, PrimarySeq,
LocatableSeq, RelSegment, LiveSeq, LargeSeq, RichSeq, SeqWithQuality, SeqI), Location objects,
Interface objects and Implementation objects. Sequence Representation: Representing large sequences
(LargeSeq), Representing changing sequences (LiveSeq).
UNIT II- ACCESSING SEQUENCE DATA – USING BIOPERL: 09 Hours
Accessing sequence data from local and remote databases, Accessing remote databases
(Bio::DB::GenBank, etc), Indexing and accessing local databases (Bio::Index::*,bp_index.pl,
bp_fetch.pl, Bio::DB::*). Sequence and Alignment format Interconversion – Transforming sequence files
(SeqIO), Transforming alignment files (AlignIO). Performing Sequence analysis – Global alignment,
Local alignment, Multiple sequence alignment, Parsing BLAST alignment report and Parsing multiple
sequence alignment.
UNIT III- EXCEPTION HANDLING BIOPYTHON BIOINFORMATICS: 10 Hours Parsing DNA data files, Image manipulation, Sequence analysis – Sequence alignment (pair wise and
multiple sequence alignment), Dynamic Programming, Detecting tandem repeats and generating Hidden
Marko Models, Simulation of EST Clustering. Data mining – Text mining, Simulating Genetic algorithm.
Analysis of Microarray data – Spot finding and Measurement.
UNIT IV- OVERVIEW OF THE R LANGUAGE: 10 Hours
Defining the R project, Obtaining R, Generating R codes, Scripts, Text editors for R, Graphical User
Interfaces (GUIs) for R, Packages. R Objects and data structures: Variable classes, Vectors and matrices,
Data frames and lists, Data sets included in R packages, Summarizing and exploring data, Reading data
from external files, Storing data to external files, Creating and storing R workspaces.
UNIT V- MANIPULATING OBJECTS IN R: 10 Hours Mathematical operations (recycling rules, propagation of names, dimensional attributes, NA handling),
Basic matrix computation (element-wise multiplication, matrix multiplication, outer product, transpose,
eigenvalues, eigenvectors), Textual operations, Basic graphics (high-level plotting, lowlevel plotting,
interacting with graphics.
UNIT VI – Recent advances and research being done in the topics mentioned above units
REFERENCES :
1. John Lewis, Peter Joseph DePasquale, Joseph Chase, Joe Chase, Java Foundations Addison-
Wesley, 2010.
2. D. Curtis Jamison, Perl Programming for Biologists Wiley-IEEE, 2003.
3. Mitchell L Model, Bioinformatics Programming Using Python O’Reilly Media, Inc., 2009.
4. Alain F. Zuur, Elena N. Ieno, and Erik Meesters. A Beginner’s Guide to R. Use R.
Springer,2009.
5. Florian Hahne, Wolfgang Huber, Robert Gentleman, Seth Falcon. Bioconductor case studies.
Springer, 2008
6. Robert Gentleman, Bioinformatics with R. Chapman & Hall/CRC, Boca Raton, FL, 2008.
7. Robert Gentleman. R Programming for Bioinformatics. Computer Science & Data Analysis.
Chapman & Hall/CRC, Boca Raton, FL, 2008.
8. Peter Dalgaard. Introductory Statistics with R. Springer, 2nd edition, 2008.
9. Python for Bioinformatics (Chapman & Hall/CRC), Sebastian Bassi, 2009. BI-21
COURSE OUTCOMES:
At the end of the course, the students will be able to:
CO1. Use various algorithms used in software development.
CO2: Apply knowledge about various software’s and their applications
CO3: Draw the graphics and create and store R workspaces
CO4: Relate the libraries available in Perl and R to solve bioinformatics challenges
CO5: Investigate models to retrieve data from biological data sets
Scheme of Examination
CIE -50
Marks
Test I (Unit I,II, & III)-15 Marks
Test II (Unit IV & V) -15 Marks
Quiz/ AAT = 5 Marks
Unit- VI(AAT) =15 Marks
Total: Marks 50
SEE-100
Marks
Answer Five Full Questions
Unit which have 10 Hours shall have internal
choice
20*3=60
Marks Total : Marks
100 Unit which have 09 Hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced for 50 marks
Mapping of course Outcomes(Cos) to Program Outcomes(Pos)
PO1 PO2 PO3
CO1 3
CO2 3
CO3 2
CO4 3
CO5 3 2
1. LOW, 2. MEDIUM, 3.HIGH
BI-22
Course Code 18BI1E2A M.Tech(Bioinformatics)
Category Theory-Professional Elective
Course Title ADVANCED BIOCHEMISTRY AND IMMUNOLOGY
Scheme and Credits No. of Hours/Week Semester-I
L T P SS Credits
4 0 0 0 4
CIE Marks:50 SEE Marks:50 Total Marks:50 Duration of SEE:03 Hours
Prerequisite(if any):
COURSE OBJECTIVES
The course will enable the student to:
1. Understand the underlying principles of biochemistry and immunology which form the basis
of biosciences.
2. Understanding Proteins and Carbohydrates.
3. Understanding Lipids, Nucleic acids and Enzymes.
4. Introducing the basics of immune system.
5. Understanding antigen-antibody reaction.
UNIT I- PROTEINS AND CARBOHYDRATES: 09 Hours
Proteins: amino acids– physical and chemical properties of amino acids, peptides, Ramachandran plot,
Amino acid biosynthesis, Metabolism – Urea cycle. Sugars and polysaccharides: Monosaccharides,
polysaccharides and glycoprotein, Metabolism of carbohydrates – Glycolysis-TCA cycle –
gluconeogenesis- glycogen metabolism.
UNIT II- LIPIDS, NUCLEIC ACIDS AND ENZYMES: 09 Hours Lipids: Lipid classification, properties of lipid aggregates, Biological membrane, Lipid linked proteins
and lipoproteins, Biosynthesis – fatty acids, triglycerides, Cholesterol. Metabolism – oxidation of fatty
acid, ATP synthesis. Nucleic acids: Structure of DNA, Forms of DNA - A, B, Z Structures, classification
of RNA.
UNIT III- ENZYMES: 10 Hours Enzymes: Nomenclature, classification, substrate specificity, coenzymes, regulation of enzyme activity.
Rate of enzyme reaction, kinetics, inhibition, effect of pH and temperature.
UNIT IV- IMMUNE SYSTEM: 10 Hours Innate vs. Acquired, humoral and cell mediated immunity, Immunity at Body Surfaces. Cells of the
immune system, Organs of the immune system – primary and secondary lymphoid organ, Antibody
structure and isotypes, Antigens.
UNIT V- IMMUNE RESPONSE: 10 Hours Major histocompatibility complex, HLA typing, Antigen processing and presentation Pathways.
Lymphokines and Cytokines: The complement system, Cell-mediated effectors responses (CTL, NK,
DH). Vaccines. Autoimmunity: Breakdown in Self-Tolerance. Transplantation: tissue and organ grafting.
UNIT VI- Recent advances and research being done in the topics mentioned above units
REFERENCES
1. VoetD. and J.G. Voet, “Biochemistry”, Wiley Publications, Second Edition, 2005.
2. D.L Nelson and M.M Cox, “Lehninger’s Principles of Biochemistry”, W.H FreemanPublications,
5thedition, 2008.
3. Thomas Devlin,“Textbook of Biochemistry with Clinical Correlations”, 7th edition,John Wiley
&Sons,2010.
4. Roitt, “Essential Immunology”, 10 thedition. Blackwell Science, 2005.
5. Richard A. Goldsby, Thomas J. Kindt and Barbara A. Osborne, Kuby“Immunology”,4thedition,
W. H. Freeman & Company, 2000.
6. Janeway et al., “Immunobiology”, 4th edition, Current Biology Publications, 1999.
7. William E. Paul, “Fundamental Immunology”, 4th edition, Lippencott Raven, 1999.
COURSE OUTCOMES:
At the end of the course, the students will be able to:
CO1: Explore Nucleic acids, Structure of DNA, Forms of DNA - A, B, Z Structures, classification of
RNA.
CO2: Identify Cells of the immune system, Organs of the immune system
BI-23
CO3: Get properties of lipid aggregates, Biological membrane, Lipid linked proteins and lipoproteins
CO4: Determines levels of immunity at organism and cellular level
CO5: Investigate auto immunity process
Scheme of Examination
CIE -50
Marks
Test I (Unit I,II, & III)-15 Marks
Test II (Unit IV & V) -15 Marks
Quiz/ AAT = 5 Marks
Unit- VI(AAT) =15 Marks
Total: Marks 50
SEE-100
Marks
Answer Five Full Questions
Unit which have 10 Hours shall have internal
choice
20*3=60
Marks Total : Marks
100 Unit which have 09 Hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced for 50 marks
Mapping of course Outcomes(COs) to Program Outcomes(POs)
PO1 PO2 PO3
CO1 3
CO2 3
CO3 3
CO4 1
CO5 2 3
1. LOW, 2. MEDIUM, 3.HIGH
BI-24
Course Code 18BI1E2B M.Tech(Bioinformatics)
Category Theory-Professional Elective
Course Title METABOLIC ENGINEERING
Scheme and Credits No. of Hours/Week Semester-I
L T P SS Credits
4 0 4
CIE Marks:50 SEE Marks:50 Total Marks:50 Duration of SEE:03 Hours
Prerequisite(if any):
COURSE OBJECTIVES:
The course will enable the student to:
1. Metabolome and its study
2. Applications of Metabolomics
3. Comprehensive models cellular reactions
4. Metabolic flux analysis and its applications
UNIT I- METABOLOMICS: 09 Hours
Overview- Background and definitions of Metabolomics- importance of Metabolomics.
UNIT II- TECHNOLOGIES IN METABOLOMICS: 10 Hours
Technologies-Mass spectrometry: principles, definitions, nomenclature, Metabolite isolation and
analysis by Mass Spectrometry, metabolite library, HPLC- capillary electrophoresis coupled with Mass
spectrometry.
UNIT III- APPLICATIONS: 10 Hours Applications of Metabolomics to biology: examples and case studies, Metabolome informatics, data
integration and mining.
UNIT IV- METABOLIC ENGINEERING: 09 Hours Metabolic engineering: introduction, mass balance, black box, metabolic flux analysis, stoichiometry,
Principles of metabolic engineering
UNIT V- FLUX BALANCE ANALYSIS: 10 Hours Flux balance analysis, flux balance methods, group based flux balance, metabolic control analysis:
overview, control coefficients, methods of measuring control. Flux analysis of networks- top down
approach, bottom up approach.
UNIT VI- Recent advances and research being done in the topics mentioned above units
REFERENCES
1. Tomita M., T. Nishioka, “Metabolomics: The Frontier of Systems Biology”, Springer, 2003.
2. Gregory N. Stephanopoulos, “Metabolic Engineering: Principles and
Methodologies”,Academic press, First Edition, 1998.
3. Wolfram Weckwerth, “Metabolomics: Methods and Protocols”, Humana Press, 2007.
4. Sang Yup Lee, E. Terry Papoutsakis, “Metabolic engineering”, CRC Press, 1999
5. William J. Griffiths, “Metabolomics, metabonomics and metabolite profiling”, RoyalSociety of
Chemistry, 2008.
COURSE OUTCOMES:
At the end of the course, the students will be able to:
CO1: Technologies in metabolomics and -Mass spectrometry
CO2: Do flux balance analysis, flux balance methods, group based flux balance analysis
CO3: Take up case studies and Applications of Metabolomics to biology
CO4: Compare and Anlyze mass stoichiometry and HPLC techniques used in metabolomics
CO5: Design and Develop prediction model for flux analysis in biological networks
Scheme of Examination
CIE -50
Marks
Test I (Unit I,II, & III)-15 Marks
Test II (Unit IV & V) -15 Marks
Quiz/ AAT = 5 Marks
Unit- VI(AAT) =15 Marks
Total: Marks 50
SEE-100
Marks
Answer Five Full Questions
Unit which have 10 Hours shall have internal
choice
20*3=60
Marks Total : Marks
100 Unit which have 09 Hours shall not have
internal choice
20*2=40
Marks
BI-25
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced for 50 marks
Mapping of course Outcomes(COs) to Program Outcomes(POs)
PO1 PO2 PO3
CO1 3
CO2 3
CO3 3
CO4 3
CO5 3 2
1. LOW, 2. MEDIUM, 3.HIGH
BI-26
Course Code 18BI1E2C M.Tech(Bioinformatics)
Category Theory-Professional Elective
Course Title BIOSTATISTICS AND APPLICATIONS
Scheme and Credits No. of Hours/Week Semester-I
L T P SS Credits
4 0 4
CIE Marks:50 SEE Marks:50 Total Marks:50 Duration of SEE:03 Hours
Prerequisite(if any):
COURSE OBJECTIVES:
The course will enable the student to:
1. Know the techniques of numerical methods
2. Learn the basics of Biostatistics
3. Understand the concept of hypothesis
UNIT I- FUNDAMENTALS OF STATISTICS: 09 Hours
Introduction to statistics, Measurement of data scale and Central tendency, Measures of dispersion: range,
percentile, variance and standard deviation. Data handling and statistical variables. Characteristics of
biological data, Elementary theory of statistical errors. Continuous random variables-normal distribution,
discrete random variables-Binomial and poisons distribution. Logarithmic transformations. Application
of statistics to biological problems and their interpretation.
UNIT II- INFERENTIAL STATISTICS: 10 Hours Basics of experimental design, Random block design, stratified design; cohort studies, case-control
studies, and odd ratio. Principles of statistical inference: Parameter estimation, hypothesis testing.
Statistical inference on categorical variables; categorical data and Single- and Double-blind experiments;
Sampling distributions: Bivariate distribution-conditional and marginal distribution-Discrete
distribution-Binomial, Poisson, geometric distribution-Continuous distribution, Normal, simple
problems-properties.
UNIT III- HYPOTHESIS TESTING: 10 Hours Null and alternative hypotheses, decision criteria, critical values, type I and type II errors, Meaning of
statistical significance; Power of a test; One sample hypothesis testing: Normally distributed data: z, t
and chi-square tests; F-tests. Binomial proportion testing. Independent and dependent sample
comparison, Wilcoxon Signed Rank Test, Wilcoxon-Mann-Whitney Test, Kruskal-Wallis test and
Analysis of variance: One-way and Two-way Tables.
UNIT IV- STATISTICAL CURVE FITTING: 09 Hours Correlation-Correlation coefficient, properties-problems on Karl Pearson and Spearman Rank correlation
coefficient, Rank correlation-Regression equations problems-curve fitting by the method of least squares-
fitting curves of the form ax+b,ax2 +bx+c,abx and axb - Bivariate correlation application to biological
problems. Regression: simple linear regression; Least squares method; Multiple linear regression model,
Optimization strategies with case studies.
UNIT V - STATISTICS IN MICROARRAY 10 Hours Genome mapping and bioinformatics: Types of microarray, objectives of the study, experimental designs
for micro array studies, microarray analysis, interpretation, validation and microarray informatics.
Genome mapping, discrete sequence matching, programs for mapping sequences with case studies.
UNIT VI - Recent advances and research being done in the topics mentioned above units
REFERENCES
1. Wayne W. Daniel , Chad L. Cross., “Biostatistics : Basic Concepts and Methodology for the
Health Sciences” Wiley India Pvt Ltd. 10th Edition, 2014. ISBN: 9788126551897.
2. Shalabh Helge Toutenburg., “Statistical Analysis of Designed Experiments”, Wiley Publication,
Third Edition, 2010. ISBN-13: 978-1441911476.
3. Arora P.N and Malhan P.K. “Biostatistics”, Himalaya Publishing House, 2013 ISBN: 81-8318-
691-2.
COURSE OUTCOMES:
At the end of the course, the students will be able to:
CO1: Understanding of statistical methods and numerical methods.
CO2: Apply principles of statistical inference, Parameter estimation, hypothesis testing
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CO3: Use Correlation-and Rank correlation
CO4: Sketch the role of different curve fitting techniques in correlation analysis
CO5: Formulate the design process for use of microarrays in genome mapping
Scheme of Examination
CIE -50
Marks
Test I (Unit I,II, & III)-15 Marks
Test II (Unit IV & V) -15 Marks
Quiz/ AAT = 5 Marks
Unit- VI(AAT) =15 Marks
Total: Marks 50
SEE-100
Marks
Answer Five Full Questions
Unit which have 10 Hours shall have internal
choice
20*3=60
Marks Total : Marks
100 Unit which have 09 Hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced for 50 marks
Mapping of course Outcomes(COs) to Program Outcomes(POs)
PO1 PO2 PO3
CO1 3
CO2 3
CO3 3
CO4 3
CO5 3 2
1. LOW, 2. MEDIUM, 3.HIGH
BI-28
Scheme of Examination
For selected application, the student have to demonstrate different phase of software development life
cycle
Continuous Internal
Evaluation(Lab=50)
Marks Semester End Evaluation (SEE) Marks
Performance of the student in the
lab every week
20 Write-Up 20
Test at end of the semester 20 Experiment/Execution 70
Vice-Voce 20 Vice-Voce 10
Total(CIE) 50 Total(SEE) 50*
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced for 50 marks
Course Code 18BIL01 M.Tech(Bioinformatics)
Category Practical
Course Title ADVANCED BIOINFORMATICS LAB
Scheme and Credits No. of Hours/Week Semester-I
L T P SS Credits
0 0 4 2
CIE Marks:50 SEE Marks:50 Total Marks:50 Duration of SEE:03 Hours
Prerequisite(if any):
COURSE OBJECTIVES:
The course will enable the student to
1. Learn about developing bench skills through lab exercises, oriented towards utilizing various
web based tools for bioinformatics projects
Each student has to work individually on assigned lab exercises. Lab sessions could be scheduled as
one contiguous four-hour session per week. It is recommended that all implementations are carried out
in suitable tools. Exercises should be designed to cover the following topics:
1. Sequence retrieval from nucleic acid and protein databases.
2. Retrieval of information about structure, bioassay chemical compounds (such as Drugs and
naturally occurring compounds).
3. 3 Retrieval of information about physical and chemical properties of chemical compounds (such
as Drugs and naturally occurring compounds).
4. Gene sequence assembly and contig mapping and identification of Gene.
5. Primer and Promoter design for a given sequences
6. Sequence searches using FASTA and BLAST, and Phylogenetic analysis.
7. Prediction of secondary structure for given protein and RNA sequences.
8. Retrieval of protein structure from PDB and its visualization and modification.
9. Prediction of 3D structure of unknown protein sequence.
10. Prediction of protein-protein interactions.
11. EST clustering and EST mapping, and Genome annotation
12. Microarray data analysis- normalization, clustering.
Study of Profiles, Patterns and PSSMs
COURSE OUTCOMES:
At the end of the course, the students will be able to:
CO1. Design and implement retrieval of information about physical and Chemical properties of chemical
compounds.
CO2. Retrieval of protein structure from PDB and its visualization and modification.
CO3. Prediction of protein-protein interactions.
CO4: Design and develop Sequence retrieval from nucleic acid and protein database.
CO5: Investigate the performance of proteins structure predication techniques.
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Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced for 50 marks
Mapping of course Outcomes(COs) to Program Outcomes(POs)
PO1 PO2 PO3
CO1 3
CO2 3
CO3 3
CO4 3
CO5 3 2
1. LOW, 2. MEDIUM, 3.HIGH
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22
Course Code 18CS1M01 M.Tech(Bioinformatics)
Category Mandatory Audit
Course title RESEARCH METHODOLOGY AND IPR
Scheme and
Credits
No. of Hours/Week Semester – I
L T P SS Credits
2 0 - - 2
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3
Hrs
Prerequisites (if any):
COURSE OBJECTIVES
This course will enable students to
1. Understand the formulation of research problem, scope and objectives of research
problem
2. Use methods for effective technical writing skills
3. Analyze Approaches of investigation of solutions for research problem
4. Evaluate the format of research proposal , intellectual property and patent
5. Create patent, research paper
UNIT -I RESEARCH PROBLEM: 3 Hours
Meaning of research problem, Sources of research problem, Criteria Characteristics of a good
research problem, Errors in selecting a research problem, Scope and objectives of research
problem. Approaches of investigation of solutions for research problem, data collection,
analysis, interpretation, Necessary instrumentations
UNIT- II RESEARCH REQUIREMENTS: 3 Hours
Effective literature studies approaches, analysis Plagiarism, Research ethics,
UNIT- III EFFECTIVE TECHNICAL WRITING: 6 Hours
Effective technical writing, how to write report, Paper Developing a Research Proposal, Format of
research proposal, a presentation and assessment by a review committee
UNIT- IV NATURE OF INTELLECTUAL PROPERTY: 6 Hours
Patents, Designs, Trade and Copyright. Process of Patenting and Development: technological
research, innovation, patenting, development. International Scenario: International cooperation on
Intellectual Property. Procedure for grants of patents, Patenting under PCT.
UNIT- V PATENT RIGHTS: 6 Hours Scope of Patent Rights. Licensing and transfer of technology. Patent information and databases.
Geographical Indications.
UNIT- VI NEW DEVELOPMENTS IN IPR:
Administration of Patent System. New developments in IPR; IPR of Biological Systems, Computer
Software etc. Traditional knowledge Case Studies, IPR and IITs.
REFERENCES
1. Stuart Melville and Wayne Goddard, “Research methodology: an introduction for
science & engineering students’”
2. Wayne Goddard and Stuart Melville, “Research Methodology: An Introduction”
3. Ranjit Kumar, 2nd Edition, “Research Methodology: A Step by Step Guide for
beginners” Halbert, “Resisting Intellectual Property”, Taylor & Francis Ltd ,2007.
4. Mayall, “Industrial Design”, McGraw Hill, 1992.
5. Niebel, “Product Design”, McGraw Hill, 1974.
6. Asimov, “Introduction to Design”, Prentice Hall, 1962.
7. Robert P. Merges, Peter S. Menell, Mark A. Lemley, “ Intellectual Property in New
Technological Age”, 2016.
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23
8. T. Ramappa, “Intellectual Property Rights Under WTO”, S. Chand, 2008
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1: Understand research problem formulation. Analyze research related information and
follow research ethics
CO2: Understanding that when IPR would take such important place in growth of
individuals and nation, it is needless to emphasis the need of information about
Intellectual Property Right to be promoted among students in general & engineering
in particular.
CO3: Understand that IPR protection provides an incentive to inventors for further research
work and investment in R & D, which leads to creation of new and better products,
and in turn brings about, economic growth and social benefits.
CO4: Analyze research related information
CO5: Follow research ethics
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 6 hours shall have internal
choice
20*3=60
Marks Total:
Marks 100 Unit which have 3 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3
CO2 3
CO3 3
CO4
CO5 3 3
1: Low 2: Medium 3:High
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24
COURSE LEARNING OBJECTIVES:
The objectives of the SEMINAR-I is to prepare the students to learn to:
1. Carry out the literature review of general research area/current topic and analyse the
same effectively.
2. Prepare a technical report, reflecting his/her depth of understanding, on the selected
area/topic and prepare content rich presentation.
3. Acquire communication and time management skills for effective and impactful
presentation. Interact with peers to acquire the qualities of thoughtfulness, friendliness,
adaptability, responsiveness, and politeness in-group discussion. Overcome stage fear
during the presentation.
GUIDE LINES
1. Seminar preparation and presentation is an individual student activity.
2. Topic may be of general/ specific interest to program of engineering or electives not
offered in the semester and to be selected in consultation with the faculty/Guide.
3. Select one pertinent research paper for the seminar presentation.
4. Prepare and submit a detailed technical report of the seminar topic.
COURSE OUTCOMES:
Students shall be able to:
1. Carry out the literature survey of topic of seminar.
2. Prepare a technical report on the selected area/topic.
3. Make an effective presentation with seamless flow of content within the time allocated.
Overcome inhibition in interacting with peers and hence develop the spirit of team
work. Overcome stage fear during the presentation.
Course Code 18BI1S01 M.Tech (Bioinformatics)
Category Seminar Semester: I
Course title SEMINAR - I
Scheme and Credits
No. of Hours/Week
Total hours = 24 L T P S Credits
0 0 2 0 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any): NIL
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SCHEME OF EXAMINATION
CIE – 50
marks
Phase -1 Marks =10 Seminar Report
Marks =20
Total:50
Marks Phase -2 Marks =20
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2 3
CO2 2 3 3
CO3 2
1. Low, 2. Medium, 3. High
Scheme of Continuous Internal Evaluation (CIE):
Evaluation would be carried out in TWO phases. The Seminar Evaluation Committee
shall comprise of Chairman of the Department, Faculty/Guide and one more faculty member
nominated by Chairman. The evaluation criteria shall be as per the rubrics given below:
Rubrics for Evaluation:
Topic - Technical Relevance, Sustainability and Societal Concerns : 15%
Review of literature and Technical Content : 35%
Presentation Skills : 25%
Report : 25%
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Course Code 18CS1M02 M.Tech(Bioinformatics)
Category Audit Course-I
Course title TECHNICL PAPER WRITING
Scheme and
Credits
No. of Hours/Week Semester – I
L T P SS Credits
2 0 - - 1
CIE Marks: 50 SEE Marks: -- Total Max. Marks: 50
Prerequisites (if any):
COURSE OBJECTIVES
This course will enable students to
1. Understand the planning section of research paper and preparation of paper writing
2. Apply key skill while writing research paper and know about what to write in each
section
3. Analyse literature, methods,
4. Evaluate research paper, paraphrasing paper
5. Create good research paper
UNIT-I PLANNING AND PREPARATION: 6 Hours
Planning and Preparation, Word Order, Breaking up long sentences, Structuring Paragraphs
and Sentences, Being Concise and Removing Redundancy, Avoiding Ambiguity and
Vagueness
UNIT- II CLARIFYING: 3 Hours
Clarifying Who Did What, Highlighting Your Findings, Hedging and Criticising, Paraphrasing
and Plagiarism, Sections of a Paper, Abstracts. Introduction
UNIT- III REVIEW OF THE LITERATURE: 6 Hours
Review of the Literature, Methods, Results, Discussion, Conclusions, The Final Check.
UNIT- IV KEY SKILLS: 6 Hours
Key skills are needed when writing a Title, key skills are needed when writing an Abstract,
key skills are needed when writing an Introduction, skills needed when writing a Review of
the Literature,
UNIT- V METHODS: 3 Hours
skills are needed when writing the Methods, skills needed when writing the Results, skills are
needed when writing the Discussion, skills are needed when writing the Conclusions.
UNIT- VI NEW DEVELOPMENTS IN RESEARCH PAPER WRITING:
useful phrases, how to ensure paper is as good as it could possibly be the first- time submission
REFERENCES
1. Goldbort R (2006) Writing for Science, Yale University Press (available on Google
Books)
2. Day R (2006) How to Write and Publish a Scientific Paper, Cambridge University Press
3. Highman N (1998), Handbook of Writing for the Mathematical Sciences, SIAM.
Highman’sbook.
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27
4. Adrian Wallwork, English for Writing Research Papers, Springer New York Dordrecht
Heidelberg London, 2011
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1: List of section of research paper and preparation of paper writing
CO2: Determine key skill while writing research paper
CO3: Analyse literature, methods
CO4: Assess research paper, do paraphrasing paper
CO5: Formulate research paper and results of simulation
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=20 Marks Total: Marks
50
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3
CO2 3
CO3 3
CO4 3
CO5 3
1: Low 2: Medium 3:High
BI-36
28
SEMISTER-II
BI-37
29
Course Code 18CS2C01 M.Tech(Bioinformatics)
Category Theory-Professional Core
Course title ADVANCED DATA STRUCTURES AND ALGORITHMS
Scheme and
Credits
No. of Hours/Week Semester – II
L T P SS Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3
Hrs
Prerequisites (if any):
COURSE OBJECTIVE
The course will enable the student to:
1. Learn various data structures and its usage in designing algorithms.
2. Understand to the advanced methods of designing and analysing algorithms.
3. Learn various string matching and graph algorithms.
4. Acquire the knowledge of polynomial, non-polynomial and approximation problems.
5. Understand the recent developments in the area of algorithmic design
UNIT-1 REVIEW OF ANALYSIS TECHNIQUES 09 Hours
Growth of Functions: Asymptotic notations; Standard notations and common functions;
Recurrences -The substitution method, recursion-tree method, the master method, Probabilistic
Analysis and Randomized Algorithms.
UNIT- II BASIC DATA STRUCTURES 09 Hours
Stacks and Queues, Vectors, Lists, and Sequences, Trees, Priority Queues and Heaps,
Dictionaries and Hash Tables, Search Trees and Skip lists- Ordered Dictionaries and Binary
Search Trees, AVL Trees, Bounded-Depth Search Trees, Splay Trees, Skip Lists.
UNIT -III DYNAMIC PROGRAMMING 10 Hours
Matrix-Chain multiplication, Elements of dynamic programming, longest common
subsequence’s. Graph algorithms: Bellman - Ford Algorithm; Single source shortest paths in a
DAG; Johnson’s Algorithm for sparse graphs; Flow networks and Ford-Fulkerson method.
.
UNIT- IV TRIES AND STRING MACHING ALGORITHMS 10 Hours
Quad trees and K-d trees. String-Matching Algorithms: Naïve string Matching; Rabin - Karp
algorithm; String matching with finite automata; KnuthMorris-Pratt algorithm.
UNIT- V NP-COMPLETENESS 10 Hours
Polynomial time, Polynomial time verification, NP-Completeness and reducibility, NP-Complete
problems. Approximation Algorithms: vertex cover problem, the set – covering problem,
randomization and linear programming, the subset – sum problem.
UNIT- VI Recent advances and research being done in the topics mentioned above units
BI-38
30
REFERENCES
1. Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein,” Introduction to
Algorithms”, Third Edition, Prentice-Hall, 2011.
2. M T Goodrich, Roberto Tamassia, “Algorithm Design”, John Wiley, 2002.
3. Mark Allen Weiss, “Data Structures and Algorithm Analysis in C++”, 4th Edition, Pearson,
2014.
4. Alfred V. Aho, John E. Hopcroft, Jeffrey D. Ullman, ―Data Structures and Algorithms‖,
Pearson Education, Reprint 2006.
5. Ellis Horowitz, Sartaj Sahni, Dinesh Mehta, “Fundamentals of Data Structures in C”, Silicon
Pr, 2007.
6. Aho, Hopcroft and Ullman, Data structures and algorithms, 1st edition, Pearson Education,
India, 2002, ISBN: 8177588265, 978817758826
COURSE OUTCOMES
On completion of the course, the student will be able to:
CO1: Develop and analyze algorithms for red-black trees, B-trees and Splay trees and for text
processing applications.
CO2: Identify suitable data structures and develop algorithms for solving a particular set of
problems
CO3: Analyze the complexity/ performance of different algorithms.
CO4: Categorize the different problems in various classes according to their complexity.
CO5: Use appropriate data structure and algorithms in real time applications.
Scheme of Examination
CIE -50
Marks
Test I (Unit I,II, & III)-
15 Marks
Test II (Unit IV & V) -15
Marks
Quiz/ AAT = 5 Marks
Unit- VI(AAT) =15 Marks
Total: Marks
50
SEE-100
Marks
Unit which have 10 Hours shall have
internal choice
20*3=60
Marks
Total:
Marks 100 Unit which have 09 Hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced for
50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2 2
CO3 2 2
CO4 2
CO5 2 2
1: Low 2: Medium 3:High
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Course Code 18CS2C02 M.Tech (Bioinformatics)
Category Theory-Professional Core
Course title ADVANCED OPERATING SYSTEMS
Scheme and
Credits
No. of Hours/Week Semester – II
L T P SS Credits
4 0 - - 4
CIE Marks: 50 SEE Marks:
50
Total Max. Marks: 100 Duration of SEE: 3
Hrs
Prerequisites (if any):
COURSE OBJECTIVES
The course will enable the students to:
1. Understand the Design Approaches and Issues related to Advanced Operating Systems.
2. Apply the Knowledge on Distributed Operating Systems Concepts to develop Clocks,
Mutual Exclusion Algorithms.
3. Analyze the Distributed Deadlock Detection Algorithms and Agreement Protocols.
4. Evaluate the Algorithms for Distributed Scheduling, Examine the Commit Protocols and
review Concurrency Control Algorithms.
5. Create Advanced Operating Systems Applications with recent technologies
UNIT- I INTRODUCTION 09 Hours
Concept of Batch system, Multiprogramming, Time Sharing, Parallel, Distributed and Real-time
System, Process Management: Concept of Process, Synchronization, CPU Scheduling, IPC,
Deadlock: Concept of Deadlock Prevention, Avoidance, Detection and its Recovery. Memory
Management: Contiguous allocation, Paging and Segmentation. Virtual memory: Demand
Paging, Page Replacement Algorithms. Distributed OS- Design Approaches and Issues in DOS.
Message Passing Model and RPC.
UNIT -II CLOCKS AND DISTRIBUTED MUTUAL EXCLUSION: 10 Hours
Concept of Lamport’s Logical Clock and Vector Clocks, Termination Detection. A simple
solution to distributed mutual exclusion, Non Token based algorithms: Lamport’s algorithm,
Ricart Agarwala’s algorithm, Maekawa’s algorithm, Token based algorithms: Suzuki Kasami’s
broadcast algorithm, Raymond’s tree based algorithm.
UNIT -III DISTRIBUTED DEADLOCK DETECTION: 10 Hours
Deadlock handling, Strategies in Distributed Systems, Issues in Deadlock detection and
resolution, Control Organization for distributed deadlock detection, Centralized deadlock
detection algorithm: The Ho Ramamoorthy’s algorithm, Distributed deadlock detection
algorithms: A path- pushing algorithm and Edge chasing algorithm, Hierarchical deadlock
detection algorithms: The Menasce- Muntz Algorithm, The Ho Ramamoorthy’s algorithm.
Agreement Protocols: The Byzantine Agreement Problem, Solution to the Byzantine Agreement
Problem- Lamport -Shostak- Pease algorithm, Dolev et al.’s algorithm
UNIT –IV DISTRIBUTED SCHEDULING AND FAULT TOLERANCE 10 Hours
Issues in load distribution, components of a load distributing algorithms, load Distributing
algorithms, performance comparison, selecting suitable load sharing algorithms, Requirements
of load sharing policies. Commit Protocols, Nonblocking Commit Protocols, Voting Protocols,
Dynamic Voting Protocols, The Majority Based Dynamic Voting Protocol, Dynamic Vote
Reassignment Protocols.
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UNIT -V DATABASE OS & CONCURRENCY CONTROL 09 Hours
Requirement of Database OS, A Concurrency Control Model of a Database System, The Problem
of concurrency control, Serializability Theory, Concurrency control algorithms, Basic
Synchronization Primitives, Lock Based algorithms, timestamp based algorithms, Optimistic
algorithms, and Concurrency Control algorithms for data replication.
UNIT- VI Recent advances and research being done in the topics mentioned above units
REFERENCES
1. Mukesh Singhal and Niranjan G Shivaratri, Advanced Concepts in Operating Systems, Tata
Mcgraw Hill, 2002.
2. A Silberchatz and Peter Baer Galvin, Operating System Concepts, 10th Edition, John Wiley
and Sons, 2018.
3. William Stallings, Operating System: Internals and Design Principles, 9th Edition, Prentice
Hall India, 2017.
4. Andrew S Tennenbaum and Albert S Woodhull, Operating System Design and
Implementation, 3rd Edition, Pearson Education Inc., 2006.
5. Ceri S and Pelagorthi S, Distributed Databases: Principles and Systems, 1984, McGraw Hill.
COURSE OUTCOMES
On completion of the course, the student would be able to:
CO1: Explain the Fundamentals of Advanced Operating Systems, Design issues.
CO2: Determine the various Clock Synchronization Principles and Implement Mutual
Exclusion Algorithms.
CO3: Differentiate the various Distributed Deadlock Detection Algorithms and Analyze the
Agreement Protocols.
CO4: Select the appropriate Distributed Scheduling Algorithms, Commit Protocols and
Concurrency Control Algorithms.
CO5: Integrate the Concepts of Advanced Operating Systems with recent trends and
technologies to Design and Develop Applications.
Scheme of Examination
CIE -50
Marks
Test I (Unit I,II, & III)-15
Marks
Test II (Unit IV & V) -15
Marks
Quiz/ AAT = 5 Marks
Unit- VI(AAT) =15 Marks
Total: Marks 50
SEE-
100
Marks
Unit which have 10 Hours shall have internal
choice
20*3=60
Marks Total:
Marks 100 Unit which have 09 Hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced for 50
marks
Mapping of course Outcomes(COs) to Program Outcomes(POs)
PO1 PO2 PO3
CO1 1 -
CO2 1 2
CO3 1 2
CO4 1 3
CO5 3 2 2
1. LOW, 2. MEDIUM, 3.HIGH
BI-41
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Course Code 18BI2C03 M.Tech(Bioinformatics)
Category Theory-Professional Core
Course Title STRUCTURAL BIOINFORMATICS
Scheme and Credits No. of Hours/Week Semester-II
L T P SS Credits
4 0 4
CIE Marks:50 SEE Marks:50 Total Marks:50 Duration of SEE:03 Hours
Prerequisite(if any):
COURSE OBJECTIVES:
The course will enable the students to:
1. Understand various data format for structural databases
2. Learn importance of structure-function relationship of biomolecules
3. Learn how various interactions played major for biomolecules
4. Knowledge about predicting the structure of biomolecules
5. Understand the essence of structural validation
UNIT I - DATA REPRESENTATION AND DATABASES 09 Hours
PDB, mmCIF and other formats, structure based databases for proteins and nucleic acids. Comparative
features-the CATH domain structure Database, Protein structure evolution and the SCOP Database.
UNIT II - DATA INTEGRITY AND COMPARATIVE FEATURES 09 Hours
Structural Quality Assurance, Structure Comparison and Alignment. Structure and Functional
Assignment-Identifying Structural Domains in Proteins, Inferring Protein Function from Structure.
UNIT III - BIOMOLECULES INTERACTION 10 Hours Electrostatic interactions, Prediction of Protein- protein interactions, Prediction of Protein- nucleic acid
interactions, Docking Methods: Introduction, Docking and scoring, Application in the drug design
UNIT IV - STRUCTURAL MODELING 10 Hours Scoring functions: force fields, surface area based functions, knowledge based potentials, searching
procedures: grid based, stochastic methods, building complete protein structures using homology
modelling, fold recognition, Ab initio methods, Analysis of Folds.
UNIT V - STRUCTURAL VALIDATION AND APPLICATION 10 Hours
Validation: CASP and CAFASP experiments and their findings, Structural bioinformatics in drug design:
Modern drug discovery, Drug target, Lead identification, Lead Optimization.
UNIT VI - Recent advances and research being done in the topics mentioned above units
REFERENCES BOOKS:
1. Philip E. Bourne, HelgeWeissig, “Structural Bioinformatics”, John Wiley & Sons, Inc, 2003.
2. Becker OM., MackKerell AD Jr., Roux B., Watanabe M (Eds.), “Computational Biochemistry and
Biophysics”, Dekker, 2001.
3. Hinchliffe A., “Molecular Modelling for Beginners”, Wiley, 2003.
4. Orengo CA, Jones DT, Thornton, JM (Eds.), “Bioinformatics - Genes, Proteins and Computers”,
Bios Scientific Publishers Ltd., 2003..
COURSE OUTCOMES:
On completion of the course, the student would be able to:
CO1: Identify structure based databases for proteins and nucleic acids.
CO2: Apply biomolecules interaction and structural modelling to design of drugs
CO3: Analyse CASP and CAFASP experiments and their findings
CO4: Evaluate modern drug discovery, drug target, lead identification, lead Optimization
CO5: Build complete protein structures using homology modelling
Scheme of Examination
BI-42
34
CIE -50
Marks
Test I (Unit I,II, & III)-15 Marks
Test II (Unit IV & V) -15 Marks
Quiz/ AAT = 5 Marks
Unit- VI(AAT) =15 Marks
Total: Marks 50
SEE-100
Marks
Answer Five Full Questions
Unit which have 10 Hours shall have internal
choice
20*3=60
Marks Total : Marks
100 Unit which have 09 Hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced for 50 marks
Mapping of course Outcomes(COs) to Program Outcomes(POs)
PO1 PO2 PO3
CO1 3
CO2 3
CO3 2
CO4 3
CO5 3 2
1. LOW, 2. MEDIUM, 3.HIGH
BI-43
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Course Code 18BI2E1A M.Tech(Bioinformatics)
Category Theory-Professional Elective
Course Title ENZYME KINETICS
Scheme and Credits No. of Hours/Week Semester-II
L T P SS Credits
4 0 4
CIE Marks:50 SEE Marks:50 Total Marks:50 Duration of SEE:03 Hours
Prerequisite(if any):
COURSE OBJECTIVES:
The course will enable the students to:
1. Enhance skills in the areas of biochemical processes
2. Provide the fundamental background of biological systems,
3. Bio-molecules, micro-organisms, fermentation processes, Bioreactors and kinetics.
UNIT I - ENZYMES AND PROTEINS 09 Hours
Enzymes and Proteins: Detailed structure of proteins and enzymes. Functions. Methods of Production
and purification of Enzymes. Nomenclature and Classification of enzymes. Kinetics and mechanism of
Enzyme action: Michaelis–Menten and Briggs Haldane approach. Derivation.
UNIT II - KINETICS OF ENZYME ACTION: 09 Hours
Reversible Enzyme. Two-substrate. Multi-complexes enzyme kinetics (Derivation of rate equations).
Experimental determination of rate parameters: Batch and continuous flow experiments. Lineweaver–
Burk, Eadie-Hofstee and Hanes-Woolf Plots. Batch Kinetics (Integral and Differential methods).
UNIT III - ENZYME INHIBITION: 10 Hours
Effect of Inhibitors (Competitive, non-competitive, uncompetitive, substrate and product inhibitions),
Temperature and pH on the rates enzyme catalyzed reactions. Determination of kinetic parameters for
various types of inhibitions. Dixon method. Enzyme immobilization: Uses. Methods of enzyme
immobilization.
UNIT IV - FERMENTATION TECHNOLOGY: 10 Hours
Ideal reactors: A review of Batch and Continuous flow reactors for biokinetic measurements.
Microbiological reactors: Operation and maintenance of typical aseptic aerobic fermentation processes.
Formulation of medium: Sources of nutrients. Alternate bioreactor configurations. Introduction to
sterilization of bioprocess equipment.
UNIT V - GROWTH KINETICS OF MICROORGANISMS: 10 Hours
Transient growth kinetics (Different phases of batch cultivation). Quantification of growth kinetics:
Substrate limited growth, Models with growth inhibitors, Logistic equation, Filamentous cell growth
model. Continuous culture: Optimum Dilution rate and washout condition in Ideal Chemostat.
Introduction to Fed-batch reactors.
UNIT VI - Recent advances and research being done in the topics mentioned above units
REFERENCES :
1. Biochemical Engineering Fundamentals, Bailey and Ollis, II Edition, McGraw Hill, 1976.
2. Bioprocess Engineering, Shuler M. L. and Kargi F., 2ndEdition, Prentice Hall, 2002.
3. Biochemical Engineering, James Lee, Prentice Hall, 1992.
4. Biochemical Reactors, Atkinson B, Pion Ltd., London, 1974.
5. Industrial Microbiology, Casida, Wiley, New York, 1968
6. Principles of Fermentation Technology, Stanbury and Whitekar, 2ndEdition, Butterworth-
Heinemann An Imprint of Elsevier
COURSE OUTCOMES:
On completion of the course, the student would be able to:
CO1: Explain structure of proteins and enzymes , nomenclature and classification of enzymes
CO2: Determination of kinetic parameters for various types of inhibitions.
CO3: Differentiate batch and continuous flow reactors
CO4 : Assess substrate limited growth, Models with growth inhibitors, Logistic equation, Filamentous
cell growth model
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CO5: Develop methods of enzyme immobilization.
Scheme of Examination
CIE -50
Marks
Test I (Unit I,II, & III)-15
Marks
Test II (Unit IV & V) -15 Marks
Quiz/ AAT = 5 Marks
Unit- VI(AAT) =15 Marks
Total: Marks 50
SEE-100
Marks
Unit which have 10 Hours shall have internal
choice
20*3=60
Marks Total : Marks
100 Unit which have 09 Hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced for 50 marks
Mapping of course Outcomes(COs) to Program Outcomes(POs)
PO1 PO2 PO3
CO1 2
CO2 2
CO3 3
CO4 3
CO5 3 2
1. LOW, 2. MEDIUM, 3.HIGH
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Course Code 18BI2E1B M.Tech(Bioinformatics)
Category Theory-Professional Elective
Course Title NEXT GENERATION SEQUENCING TECHNOLOGIES
Scheme and Credits No. of Hours/Week Semester-II
L T P SS Credits
4 0 4
CIE Marks:50 SEE Marks:50 Total Marks:50 Duration of SEE:03 Hours
Prerequisite(if any):
COURSE OBJECTIVES:
The course will enable the students to:
1. Know the application of NGS in various areas of research.
2. Highlight the various techniques (software’s and tools) used for NGS analysis.
3. Provide in-depth understanding of the use of NGS in clinical and medical diagnostic approach.
UNIT I - NEXT GENERATION SEQUENCING 10 Hours
Sanger sequencing principles - history and landmarks, of Sequencing Technology Platforms, A survey
of next-generation sequencing technologies, A review of DNA enrichment technologies, Application of
High-Throughput Sequencing, Application of NGS to the diagnosis of genetic disorders,
Computational Infrastructure and Basic Data Analysis.
UNIT II - INTERACTION ANALYSIS OF CHIP-SEQ 10 Hours
Base-Calling for Bio-informaticians, De Novo Short-read Assembly, Short-Read Mapping, DNA-Protein
Interaction Analysis (CHIP-Sequence), Generation and Analysis of Genome-Wide DNA Methylation
Maps, Differential Expression for RNA Sequencing (RNA-Sequence) Data: Mapping, summarization,
statistical Analysis and Experimental Design.
UNIT III - ANALYSIS OF METAGENOMIC DATA 09 Hours MicroRNA Expression Profiling and Discovery, Dissecting Splicing Regulatory Network by Integrative
Analysis of CLIP-Sequence Data, Analysis of Metagenomic Data, NGS-based noninvasive prenatal
diagnosis, Diagnosis of inherited neuromuscular disorders by NGS Application of NGS in hearing loss
diagnosis.
UNIT IV - EXOME SEQUENCING 10 Hours
Exome sequencing as a discovery and a diagnostic tool, Challenges of NGS based molecular diagnostics,
NGS-Based Clinical Diagnosis of Genetically Heterogeneous Disorders, Molecular Diagnosis of
Congenital Disorders of Glycosylation (CDG), NGS improves the Diagnosis of X-Linked Intellectual
Disability (XLID), NGS Analysis of Heterogeneous Retinitis Pigmentosa.
UNIT V - NGS ANALYSIS OF THE WHOLE MITOCHONDRIAL GENOME NGS 09 Hours
Analysis of the Whole Mitochondrial Genome, Noninvasive Prenatal Diagnosis Using Next-Generation
Sequencing, High-Throughput Sequencing Data Analysis Software: Current state and future
developments.
UNIT VI - Recent advances and research being done in the topics mentioned above units
Reference Books:
1. Valencia, C.A., Pervaiz, M.A., Husami, A., Qian, Y., Zhang, K, “Next Generation Sequencing
Technologies in Medical Genetics”, Springer, 2013.
2. Lee-Jun C. Wong, “Next Generation Sequencing: Translation to Clinical Diagnostics”, Springer,
2013.
3. Naiara Rodríguez-Ezpeleta, Michael Hackenberg, “Bioinformatics for High Throughput
Sequencing”, Springer, 2012.
4. Masoudi-Nejad, Ali, Narimani, Zahra, Hosseinkhan, Nazanin, “Next Generation Sequencing and
Sequence Assembly: Methodologies and Algorithms”, Springer, 2013.
5. Wu, Wei, Choudhry, Hani (Eds.), “Next Generation Sequencing in Cancer Research: Volume 1:
Decoding the Cancer Genome”, Springer, 2013.
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COURSE OUTCOMES:
On completion of the course, the student would be able to:
CO1: Understand Exome sequencing as a discovery and a diagnostic tool
CO2: Discuss MicroRNA expression profiling and discovery
CO3: Analyse application of NGS to the diagnosis of genetic disorders
CO4: Evaluate diagnosis of inherited neuromuscular disorders by NGS Application of NGS in hearing
loss diagnosis.
CO5: Design and develop Sequencing Data Analysis Software
Scheme of Examination
CIE -50
Marks
Test I (Unit I,II, & III)-15
Marks
Test II (Unit IV & V) -15 Marks
Quiz/ AAT = 5 Marks
Unit- VI(AAT) =15 Marks
Total: Marks 50
SEE-100
Marks
Unit which have 10 Hours shall have internal
choice
30*2=60
Marks Total : Marks
100 Unit which have 09 Hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced for 50 marks
Mapping of course Outcomes(COs) to Program Outcomes(POs)
PO1 PO2 PO3
CO1 3
CO2 2
CO3 3
CO4 3
CO5 3 2
1. LOW, 2. MEDIUM, 3.HIGH
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Course Code 18BI2E1C M.Tech(Bioinformatics)
Category Theory-Professional Elective
Course Title MICROARRAY BIOINFORMATICS
Scheme and Credits No. of Hours/Week Semester-II
L T P SS Credits
4 0 4
CIE Marks:50 SEE Marks:50 Total Marks:50 Duration of SEE:03 Hours
Prerequisite(if any):
COURSE OBJECTIVES:
The course will enable the students to:
1. DNA Microarray and its statistical analysis.
2. Analysis of RNA data. Statistical computing and Statistical Genetics.
3. Hierarchical Clustering, Self-Organization Maps (SOM), Identifying Genes
UNIT-I DNA MICROARRAY 09 Hours The Technical Foundations, Why are MicroArray Important?, What is a DNA MicroArray?, Designing
a MicroArray Experiment-The Basic steps, Types of MicroArray.
UNIT –II MICROARRAY DATABASES 09 Hours
NCBI and MicroArray Data Management, GEO (Gene Expression Omnibus), MAML, The benefits of
GEO and MAML, The Promise of MicroArray Technology in Treating Disease.
UNIT–III: MICROARRAY DATA NORMALIZATION 10 Hours
Micro-Array Data Pre-processing, Data-Data normalization, Measuring Dissimilarity of Expression
Pattern-Distance Motifs and Dissimilarity measures, Visualizing Micro-Array Data-Principal
Component Analysis, Micro-Array Data.
UNIT –IV MICROARRAY DATA ANALYSIS 10 Hours
KMeans Clustering, Hierarchical Clustering, Self-Organization Maps (SOM), Identifying Genes:
Expressed usually in a sample- Expressed significantly in population-Expressed differently in two
populations, Classifying Samples from two populations using Multilayer Perceptron, Support Vector
Machines and their applications, Using genetic algorithm and Perceptron for feature selection and
supervised classification..
UNIT-V MICROARRAY APPLICATIONS 10 Hours
Gene Ontology and pathway analysis, Promoter analysis and gene regulatory network, Coexpression
analysis, CGH & Genotyping chips, Chromosome aberration and polymorphism via genome-wide
scanning, Future direction of microarray approach, Pharmacogenomics, Toxicogenomics, Data mining.
UNIT- VI Recent advances and research being done in the topics mentioned above units
REFERENCE BOOKS:
1. ArunJogota, “Microarray Data Analysis and Visualization”, the Bay Press, 2001.
2. Ernst Wit and John McClure, “Statistics for Microarrays Design, Analysis and Inference”, John
Wiley & Sons, 2004.
3. Steen Knudsen, “Guide to analysis of DNA Microarray data”, John Wiley & Sons, 2004.
4. DovStekel, “Microarray Bioinformatics”, Cambridge University Press, 2003.
5. Draghic S., Chapman, “Data Analysis tools for DNA Microarray”, Hall/ CRC Press, 2002.
6. Uwe R. Müller, Dan V. Nicolau,“Microarray Technology and Its Applications”, Springer, 2005.
COURSE OUTCOMES:
On completion of the course, the student would be able to:
CO1: K-Means Clustering, Hierarchical Clustering, Self-Organization Maps (SOM), Identifying Genes
CO2: Using genetic algorithm and Perceptron for feature selection and supervised classification.
CO3: Apply Gene Ontology and pathway analysis, Promoter analysis and gene regulatory network
CO4: Evaluate Support Vector Machines and their applications
CO5: Develop CGH & Genotyping chips, Chromosome aberration and polymorphism via genome-
wide scanning
Scheme of Examination
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CIE -50
Marks
Test I (Unit I,II, & III)-15
Marks
Test II (Unit IV & V) -15 Marks
Quiz/ AAT = 5 Marks
Unit- VI(AAT) =15 Marks
Total: Marks 50
SEE-100
Marks
Unit which have 10 Hours shall have internal
choice
20*3=60
Marks Total : Marks
100 Unit which have 09 Hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced for 50 marks
Mapping of course Outcomes(COs) to Program Outcomes(POs)
PO1 PO2 PO3
CO1 3
CO2 2
CO3 3
CO4 3
CO5 3 2
1. LOW, 2. MEDIUM, 3.HIGH
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Course Code 18BI2E2A M.Tech(Bioinformatics)
Category Theory-Professional Elective
Course Title MOLECULAR MECHANICS AND SIMULATION
Scheme and Credits No. of Hours/Week Semester-II
L T P SS Credits
4 0 4
CIE Marks:50 SEE Marks:50 Total Marks:50 Duration of SEE:03 Hours
Prerequisite(if any):
COURSE OBJECTIVES:
The course will enable the students to:
1. Understand the basic concepts in Molecular Mechanics.
2. Learn empirical force field models.
3. Understand computer simulation techniques and conformational analysis
UNIT-I CONCEPTS IN MOLECULAR MECHANICS 09 Hours Concepts In Molecular Mechanics: Introduction, Coordinate systems, Units of Length and Energy,
Potential Energy surfaces, other surfaces, Molecular Graphics. .
UNIT –II COMPUTATIONAL QUANTUM MECHANICS 09 Hours
Computational Quantum Mechanics: One-electron atoms, Poly electron atoms and molecules, Molecular
orbitals, Hartree-Fock Equations, Molecular Properties using ab initio methods, Semi-empirical methods,
Huckel Theory..
UNIT –III EMPIRICAL FORCE FIELD METHODS 10 Hours
Empirical Force Field Methods: Bond Stretching, Angle Bending, Torsional Terms, Non bonded and
ectrostatic interactions, Van der Waals Interaction, Hydrogen bonding parameterization, United atom
force field representation, Force field parameterization.
UNIT –IV COMPUTER SIMULATION METHODS 10 Hours
Computer Simulation Methods: Simple Thermodynamic properties, Phase space, Practical aspects of
Computer simulation, Boundaries, Truncating the potential, Minimum Image convention, Long range
forces. Conformational Analysis: Systematic methods for exploring conformational space, Random
search methods, Evolutionary algorithms, Simulated Annealing, Restrained molecular methods,
Molecular fitting, Clustering algorithm, Reducing dimensionality of data set, Pooling.
UNIT-V MONTE CARLO SIMULATIONS 10 Hours
Monte Carlo Simulations: Calculating properties by integration, metropolis methods- metropolis Monte
Carlo methods- simulations of molecules- models- biased methods- different ensembles calculating
chemical potentials- Gibbs ensemble methods.
UNIT-VI Recent advances and research being done in the topics mentioned above units
References:
1. Andrew R. Leach, “Molecular Modeling: Principles and applications”, Prentice Hall, 2ndedition,
1996.
2. Alan Hinchliffe, “Modelling Molecular Structures”, John Wiley, 2000.
3. Ramachandran K. I., G. Deepa, K.Namboori,“Computational Chemistry and Molecular Modeling:
Principles and Applications”, Springer, 2008.
4. Charles R. Cantor, Paul ReinhardSchimmel, “Biophysical Chemistry: The Behavior of Biological
Macromolecules PART III”, W. H. Freeman, 1980.
COURSE OUTCOMES:
On completion of the course, the student would be able to:
CO1: Explain Monte Carlo Simulations for application
CO2: Apply Computational Quantum Mechanics for analysis
CO3: Verify systematic methods for exploring conformational space, Random search methods
CO4: Assess different ensembles calculating chemical potentials- Gibbs ensemble methods.
CO5: Investigate exploring conformational space, Random search methods, Evolutionary algorithms
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Scheme of Examination
CIE -50
Marks
Test I (Unit I,II, & III)-15
Marks
Test II (Unit IV & V) -15 Marks
Quiz/ AAT = 5 Marks
Unit- VI(AAT) =15 Marks
Total: Marks 50
SEE-100
Marks
Unit which have 10 Hours shall have internal
choice
20*3=60
Marks Total : Marks
100 Unit which have 09 Hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced for 50 marks
Mapping of course Outcomes(COs) to Program Outcomes(POs)
PO1 PO2 PO3
CO1 2
CO2 3
CO3 3
CO4 3
CO5 3 2
1. LOW, 2. MEDIUM, 3.HIGH
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Course Code 18BI2E2B M.Tech(Bioinformatics)
Category Theory-Professional Elective
Course Title SYSTEM BIOLOGY
Scheme and Credits No. of Hours/Week Semester-II
L T P SS Credits
4 0 4
CIE Marks:50 SEE Marks:50 Total Marks:50 Duration of SEE:03 Hours
Prerequisite(if any):
COURSE OBJECTIVES:
The course will enable the students to:
1. Learning the Principles of Systems Biology
2. Learning the Standard models and approaches
3. Understand signal transduction and other biological processes
4. Understand modeling of gene expression.
UNIT-I MATLAB USAGE 09 Hours Data types, data structures, conditional loops, 2D and 3D plots, matrix operations, ODE solvers, curve
fitting. MM kinetics, numerical solutions to first order ordinary differential equations (ODE) using
MATLAB. Introduction to system biology and mathematical modelling.
UNIT –II STATIC NETWORK MODELS 10 Hours
Interaction graphs, Bayesian reconstruction of interaction networks, signalling networks, metabolic
networks, modelling with ODE's with examples. Discrete and continuous linear system models,
continuous non-linear systems, stability analysis, parameter sensitivity, parameter estimation, linear
regression of several variables. Physiological modelling: simple models of oscillations with heart as an
example, few more examples..
UNIT-III GENE REGULATION 10 Hours
Models of regulation, transcription factors, gene interaction network, Lac Operan as an example. Protein
system, proteins as enzymes, transporters and carriers, protein protein interaction network, protein-
promoter interactions, comparison of system biology between prokaryotes Vs. Complex eukaryotes.
UNIT – IV METABOLIC PATHWAYS AND THEIR REPRESENTATION 10 Hours
KEGG. Mathematical formulation of elementary biochemical reactions, metabolic flux analysis,
modelling metabolic pathways with ODE, Pharmaco kinetic models (PBPK) with examples, signal
transduction systems. Population systems: Population growth, models of population growth, population
dynamics under external perturbations.
UNIT-V RESOURCE USAGE 09 Hours Resource usage with case studies, in protein-protein interactions, protein-promoter interactions,
pathways and cross-talk between pathways. Comparison of systems biology for prokaryotes vs. complex
eukaryotes.
UNIT –VI Recent advances and research being done in the topics mentioned above units
REFERENCES:
1. Advanced analysis of gene expression microarray data; A. Zhang; World Scientific Publishing,
2006.
2. Systems biology: A textbook; E. Klipp et.al. Wiley, 2009.
3. DNA microarrays; M. Schena; Scion Publishing, 2006.
4. Computational Systems Biology of Cancer; E. Barillot, L. Calzone, P. Hupe and J-P Vert;
CRC Press; 2013.
5. Matlab: A practical introduction to programming and problem solving; S. Attaway,
Butterworth-Hienemann, 2009.
6. Matlab for neuroscientists: An introduction to scientific computing in matlab; P Wallisch, M
Lusignan, M Benayoun, and T. I. Baker, Elsevier, 2009.
7. Essentials of MATLAB programming; S. J. Chapman, 2nd Edition, BAE SYSTEMS,
Australia.
COURSE OUTCOMES:
On completion of the course, the student would be able to:
CO1: Explain bayesian reconstruction of interaction networks, modelling metabolic pathways with ODE
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CO2: Apply Physiological modelling to determine models of oscillations with heart
CO3: Analyse formulation of elementary biochemical reaction
CO4: Evaluate resource usage with case studies
CO5: Design evolution model and self-organization
Scheme of Examination
CIE -50
Marks
Test I (Unit I,II, & III)-15
Marks
Test II (Unit IV & V) -15 Marks
Quiz/ AAT = 5 Marks
Unit- VI(AAT) =15 Marks
Total: Marks 50
SEE-100
Marks
Unit which have 10 Hours shall have internal
choice
20*3=60
Marks Total : Marks
100 Unit which have 09 Hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced for 50 marks
Mapping of course Outcomes(COs) to Program Outcomes(POs)
PO1 PO2 PO3
CO1 2
CO2 2
CO3 3
CO4 3
CO5 3 2
1. LOW, 2. MEDIUM, 3.HIGH
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Course Code 18BI2E2C M.Tech(Bioinformatics)
Category Theory-Professional Elective
Course Title PYTHON FOR BIOINFORMATICS
Scheme and Credits No. of Hours/Week Semester-II
L T P SS Credits
4 0 4
CIE Marks:50 SEE Marks:50 Total Marks:50 Duration of SEE:03 Hours
Prerequisite(if any):
COURSE OBJECTIVES:
The course will enable the students to:
1. Apply Python for bioinformatics applications.
2. Describe Object oriented programming in Python and different module
3. Biological sequence analysis using Python. Describe advanced analysis techniques using
Python. Describe expression analysis using Python
UNIT- I PYTHON FUNDAMENTALS: 09 Hours Running programs, types and operations, Functions, modules, classes, Exceptions.
UNIT –II OBJECT ORIENTED PROGRAMMING MODULES: 10 Hours
Object Oriented Programming, Threads, process, synchronization, databases and persistence, NumPy,
SciPy, image manipulation, Akando and Dancer modules.
UNIT –III BIOLOGICAL SEQUENCE ANALYSIS: 10 Hours Biopython: Parsing DNA data files, Sequence Alignment, Dynamic programming, Hidden Markov
Model, Genetic algorithms, Multiple Sequence Alignment, gapped alignment.
UNIT –IV ADVANCED ANALYSIS TECHNIQUES : 10 Hours
Trees, text mining, clustering, Self-Organizing Map, Principal Component Analysis, Fourier transforms,
Numerical Sequence Alignment.
UNIT-V EXPRESSION ANALYSIS: 09 Hours
Gene expression array analysis, Spot finding and Measurement, Spreadsheet Arrays and Data Displays,
Applications with Expression Arrays
UNIT –VI Recent advances and research being done in the topics mentioned above units
REFERENCES BOOKS
1. Jason Kinser, “Python for Bioinformatics”, Jones & Bartlett Publishers, 2008.
2. Mark Lutz, “Learning Python”, 3rd edition, O'Reilly, 2007.
3. Alex Martelli, David Ascher, “Python cookbook”, O'Reilly, 2002.
COURSE OUTCOMES:
On completion of the course, the student would be able to:
CO1: Understand python, Object Oriented Programming, Threads, process, synchronization, databases
and persistence
CO2: Apply Python for bioinformatics applications
CO3: Evaluate program of Trees, text mining, clustering
CO4: Implement Bio-python programs for Parsing DNA data files
CO5: Design program for Spreadsheet Arrays and Data Displays
Scheme of Examination
CIE -50
Marks
Test I (Unit I,II, & III)-15 Marks
Test II (Unit IV & V) -15 Marks
Quiz/ AAT = 5 Marks
Unit- VI(AAT) =15 Marks
Total: Marks 50
SEE-100
Marks
Unit which have 10 Hours shall have internal
choice
20*3=60
Marks Total : Marks
100 Unit which have 09 Hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced for 50 marks Mapping of course Outcomes(COs) to Program Outcomes(POs)
PO1 PO2 PO3
CO1 3
CO2 3
CO3 3
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CO4 3
CO5 3 2
1. LOW, 2. MEDIUM, 3.HIGH
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Course Code 18CS2L01 M.Tech(Bioinformatics)
Category Practical
Course title ADVANCED DATS STRUCTURES AND ALGORITHMS LABORATORY
Scheme and
Credits
No. of Hours/Week Semester – II
L T P SS Credits
0 0 4 0 2
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
1. Data structures and Algorithm
2. Java Programming
Course Objectives: The course will enable the students to:
1. Acquire the knowledge of using advanced data structures
2. Acquire the knowledge of sorting and balancing the tree structure
3. Understand the usage of graph structures and string matching.
4. Understand the implementation of various string matching algorithms.
5. learn to solve the various NP complete problems
Each student has to work individually on assigned lab exercises. Lab sessions could be scheduled as
one contiguous four-hour session per week. It is recommended that all implementations are carried
out in Java. Exercises should be designed to cover the following topics:
1. Doubly Circular Linked List
2. AVL Tree
3. Efficiency of Heap Sort & Quick Sort
4. Travelling Salesman Problem (Dynamic Programming)
5. N Queens Problem (Backtracking/ Branch & Bound)
6. Bellman-Ford algorithm
7. Shortest paths in a DAG
8. Ford-Fulkerson algorithm
9. Robin-Karp algorithm
10. Knuth-Morris-Pratt algorithms
11. String matching with Finite Automata
12. Vertex Cover problem
13. The Set Covering problem
14. The Subset-Sum problem
15. Maximum Bipartite algorithm
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1: Design and implement basic and advanced data structures extensively.
CO2: Design and apply graph structures for various applications.
CO3: Design and develop efficient algorithms with minimum complexity using design techniques.
CO4: Design and develop advanced string matching and NP Complete problems.
CO5: Achieve proficiency in Java programming.
Scheme of Examination
For examination an experiment shall be set
Continuous Internal
Evaluation(Lab=50)
Marks Semester End Evaluation (SEE) Marks
Performance of the student in the
lab every week
20 Write-Up 20
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1. Low, 2. Medium, 3. High
Test at end of the semester 20 Experiment/Execution 70
Vice-Voce 20 Vice-Voce 10
Total(CIE) 50 Total(SEE) 50*
Note: *= SEE shall be conducted for 100 marks and marks obtained shall be reduced for 50
marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2
CO2 2
CO3 2
CO4 2
CO5 2
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COURSE LEARNING OBJECTIVES:
The objectives of the SEMINAR-II is to prepare the students to learn to:
1. Carry out the literature review of general research area/current topic and analyse the
same effectively.
2. Prepare a technical report, reflecting his/her depth of understanding, on the selected
area/topic and prepare content rich presentation.
3. Acquire communication and time management skills for effective and impactful
presentation. Interact with peers to acquire the qualities of thoughtfulness, friendliness,
adaptability, responsiveness, and politeness in-group discussion. Overcome stage fear
during the presentation.
GUIDE LINES
1. Seminar preparation and presentation is an individual student activity.
2. Topic may be of general/ specific interest to program of engineering or electives not
offered in the semester and to be selected in consultation with the faculty/Guide.
3. Select one pertinent research paper for the seminar presentation.
4. Prepare and submit a detailed technical report of the seminar topic.
COURSE OUTCOMES:
Students shall be able to:
1. Carry out the literature survey of topic of seminar.
2. Prepare a technical report on the selected area/topic.
3. Make an effective presentation with seamless flow of content within the time allocated.
Overcome inhibition in interacting with peers and hence develop the spirit of team
work. Overcome stage fear during the presentation.
Course Code 18BI2S01 M.Tech (Bioinformatics)
Category Seminar Semester: II
Course title SEMINAR - II
Scheme and Credits
No. of Hours/Week
Total hours = 24 L T P S Credits
0 0 2 0 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any): NIL
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SCHEME OF EXAMINATION
CIE – 50
marks
Phase -1 Marks =10 Seminar Report
Marks =20
Total:50
Marks Phase -2 Marks =20
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2 3
CO2 2 3 3
CO3 2
1. Low, 2. Medium, 3. High
Scheme of Continuous Internal Evaluation (CIE):
Evaluation would be carried out in TWO phases. The Seminar Evaluation Committee
shall comprise of Chairman of the Department, Faculty/Guide and one more faculty member
nominated by Chairman. The evaluation criteria shall be as per the rubrics given below:
Rubrics for Evaluation:
Topic - Technical Relevance, Sustainability and Societal Concerns : 15%
Review of literature and Technical Content : 35%
Presentation Skills : 25%
Report : 25%
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Course Code 18CS2M01 M.Tech(Bioinformatics)
Category Audit Course - II
Course title PEDAGOGY STUDIES
Scheme and Credits No. of Hours/Week Semester – II
L T P SS Credits
2 0 - - 1
CIE Marks: 50 SEE Marks: -- Total Max. Marks: 50
Prerequisites (if any):
COURSE OBJECTIVES
This course will enable students to
1. Understand the Thematic Overview and Pedagogical practices
2. Apply professional classroom practices , curriculum and assessment
3. Analyse methodology for quality assessment of school curriculum teacher
4. Evaluate pedagogic theory and pedagogical approaches
5. Create contexts pedagogy, new curriculum and assessment metrics for future
UNIT- I INTRODUCTION AND METHODOLOGY: 6 Hours
Aims and rationale, Policy background, Conceptual framework and terminology Theories of
learning, Curriculum, Teacher education. Conceptual framework, Research questions.
Overview of methodology and Searching.
UNIT- II THEMATIC OVERVIEW: 3 Hours
Pedagogical practices are being used by teachers in formal and informal classrooms in
developing countries. Curriculum, Teacher education
UNIT- III PEDAGOGICAL PRACTICES: 6 Hours
Evidence on the effectiveness of pedagogical practices Methodology for the in depth stage:
quality assessment of included studies. How can teacher education (curriculum and
practicum) and the school curriculum and guidance materials best support effective
pedagogy? Theory of change. Strength and nature of the body of evidence for effective
pedagogical practices. Pedagogic theory and pedagogical approaches. Teachers’ attitudes
and beliefs and Pedagogic strategies.
UNIT- IV PROFESSIONAL DEVELOPMENT: 6 Hours
Professional development: alignment with classroom practices and follow-up support Peer
support Support from the head teacher and the community. Curriculum and assessment
Barriers to learning: limited resources and large class sizes
UNIT- V RESEARCH GAPS AND FUTURE DIRECTIONS: 3 Hours
Research design Contexts Pedagogy Teacher education Curriculum and assessment
Dissemination and research impact.
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UNIT- VI NEW DEVELOPMENTS IN PEDAGOGY:
REFERENCES
1. Ackers J, Hardman F (2001) Classroom interaction in Kenyan primary schools,
Compare, 31 (2): 245-261.
2. Agrawal M (2004) Curricular reform in schools: The importance of evaluation,
Journal of Curriculum Studies, 36 (3): 361-379.
3. Akyeampong K (2003) Teacher training in Ghana - does it count? Multi-site teacher
education research project (MUSTER) country report 1. London: DFID.
4. Akyeampong K, Lussier K, Pryor J, Westbrook J (2013) Improving teaching and
learning of basic maths and reading in Africa: Does teacher preparation count?
International Journal Educational Development, 33 (3): 272–282.
5. Alexander RJ (2001) Culture and pedagogy: International comparisons in primary
education. Oxford and Boston: Blackwell.
6. Chavan M (2003) Read India: A mass scale, rapid, ‘learning to read’ campaign
7. www.pratham.org/images/resource%20working%20paper%202.pdf.
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1: What pedagogical practices are being used by teachers in formal and informal
classrooms in developing countries?
CO2: What is the evidence on the effectiveness of these pedagogical practices, in what
conditions, and with what population of learners?
CO3: How can teacher education (curriculum and practicum) and the school curriculum
and guidance materials best support effective pedagogy
CO4: Assess pedagogic theory and pedagogical approaches
CO5: Design new curriculum and assessment metrics for future
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks 50
Mapping of Course Outcomes (COS) to Program Outcomes (POs) PO1 PO2 PO3
CO1 3
CO2 3
CO3 3
CO4 3
CO5 3
1: Low 2: Medium 3:High
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SEMISTER-III
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Course Code 18BI3E1A M.Tech(Bioinformatics)
Category Theory-Professional Elective
Course Title PROTEIN AND INSILICO DRUG DESIGN
Scheme and Credits No. of Hours/Week Semester-III
L T P SS Credits
4 0 4
CIE Marks:50 SEE Marks:50 Total Marks:50 Duration of SEE:03 Hours
Prerequisite(if any):
COURSE OBJECTIVES:
The course will enable the students to:
1. Understand protein structure and engineering.
2. Comprehend the construction and structure generation by molecular modelling.
3. Design drugs through molecular modelling.
4. Describe various docking methods. Explain the computer assisted and new Lead drug discovery
strategies
Unit- I PROTEIN STRUCTURE PREDICTION AND ENGINEERING: 10 Hours
Primary structure and its determination, secondary structure prediction and determination of motifs,
profiles, patterns, fingerprints, super secondary structures, protein folding pathways, tertiary structure,
quaternary structure, methods to determine tertiary and quaternary structure, post translational
modification. Methods of protein isolation, purification and quantification; large scale synthesis of
engineered proteins, design and synthesis of peptides; methods of detection and analysis of proteins.
Protein database analysis, methods to alter primary structure of proteins, examples of engineered proteins
Unit –II MOLECULAR MODELING: 09 Hours
Constructing an Initial Model, Refining the Model, Manipulating the Model, Visualization. Structure
Generation or Retrieval, Structure Visualization, Conformation Generation, Deriving Bioactive
Conformations, Molecule Superposition and Alignment, Deriving the Pharmacophoric Pattern, Receptor
Mapping, Estimating Biological Activities, Molecular Interactions: Docking, Calculation of Molecular
Properties
UNIT –III INSILICO DRUG DESIGN: 10 Hours
Generation of Rational Approaches in Drug Design, Molecular Modelling: The Second Generation,
Conceptual Frame and Methodology of Molecular Modelling, The Field Currently Covered, Importance
of the "Bioactive Conformation", Molecular Mimicry and Structural Similarities, and Superimposition
Techniques, Rational Drug Design and Chemical Intuition, An Important Key and the Role of the
Molecular Model, Limitations of Chemical Intuition
UNIT-IV DOCKING METHODS: 09 Hours
Three - Dimensional Description of Binding Site Environment and Energy Calculation, Automatic
Docking Method, Three-Dimensional Database Search Approaches, Automated Structure Construction
Methods, Structure Construction Methods with known Three-Dimensional Structure of the Receptor,
Structure Construction in the case of Unknown Receptor Structure. Points for Consideration in Structure
Construction Methods, Handling of X-Ray Structures of Proteins, Future Perspectives. Other web based
programs available for molecular modelling, molecular docking and energy minimization techniques –
Scope and limitations, interpretation of results.
UNIT-V COMPUTER ASSISTED NEW LEAD DESIGN AND DRUG DISCOVERY: 10 Hours
Introduction, Basic Concepts, Molecular Recognition by Receptor and Ligand Design, Active
Conformation, Approaches to Discover New Functions, Approaches to the Cases with known and
unknown receptor structure, The Drug Development Process, Introduction, The Discovery and
Development Process, New Lead Discovery Strategies, Composition of Drug Discovery Teams, The
Practice of Computer-Assisted Drug Discovery (CADD), Current Practice of CADD in the
pharmaceutical Industry, Management Structures of CADD Groups, Contributions and Achievements of
CADD Groups, Limitations of CADD Support, Inherent Limitations of CADD Support, State of Current
Computational Models, Software and Hardware Constraints.
UNIT –VI Recent advances and research being done in the topics mentioned above units
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REFERENCES :
1. The molecular modeling perspective in drug design by N Claude Cohen, 1996, Academic Press.
2. Protein Engineering by Moody P.C.E. and A.J. Wilkinson, IRL Press, Oxford, 1990.
3. Biochemistry by Voet and Voet, Wiley New York.
4. Bioinformatics Methods & Applications-Genomics, Proteomics & Drug Discovery by S C
Rastogi, N Mendiratta & P Rastogi, PHI, 2006.
5. Fundamentals of Biochemistry by John Willey, 3rd edition, 2004.
COURSE OUTCOMES:
On completion of the course, the student would be able to:
CO1: Explain primary structure and its determination, secondary structure prediction and determination
of motifs,
CO2: Apply the Drug Development Process
CO3: Analyse design methodology of molecular modelling
CO4: Evaluate Approaches to discover new lead design and drug discovery
CO5: Design Docking, Calculation of Molecular Properties
Scheme of Examination
CIE -50
Marks
Test I (Unit I,II, & III)-15
Marks
Test II (Unit IV & V) -15 Marks
Quiz/ AAT = 5 Marks
Unit- VI(AAT) =15 Marks
Total: Marks 50
SEE-100
Marks
Unit which have 10 Hours shall have internal
choice
20*3=60
Marks Total : Marks
100 Unit which have 09 Hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced for 50 marks
Mapping of course Outcomes(COs) to Program Outcomes(POs)
PO1 PO2 PO3
CO1 2
CO2 3
CO3 3
CO4 3
CO5 3 2
1. LOW, 2. MEDIUM, 3.HIGH
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Course Code 18BI3E1B M.Tech(Bioinformatics)
Category Theory-Professional Elective
Course Title RECOMBINANT DNA TECHNOLOGY
Scheme and Credits No. of Hours/Week Semester-III
L T P SS Credits
4 0 4
CIE Marks:50 SEE Marks:50 Total Marks:50 Duration of SEE:03 Hours
Prerequisite(if any):
COURSE OBJECTIVES:
The course will enable the students to:
1. Acquire knowledge of central dogma of molecular biology and rDNA technology.
2. Study the techniques of Recombinant DNA technology.
3. Acquire the various methods of genetic transformation of living systems, and selection,
screening and analysis of recombinants.
4. Know various advanced techniques of genetic manipulation of microbes, plants and animals.
UNIT-I CENTRAL DOGMA OF MOLECULAR BIOLOGY: 09 Hours
Molecular structure of genes and chromosomes, Replication, transcription and translation in
prokaryotes and eukaryotes. Gene regulation: Gene regulation and Operon concept, Constitutive,
Inducible and Repressible systems; Operators and Regulatory elements; Positive and negative
regulation of operon: lac, trp, ara, his, and gal. Promoters and enhancers, Structure and function of
different types of RNA and mRNPs. Regulation of Translation: global vs mRNA-specific. Translation
inhibitors, Posttranslational modifications of proteins. Protein trafficking and transport.
UNIT –II COMPONENTS OF RDNA TECHNOLOGY: 10 Hours
Isolation and purification of DNA (genomic and plasmid) and RNA. Chemical synthesis of DNA:
Phosphoramidite method, use of synthesized oligonucleotides. Labelling nucleic acids: Radioactive and
non-radioactive, end labeling, nick translation, primer extension. Nucleic acid hybridization, Gel
electrophoresis. Restriction enzymes, DNA modifying enzymes (Nucleases, Polymerases), DNA ligases.
Host cells: Prokaryotic and eukaryotic hosts. Vectors: plasmid, bacteriophage and other viral vectors,
cosmids, Ti plasmid, Ri plasmids, Yeast Episomal Plasmids (YEPs), Yeast integrative plasmids (Yips),
Yeast replicative plasmids, Yeast Artificial Chromosome (YAC), mammalian and plant expression
vectors, Gate-way vectors.
UNIT –III GENETIC TRANSFORMATION AND CLONING STRATEGIES: 10 Hours Transformation and transfection, Packaging phage DNA in vitro, Alternative DNA deliver methods:
Electroporation, microinjection, biolistic. Cloning from mRNA: synthesis if cDNA, cloning cDNA in
plasmid vectors, cloning cDNA in bacteriophage vectors. Cloning from genomic DNA: Genomic
libraries, preparation of DNA fragments for cloning, ligation, packaging, and amplification of libraries.
Advanced cloning strategies: synthesis and cloning of cDNA, Expression of cloned DNA molecules,
Cloning large DNA fragments in BAC and YAC vectors.
UNIT-IV SELECTION, SCREENING, AND ANALYSIS OF RECOMBINANTS: 09 Hours Genetic selection and screening methods: Using chromogenic substrates, Insertional inactivation,
Complementation of defined mutation, other genetic selection methods. Screening using nucleic acid
hybridization: Nucleic acid probes, Screening clone banks. Screening using PCR, Immunological
screening for expressed genes. Analysis of cloned genes: Characterization based on mRNA translation
in vitro, Restriction mapping, Blotting techniques, DNA sequencing.
UNIT-V THE APPLICATIONS OF RDNA TECHNOLOGY: 10 Hours
Production of proteins: Native and fusion proteins, Yeast expression systems, Baculovirus expression
system, mammalian cell lines. Protein engineering: Rational design, Directed evolution. RNAi
technology: si RNA and miRNA mediated gene silencing, antisense technology. Genome editing:
Clustered regularly interspaced short palindromic repeats (CRISPR)/Cas systems, Zinc finger nucleases,
Transcription activator-like effector nuclease (TALENS). Applications of synthetic Riboswitches,
Identification of genes responsible for human diseases. Gene therapy, DNA profiling, Transgenic plants
and animals. Ethical and regulatory issues.
UNIT –VI Recent advances and research being done in the topics mentioned above units
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REFERENCES :
1. Nicholl DST., An introduction to Genetic Engineering, Cambridge, 3rd edition, 2010,
ISBN:978-0-521-61521-1 2.
2. Glick BR, Pasternak JJ, and Patten CL, Molecular Biotechnology – Principles and applications
of recombinant DNA, ASM Press, 4th Edition. 2010. ISBN:978-1-55581-498-4.
3. Brown TA. Gene Cloning and DNA Analysis – An Introduction, Wiley-Blackwell Science, 6th
Edition, 2010, ISBN: 9781405181730.
4. Lodish H, Berk A, Kaiser CA, Krieger M, Scott MP, Bretscher A, Ploegh H and Matsudaira P,
Molecular Cell Biology, Freeman, 8th Edition, 2016, ISBN-13: 978-1464183393.
COURSE OUTCOMES:
On completion of the course, the student would be able to:
CO1: Differentiate between global vs mRNA-specific
CO2: Apply RNAi technology
CO3: Analyse Applications of synthetic Riboswitches, Identification of genes responsible for human
diseases
CO4: Evaluate selection, screening, and analysis of recombinants
CO5: Use RDNA technology to design synthetic Riboswitches for identification of genes responsible
for human diseases
Scheme of Examination
CIE -50
Marks
Test I (Unit I,II, & III)-15
Marks
Test II (Unit IV & V) -15 Marks
Quiz/ AAT = 5 Marks
Unit- VI(AAT) =15 Marks
Total: Marks 50
SEE-100
Marks
Unit which have 10 Hours shall have internal
choice
20*3=60
Marks Total : Marks
100 Unit which have 09 Hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced for 50 marks
Mapping of course Outcomes(COs) to Program Outcomes(POs)
PO1 PO2 PO3
CO1 2
CO2 2
CO3 3
CO4 3
CO5 3 2
1. LOW, 2. MEDIUM, 3.HIGH
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Course Code 18BI3E1C M.Tech(Bioinformatics)
Category Theory-Professional Elective
Course Title GENETIC ENGINEERING AND BIOTECHNOLOGY
Scheme and Credits No. of Hours/Week Semester-III
L T P SS Credits
4 0 4
CIE Marks:50 SEE Marks:50 Total Marks:50 Duration of SEE:03 Hours
Prerequisite(if any):
COURSE OBJECTIVES:
The course will enable the students to:
1. Understand types of vectors used in recombinant DNA technology
2. Learn mutagenesis, oligonucleotide derived mutagenesis
3. Acquire knowledge on synthetic biology’, and its relevance to informatics and genetic engineering
UNIT-I CONCEPTS IN RECOMBINANT DNA TECHNOLOGY: 10 Hours
Basic principles; introduction to types of vectors used in recombinant DNA technology, their specific
uses and comparison of their features; an overview of various enzymes used in recombinant DNA
technology. Types of vectors and applications. cis and trans-genesis, Agrobacterium mediated genetic
transformation and binary vectors, particle bombardment, transfections, knockouts and transgenics.
UNIT –II USE OF OLIGONUCLEOTIDES AND PCR: 09 Hours
Principles, process and application of PCR, reverse-transcription-PCR and real time PCR. Application
of primers, probes and PCR in various other techniques and research strategies.
UNIT –III GENETIC ENGINEERING: 10 Hours
Mutagenesis: deletion mutagenesis, oligonucleotide derived mutagenesis, site directed mutagenesis. Case
studies in applications of rDNA technology and genetic engineering. Concept of ‘synthetic biology’, and
its relevance to informatics and genetic engineering. Ethical considerations, and potential negative
impacts.
UNIT-IV OTHER COMMON MOLECULAR BIOLOGY TECHNIQUES : 10 Hours
Common methods in the context of questions/problems usually addressed in molecular biology research:
purification, detection and localization of DNA, RNA and proteins, and the corresponding techniques..
UNIT-V STRUCTURAL VALIDATION AND APPLICATION: 09 Hours
Validation: CASP and CAFASP experiments and their findings, Structural bioinformatics in drug design:
Modern drug discovery, Drug target, Lead identification, Lead Optimization.
UNIT –VI Recent advances and research being done in the topics mentioned above units
REFERENCES :
1. Molecular cell biology; H. Lodish, A. Berk, S.L. Zipursky, P. Matsudaira, D. Baltimore and J.
Darnell; W.H Freeman & Comp., 6th ed., 2007.
2. Molecular biology of the cell; B. Alberts et. al.; Taylor & Francis Publishers, 5th ed., 2008.
3. The cell: a molecular approach; G.M. Cooper and R.E. Hausman; ASM Press, 5th ed., 2009.
4. Lewin's Genes X; J E. Krebs, E S. Goldstein, S T. Kilpatrick. Jones & Bartlett Publishers, Inc.
2009.
5. An introduction to genetic analysis; A.J.F. Griffiths, W. H. Freeman & Co., 2008.
6. Recombinant DNA; J.D. Watson; Scientific Amercian Books, 1992
COURSE OUTCOMES:
On completion of the course, the student would be able to:
CO1: Explain vectors used in recombinant DNA technology, their specific uses and comparison of their
features
CO2: Apply CASP and CAFASP for experiments
CO3: Analyse various enzymes for recombinant DNA
CO4: Detect and localization of DNA, RNA and proteins
CO5: Develop reverse-transcription-PCR and real time PCR
Scheme of Examination
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CIE -50
Marks
Test I (Unit I,II, & III)-15
Marks
Test II (Unit IV & V) -15 Marks
Quiz/ AAT = 5 Marks
Unit- VI(AAT) =15 Marks
Total: Marks 50
SEE-100
Marks
Unit which have 10 Hours shall have internal
choice
20*3=60
Marks Total : Marks
100 Unit which have 09 Hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced for 50 marks
Mapping of course Outcomes(COs) to Program Outcomes(POs)
PO1 PO2 PO3
CO1 3
CO2 2
CO3 3
CO4 3
CO5 3 2
1. LOW, 2. MEDIUM, 3.HIGH
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Course Code 18CS3P1A M.Tech(Bioinformatics)
Category Theory-Professional Open Elective
Course title ARITIFICIAL INTELLIGENCE
Scheme and
Credits
No. of Hours/Week Semester – III
L T P SS Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
This course will enable students to
1. Understand the various characteristics of Intelligent agents
2. Understand the different search strategies in AI
3. Learn to represent knowledge in solving AI problems
4. Analyse the different ways of designing software agents
5. Evaluate the various reasoning techniques for AI.
UNIT-I INTRODUCTION 9 Hours
Introduction Definition Future of Artificial Intelligence Characteristics and Problem Solving
Approach to Typical AI problems. State Space Search and Heuristic Search Techniques
Defining problems as State Space search, Production systems and characteristics, Hill
Climbing, Breadth first and depth first search, Best first search.
UNIT-II KNOWLEDGE REPRESENTATION ISSUES 9 Hours
Representations and Mappings, Approaches to knowledge representation, Using Predicate
Logic and Representing Knowledge as Rules , Representing simple facts in logic, Computable
functions and predicates, Procedural vs Declarative knowledge, Logic Programming, Forward
vs backward reasoning.
UNIT-III SOFTWARE AGENTS 10 Hours
Architecture for Intelligent Agents Agent communication Negotiation and Bargaining
Argumentation among Agents Trust and Reputation in Multi-agent systems.
UNIT-IV REASONING I 10 Hours
Symbolic Logic under Uncertainty , Non-monotonic Reasoning, Logics for non-monotonic
reasoning, Statistical Reasoning.
UNIT-V METHODS 10 Hours
Probability and Bayes Theorem, Certainty factors, Probabilistic Graphical Models, Bayesian
Networks, Markov Networks, Fuzzy Logic.
UNIT -VI Recent advances and research being done in the topics mentioned above units
REFERENCES:
1. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach", Prentice Hall,
Third Edition, 2009.
2. Rich and Knight, Artificial Intelligence, 2nd Edition, 2013
3. I. Bratko, "Prolog: Programming for Artificial Intelligence", Fourth edition, Addison-
Wesley Educational Publishers Inc., 2011.
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4. M. Tim Jones, "Artificial Intelligence: A Systems Approach(Computer Science),
Jones and Bartlett Publishers, Inc.; First Edition, 2008
5. Nils J. Nilsson, "The Quest for Artificial Intelligence", Cambridge University
Press, 2009.
6. William F. Clocksin and Christopher S. Mellish," Programming Using
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1: Define and identify various AI concepts
CO2: illustrate different AI strategies
CO3: Sketch various knowledge representation for AI problems
CO4: Analyze agents usage for AI
CO5: Design AI inference techniques
Scheme of Examination
CIE -50
Marks
Test I (Unit I,II, & III)-15
Marks
Test II (Unit IV & V) -15
Marks
Quiz/ AAT = 5 Marks
Unit- VI(AAT) =15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 Hours shall have internal
choice
20*3=60
Marks Total:
Marks 100 Unit which have 09 Hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2
CO3 2
CO4 2
CO5 2 2
1: Low 2: Medium 3:High
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Course Code 18CS3P1B M.Tech(Bioinformatics)
Category Theory-Professional Open Elective
Course title BUSINESS ANALYTICS
Scheme and
Credits
No. of Hours/Week Semester – III
L T P SS Credits
4 0 - - 4
CIE Marks: 50 SEE
Marks: 50
Total Max. Marks:
100
Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
The course will enable the students to:
1. Understand the role of business analytics within an organization.
2. Analyse data using statistical and data mining techniques.
3. Distinguish relationships between the underlying business processes of an
organization.
4. Gain an understanding of how managers use business analytics to formulate and
solve business problems and to support managerial decision making.
5. Discuss the uses of decision-making tools and Operations research techniques.
UNIT -I BUSINESS ANALYTICS 10 Hours
Overview of Business analytics, Scope of Business analytics, Business Analytics Process,
Relationship of Business Analytics Process and organisation, competitive advantages of
Business Analytics. Statistical Tools: Statistical Notation, Descriptive Statistical methods,
Review of probability distribution and data modelling, sampling and estimation methods
overview
UNIT -II TRENDINESS AND REGRESSION ANALYSIS 9 Hours
Modelling Relationships and Trends in Data, simple Linear Regression. Important
Resources, Business Analytics Personnel, Data and models for Business analytics, problem
solving, Visualizing and Exploring Data, Business Analytics Technology
UNIT -III ORGANIZATION STRUCTURES OF BUSINESS ANALYTICS
10 Hours
Team management, Management Issues, Designing Information Policy, Outsourcing,
Ensuring Data Quality, Measuring contribution of Business analytics, Managing Changes.
Descriptive Analytics, predictive analytics, predicative Modelling, Predictive analytics
analysis, Data Mining, Data Mining Methodologies, Prescriptive analytics and its step in
the business analytics Process, Prescriptive Modelling, nonlinear Optimization
UNIT -IV FORECASTING TECHNIQUES 10 Hours
Qualitative and Judgmental Forecasting, Statistical Forecasting Models, Forecasting
Models for Stationary Time Series, Forecasting Models for Time Series with a Linear
Trend, Forecasting Time Series with Seasonality, Regression Forecasting with Casual
Variables, Selecting Appropriate Forecasting Models. Monte Carlo Simulation and Risk
Analysis: Monte Carle Simulation Using Analytic Solver Platform, New-Product
Development Model, Newsvendor Model, Overbooking Model, Cash Budget Model
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UNIT- V DECISION ANALYSIS 9 Hours
Formulating Decision Problems, Decision Strategies with the without Outcome
Probabilities, Decision Trees, The Value of Information, Utility and Decision Making
UNIT- VI Recent advances and research being done in the topics mentioned above
units
REFERENCES:
1. Business analytics Principles, Concepts, and Applications by Marc J. Schniederjans,
Dara G. Schniederjans, Christopher M. Starkey, Pearson FT Press
2. Business Analytics by James Evans, persons Education
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1. Develop the knowledge of data analytics.
CO2. Demonstrate the ability of think critically in making decisions based
on data and deep analytics
CO3. Discuss the uses of technical skills in predicative and prescriptive
modelling to support business decision-making
CO4. Demonstrate the ability to translate data into clear and actionable insights.
CO5. Evaluate and assess the forecasting techniques.
Scheme of Examination
CIE -50
Marks
Test I (Unit I,II, & III)-15
Marks
Test II (Unit IV & V) -15
Marks
Quiz/ AAT = 5 Marks
Unit- VI(AAT) =15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 Hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100 Unit which have 09 Hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 3
CO2 3 3
CO3 3 3
CO4 3 3
CO5 3 3
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Course Code 18CS3P1C M.Tech(Bioinformatics)
Category Theory-Professional Open Elective
Course title MODELING AND SIMULATION
Scheme and
Credits
No. of Hours/Week Semester – III
L T P SS Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES:
The course will enable the students to:
1. Understand the system, specify systems using natural models of computation, modelling
techniques
2. Apply natural models of computation, modelling techniques to
understand behaviour of system , and analyse the simulation data
3. Analyse simulation data, simulation tools for simulation, Terminating Simulations –
Steady state simulations.
4. Evaluate the existing simulation models for verification, calibration and validation
5. Design validation, calibration model and decision support
UNIT – I INTRODUCTION TO SIMULATION 09 Hours
Introduction Simulation Terminologies- Application areas – Model Classification Types of
Simulation- Steps in a Simulation study- Concepts in Discrete Event Simulation Example.
UNIT-II MATHEMATICAL MODELS 10 Hours
Statistical Models - Concepts – Discrete Distribution- Continuous Distribution – Poisson
Process- Empirical Distributions- Queueing Models – Characteristics- Notation Queueing
Systems – Markovian Models- Properties of random numbers- Generation of Pseudo Random
numbers- Techniques for generating random numbers-Testing random number generators
Generating Random-Variates- Inverse Transform technique Acceptance- Rejection technique –
Composition & Convolution Method.
UNIT-III ANALYSIS OF SIMULATION DATA 10 Hours
Input Modelling - Data collection - Assessing sample independence – Hypothesizing distribution
family with data - Parameter Estimation - Goodness-of-fit tests – Selecting input models in
absence of data- Output analysis for a Single system – Terminating Simulations – Steady state
simulations.
UNIT -IV VERIFICATION AND VALIDATION 09 Hours
Building – Verification of Simulation Models – Calibration and Validation of Models –
Validation of Model Assumptions – Validating Input – Output Transformations
UNIT-V SIMULATION OF COMPUT ER SYSTEMS 10 Hours
Simulation Tools – Model Input – High level computer system simulation – CPU – Memory
Simulation – Comparison of systems via simulation – Simulation Programming techniques -
Development of Simulation models.
UNIT-VI Recent advances and research being done in the topics mentioned above units
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REFERENCES
1. Jerry Banks and John Carson, “Discrete Event System Simulation”, Fourth Edition, PHI,
2005.
2. Geoffrey Gordon, “System Simulation”, Second Edition, PHI, 2006.
3. Frank L. Severance, “System Modelling and Simulation”, Wiley, 2001.
4. Averill M. Law and W. David Kelton, “Simulation Modelling and Analysis, Third
Edition, McGraw Hill, 2006.
5. Jerry Banks, “Handbook of Simulation: Principles, Methodology, Advances,
Applications and Practice”, Wiley-Inter science, 1 edition, 1998.
COURSE OUTCOMES
On Completion of the course, the student will be able to:
CO1: Explain natural models of computation, modelling techniques
CO2: Determine suitable models of computation, modelling techniques to
understand behaviour of system.
CO3: Distinguish simulation models for verification, calibration and validation
CO4: Assess the performance of different simulation models, statistical models, queuing
Systems and Markovian Models for given problem
CO5: Design goodness-of-fit tests and input models in absence of data
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I,II, & III)-15 Marks Quiz/ AAT = 5
Marks
Unit- VI(AAT) =15
Marks
Total:50
marks
Test II (Unit IV & V) -15 Marks
SEE
– 100
marks
Answer FIVE full questions Total:100 marks
Units which have 09 Hours shall not have
internal choice. 20* 2 = 40 Marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 3
CO3 3
CO4 3
CO5 3 2
1. Low, 2. Medium, 3. High
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COURSE LEARNING OBJECTIVES:
The objectives of the SEMINAR-III is to prepare the students to learn to:
1. Carry out the literature review of general research area/current topic and analyse the same effectively.
2. Prepare a technical report, reflecting his/her depth of understanding, on the selected area/topic and
prepare content rich presentation.
3. Acquire communication and time management skills for effective and impactful presentation. Interact with
peers to acquire the qualities of thoughtfulness, friendliness, adaptability, responsiveness, and politeness
in-group discussion. Overcome stage fear during the presentation.
GUIDE LINES
1. Seminar preparation and presentation is an individual student activity.
2. Topic may be of general/ specific interest to program of engineering or electives not offered in the
semester and to be selected in consultation with the faculty/Guide.
3. Select one pertinent research paper for the seminar presentation.
4. Prepare and submit a detailed technical report of the seminar topic.
COURSE OUTCOMES:
Students shall be able to:
1. Carry out the literature survey of topic of seminar.
2. Prepare a technical report on the selected area/topic.
3. Make an effective presentation with seamless flow of content within the time allocated. Overcome
inhibition in interacting with peers and hence develop the spirit of team work. Overcome stage fear during
the presentation.
Course Code 18BI3S01 M.Tech (Bio Informatics)
Category Seminar Semester: III
Course title SEMINAR - III
Scheme and Credits
No. of Hours/Week
Total hours = 24 L T P S Credits
0 0 2 0 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any): NIL
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SCHEME OF EXAMINATION
CIE – 50
marks
Phase -1 Marks =10 Seminar Report
Marks =20
Total:50
Marks Phase -2 Marks =20
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2 3
CO2 2 3 3
CO3 2
1. Low, 2. Medium, 3. High
Scheme of Continuous Internal Evaluation (CIE):
Evaluation would be carried out in TWO phases. The Seminar Evaluation Committee shall comprise of
Chairman of the Department, Faculty/Guide and one more faculty member nominated by Chairman. The
evaluation criteria shall be as per the rubrics given below:
Rubrics for Evaluation:
Topic - Technical Relevance, Sustainability and Societal Concerns : 15%
Review of literature and Technical Content : 35%
Presentation Skills : 25%
Report : 25%
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INTERNSHIP
COURSE LEARNING OBJECTIVES:
Objectives of the internship
1. Provide an opportunity to see how classroom and textbook learning applies to the real world, and to
expose the students to the relevant work experience.
2. Pay close attention to all the steps that go onto completing a job, thereby, help students to become
workforce ready before entering the job market as a graduate. Provide an opportunity to select the topic
of dissertation work by evaluating the requirement of organisation.
3. Prepare and present a technical report of internship.
GUIDELINES
1. Student has to approach the concerned heads of various Industries/organization, which are related to the
field of specialization of the M. Tech program.
2. If any student gets internship, he/she has to submit the internship offer letter duly signed by the concerned
authority of the company to the Chairperson of the Department.
3. The internship on full time basis will be after the examination of II semester and during III semester for a
period of 8 weeks without affects regular class work.
4. The progress has to be reported periodically to the faculty or to the Guide assigned by the Chairperson as
per the format acceptable to the respective industry /organizations and to the Institution.
5. At the end of the internship the student has to prepare a detailed report and submit.
6. Students are advised to use ICT tools such as Skype to report their progress and submission of periodic
Course Code 18BI3I01 M.Tech (Bioinformatics)
Category Internship/ Mini Project Semester: III
Course title INTERNSHIP / MINI PROJECT
Scheme and Credits
No. of Hours/Week
Total hours = 80 L T P S Credits
--- --- 10 --- 5
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs for Batch
of Six students
Prerequisites (if any): NIL
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69
progress reports to the faculty in charge or guide.
7. Duly signed report from internal supervisor (faculty incharge or guide) and external supervisor from the
organization where internship is offered has to be submitted to the Chairperson of the Department for
his/her signature and further processing for evaluation.
The broad format of the internship final report shall contain Cover Page, Certificate from College, Certificate
from Industry / Organization of internship, Acknowledgement, Synopsis, Table of Contents, chapters of
Profile of the Organization - Organizational structure, Products, Services, Business Partners, Financials,
Manpower, Societal Concerns, Professional Practices, Activities of the Department where internship is
done, Tasks Performed and summary of the tasks performed. Specific technical and soft skills that student
has acquired during internship, References and Annexure.
COURSE OUTCOMES:
The student will be able to:
1. Apply the gained experience along with the theoretical knowledge to solve the real world problems what
engineers ready do.
2. Get equipped with experience required before entering the job market. Explore the possibility of
formulating the dissertation problem.
3. Prepare a technical report and make a presentation of details of internship.
SCHEME OF EXAMINATION
CIE 1.Marks awarded by guide (Internal examiner) = 50 marks
2.Marks awarded by the department internship monitoring committee = 50 marks
50*
Marks
SEE Presentation of internship in the presence of Guide (Internal examiner) and external
examiner=100
50**
Marks
Note: *= CIE be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
**= SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
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Rubrics for CIE:
1. Topic of internship = 10%
2. Objectives of internship = 10%
3. Specific skills acquired = 20%
4. Document = 40%
5. presentation = 20%
Rubrics for SEE:
1. Topic of internship = 10%
2. Objectives of internship = 10%
3. Specific skills acquired = 20%
4. Document = 40%
5. presentation = 20%
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 2
CO2 2 2
CO3 3
1. Low, 2. Medium, 3. High
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71
MINI PROJECT
COURSE LEARNING OBJECTIVE:
1. Understand the method of applying engineering knowledge/use application software to solve specific
problems after carrying out literature survey.
2. Apply engineering and management principles while executing the project.
3. Demonstrate the skills for good technical report writing and presentation.
COURSE CONTENT/GUIDELINES
Student shall take up small problems in the field of domain of program as mini project. It can be related to a
solution to an engineering problem, verification and analysis of experimental data available, conducting
experiments on various engineering subjects, material characterisation, studying a software tool for solution to an
engineering problem, etc.
The mini project must be carried out preferably using the resources available in the department/college and it can
be of interdisciplinary also.
COURSE OUTCOMES:
The students shall be able to:
1. Conduct experiments / use the capabilities of relevant application software/ simulation tools
Individually to generate data/ solve problems.
2. Assess the available engineering resources available in the institution.
3. Prepare and Present the technical document of mini project.
Rubrics for CIE:
Note: * = CIE be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
Sl.
no
Particulars Weightage Marks Total marks
of CIE
1 Selection of the topic & formulation of objectives 10% 10
50*
2 Modelling and simulation/algorithm
development/experiment setup
25% 25
3 Conducting experiments/implementation/testing 25% 25
4 Demonstration & Presentation 15% 15
5 Report writing 25% 25
Total 100% 100
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Rubrics for SEE:
The SEE shall be done by two examiners out of which one examiner is the guide of mini project. The following
weightage would be given for the examination. Evaluation shall be done in batches, not exceeding 6 students.
Sl.
no
Particulars Weightage Marks Total marks
of SEE
1 Brief write-up about the project 05% 05
50**
2 Presentation/demonstration of the project 20% 20
3 Methodology and Experimental Results & Discussion 30% 30
4 Report 25% 25
5 Viva Voce 20% 20
Total 100% 100
Note: ** = SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 3
CO2 2 3
CO3 2 3
1. Low, 2. Medium, 3. High
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73
COURSE LEARNING OBJECTIVES:
1. Choose a problem applying relevant knowledge and skills acquired during the course. Formulate the
specifications of the project work, identify the set of feasible solutions, prepare, and execute project plan
considering professional, cultural and societal factors. Identify the problem-solving methodology using
literature survey and present the same.
2. Develop experimental planning and select appropriate techniques and tools to conduct experiments to
Evaluate and critically examine the outcomes followed by concluding the results and identifying relevant
applications. Preparation of synopsis, preliminary report for approval of topic selected along with literature
survey, objectives and methodology.
3. Develop oral and written communication skills to effectively convey the technical content.
GUIDELINES
The Dissertation work will start in III semester and should be a problem with research potential and should
involve scientific research, design, generation/collection and analysis of data, determining solution and must
preferably bring out the individual contribution.
The Dissertation work will have to be done by only one student and the topic of dissertation must be
decided by the guide and the student. The dissertation work shall be carried out, on-campus or in an industry
or in an organisation with prior approval from the Chairperson of the Department. The student has to be in
regular contact with the guide atleast once in a week.
The report of Dissertation work phase I shall contain cover page, certificate from
College/Industry/Organisation, Acknowledgement, List of Figures and Tables Contents, Nomenclature,
Chapters of Introduction including motivation to choose topic, Literature survey, Conclusion of literature
survey, Objectives and Scope of Dissertation, Methodology to be followed, Experimental requirements,
References and Annexure.
Course Code 18BI3D01 M.Tech (Bioinformatics)
Category Dissertation Work Semester: III
Course title DISSERTATION WORK PHASE -I
Scheme and Credits
No. of Hours/Week
Total hours = 80 L T P S Credits
0 0 10 0 5
CIE Marks: 50 SEE Marks:50 Total Max. Marks: 100 Duration of SEE: 1Hour
Prerequisites (if any): NIL
BI-82
74
The preliminary results (if available) of the problem of Dissertation work may also be discussed in the
report.
COURSE OUTCOME:
The students will be able to:
1. Self learn various topics relevant to Dissertation work. Survey the literature such as books,
National/International reference journals, articles and contact resource persons for selected topics of
Dissertation.
2. Write and prepare a typical technical report.
3. Present and defend the contents of Dissertation work phase I in front of technically qualified audience
effectively.
SCHEME OF EXAMINATION
CIE 1.Marks awarded by guide (Internal examiner) = 50 marks
2.Marks awarded by the department dissertation monitoring committee = 50 marks
50*
Marks
SEE Presentation of Dissertation work Phase-I in the presence of Guide (Internal
examiner) and external examiner=100 Marks
50**
Marks
Note: *= CIE be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
**= SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
Rubrics for CIE: Weightage
1. Introduction and Justification of topic = 10%
2. Literature survey and Conclusion = 30%
3. Objectives and Scope of Dissertation work = 30%
4. Methodology to be adopted = 20%
5. Presentation of contents of Dissertation work Phase-I = 10%
Rubrics for SEE:
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75
1. Introduction and Justification of topic = 10%
2. Literature survey and Conclusion = 30%
3. Objectives and Scope of Dissertation work = 30%
4. Methodology, Experimental /Software = 20%
5. Presentation of Dissertation Phase-I = 10%
Mapping of Course Outcomes (Cos) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 3
CO2 3 3
CO3 3 3
1. Low, 2.Medium, 3. High
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76
SEMISTER-IV
BI-85
77
COURSE LEARNING OBJECTIVES:
The objectives of the SEMINAR-IV is to prepare the students to learn to:
1. Carry out the literature review of general research area/current topic and analyse the same effectively.
2. Prepare a technical report, reflecting his/her depth of understanding, on the selected area/topic and
prepare content rich presentation.
3. Acquire communication and time management skills for effective and impactful presentation. Interact with
peers to acquire the qualities of thoughtfulness, friendliness, adaptability, responsiveness, and politeness
in-group discussion. Overcome stage fear during the presentation.
GUIDE LINES
1. Seminar preparation and presentation is an individual student activity.
2. Topic may be of general/ specific interest to program of engineering or electives not offered in the
semester and to be selected in consultation with the faculty/Guide.
3. Select one pertinent research paper for the seminar presentation.
4. Prepare and submit a detailed technical report of the seminar topic.
COURSE OUTCOMES:
Students shall be able to:
1. Carry out the literature survey of topic of seminar.
2. Prepare a technical report on the selected area/topic.
3. Make an effective presentation with seamless flow of content within the time allocated. Overcome
inhibition in interacting with peers and hence develop the spirit of team work. Overcome stage fear during
the presentation.
Course Code 18BI4S01 M.Tech ( Bioinformatics)
Category Seminar Semester: IV
Course title SEMINAR - IV
Scheme and Credits
No. of Hours/Week
Total hours = 24 L T P S Credits
0 0 2 0 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any): NIL
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78
SCHEME OF EXAMINATION
CIE – 50
marks
Phase -1 Marks =10 Seminar Report
Marks =20
Total:50
Marks Phase -2 Marks =20
Scheme of Continuous Internal Evaluation (CIE):
Evaluation would be carried out in TWO phases. The Seminar Evaluation Committee shall comprise of
Chairman of the Department, Faculty/Guide and one more faculty member nominated by Chairman. The
evaluation criteria shall be as per the rubrics given below:
Rubrics for CIE:
Topic - Technical Relevance, Sustainability and Societal Concerns : 15%
Review of literature and Technical Content : 35%
Presentation Skills : 25%
Report of Seminar : 25%
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2 3
CO2 2 3 3
CO3 2
1. Low, 2. Medium, 3. High
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79
COURSE LEARNING OBJECTIVES:
1. Apply/Use different experimental techniques, equipments, software/ Computational/ Analytical /Modelling
and Simulation tools required for conducting tests and generate other relevant data. Students will also be
able to design and develop an experimental setup/test rig.
2. Analyse the results of the experiments conducted/models developed.
3. Create a detailed technical document as per format based on the outcome of dissertation work phase I and II.
GUIDELINES
Dissertation work phase II is the continuation of project work started in III semester. The report of Dissertation
work that includes the details of Dissertation work phase I and phase II should be presented in a standard
format. The candidate shall prepare a detailed report of dissertation that includes Cover Paper, Certificate
from College/Industry/Organisation, Acknowledgement, Abstract, Table of contents, List of Figures and
Table, Nomenclature, Chapter of Introduction, Literature survey, Conclusion of literature survey, Objectives
and Scope of dissertation work, Methodology, Experimentation, Results, Discussion, Conclusion, Scope for
future work, References, Annexure and full text of the publication (submitted or published).
COURSE OUTCOMES:
Students shall be able to:
1. Conduct experiments/ implement the capabilities of different Software /Computational / Analytical/
Modelling and simulation tools individually and generate data for validation of hypothesis.
2. Investigate and assess the results obtained within the scope of experiments conducted followed by
conclusions.
3. Prepare detailed technical document, present and defend the contents of Dissertation work in presence of
technically qualified audience effectively.
Course Code 18BI4D01 M.Tech ( Bioinformatics)
Category Dissertation Work Semester: IV
Course title DISSERTATION WORK PHASE -II
Scheme and Credits
No. of Hours/Week
Total hours = 150 L T P S Credits
--- --- 30 --- 15
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100
Prerequisites (if any): NIL
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80
SCHEME OF EXAMINATION
CIE
1. Marks awarded by guide = 50 marks
2. Marks awarded by the department dissertation monitoring committee
(Guide + Two faculty members )= 50 marks
100
marks
50*
marks
SEE
1. Dissertation evaluation by guide (Internal examiner) = 100 marks
2. Dissertation evaluation by external examiner=100 Marks
3. Viva- Voce examination by guide and external examiner who evaluated the
dissertation work =100 marks
300
marks
50**
marks
Note: * = CIE be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
** = SEE shall be conducted for 300 marks and the marks obtained shall be reduced for 50 marks.
Rubrics for CIE:
6. Presentation of background of dissertation work = 10%
7. Literature survey, Problem formulation and Objectives = 30%
8. Presentation of methodology and experimentation = 30%
9. Results and Discussion = 20%
10. Questions and Answers = 10%
Rubrics for SEE:
1. Originality = 05%
2. Literature survey = 15%
3. Problem formulation, Objectives and Scope of Work = 10%
4. Methodology, experimentation /Theoretical modelling = 10%
5. Results, Discussion and Conclusion = 20%
6. Questions and Answers = 20%
7. Acceptance/Publication of technical paper in Journals/Conference = 10%
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 2 3
CO2 2 2 3
CO3 3 3 3
1. Low, 2. Medium, 3. High
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81
BI-90
BANGALORE UNIVERSITY
Department of Computer Science and Engineering
UNIVERSITY VISVESVARAYA COLLEGE OF ENGINEERING
K R Circle, Bengaluru-560 001.
Choice Based Credit System (CBCS)-2018
M.Tech in Computer Science and Engineering
Specialization: Software Engineering
SE-1
BANGALORE UNIVERSITY
VISION
“To strive for excellence in education for the realization of a vibrant and inclusive
society through knowledge creation and dissemination”
MISSION
· Impart quality education to meet national and global challenges
· Blend theoretical knowledge with practical skills
· Pursue academic excellence through high quality research and publications
· Provide access to all sections of society to pursue higher education
· Inculcate right values among students while encouraging competitiveness to
promote leadership qualities
· Produce socially sensitive citizens
· Hasten the process of creating a knowledge society
· To contribute to nation building
SE-2
Bangalore University
UNIVERSITY VISVESVARAYA COLLEGE OF ENGINEERING
K R Circle, Bengaluru – 560 001.
University Visvesvaraya College of Engineering (UVCE) was started as a School of Mechanical
Engineering by Bharat Ratna Sir. M. Visvesvaraya in the year 1913 to meet the needs of the State for
skilled workers with S V Setty as its Superintendent. Later, it was converted to a full-fledged
Engineering College in the year 1917 under the name Government Engineering College and was
affiliated to the University of Mysore. It is the fifth Engineering College to be established in the country.
After the formation of Bangalore University in 1964, UVCE became one of the Constituent
Colleges of Bangalore University. This is one of the oldest Institutions in the country imparting
technical education leading to B.E., M.E, B.Arch., M.Sc. (Engineering), M.Arch. and Ph.D. degrees in
various disciplines of Engineering and Architecture. The Institution currently offers 7 Undergraduate
(B.E. / B.Arch.) Full-time, three Undergraduate (B.E.) Part-time and 24 Postgraduate (M.E. / M.Arch.)
Programmes.
VISION
The vision of UVCE is to strive for excellence in advancing engineering education through path
breaking innovations across the frontiers of human knowledge to realize a vibrant, inclusive and humane
society.
MISSION
The mission of UVCE is to prepare human resource and global leaders to achieve the above vision
through discovery, invention and develop friendly technologies to promote scientific temper for a
healthy society. UVCE shapes engineers to respond competently and confidently to the economic, social
and organizational challenges arising from globally advancing technical needs.
SE-3
Bangalore University Bengaluru
Department of Computer Science and Engineering, UVCE, Bengaluru M. Tech. DEGREE IN COMPUTER SCIENCE AND ENGINEERING under CBCS Scheme - 2K18
Specialization: Software Engineering
Vision of the Department
Strive for excellence in education for the realization of a vibrant and inclusive society through knowledge
creation and dissemination.
Mission of the Department
CSEM1. Impart quality education and promote scientific temper
CSEM2. Blend theoretical knowledge with practical skills.
CSEM3. Inculcate right values in students.
CSEM4. Providing access to all sections of the society to purse higher education.
CSEM5. Pursue academic excellence through quality teaching, research and publishing
CSEM6: Promote leadership qualities among students
CSEM7: Hasten the process of creating a knowledge society
CSEM8: Produce socially sensitive citizens
Program Outcomes (PO)
SEPO1: An ability to independently carry out research /investigation and development work to
solve practical problems
SEPO2: An ability to write and present a substantial technical report/document
SEPO3: Students should be able to demonstrate a degree of mastery over the area as per the
specialization of the program. The mastery should be at a level higher than the
requirements in the appropriate bachelor program
Program Educational Objectives (PEO)
The post graduates of M.Tech in Software Engineering will be provided the knowledge and skill to:
SE-4
Program Educational Objectives:
M. Tech (Software Engineering)
SEPE01 An ability to analyze, design and synthesize software systems from the individual
component to the entire system architecture
SEPE02 An ability to define, assess, and tailor software quality practices, software engineering
fundamentals and methodologies for development of software projects in a various of
domain.
SEPE03 Be an effective member of a multi-disciplinary software development team and
manage the projects with an awareness of individual professional and ethical
responsibilities.
SEPE04 An ability to critically analyze the issues of industry trends, communicate to varied
stakeholders and use various state-of-the-art practices and tools
.
SE-5
BANGALORE UNIVERSITY
SCHEME OF STUDIES AND EXAMINATION FOR 24MONTHS COURSE FOR THE AWARD OF
M. Tech. DEGREE IN COMPUTER SCIENCE AND ENGINEERING (Specialization: SOFTWARE ENGINEERING) under CBCS Scheme – 2K18
Semester I Sl. No Course Type/ Course Name Teaching scheme Teaching Total CIE *SEE Credits
Course Code Hrs/Week DPT Hrs/week Marks Marks
L T P S
1 18CS1C01 Mathematical Foundations of Computer Science 3 1 0 0 CSE 4 50 50 4
2 18CS1C02 Advances in Computer Networks 4 0 0 0 CSE 4 50 50 4
3 18SE1C03 Software Architecture 4 0 0 0 CSE 4 50 50 4
4
18SE1E1A Agile Software Architecture 4 0 0 0
CSE 4 50 50 4 18SE1E1B Software Reliability Metrics and Models 4 0 0 0
18SE1E1C Software Requirements Engineering 4 0 0 0
5
18SE1E2A Software Design Patterns 4 0 0 0
CSE 4 50 50 4 18SE1E2B Advances Storage Area Networks 4 0 0 0
18SE1E2C Software Verification and Validation 4 0 0 0
6 18SE1L01 Software Development Laboratory 0 0 4 0 CSE 4 50 50 2
7 18CS1M01 Research Methodology and IPR. 2 0 0 0 CSE 2 50 50 2
8 18SE1S01 Seminar -I 0 0 2 0 CSE 2 50 -- 1
9 18CS1M02 Audit Course-I (Technical Paper Writing) 2 0 0 0 English 2 50 -- 1
Total 30 450 350 26
Note*=SEE shall be conducted for 100 marks and the marks obtained is to be reduced for 50 marks.
SE-6
Semester II Sl. No Course Type/ Course Name Teaching scheme Teaching Total CIE *SEE Credits
Course Code Hrs/Week DPT Hrs/week Marks Marks
L T P S
1 18CS2C01 Advanced Data Structures and Algorithms 4 0 0 0 CSE 4 50 50 4
2 18CS2C02 Advanced Operating Systems 4 0 0 0 CSE 4 50 50 4
3 18SE2C03 Software Testing and Quality Assurance 4 0 0 0 CSE 4 50 50 4
4
18SE2E1A Software Test Automation 4 0 0 0
CSE 4 50 50 4 18WT2E1B User Interface Design and Evaluation 3 0 2 0
18SE2E1C Enterprise Resource Planning 4 0 0 0
5
18SE2E2A Software Agents 4 0 0 0 CSE
4 50 50 4 18SE2E2B Software Security 4 0 0 0
18SE2E2C Software Engineering for Web Applications
6 18CS2L01 Advanced Data Structures and Algorithms Laboratory 0 0 4 0 CSE 4 50 50 2
7 18SE2S01 Seminar -II 0 0 2 0 CSE 2 50 -- 1
8 18CS2M01 Audit Course-II (Pedagogy Studies) 2 0 0 0 CSE 2 50 -- 1
Total 28 400 300 24
Semester III
Sl. No Course Type/ Course Name Teaching scheme Teaching Total CIE *SEE Credits
Course Code Hrs/Week DPT Hrs/week Marks Marks
L T P S CSE
1 18IT3E1A Social Network 4 0 0 0
CSE 4 50 50 4 18SE3E1B Business Intelligence 4 0 0 0
18SE3E1C Software Project Management 4 0 0 0
2 Open Elective 4 0 0 0
---4 50 50 4
3 18SE3S01 Seminar -III 0 0 2 0 CSE 2 50 -- 1
4 18SE3I01 Internship / Mini Project 0 0 10 0 CSE 10 50 50 5
5 18SE3D01 Dissertation Phase -I 0 0 10 0 CSE 10 50 50 5
Total 30 250 200 19
SE-7
Semester IV Sl. No Course Type/ Course Name Teaching scheme Teaching Total CIE *SEE Credits
Course Code Hrs/Week DPT Hrs/week Marks Marks
L T P S
1 18SE4S01 Seminar -IV 0 0 2 0 CSE 2 50 -- 1
2 18SE4D01 Dissertation Phase -II - - 30 - CSE 30 50 50 15
Total 32 100 50 16
1 18SEMOOC MOOC Course 03
Grand Total of Credits 88
COURSE TYPE
SE: SOFTWARE ENGINEERING CS: COMPUTER SCIENCE AND ENGG C: PROFESSIONAL CORE E: PROFESSIONAL ELECTIVE
P: OPEN ELECTIVE M: MANDATORY AUDIT L: LABORATORY
S: SEMINAR I: INTERNSHIP/ MINI PROJECT D: DISSERTATION
L – Theory lecture, T – Tutorial, P – Lab work, S – Self study:
Numbers under teaching scheme indicates contact clock hours. Note:
1. In Any curse(Program core or Program Elective), if self-study of 4 hours per week per students is allocated, then teaching scheme of such course will be 3-0-0-4 and the
total credits will be 4.
2. *=SEE shall be conducted for 100 marks and the marks obtained is to be reduced for 50 marks.
3. #= the CIE test of the lab component of integrated course shall be conducted with the external examiners for 50 marks and shall be reduced to 25 marks
SE-8
BANGALORE UNIVERSITY
SCHEME OF STUDIES AND EXAMINATION FOR 24MONTHS COURSE FOR THE AWARD OF
M. Tech. DEGREE IN COMPUTER SCIENCE AND ENGINEERING (Specialization: SOFTWARE ENGINEERING) under CBCS Scheme – 2K18
Open Elective for M. Tech CBCS Scheme
Semester III Sl. No Course Type/ Course Name Teaching scheme Teaching Total CIE *SEE Credits
Course Code Hrs/Week DPT Hrs/week Marks Marks
L T P S
1
18CS3P1A Artificial Intelligence
4 0 0 0 CSE 4 50 50 4 18CS3P1B Business Analytics
18CS3P1C Simulation and Modelling
2
18CV3P1A Significance of National Building Codes
4 0 0 0 Civil 4 50 50 4 18CV3P1B Water Laws, Rights and Administration
18CV3P1C Waste To Energy
18CV3P1D Remote Sensing and Geographic information System
3 18ME3P1A Composite and Smart Materials 4 0 0 0 Mech 4 50 50 4
18ME3P1B Industrial Safety
4
18EE3P1A Real Time Embedded Systems
4 0 0 0
EEE4 50 50 4
18EE3P1B Robotics and Automation
18EE3P1C Solar and Wind Energy
5
18EC3P1A Reliability and Engineering
4 0 0 0 ECE 4 50 50 4 18EC3P1B M-Commerce and Applications
18EC3P1C Optimization Techniques
SE-9
1
Course Code 18CS1C01 M. Tech(Software Engineering)
Category Theory-Professional Core
Course title MATHEMATICAL FOUNDATIONS OF COMPUTER SCIENCE
Scheme and
Credits
No. of Hours/Week Semester – I
L T P SS Credits
3 1 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
1. Basics of probability
2. Basics of graph theory
COURSE OBJECTIVES
The course will enable the students to:
1. Understand the concepts of number theory and solve related problems.
2. Apply the concepts of stochastic process and queuing theory required to devise
analytical models for the real problems of computer science.
3. Analyze the various concepts of arranging, selecting and combining objects from a
set.
4. Understand the concept of advanced graph theory that can be used to model any
network, physical or conceptual.
UNIT -I NUMBER THEORY: 10 Hours
The Division algorithm, the Greatest Common Divisor, the Euclidean algorithm. The basic
properties of Congruencies, Binary and decimal representation of integer, linear congruence,
Chinese-Reminder Theorem, Fermat‟s Little theorem, The sum and number of Divisors, The
mobius inversion formula, The Greatest integer function (No theorem proofs).
UNIT -II PROBABILITY AND QUEUING THEORY: 10 Hours
Random Variables, Probability Distribution, Binomial Distribution, Poisson Distribution,
Geometric Distribution, Exponential Distribution, Normal Distribution, Uniform
Distribution. Introduction to Stochastic Processes, Markov process, Markov chain, one step
and n-step Transition Probability, Chapman Kolmogorov theorem (Statement only),
Transition Probability Matrix, Classification of States of a Markov chain. Introduction to
Markovian queuing models, Single Server Model with Infinite system capacity,
Characteristics of the Model (M/M/1) : (∞/FIFO), Single Server Model with Finite System
Capacity, Characteristics of the Model (M/M/1) : (K/FIFO).
UNIT -III COMBINATORICS: 10 Hours
Basics of Counting: Permutations, Permutations with Repetitions, Circular Permutations,
Restricted Permutations, Combinations: Restricted Combinations, Generating Functions of
Permutations and Combinations, Binomial and Multinomial Coefficients, Binomial and
Multinomial Theorems, The Principles of Inclusion Exclusion, Pigeonhole Principle and its
Application.
UNIT -IV RECURRENCE RELATIONS: 09 Hours Generating Functions, Function of Sequences, Partial Fractions, Calculating Coefficient of
Generating Functions, Recurrence Relations, Formulation as Recurrence Relations, Solving
Recurrence Relations by Substitution and Generating Functions, Method of Characteristic
Roots, Solving Inhomogeneous Recurrence Relations.
UNIT –V GRAPH THEORY: 09 Hours
Basic Concepts of Graphs, Sub graphs, Matrix Representation of Graphs: Adjacency
Matrices, Incidence Matrices, Isomorphic Graphs, Paths and Circuits, Eulerian and
Hamiltonian Graphs, Multi-graphs, Planar Graphs, Euler„s Formula, Graph Colouring and
Covering, Chromatic Number, Spanning Trees, Algorithms for Spanning Trees (Concepts
and Problems Only, Theorems without Proofs).
UNIT-VI Recent advances and research being done in the topics mentioned above
units
SE-10
2
REFERENCES
1. David M Burton, “Elementary Number Theory”, Allyn and Bacon, 1980.
2. K. S. Trivedi, “Probability and Statistics with Reliability, Queuing for Computer
Science Applications”, John Wiley and Sons, II Edition, 2008.
3. Gunter Bolch, Stefan Greiner, Hermann De Meer, K S Trivedi, “Queuing Networks
and Markov Chains”, John Wiley and Sons, II Edition, 2006.
4. Richard A Brualdi, Introductory Combinatorics 5th
Edition, Pearson 2009
5. J. A. Bondy and U. S. R. Murty, “Graph Theory and Applications”, Macmillan
Press, 1982.
COURSE OUTCOMES
On completion of the course, the student would be able to:
CO1. Solve problems related to number theory.
CO2: Design the analytical models using the concepts of probability and stochastic process.
CO3: Compare the various methods of counting using permutations and combinations.
CO4: Solve the problems of recurrence relations.
CO5: Apply the graph theory concepts in solving problems related to computer science.
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100 Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 1
CO2 2
CO3 1 1
CO4 1
CO5 2
1: Low 2: Medium 3:High
SE-11
3
Course Code 18CS1C02 M. Tech(Software Engineering)
Category Theory-Professional Core
Course title ADVANCES IN COMPUTER NETWORKS
Scheme and
Credits
No. of Hours/Week Semester – I
L T P SS Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVE
The course will enable the student to:
1. Understand the requirement of various high speed networks
2. Learn the effect of congestion and its control.
3. Understand Network Traffic Management for reliable delivery.
4. Understand integrated and differentiated architecture and services.
5. Learn the effect of traffic in the networks on various QoS parameters
UNIT I- INTRODUCTION 9 Hours
OSI and TCP/ IP Reference Model, RS-232C, RS-449, Devices used in Physical Layers,
Framing, Flow and Error Control, Error Detection and Correction, Checksum, Sliding
Window Protocols-ARQ.
UNIT II- DATA LINK LAYER 10 Hours Multiple Access Control Protocols(MAC), ALOHA, CSMA/CD (802.3), Data Link
Protocol- HDLC,PPP, Wired LAN‟s : Fast Ethernet, Gigabit Ethernet, Fibre Channel,
Wireless LAN‟s(802.11), Broadband Wireless(802.16).
UNIT III- ROUTING AND CONGESTION CONTROL 10 Hours Design issues, Routing Algorithms, Distance Vector Routing, Link State Routing, Routing
in Mobile Host, Broadcast Routing, Reverse Path Forwarding, OSPF, IP Protocols (IPv4 -
ARP, RARP, IPv6), Queuing Analysis- Queuing Models – Single Server Queues –
Effects of Congestion – Congestion Control – Traffic Management – Congestion Control
in Packet Switching Networks.
UNIT IV – TRANSPORT LAYER AND INTEGRATED SERVICES 10 Hours
TCP Header, TCP Flow control – TCP Congestion Control – Retransmission – Timer
Management – Exponential RTO back-off – KARN‟s Algorithm – Window
management. Integrated Services Architecture – Approach, Components, Services-
Queuing Discipline, FQ, PS, BRFQ, GPS, WFQ – Random Early Detection,
Differentiated Services.
UNIT V - PROTOCOLS FOR QOS SUPPORT 09 Hours
RSVP – Goals & Characteristics, Data Flow, RSVP operations, Protocol
Mechanisms – Multiprotocol Label Switching – Operations, Label Stacking, Protocol
details – RTP – Protocol Architecture, Data Transfer Protocol, RTCP.
UNIT VI- To understand latest innovative networks such as Software Defined
Networks(SDN).
REFERENCES
1. Behrouz A Forouzan and Firouz Mosharraf, “Computer Networks, A Top-Down
Approach”, TMH, 2012.
2. Andrew S. Tanenbaum and David J. Wetherall, “Computer Networks”, Pearson
Education, 5th Edition,2011.
3. William Stallings, “High Speed Networks and Internet”, , Second Edition, 2012.
4. Prakash C Guptha, “Data Communication and Computer Networks”, PHI , 6th
printing 2012.
5. Larry L. Peterson and Bruce S Davis , “Computer Network A System
Approach”, Elsevier, 5th
edition 2010.
SE-12
4
COURSE OUTCOMES
On Completion of the course, the student will be able to:
CO1: Apply the networking principles to manage the network traffic.
CO2: Control the various anomalies in the network to improve the QoS.
CO3: Study the relation and effect of one QoS parameter on the other.
CO4: Apply the efficient techniques to achieve effective and reliable communication.
CO5: Develop new protocols to mitigate emerging problems.
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100 Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2
CO3 3 2 2
CO4 3 2
CO5 2 2 2
1: Low 2: Medium 3:High
SE-13
5
Course Code 18SE1C03 M. Tech(Software Engineering)
Category Theory-Professional Core
Course title SOFTWARE ARCHITECTURE
Scheme and
Credits
No. of Hours/Week Semester – I
L T P SS Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
Software Engineering
COURSE OBJECTIVES The course will enable the student to:
1. Gain knowledge on the fundamentals of software architecture.
2. Understand various software architecture
3. Evaluate the various software architecture methodologies.
4. Develop the software architecture and quality attributes.
5. Analyze software architecture and software quality.
UNIT- I INTRODUCTION: 09Hours
Basic Concepts of Software Architecture - Architecture business cycle - architectural patterns
- reference models - architectural structures, views ; Introduction to Styles - Simple Styles -
Distributed and Networked Architectures - Architecture for network based applications -
Decentralized Architectures.
UNIT - II DESIGN METHODOLOGIES: 10Hours
Structured Design - Design Practices – Stepwise Refinement – Incremental Design –
Structured System-Analysis and Design – Jackson Structured Programming – Jackson
System Development
UNIT- III ARCHITECTURE DESCRIPTION DOCUMENTATION AND
EVALUATION: 09 Hours
Early Architecture Description Languages –Domain and Style Specific ADLs –Extensible
ADL Documenting Software architecture –Architecture Evaluation –ATAM. Baseline.
UNIT - IV ARCHITECTURE DESIGN: 10 Hours Typical Architectural Design - Data Flow - Independent Components - Call and Return –
Using Styles in Design – choices of styles – Architectural design space – Theory of Design
Spaces –Design space of Architectural Elements – Design space of Architectural styles.
UNIT- V CREATING AN ARCHITECTURE: 10 Hours Understanding Quality Attributes - Functionality and Architecture –Architecture and Quality-
Attributes-System Quality Attributes –Quality attribute Scenarios in Practice - Introducing
Tactics -Availability Tactics –Modifiability Tactics –Performance Tactics -Security Tactics –
Testability Tactics –Usability Tactics –Relationship of Tactics to Architectural Patterns –
Architectural Patterns and Styles.
UNIT-VI Recent advances and research being done in the topics mentioned above units
REFERENCES 1. Len Bass, Paul Clements, Rick Kazman, ―Software Architecture in Practice, Third
Edition, Addison, Wesley, 2012.
2. David Budgen, "Software Design", Second Edition, Pearson Education, 2004.
3. Richard N.Taylor, NenadMedvidovic and Eric M.Dashofy, ―Software Architecture,
Foundations, Theory and Practice, Wiley 2010.
4. Hong Zhu, ―Software Design Methodology from Principles to Architectural Styles,
Elsevier, 2005.
SE-14
6
5. Mary shaw and David Garlan, Software Architecture –Perspectives on an emerging
discipline, Pearson education, 2008.
COURSE OUTCOMES At the end of the course, the students will be able to:
CO1: Classify various software architecture
CO2: Explain software architecture and architecture design.
CO3: Demonstrate distributed and networked architectures.
CO4: Design methods for improving software quality from the perspective of software
architecture.
CO5: Evaluate the software architecture and software quality.
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks 50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100 Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2
CO3 3
CO4 3
CO5 2 3
1: Low 2: Medium 3:High
SE-15
7
Course Code 18SE1E1A M. Tech(Software Engineering)
Category Theory-Professional Elective
Course title AGILE SOFTWARE ENGINEERING
Scheme and
Credits
No. of Hours/Week Semester – I
L T P SS Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
SOFTWARE ENGINEERING
COURSE OBJECTIVES
The course will enable the student to:
1. Uunderstand an iterative, incremental development process leads to faster
delivery of more useful software
2. Apply agile development methods
3. Analyse the roles of prototyping in the software process
4. Explore the principles and practices of extreme programming
5. Create the concept of Mastering Agility
UNIT-I AGILE METHODS: 9 Hours Introduction to Agile: Understanding Success, Beyond Deadlines, The Importance of
Organizational Success, Enter Agility, Agile Methods,
UNIT-II UNDERSTANDING XP: 9 Hours The XP Lifecycle, The XP Team, XP Concepts, Adopting XP: Assess Agility-
UNIT-III PRACTICING XP: 10 Hours Thinking: Pair Programming, Energized Work, Informative Workspace, Root-Cause
Analysis, Retrospectives, Collaborating: Trust, Sit Together, Real Customer Involvement,
Ubiquitous Language, Stand-Up Meetings, Coding Standards, Iteration Demo, Reporting,
Releasing:“Done Done”, No Bugs, Version Control, Ten-Minute Build, Continuous
Integration, Collective Code Ownership, Documentation. Planning: Vision, Release
Planning, The Planning Game, Risk Management, Iteration Planning, Slack, Stories,
Estimating. Developing: Incremental requirements, Customer Tests, Test-Driven
Development, Refactoring, Simple Design ,Incremental Design and Architecture, Spike
Solutions, Performance Optimization, Exploratory Testing
UNIT-IV MASTERING AGILITY: 10Hours Values and Principles: Commonalities, About Values, Principles, and Practices, Further
Reading, Improve the Process: Understand Your Project, Tune and Adapt, Break the Rules,
Rely on People :Build Effective Relationships, Let the Right People Do the Right Things,
Build the Process for the People, Eliminate Waste :Work in Small, Reversible Steps, Fail
Fast, Maximize Work Not Done, Pursue Throughput
UNIT-V DELIVER VALUE: 10 Hours Exploit Agility, Only Releasable Code Has Value, Deliver Business Results, Deliver
Frequently, Seek Technical Excellence :Software Doesn‟t Exist, Design Is for
Understanding, Design Trade-offs, Quality with a Name, Great Design, Universal Design
Principles, Principles in Practice, Pursue Mastery
UNIT-VI Recent advances and research being done in the topics mentioned above units
REFERENCES 1. James shore, Chromatic, “The Art of Agile Development (Pragmatic guide to
agile software development)”, O'Reilly Media, Shroff Publishers &Distributors,
2007.
2. Robert C. Martin, “ Agile Software Development, Principles, Patterns, and
SE-16
8
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 3
CO3 2
CO4 2
CO5 2
1: Low 2: Medium 3:High
Practices” Prentice Hall; 1stedition, 2002.
3. Craig Larman, “Agile and Iterative Development A Manger‟s Guide”,Pearson
Education, First Edition, India, 2004.
4. David J. Anderson; Eli Schragenheim, ―Agile Management for Software
Engineering: Applying the Theory of Constraints for Business Results, Prentice
Hall, 2003
5. Hazza&Dubinsky, ―Agile Software Engineering, Series: Undergraduate Topics
inComputer Science, Springer, VIII edition, 2009
6. Craig Larman, ―Agile and Iterative Development : A manager ̳s Guide, Addison-
Wesley, 2004
COURSE OUTCOMES At the end of the course, the students will be able to:
CO1:Identify various software development process and Agile methods
CO2:Understand The XP Lifecycle, XP Concepts, Adopting XP
CO3:Work on Pair Programming, Root-Cause Analysis, Retrospectives, Planning,
Incremental Requirements, Customer Tests
CO4: Analyse Agile principles and practices
CO5: Implement Concepts to Eliminate Waste
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
SE-17
9
Course Code 18SE1E1B M. Tech(Software Engineering)
Category Theory-Professional Elective
Course title SOFTWARE RELIABILITY METRICS AND MODELS
Scheme and
Credits
No. of Hours/Week Semester – I
L T P SS Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
Software Engineering
COURSE OBJECTIVES
The course will enable the student to:
1. Understand software reliability and software quality
2. Remember different notions of defects and classify them
3. Apply software metrics in a rigorous way to measure the software quality
4. Analyse resource usage model, resource utilization, recommended Models
5. Evaluate reliability of software
UNIT - I INTRODUCTION TO SOFTWARE RELIABILITY: 9 Hours Basic Concepts, Failure and Faults, Environment, Availability, Modeling, uses,
requirements reliability metrics, design &code reliability metrics, testing reliability
metrics
UNIT- II SOFTWARE RELIABILITY MODELING: 10 Hours Concepts, General Model Characteristic, Historical Development of models, Model
Classification scheme, Markovian models, General concepts, General Poisson Type
Models, Binomial Type Models, Poisson Type models, Fault reduction factor for Poisson
Type models.
UNIT-III COMPARISON OF SOFTWARE RELIABILITY MODELS: 10 Hours Comparison Criteria, Failure Data, Comparison of Predictive Validity of Model Groups,
Recommended Models, Comparison of Time Domains, Calendar Time Modeling, Limiting
Resource Concept, Resource Usage model, Resource Utilization, Calendar Time
Estimation and confidence Intervals
UNIT-IV FUNDAMENTALS OF MEASUREMENT: 9 Hours Measurements in Software Engineering, Scope of Software metrics, Measurements theory,
Goal based Framework, Software Measurement Validation.
UNIT-V MEASURING SOFTWARE PRODUCT: 10 Hours Measurement of Internet Product Attributes, Size and Structure, External Product
Attributes, Measurement of Quality, Software Reliability: Measurement and Prediction.
UNIT-VI Recent advances and research being done in the topics mentioned above
units
REFERENCES
1. Norman Fenton, James Bieman, Software Metrics: A Rigorous and Practical
Approach, 3 rd edition, CRC Press, 2015
2. John D. Musa, Anthony Iannino, KazuhiraOkumoto, Software Reliability,
Measurement, Prediction, Application, Series in Software Engineering
andTechnology, McGraw Hill, 1987
3. John D. Musa, Software Reliability Engineering, Tata McGraw Hill, 1999
SE-18
10
COURSE OUTCOMES At the end of the course, the students will be able to:
CO1: Define software reliability and software quality
CO2: Explain different notions of defects and defects classification
CO3: Implement some software metrics fore measurement of software quality
CO4: Compare various software reliability models
CO5: Investigate software reliability of given application
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2
CO3 2
CO4 3
CO5 2 3
1: Low 2: Medium 3:High
SE-19
11
Course Code 18SE1E1C M. Tech(Software Engineering)
Category Theory-Professional Elective
Course title SOFTWARE REQUIREMENTS ENGINEERING
Scheme and
Credits
No. of Hours/Week Semester – I
L T P SS Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
Software Engineering
COURSE OBJECTIVES
The course will enable the student to:
1. Understand the basics of requirements engineering
2. Apply different techniques for requirements elicitation
3. Analyse need of requirements analysis in requirement integration
4. Evaluate various methodologies for requirements development
5. Formulate the current trends in requirements prioritization and validation.
UNIT -I INTRODUCTION TO SOFTWARE RELIABILITY: 9 Hours Software Requirement Overview, Software Development Roles, Software Development
Process Kernels, Commercial Life Cycle Model, Vision Development, Stakeholders
Needs &Analysis, Stakeholder needs, Stakeholder activities.
UNIT -II REQUIREMENTS ELICITATION: 10 Hours The Process of Requirements Elicitation, Requirements Elicitation Problems, Problems
of Scope, Problems of Understanding, Problems of Volatility, Current Elicitation
Techniques, InformationGathering, Requirements Expression and Analysis, Validation,
An Elicitation Methodology Framework, A Requirements Elicitation Process Model,
Methodology over Method, Integration of Techniques, Fact-Finding, Requirements
Gathering, Evaluation and Rationalization, Prioritization, Integration and Validation.
UNIT -III REQUIREMENTS ANALYSIS: 10 Hours Identification of Functional and Non Functional Requirements, Identification of
Performance Requirements, Identification of safety Requirements, Analysis, Feasibility
and Internal Compatibility of System Requirements, Definition of Human Requirements
Baseline.
UNIT -IV REQUIREMENTS DEVELOPMENT: 10 Hours Requirements analysis, Requirements Documentation, Requirements Development
Workflow, Fundamentals of Requirements Development, Requirements Attributes
Guidelines Document, Supplementary Specification Document, Use Case Specification
Document, Methods for Software Prototyping, Evolutionary prototyping, Throwaway
prototyping.
UNIT -V REQUIREMENTS VALIDATION: 9 Hours Validation objectives, Analysis of requirements validation, Activities, Properties,
Requirement reviews, Requirements testing, Case tools for requirements engineering.
UNIT-VI Recent advances and research being done in the topics mentioned above
units
REFERENCES 1. Ian Sommerville, Pete Sawyer, Requirements Engineering: A Good Practice Guide,
Sixth Edition,Pearson Education, 2004
2. Dean Leffingwe, Don Widrig, Managing Software Requirements A Use Case
Approach, Second Addition, Addison Wesley, 2003
3. Karl Eugene Wiegers, Software Requirements, Word Power Publishers, 2000
4. Ian Graham, Requirements Engineering and Rapid Development, Addison Wesley,
1998
5. Wiegers, Karl, Joy Beatty, Software requirements, Pearson Education, 2013
SE-20
12
COURSE OUTCOMES At the end of the course, the students will be able to:
CO1. Summarize the basics of requirements engineering
CO2: Illustration of requirements elicitation
CO3: Abstract need of requirements analysis in requirement integration
CO4: Assess various methodologies for requirements development
CO5: Investigate new techniques in requirements prioritization and validation
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2
CO3 3
CO4 3
CO5 3 3
1: Low 2: Medium 3:High
SE-21
13
Course Code 18SE1E2A M. Tech(Software Engineering)
Category Theory-Professional Elective
Course title SOFTWARE DESIGN PATTERNS
Scheme and
Credits
No. of Hours/Week Semester – I
L T P SS Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
Software Engineering
COURSE OBJECTIVES
The course will enable the student to:
1. Identify appropriate design patterns for design problems
2. Understand common design patterns
3. Apply incremental/iterative development
4. Evaluate the quality of software source code
5. Develop design pattern for Reframe badly designed program
UNIT – I INTRODUCTION TO DESIGN PATTERNS: 09 Hours
Design Patterns Arose from Architecture and Anthropology - Architectural to Software
Design Patterns - Advantages of Design Patterns - Adapter Pattern - Strategy Pattern -
Bridge Pattern - Abstract Factory Pattern
UNIT-II NEW PARADIGM OF DESIGN: 10 Hours
Principles and Strategies of Design Patterns - Open-Closed Principle – Designing from
Context - Encapsulating Variation. Commonality and Variability Analysis - Analysis
Matrix - Decorator Pattern - Open Closed Principle – The Principle of encapsulating
variation – Abstract Classes vs Interfaces
UNIT- III VALUES OF PATTERNS: 09 hours
Observer Pattern - Categories of Patterns - Template Method Pattern – Applying the
Template Method to the Case Study - Using Template Method Pattern to Reduce
Redundancy.
UNIT-IV APPLYING DESIGN PATTERNS: 10 Hours
Design Patterns: Factories - Singleton Pattern and the Double-Checked Locking Pattern -
Applying Singleton Pattern to Case Study. Object Pool Pattern - 31Management of Objects.
Factory Method Pattern - Factory Method Pattern – Object Oriented Pool Pattern
UNIT–V CASE STUDIES: 10 Hours
What to Expect from Design Patterns - The Pattern Community An Invitation – A Parting
Thought - A Case Study : Designing a Document Editor : Design Problems, Document
Structure, Formatting, Embellishing the User Interface, Supporting Multiple Look-and-Feel
Standards, Supporting Multiple Window Systems, User Operations Spelling Checking and
Hyphenation.
UNIT-VI Recent advances and research being done in the topics mentioned above
units
REFERENCES 1. Jason McC. Smith, “Elemental design Patterns”, Pearson, 2012.
2. Alan Shalloway and James R.Trott, “Design Patterns explained: A new
perspective on Object-Oriented Design, 2006.
3. Erich Gamma, Richard Helm, Ralph Johnson, John Vlissides, “Design Patterns:
Elements of Reusable Object-Oriented Software”, Addison-Wesley, 2003.
4. Eric Freeman, Elisabeth Freeman, Kathy Sierra, Bert Bates, “Head First Design
Patterns A Brain-Friendly Guide
SE-22
14
COURSE OUTCOMES At the end of the course, the students will be able to:
CO1: Outline appropriate design patterns for various problems
CO2: Apply principles in the design of object oriented systems.
CO3: Examine an understanding of a range of design patterns.
CO4: Comprehending a design presented using this vocabulary.
CO5: Assess and apply suitable patterns in specific contexts
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2
CO3 3
CO4 3
CO5 3
1: Low 2: Medium 3:High
SE-23
15
Course Code 18SE1E2B M. Tech(Software Engineering)
Category Theory-Professional Elective
Course title ADVANCED STORAGE AREA NETWORKS
Scheme and Credits No. of Hours/Week Semester – I
L T P SS Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
1.Computer Networks
2.Database Management Systems
3.Operating Systems
COURSE OBJECTIVES
This course will enable the students to
1. Understand storage centric and server centric systems
2. Apply various metrics used for designing storage area networks
3. Analysis RAID concepts
4. Evaluate data maintains at data centres with the concepts of backup
5. Create techniques for data storage management at data centres
UNIT -I INTRODUCTION: 10 Hours
Server Centric IT Architecture and its Limitations; Storage – Centric IT Architecture and its
advantages. Case study: Replacing a server with Storage Networks The Data Storage and Data
Access problem; The Battle for size and access. Intelligent Disk Subsystems: Architecture of
Intelligent Disk Subsystems; Hard disks and Internal 8 Hours I/O Channels; JBOD, Storage
virtualization using RAID and different RAID levels; Caching: Acceleration of Hard Disk
Access; Intelligent disk subsystems, Availability of disk subsystems.
UNIT -II I/O TECHNIQUES: 10 Hours
The Physical I/O path from the CPU to the Storage System; SCSI; Fibre Channel Protocol
Stack; Fibre Channel SAN; IP Storage. Network Attached Storage: The NAS Architecture, The
NAS hardware Architecture, The NAS Software Architecture, Network connectivity, NAS as a
storage system. File System and NAS: Local File Systems; Network file Systems and file
servers; Shared Disk file systems; Comparison of fibre Channel and NAS.
UNIT -III STORAGE VIRTUALIZATION: 10 Hours
Definition of Storage virtualization; Implementation Considerations; Storage virtualization on
Block or file level; Storage virtualization on various levels of the storage Network; Symmetric
and Asymmetric storage virtualization in the Network.
UNIT- IV SAN ARCHITECTURE AND HARDWARE DEVICES: 9 Hours
Overview, Creating a Network for storage; SAN Hardware devices; The fibre channel switch;
Host Bus Adaptors; Putting the storage in SAN; Fabric operation from a Hardware perspective.
Software Components of SAN: The switch‟s Operating system; Device Drivers; Supporting the
switch‟s components; Configuration options for SANs.
UNIT–V MANAGEMENT OF STORAGE NETWORK: 9 Hours
System Management, Requirement of management System, Support by Management System,
Management Interface, Standardized Mechanisms, Property Mechanisms, In-band Management,
Use of SNMP, CIM and WBEM, Storage.
UNIT-VI Recent advances and research being done in the topics mentioned above units
REFERENCES
SE-24
16
1. Ulf Troppens, Rainer Erkens and Wolfgang Muller: Storage Networks Explained, Wiley
India 2013.
2. Robert Spalding: “Storage Networks The Complete Reference”, Tata McGraw-Hill, 2011.
3. Marc Farley: Storage Networking Fundamentals – An Introduction to Storage Devices,
Subsystems, Applications, Management, and File Systems, Cisco Press, 2005.
4. Richard Barker and Paul Massiglia: “Storage Area Network Essentials A Complete Guide to
understanding and Implementing SANs”, Wiley India, 2006.
COURSE OUTCOMES :
The students should be able to:
CO1: Distinguish storage centric and server centric systems
CO2: Determine the need for performance evaluation and the metrics used for it
CO3: Extrapolate RAID and different RAID levels
CO4: Validate data maintained at data centres
CO5: Develop techniques for storage management
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2
CO3 3
CO4 3
CO5 1 2
1: Low 2: Medium 3:High
SE-25
17
Course Code 18SE1E2C M. Tech(Software Engineering)
Category Theory-Professional Elective
Course title SOFTWARE VERIFICATION AND VALIDATION
Scheme and
Credits
No. of Hours/Week Semester – I
L T P SS Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
Software Engineering
COURSE OBJECTIVES
The course will enable the student to:
1. Understand the principles of verification and validation
2. Apply the various verification and validation techniques
3. Analyze usage of appreciate tools for verification and validation
4. Evaluate tracing tools, tools for testing and tools for verification and validation
5. Create UML behavioral diagrams –probabilistic model
UNIT -I INTRODUCTION: 09 Hours
Principles of verification and validation – software architecture frameworks – model driven
architecture – UML – systems modeling language – verification, validation and
accreditation.
UNIT -II METHODS OF SOFTWARE VERIFICATION: 09 Hours
Verification and validation life cycle – traceability analysis – interface analysis – design and
code verification – test analysis - Reviews – inspections - walkthroughs – audits – tracing –
formal proofs – Model based verification and validation - Program verification techniques –
formal methods of software verification – clean room methods.
UNIT -III TESTING: 10 Hours
Stages of Testing: Test Planning – Test design – Test case definition – Test procedure – Test
reporting – Unit testing: white box , black box and performance testing – system testing:
Function, performance, interface, operations, resource, security, portability, reliability,
maintainability, safety, regression and stress testing – integration testing – acceptance
testing: capability, constraint testing - structured testing – structured integration testing
UNIT -IV TOOLS FOR SOFTWARE VERIFICATION: 10 Hours
Tools for verification and validation: static analyzer – configuration management tools –
reverse engineering tools – tracing tools – tools for formal analysis – tools for testing – test
case generators – test harnesses – debuggers – coverage analyzers – performance analysers
– test management tools.
UNIT -V ADVANCED APPROACHES: 10 Hours
Automatic approach for verification and validation – validating UML behavioral diagrams –
probabilistic model checking of activity diagrams in SysML – metrics for verification and
validation
UNIT -VI Recent advances and research being done in the topics mentioned above
units
REFERENCES 1. Mourad Debbabi, Hassaine F, Jarrya Y., Soeanu A., Alawneh L.,”Verification
and Validation in Systems Engineering”, Springer, 2010
2. Marcus S. Fisher, “Software Verification and Validation: An Engineering and
Scientific Approach”, Springer, 2007
3. ESA Board for Software Standardization and Control (BSSC), “Guide to
software verification and Validation”, European Space Agency ESA PSS-05-10
Issue 1 Revision 1, 1995
4. Avner Engel, “Verification, Validation & Testing of Engineered
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Systems”,Wiley series in systems Engineering and Management, 2010.
COURSE OUTCOMES At the end the students will be able to:
CO1: Identify the different techniques for verification and validation
CO2: Determine available traceability analysis tools on sample requirements
CO3: Demonstrate coverage analyzers in terms of functionality or features used
CO4: Chart the various stages of testing, test planning
CO5: Design system test cases for various testing techniques
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2
CO3 3
CO4 3
CO5 2 2
1: Low 2: Medium 3:High
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Course Code 18SE1L01 M. Tech(Software Engineering)
Category Practical
Course title SOFTWARE DEVELOPMENT LAB
Scheme and
Credits
No. of Hours/Week Semester – I
L T P SS Credits
- - 4 - 2
CIE Marks: 50 SEE Marks:
50
Total Max. Marks:
100
Duration of SEE: 3 Hrs
Prerequisites (if any): Fundamentals of software engg.
COURSE OBJECTIVES
The course will enable the student to
1. Understand software development life cycle of an application
2. Apply software development lifecycle to an application,
3. Analyse SRS and design document,
4. Validate codes, documentation and test cases at appropriate stages of software
development.
5. Create project plan
Choose any one application for performing the following phases.
1. Program Analysis and Project Planning.
Thorough study of the problem, Identify project scope, Objectives, Infrastructure., PROJECT
PLAN DOCUMENTATION
2. Software requirement Analysis
Describe the individual Phases / Modules of the project, Identify deliverables., SRS
DOCUMENTATION
3. Data Modeling
Use work products, Data dictionary, Use case diagrams and activity diagrams, build and test
class diagrams, Sequence diagrams , add interface to class diagrams., DESIGN
DOCUMENTATION
4. Software Development and Debugging :
Use technology of your choice to develop and debug the application, CODE
DOCUMENTATION
5. Software Testing :
Perform validation testing, Coverage analysis, memory leaks, develop test case hierarchy,
Site check and Site monitor., TEST CASE DOCUMENTATION
COURSE OUTCOMES At the end of the course, the students will be able to:
CO1: Identify software development phases
CO2: illustrate use case diagrams and activity diagrams
CO3: Verify SRS, design document,
CO4: Validate codes, documentation and test case at appropriate stages of software
development.
CO5: Investigate correctness of designed software
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Scheme of Examination
For selected application, the student have to demonstrate different phase of software
development life cycle
Continuous Internal
Evaluation(Lab=50)
Marks Semester End Evaluation (SEE) Marks
Performance of the student in
the lab every week
20 Write-Up 20
Test at end of the semester 20 Experiment/Execution 70
Vice-Voce 20 Vice-Voce 10
Total(CIE) 50 Total(SEE) 50*
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced for 50
marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2
CO3 3
CO4 3
CO5 2
1: Low 2: Medium 3:High
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Course Code 18CS1M01 M.Tech (Software Engineering)
Category Mandatory Audit
Course title RESEARCH METHODOLOGY AND IPR
Scheme and Credits No. of Hours/Week Semester – I
L T P SS Credits
2 0 - - 2
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
This course will enable students to
1. Understand the formulation of research problem, scope and objectives of research problem
2. Use methods for effective technical writing skills
3. Analyse Approaches of investigation of solutions for research problem
4. Evaluate the format of research proposal , intellectual property and patent
5. Create patent, research paper
UNIT -I RESEARCH PROBLEM: 3 Hours Meaning of research problem, Sources of research problem, Criteria Characteristics of a good
research problem, Errors in selecting a research problem, Scope and objectives of research problem.
Approaches of investigation of solutions for research problem, data collection, analysis,
interpretation, Necessary instrumentations
UNIT- II RESEARCH REQUIREMENTS: 3 Hours
Effective literature studies approaches, analysis Plagiarism, Research ethics,
UNIT- III EFFECTIVE TECHNICAL WRITING: 6 Hours Effective technical writing, how to write report, Paper Developing a Research Proposal, Format of research
proposal, a presentation and assessment by a review committee
UNIT- IV NATURE OF INTELLECTUAL PROPERTY: 6 Hours Patents, Designs, Trade and Copyright. Process of Patenting and Development: technological research,
innovation, patenting, development. International Scenario: International cooperation on Intellectual Property.
Procedure for grants of patents, Patenting under PCT.
UNIT- V PATENT RIGHTS: 6 Hours Scope of Patent Rights. Licensing and transfer of technology. Patent information and databases. Geographical
Indications.
UNIT- VI NEW DEVELOPMENTS IN IPR: Administration of Patent System. New developments in IPR; IPR of Biological Systems, Computer Software
etc. Traditional knowledge Case Studies, IPR and IITs.
REFERENCES
1. Stuart Melville and Wayne Goddard, “Research methodology: an introduction for science &
engineering students‟”
2. Wayne Goddard and Stuart Melville, “Research Methodology: An Introduction”
3. Ranjit Kumar, 2nd Edition, “Research Methodology: A Step by Step Guide for beginners”
Halbert, “Resisting Intellectual Property”, Taylor & Francis Ltd ,2007.
4. Mayall, “Industrial Design”, McGraw Hill, 1992.
5. Niebel, “Product Design”, McGraw Hill, 1974.
6. Asimov, “Introduction to Design”, Prentice Hall, 1962.
7. Robert P. Merges, Peter S. Menell, Mark A. Lemley, “ Intellectual Property in New
Technological Age”, 2016.
8. T. Ramappa, “Intellectual Property Rights Under WTO”, S. Chand, 2008
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COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1: Understand research problem formulation. Analyze research related information and
follow research ethics
CO2: Understanding that when IPR would take such important place in growth of
individuals and nation, it is needless to emphasis the need of information about
Intellectual Property Right to be promoted among students in general & engineering
in particular.
CO3: Understand that IPR protection provides an incentive to inventors for further research
work and investment in R & D, which leads to creation of new and better products,
and in turn brings about, economic growth and social benefits.
CO4: Analyze research related information
CO5: Follow research ethics
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 6 hours shall have internal
choice
20*3=60
Marks Total:
Marks 100
Unit which have 3 hours shall not have internal
choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3
CO2 3
CO3 3
CO4
CO5 3 3
1: Low 2: Medium 3:High
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COURSE LEARNING OBJECTIVES:
The objectives of the SEMINAR-I is to prepare the students to learn to:
1. Carry out the literature review of general research area/current topic and analyse the
same effectively.
2. Prepare a technical report, reflecting his/her depth of understanding, on the selected
area/topic and prepare content rich presentation.
3. Acquire communication and time management skills for effective and impactful
presentation. Interact with peers to acquire the qualities of thoughtfulness, friendliness,
adaptability, responsiveness, and politeness in-group discussion. Overcome stage fear
during the presentation.
GUIDE LINES
1. Seminar preparation and presentation is an individual student activity.
2. Topic may be of general/ specific interest to program of engineering or electives not offered
in the semester and to be selected in consultation with the faculty/Guide.
3. Select one pertinent research paper for the seminar presentation.
4. Prepare and submit a detailed technical report of the seminar topic.
COURSE OUTCOMES:
Students shall be able to:
1. Carry out the literature survey of topic of seminar.
2. Prepare a technical report on the selected area/topic.
3. Make an effective presentation with seamless flow of content within the time allocated.
Overcome inhibition in interacting with peers and hence develop the spirit of team
work. Overcome stage fear during the presentation.
SCHEME OF EXAMINATION
CIE –
50
marks
Phase -1 Marks =10 Seminar Report
Marks =20
Total:50
Marks Phase -2 Marks =20
Course Code 18SE1S01 M.Tech (Software Engineering)
Category Seminar Semester: I
Course title SEMINAR - I
Scheme and Credits
No. of Hours/Week
Total hours = 24 L T P S Credits
0 0 2 0 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any): NIL
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Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2 3
CO2 2 3 3
CO3 2
1. Low, 2. Medium, 3. High
Scheme of Continuous Internal Evaluation (CIE):
Evaluation would be carried out in TWO phases. The Seminar Evaluation Committee
shall comprise of Chairman of the Department, Faculty/Guide and one more faculty member
nominated by Chairman. The evaluation criteria shall be as per the rubrics given below:
Rubrics for Evaluation:
Topic - Technical Relevance, Sustainability and Societal Concerns : 15%
Review of literature and Technical Content : 35%
Presentation Skills : 25%
Report : 25%
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Course Code 18CS1M02 M.Tech(Software Engineering)
Category Audit Course-I
Course title TECHNICAL PAPER WRITING
Scheme and Credits No. of Hours/Week Semester – I
L T P SS Credits
2 0 - - 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any):
COURSE OBJECTIVES
This course will enable students to
1. Understand the planning section of research paper and preparation of paper writing
2. Apply key skill while writing research paper and know about what to write in each section
3. Analyse literature, methods,
4. Evaluate research paper, paraphrasing paper
5. Create good research paper
UNIT-I PLANNING AND PREPARATION: 6 Hours
Planning and Preparation, Word Order, Breaking up long sentences, Structuring Paragraphs and
Sentences, Being Concise and Removing Redundancy, Avoiding Ambiguity and Vagueness
UNIT- II CLARIFYING: 3 Hours
Clarifying Who Did What, Highlighting Your Findings, Hedging and Criticising, Paraphrasing and
Plagiarism, Sections of a Paper, Abstracts. Introduction
UNIT- III REVIEW OF THE LITERATURE: 6 Hours
Review of the Literature, Methods, Results, Discussion, Conclusions, The Final Check.
UNIT- IV KEY SKILLS: 6 Hours
Key skills are needed when writing a Title, key skills are needed when writing an Abstract, key skills
are needed when writing an Introduction, skills needed when writing a Review of the Literature,
UNIT- V METHODS: 3 Hours
skills are needed when writing the Methods, skills needed when writing the Results, skills are
needed when writing the Discussion, skills are needed when writing the Conclusions.
UNIT- VI NEW DEVELOPMENTS IN RESEARCH PAPER WRITING:
useful phrases, how to ensure paper is as good as it could possibly be the first- time submission
REFERENCES
1. Goldbort R (2006) Writing for Science, Yale University Press (available on Google Books)
2. Day R (2006) How to Write and Publish a Scientific Paper, Cambridge University Press
3. Highman N (1998), Handbook of Writing for the Mathematical Sciences, SIAM.
Highman‟sbook.
4. Adrian Wallwork, English for Writing Research Papers, Springer New York Dordrecht
Heidelberg London, 2011
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1: List of section of research paper and preparation of paper writing
CO2: Determine key skill while writing research paper
CO3: Analyse literature, methods
CO4: Assess research paper, do paraphrasing paper
CO5: Formulate research paper and results of simulation
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Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=20 Marks
Total: Marks
50
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3
CO2 3
CO3 3
CO4 3
CO5 3
1: Low 2: Medium 3:High
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SEMISTER-II
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Course Code 18CS2C01 M.Tech(Software Engineering)
Category Theory-Professional Core
Course title ADVANCED DATA STRUCTURES AND ALGORITHMS
Scheme and
Credits
No. of Hours/Week Semester – II
L T P SS Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVE
The course will enable the student to:
1. Learn various data structures and its usage in designing algorithms.
2. Understand to the advanced methods of designing and analysing algorithms.
3. Learn various string matching and graph algorithms.
4. Acquire the knowledge of polynomial, non polynomial and approximation problems.
5. Understand the recent developments in the area of algorithmic design
UNIT-1 REVIEW OF ANALYSIS TECHNIQUES: 09 Hours
Growth of Functions: Asymptotic notations; Standard notations and common functions;
Recurrences -The substitution method, recursion-tree method, the master method,
Probabilistic Analysis and Randomized Algorithms.
UNIT- II BASIC DATA STRUCTURES: 09 Hours Stacks and Queues, Vectors, Lists, and Sequences, Trees, Priority Queues and Heaps,
Dictionaries and Hash Tables, Search Trees and Skip lists- Ordered Dictionaries and
Binary Search Trees, AVL Trees, Bounded-Depth Search Trees, Splay Trees, Skip Lists.
UNIT -III DYNAMIC PROGRAMMING: 10 Hours
Matrix-Chain multiplication, Elements of dynamic programming, longest common
subsequences. Graph algorithms: Bellman - Ford Algorithm; Single source shortest paths
in a DAG; Johnson‟s Algorithm for sparse graphs; Flow networks and Ford-Fulkerson
method. .
UNIT- IV TRIES AND STRING MACHING ALGORITHMS: 10 Hours
Quad trees and K-d trees. String-Matching Algorithms: Naïve string Matching; Rabin -
Karp algorithm; String matching with finite automata; KnuthMorris-Pratt algorithm.
UNIT- V NP-COMPLETENESS: 10 Hours
Polynomial time, Polynomial time verification, NP-Completeness and reducibility, NP-
Complete problems. Approximation Algorithms: vertex cover problem, the set – covering
problem, randomization and linear programming, the subset – sum problem.
UNIT-VI Recent advances and research being done in the topics mentioned above
units
REFERENCES
1. Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein,”
Introduction to Algorithms”, Third Edition, Prentice-Hall, 2011.
2. M T Goodrich, Roberto Tamassia, “Algorithm Design”, John Wiley, 2002.
3. Mark Allen Weiss, “Data Structures and Algorithm Analysis in C++”, 4th
Edition,
Pearson, 2014.
4. Alfred V. Aho, John E. Hopcroft, Jeffrey D. Ullman, ―Data Structures and
Algorithms‖, Pearson Education, Reprint 2006.
5. Ellis Horowitz, Sartaj Sahni, Dinesh Mehta, “Fundamentals of Data Structures in C”,
Silicon Pr, 2007.
6. Aho, Hopcroft and Ullman, Data structures and algorithms, 1st edition, Pearson
Education, India, 2002, ISBN: 8177588265, 978817758826
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29
COURSE OUTCOMES
On completion of the course, the student will be able to:
CO1: Develop and analyze algorithms for red-black trees, B-trees and Splay trees and for
text processing applications.
CO2: Identify suitable data structures and develop algorithms for solving a particular set of
problems
CO3: Analyze the complexity/ performance of different algorithms.
CO4: Categorize the different problems in various classes according to their complexity.
CO5: Use appropriate data structure and algorithms in real time applications..
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2
CO3
CO4 2
CO5 2 2
1: Low 2: Medium 3:High
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Course Code 18CS2C02 M.Tech(Software Engineering)
Category Theory-Professional Core
Course title ADVANCED OPERATING SYSTEMS
Scheme and
Credits
No. of Hours/Week Semester – II
L T P SS Credits
4 - - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
The course will enable the students to:
1. Understand the Design Approaches and Issues related to Advanced Operating Systems.
2. Apply the Knowledge on Distributed Operating Systems Concepts to develop Clocks,
Mutual Exclusion Algorithms.
3. Analyze the Distributed Deadlock Detection Algorithms and Agreement Protocols.
4. Evaluate the Algorithms for Distributed Scheduling, Examine the Commit Protocols
and review Concurrency Control Algorithms.
5. Create Advanced Operating Systems Applications with recent technologies
UNIT- I INTRODUCTION: 09 Hours
Concept of Batch system, Multiprogramming, Time Sharing, Parallel, Distributed and Real-
time System, Process Management: Concept of Process, Synchronization, CPU Scheduling,
IPC, Deadlock: Concept of Deadlock Prevention, Avoidance, Detection and its Recovery.
Memory Management: Contiguous allocation, Paging and Segmentation. Virtual memory:
Demand Paging, Page Replacement Algorithms. Distributed OS- Design Approaches and
Issues in DOS. Message Passing Model and RPC.
UNIT -II CLOCKS AND DISTRIBUTED MUTUAL EXCLUSION: 10 Hours
Concept of Lamport‟s Logical Clock and Vector Clocks, Termination Detection. A simple
solution to distributed mutual exclusion, Non Token based algorithms: Lamport‟s algorithm,
Ricart Agarwala‟s algorithm, Maekawa‟s algorithm, Token based algorithms: Suzuki Kasami‟s
broadcast algorithm, Raymond‟s tree based algorithm.
UNIT -III DISTRIBUTED DEADLOCK DETECTION: 10 Hours
Deadlock Handling, Strategies in Distributed Systems, Issues in Deadlock Detection And
Resolution, Control Organization for Distributed Deadlock Detection, Centralized Deadlock
Detection Algorithm: Ho-Ramamoorthy‟s Algorithm, Distributed Deadlock Detection
Algorithms: A Path- Pushing Algorithm and Edge Chasing Algorithm, Hierarchical Deadlock
Detection Algorithms: Menasce- Muntz Algorithm, Ho-Ramamoorthy‟s Algorithm.
Agreement Protocols: Byzantine Agreement Problem, Solution to The Byzantine Agreement
Problem- Lamport -Shostak- Pease Algorithm, Dolev et al.‟s Algorithm
UNIT –IV DISTRIBUTED SCHEDULING AND FAULT TOLERANCE: 10 Hours Issues in Load Distribution, Components of a Load Distributing Algorithms, Load Distributing
Algorithms, Performance Comparison, Selecting Suitable Load Sharing Algorithms,
Requirements of Load Sharing Policies. Commit Protocols, Nonblocking Commit Protocols,
Voting Protocols, Dynamic Voting Protocols, The Majority Based Dynamic Voting Protocol,
Dynamic Vote Reassignment Protocols.
UNIT -V DATABASE OS & CONCURRENCY CONTROL 09 Hours
Requirement of Database OS, A Concurrency Control Model of a Database System, The
Problem of Concurrency Control, Serializability Theory, Concurrency Control Algorithms,
Basic Synchronization Primitives, Lock Based Algorithms, Timestamp Based Algorithms,
Optimistic Algorithms, Concurrency Control Algorithms for Data Replication.
UNIT-VI Recent advances and research being done in the topics mentioned above units
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REFERENCES
1. Mukesh Singhal and Niranjan G Shivaratri, Advanced Concepts in Operating Systems, Tata
Mcgraw Hill, 2002.
2. A Silberchatz and Peter Baer Galvin, Operating System Concepts, 10th Edition, John Wiley
and Sons, 2018.
3. William Stallings, Operating System: Internals and Design Principles, 9th Edition, Prentice
Hall India, 2017.
4. Andrew S Tennenbaum and Albert S Woodhull, Operating System Design and
Implementation, 3rd Edition, Pearson Education Inc., 2006.
5. Ceri S and Pelagorthi S, Distributed Databases: Principles and Systems, 1984, McGraw Hill.
COURSE OUTCOMES
On completion of the course, the student would be able to:
CO1: Explain the Fundamentals of Advanced Operating Systems, Design issues.
CO2: Determine the various Clock Synchronization Principles and Implement Mutual
Exclusion Algorithms.
CO3: Differentiate the various Distributed Deadlock Detection Algorithms and Analyze the
Agreement Protocols.
CO4: Select the appropriate Distributed Scheduling Algorithms, Commit Protocols and
Concurrency Control Algorithms.
CO5: Integrate the Concepts of Advanced Operating Systems with recent trends and
technologies to Design and Develop Applications.
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 1 -
CO2 1 2
CO3 1 2
CO4 1 3
CO5 3 2 2
1: Low 2: Medium 3:High
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Course Code 18SE2C03 M.Tech(Software Engineering)
Category Theory-Professional Core
Course title SOFTWARE TESTING AND QUALITY ASSURANCE
Scheme and
Credits
No. of Hours/Week Semester – II
L T P SS Credits
4 0 0 - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
The course will enable the students to:
1. Understand software and the quality metrics of various softwares.
2. Apply quality metrics for quality assurance to various softwares.
3. Analyse methodologies in making Software.
4. Evaluate the product finally to check the product Quality.
5. Create new quality metrics for quality assurance
UNIT -I INTRODUCTION: 09 Hours Introduction to Software Quality - Challenges – Objectives – Quality Factors – Components of
SQA – Contract Review – Development and Quality Plans – SQA Components in Project Life
Cycle – SQA Defect Removal Policies – Reviews.
UNIT -II TESTING METHODOLOGIES: 09 Hours Basics of Software Testing – Test Generation from Requirements – Finite State Models –
Combinatorial Designs - Test Selection, Minimization and Prioritization for Regression
Testing – Test Adequacy, Assessment and Enhancement.
UNIT -III TEST STRATEGIES: 10 Hours Testing Strategies – White Box and Black Box Approach – Integration Testing – System and
Acceptance Testing – Performance Testing – Regression Testing - Internationalization Testing
– Ad-hoc Testing – Website Testing – Usability Testing – Accessibility Testing.
UNIT- IV TEST AUTOMATION AND MANAGEMENT: 10 Hours Test plan – Management – Execution and Reporting – Software Test Automation – Automated
Testing tools - Hierarchical Models of Software Quality – Configuration Management –
Documentation Control.
UNIT -V SQA IN PROJECT MANAGEMENT: 10 Hours Project progress control – costs – quality management standards – project process standards –
management and its role in SQA – SQA unit.
UNIT-VI Recent advances and research being done in the topics mentioned above units
COURSE OUTCOMES At the end of the course, the students will be able to:
CO1: Explain different quality metrics for various softwares
CO2: Illustrate usage of quality metrics to analyse the product Quality.
CO3: Evaluate the test plan and various testing methods.
CO4: Assess software quality standards.
CO5:Develop new quality metrics for software to assure quality
References
1. Daniel Galin, “Software Quality Assurance – from Theory to Implementation”,
Pearson Education, 2009
2. Yogesh Singh, "Software Testing", Cambridge University Press, 2012
3. Aditya Mathur, “Foundations of Software Testing”, Pearson Education, 2008
4. Ron Patton, “Software Testing” , Second Edition, Pearson Education, 2007
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5. Srinivasan Desikan, Gopalaswamy Ramesh, “Software Testing – Principles and
Practices”, Pearson Education, 2006
6. Alan C Gillies, “Software Quality Theory and Management”, Cengage Learning,
Second Edition, 2003.
7. Robert Furtell, Donald Shafer, and Linda Shafer, "Quality Software Project
Management", Pearson Education Asia, 2002.
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2
CO3 3
CO4 3
CO5 3
1: Low 2: Medium 3:High
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Course Code 18SE2E1A M.Tech(Software Engineering)
Category Theory-Professional Elective
Course title SOFTWARE TEST AUTOMATION
Scheme and
Credits
No. of Hours/Week Semester – I I
L T P SS Credit
s
4 - 0 - 3
CIE Marks:
50
SEE Marks: 50 Total Max. Marks:
100
Duration of SEE: 3 Hrs
Prerequisites (if any): Fundamentals of software engineering.
COURSE OBJECTIVES
The course will enable the students to:
1. Understand the basics of test automation
2. Appreciate the different aspects of test tool evaluation and test automation approach
selection
3. Analyse the role played by test planning and design in test execution
4. Evaluate various testing tools for testing varied applications
5. Create test automation for given case studies
UNIT-I INTRODUCTION: 9 Hours
Fundamentals of test automation Management issues technical issues Background
on software testing Automated test life cycle methodology (ATLM) –Test Maturity
Model – Test Automation Development – Overcoming false expectations of
automated testing – benefits – test tool proposal
UNIT -II TEST AND AUTOMATION FRAMEWORK 10 Hours
Test Tool Evaluation and selection – organisations‗ system engineering environment–
tools that support the testing life cycle – test process analysis – test tool consideration
Test framework – Test Library Management – selecting the test automation approach –
test team management
UNIT -III TEST PLANNING AND DESIGN: 10 Hours Test planning – Test program scope – Test requirements management – Test Events,
Activities and Documentation – Test Environment – Evolving a Test plan Test analysis
and design – Test requirements analysis – Test program design – Test procedure design –
Test development architecture – guidelines – automation infrastructure – test execution
and review – test metrics
UNIT -IV TESTING THE APPLICATIONS: 10Hours Testing Web Applications – Functional Web testing with Twill – Selenium – Testing a
simple Web Application – Testing Mobile Smartphone Applications – Running
automated test scripts – Test tools for Browser based applications – Test Automation
with Emulators
UNIT -V CASE STUDIES: 9 Hours
Test automation and agile project management – database automation – test automation
in cloud – Mainframe and Framework automation – Model based test case generation
– Model based testing of Android applications – exploratory test automation
UNIT-VI Recent advances and research being done in the topics mentioned above
units
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COURSE OUTCOMES At the end of the course, the students will be able to:
CO1: Identify the different automation testing tools
CO2: Demonstrate usage of available testing tools to test software applications
CO3: Analyse test metrics based on functionality or features used
CO4: Assess test scripts for automating test execution
CO5: Design test cases and execute them
REFERENCES
1. Elfriede Dustin, Jeff Rashka, “Automated software testing: Introduction,
Management and Performance”, Pearson Education, 2008
2. C. Titus Brown, Gheorghe Gheorghiu, Jason Huggins, “An Introduction to
Testing Web Applications with twill and Selenium”, O'Reilly Media, Inc.,
2007
3. Dorothy Graham, Mark Fewster, “Experiences of Test Automation: Case
Studies of Software Test Automation”, illustrated Edition, Addison-Wesley
Professional, 2012
4. Kanglin Li, Mengqi Wu, “Effective Software Test Automation: Developing
an Automated Software Testing Tool”,John Wiley & Sons, 2006
5. Linda Hayes, “The Automated Testing Handbook”, Software testing Inst.,
1995
6. Julian Harty, “A Practical Guide to Testing Mobile Smartphone Applications,
Vol. 6 of Synthesis Lectures on Mobile and Pervasive Computing Series”,
Morgan & Claypool Publishers, 2009
7. Mark Fewster, Dorothy Graham, “Software Test Automation”, Addison
Wesley, 1999
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3
CO2 3
CO3 3
CO4 3
CO5 3
1: Low 2: Medium 3:High
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Course Code 18WT2E1B M.Tech(Software Engineering)
Category Theory-Professional Integrated
Course title USER INTERFACE DESIGN AND EVALUVATION
Scheme and
Credits
No. of Hours/Week Semester – I I
L T P SS Credits
3 - 2 - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
1. Overview of user-centred design field.
2. Describing requirements.
3. Importance of Evaluation.
COURSE OBJECTIVES
The course will enable the students to:
1. Understand the benefits of user centred approach to the design of software,
computer systems and websites.
2. Developing interaction design from user requirements and user interface design
evaluation.
3. Evaluate the user interface design
4. Analyze an established Human computer interaction topics like visibility,
affordance, feedback, metaphors and mental models
5. Apply the design evaluation for the real world applications.
UNIT-I INTRODUCTION: 09 Hours
Overview of the user-interface design. Designing for users, Knowledge needed for UI
designs.
UNIT -II REQUIRMENTS FOR DESIGN EVALUVATION: 10 Hours
How to gather requirements; Users and the domain; Tasks and work; Thinking about and
describing requirements; Case study on requirements;
UNIT -III DESIGN: 10 Hours
Work reengineering and conceptual design; Design rationale and Principles; Interaction
design; Interaction Styles; Choosing interaction devices; Hardware; Choosing interaction
elements; Software components; Case study on design; Style guides; guidelines and user-
centred design; Designing GUI; Designing for web; Design embedded computer systems
and small devices.
UNIT -IV EVALUATIONS: 10 Hours Why Evaluation?; deciding on what to evaluate, the strategy; Planning; Analysis and
Interpretation of user-observation evaluation data; Inspections of the user Interface;
Variations and More Comprehensive evaluations; .
UNIT -V PERSUVASION: 09Hours
Communication and using findings; Winning and Maintaining support for user-centred
Design.
UNIT-VI Recent advances and research being done in the topics mentioned above
units
UNIT-VII (Practical)- Lab exercise using a suitable software to the topics studied in
UNIT-I, UNIT-II, UNIT-III, UNIT-IV and UNIT-V 24 Hours
REFERENCES
1. Ben Shneiderman and Catherine Plaisant, “Designing the User Interface: Strategies
for Effective Human-Computer Interaction”, 5th
Edition, 2014, Pearson
Publications, ISBN:0321537351.
2. Debbie Stone, Caroline Jarrett, Mark woodroffe, Shailey Minocha, “User Interface
Design and Evaluation”,1st Edition Elsevier, 2005.
3. Wilbert O Galitz, ““The essential guide to user interface design”, Wiley, 3rd
Ed,
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Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3
CO2 3
CO3 2
CO4 3
CO5 3
1: Low 2: Medium 3:High
2007, ISBN:978-0-471-27139-0.
4. Prece, Rogers and Sharps, “Interaction Design”, 3rd
Edition, 2011, Wiley,
ISBN:978-1-119-02075-2.
5. Alan Dix, Janet Fincay, GRe Goryd, Abowd, Russel Bealg, “Human-Computer
Interactio”, Pearson 3rd Edition, 2004, ISBN 0-13-046109-1.
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1. Identify the benefits of user centred approach to the design field.
CO2: List out the requirements for design evaluvation.
CO3: Illustrate the need of user interface design
CO4: Evaluate the importance of evaluvation and user interface design
CO5: Design Case Study on user interface Design.
Scheme of Examination
CIE -
Practical
Conduction of experiments, performance of student in
every week lab and completion of lab record=50#
Total: Marks
50
Lab Test: Test =20, Record=20 and Vice-Voce=10
Marks(50##
)
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
# = CIE Marks for laboratory performance is to be evaluated for 50 marks and the
marks obtained shall be reduced for 25 marks
## = Lab test is to be conducted for 50 marks and the marks obtained shall be
reduced for 25 marks. Lab test shall be conducted by two examiners out of which
one examiner is the faculty taught the course. There is no SEE for the practical ‘s
portion of integrated course
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Course Code 18SE2E1C M.Tech (Software Engineering)
Category Theory-Professional Elective
Course title ENTERPRISE RESOURCE PLANNING
Scheme and
Credits
No. of Hours/Week Semester – II
L T P SS Credits
4 - - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
The course will enable the students to:
1. Understand concepts of ERP.
2. Execute procurement, production, and sales business processes using ERP software.
3. Emphasis need of business process knowledge and its application to the business
environment.
4. Evaluate ERP Implementation Success & Failure for an application.
5. Create appreciate ERP in various public and private sector.
UNIT -I INTRODUCTION: 09 Hours Overview – Benefits of ERP – ERP and Related Technologies – ERP Risks – Benefits -
Data Warehousing – Data Mining – On–line Analytical Processing – Data Migration –
ERP, Internet and WWW
UNIT-II ERP IMPLEMENTATION: 09 Hours Implementation Life Cycle – cost model - Implementation Methodology – Hidden Costs –
Organizing Implementation – Vendors, Consultants and Users – Contracts – ERP Project
Management and Monitoring - Business case and ROI analysis - ERP and business process
reengineering..
UNIT -III BUSINESS MODULES: 10 Hours Finance Management – Manufacturing Management – Human capital Management –
Procurement and Inventory Management – Supplier Relationship Management – Supply
chain planning & Management - Logistics Management - Plant Maintenance – Materials
Management – Quality Management – Sales and Distribution – Enterprise Asset
Management Product Lifecycle Management.
UNIT -IV ERP MARKET: 10 Hours ERP & E-business – ERP & CRM - ERP Market Place – SAP–ERP financials – Auditing
ERP – ERP Business Intelligence and Performance Management – ERP for manufacturing:
Auto, Pharma, Consumer Products, Mining – ERP for service sector: Retail, Healthcare,
Telecom, Banking, Insurance, Educational Institutions.
UNIT -V ERP – APPLICATIONS: 10 Hours Lean manufacturing and ERP - Turbo Charge the ERP System – EIA Study of ERP
selection process – Big Bang ERP implementation – Impact of ERP systems on
organizational effectiveness – Knowledge management for enterprise systems – Managing
ERP security
UNIT-VI Recent advances and research being done in the topics mentioned above
units
COURSE OUTCOMES At the end of the course, the students will be able to:
CO1: Give examples for an ERP.
CO2: Explain the structure of an ERP system
CO3: Illustrate procurement, production, and sales business processes using ERP software.
CO4: Recommend ERP suitable to Industry and Information Technology Companies
CO5: Design ERP for Retail, Healthcare, Telecom, Banking, Insurance, Educational
Institutions.
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References
1. Alexis Leon, “Enterprise Resource Planning”, Tata McGraw Hill, 2nd Edition,
2008
2. Ray, “Enterprise Resource Planning”, Tata McGraw Hill, 2011
3. Veena Bansal, “Enterprise Resource Planning”, Pearson Education India. 2013
4. Marianne Bradford, “Modern ERP – Select, Implement and Use” – Today‟s
Advanced Business Systems, North Carolina State University, Second Edition,
2010
5. V. Narayanan, “Implementing SAR-ERP Financials – A configuration Guide”,
Tata McGraw Hill, 2010
6. Joseph A. Brady, Ellen F. Monk, Bret J. Wangner, “Concepts in Enterprise
Resource Planning”, Thomson Learning, 2001.
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2
CO3 3
CO4 3
CO5 3
1: Low 2: Medium 3:High
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Course Code 18SE2E2A M.Tech(Software Engineering)
Category Theory-Professional Elective
Course title SOFTWARE AGENTS
Scheme and
Credits
No. of Hours/Week Semester – II
L T P SS Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES The course will enable the students to:
1. Have an overview of the agent systems and software agents.
2. Understand the basic concepts of intelligent software agents.
3. Explore the use of software agents
4. Analyse and share information to coordinate activities of the agents for the
purpose of group problem solving.
5. Design recurred systems using agents
UNIT - I INTRODUCTION TO AGENTS: 9 hours Introduction to software agent, Applivations, uses and classification of software agent;
Agent Programming Paradigms, Agent Vs Object, Aglet, Mobile Agents, Agent
Frameworks, Agent Reasoning.
UNIT - II JAVA AGENTS: 9 hours Processes, Threads, Daemons, Components, Java Beans, ActiveX, Sockets, RPCs,
Distributed Computing, Aglets Programming, Jini Architecture, Actors and Agents, Typed
and proactive messages.
UNIT – III MULTIAGENT SYSTEMS: 10 hours Interaction between agents, Reactive Agents, Cognitive Agents, Interaction protocols,
Agent oordination, Agent negotiation, Agent Cooperation, Agent Organization, Self-
Interested agents in Electronic Commerce Applications.
UNIT- IV INTELLIGENT SOFTWARE AGENTS: 10 hours Interface Agents, Agent Communication Languages, Agent Knowledge Representation,
Agent Adaptability, Belief Desire Intension, Mobile Agent Applications.
UNIT- V AGENTS AND SECURITY: 10 hours
Agent Security Issues, Mobile Agents Security, Protecting Agents against Malicious Hosts,
Untrusted Agent, Black Box Security, Authentication for agents, Security issues for
Aglets.
UNIT- VI Recent advances and research being done in the topics mentioned above
units
COURSE OUTCOMES At the end of the course, the students will be able to:
CO1: Interpret the basics of agents
CO2: Create / develop an agent based system for a particular task.
CO3: Design an application that uses different security issues for intelligent agents.
CO4: Effectively apply agent-based technologies in distributed systems
CO5:Validate the application of distributed information systems that use software agents.
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REFERENCES 1. Bradshaw, " Software Agents ", MIT Press, 2010
2. Russel, Norvig, "Artificial Intelligence: A Modern Approach", Second Edition, Pearson
Education, 2003
3. Richard Murch, Tony Johnson, "Intelligent Software Agents", Prentice Hall, 2000
4. Gerhard Weiss, Multi Agent Systems, A Modern Approach to Distributed Artificial
Intelligence, MIT Press, 2000.
5. Bigus&Bigus, " Constructing Intelligent agents with Java ", Wiley, 1997
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3
CO2 1 2
CO3 2
CO4 1 2 2
CO5 2
1: Low 2: Medium 3:High
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Course Code 18SE2E2B M.Tech(Software Engineering)
Category Theory-Professional Elective
Course title SOFTWARE SECURITY
Scheme and
Credits
No. of Hours/Week Semester – II
L T P SS Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
The course will enable the students to
1. Understand the basics of secure programming.
2. Describe most frequent programming errors leading to software vulnerabilities.
3. Analyze security problems in software.
4. Evaluate security threats and software vulnerabilities.
5. Effectively design secure software system.
UNIT -I INTRODUCTION TO SECURITY: 9 Hours
Introduction to Security: Need for security, Security approaches, Principles of security,
Types of attacks. Encryption Techniques: Plaintext, Cipher text, Substitution &
Transposition techniques, Encryption & Decryption, Types of attacks, Key range & Size.
Symmetric & Asymmetric Key Cryptography: DES,RSA.
UNIT -II INTRODUCTION TO SOFTWARE SECURITY: 10 Hours Managing software security risk, Selecting software development technologies, An open
source and closed source, Guiding principles for software security, Auditing software,
Buffet overflows, Access control, Race conditions, Input validation, Password
authentication
UNIT-III SECURE RISK MANAGEMENT: 9 Hours Anti-tampering, Protecting against denial of service attack, Copy protection schemes,
Client-side security, Database security, Applied cryptography, Randomness and
determinism
UNIT- IV SECURITY TESTING: 10 Hours Buffer Overrun, Format String Problems, Integer Overflow, and Software Security
Fundamentals SQL Injection, Command Injection, Failure to Handle Errors, and Security
Touchpoints
UNIT- V ADVANCED SOFTWARE SECURITY 10 Hours Cross Site Scripting, Magic URLs, Weak Passwords, Failing to Protect Data, Weak
random numbers, improper use of cryptography Information Leakage, Race Conditions,
Poor usability, Failing to protect network traffic, improper use of PKI, trusting networ
k name resolution
UNIT- VI Recent advances and research being done in the topics mentioned above
units
REFERENCES 1. J. Viega, G. McGraw. Building Secure Software, Addison Wesley -2011
2. Theodor Richardson, Charles N Thies, Secure Software Design, Jones & Bartlett-
2012
3. Kenneth R. van Wyk, Mark G. Graff, Dan S. Peters, Diana L. Burley, Enterprise
Software Security, Addison Wesley. -2010
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COURSE OUTCOMES At the end the student will be able to
CO1: Identify various risk in the softwares.
CO2: illustrate security problems in the open source software.
CO3: Relate security and software engineering.
CO4: Assess real-time software and its vulnerabilities
CO5: Investigate security flaws in software
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3
CO2 2 2
CO3 2
CO4 3
CO5 3
1: Low 2: Medium 3:High
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Course Code 18SE2E2C M.Tech(Software Engineering)
Category Theory-Professional Elective
Course title SOFTWARE ENGINEERING FOR WEB APPLICATIONS
Scheme and
Credits
No. of Hours/Week Semester – II
L T P SS Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES:
The course will enable the students to:
1. Know the importance of web technologies for the real world applications
2. Learn appropriate scripting languages
3. Know the testing techniques to test the product
4. Gain the skills and project based experience needed for entry into web design and
development careers
5. To use a variety of strategies and tools to create websites
UNIT-I SITE ORGANIZATION AND NAVIGATION: 9 Hours
User centered design, Web medium, Web design process, Evaluating process, Site types
and architectures, Navigation theory, Basic navigation practices, Search, Site maps
UNIT-II ELEMENTS OF PAGE DESIGN: 10 Hours
Browser compatible design issues Pages and Layout, Templates, Text, Color, Images,
Graphics and Multimedia GUI Widgets and Forms, Web Design patterns
UNIT- III SCRIPTING LANGUAGES: 10 Hours
Client side scripting: XHTML, DHTML, JavaScript, XML Server side scripting: Perl,
PHP,ASP/JSP Designing a Simple web application
UNIT -IV PREPRODUCTION MANAGEMENT: 9 Hours
Principles of Project Management, Web Project Method, Project Road Map, Project
Clarification, Solution Definition, Project Specification, Content, Writing and Managing
content
UNIT -V PRODUCTION, MAINTENANCE AND EVALUATION: 10Hours Design and Construction, Testing, Launch and Handover, Maintenance, Review and
Evaluation, Case Study
UNIT -VI Recent advances and research being done in the topics mentioned above
units
REFERENCES
1. Soumen Chakrabarti, Mining the Web, Morgan Kaufmann Publishers, Reprint 2016
2. Bing Liu, Web Data Mining: Exploring Hyperlinks, Contents and Usage Data,
Springer, Second Edition, 2011
3. Paulraj Ponniah, “Data Warehousing Fundamentals”, John Wiley, 2012
4. Jiawei Han and Micheline Kamber, Data Mining, Concepts and Techniques, Elsevier
Publication, 2nd
Edition, 2011
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1. Understand the importance of Web technologies for the real world applications.
CO2. Apply various scripting languages for the development of web applications
CO3. Discuss the Web design standards.
CO4. Develop websites for local community organizations.
CO5. Verify and analyse the web applications.
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45
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3
CO2 1 3
CO3 2 3
CO4 3 3
CO5 3 3
1: Low 2: Medium 3:High
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Course Code 18CS2L01 M.Tech(Software Engineering)
Category Practical
Course title ADVANCED DATS STRUCTURES AND ALGORITHMS
LAB
Scheme and
Credits
No. of Hours/Week Semester – II
L T P SS Credits
0 0 4 0 2
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
3. Data structures and Algorithm
4. Java Programming
Course Objectives: The course will enable the students to:
1. acquire the knowledge of using advanced data structures
2. acquire the knowledge of sorting and balancing the tree structure
3. understand the usage of graph structures and string matching
4. learn to solve the various NP complete problems
Each student has to work individually on assigned lab exercises. Lab sessions could be
scheduled as one contiguous four-hour session per week. It is recommended that all
implementations are carried out in Java. Exercises should be designed to cover the
following topics:
1. Doubly Circular Linked List
2. AVL Tree
3. Efficiency of Heap Sort & Quick Sort
4. Travelling Salesman Problem (Dynamic Programming)
5. N Queens Problem (Backtracking/ Branch & Bound)
6. Bellman-Ford algorithm
7. Shortest paths in a DAG
8. Ford-Fulkerson algorithm
9. Robin-Karp algorithm
10. Knuth-Morris-Pratt algorithms
11. String matching with Finite Automata
12. Vertex Cover problem
13. The Set Covering problem
14. The Subset-Sum problem
15. Maximum Bipartite algorithm
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47
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1. Design and implement basic and advanced data structures extensively.
CO2. Design and apply graph structures for various applications.
CO3. Design and develop efficient algorithms with minimum complexity using design
techniques.
CO4: Design and develop advanced string matching and NP Complete problems
Scheme of Examination
For examination an experiment shall be set
Continuous Internal
Evaluation(Lab=50)
Marks Semester End Evaluation (SEE) Marks
Performance of the student in
the lab every week
20 Write-Up 20
Test at end of the semester 20 Experiment/Execution 70
Vice-Voce 20 Vice-Voce 10
Total(CIE) 50 Total(SEE) 50*
Note: *=SEE shall be conducted for 100 marks and marks obtained shall be reduced for
50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2
CO2 2
CO3 2
CO4 2
CO5 2 2
1: Low 2: Medium 3:High
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COURSE LEARNING OBJECTIVES:
The objectives of the SEMINAR-II is to prepare the students to learn to:
1. Carry out the literature review of general research area/current topic and analyse the
same effectively.
2. Prepare a technical report, reflecting his/her depth of understanding, on the selected
area/topic and prepare content rich presentation.
3. Acquire communication and time management skills for effective and impactful
presentation. Interact with peers to acquire the qualities of thoughtfulness, friendliness,
adaptability, responsiveness, and politeness in-group discussion. Overcome stage fear
during the presentation.
GUIDE LINES
1. Seminar preparation and presentation is an individual student activity.
2. Topic may be of general/ specific interest to program of engineering or electives not
offered in the semester and to be selected in consultation with the faculty/Guide.
3. Select one pertinent research paper for the seminar presentation.
4. Prepare and submit a detailed technical report of the seminar topic.
COURSE OUTCOMES:
Students shall be able to:
1. Carry out the literature survey of topic of seminar.
2. Prepare a technical report on the selected area/topic.
3. Make an effective presentation with seamless flow of content within the time allocated.
Overcome inhibition in interacting with peers and hence develop the spirit of team
work. Overcome stage fear during the presentation.
SCHEME OF EXAMINATION
CIE – 50
marks
Phase -1 Marks =10 Seminar Report
Marks =20
Total:50
Marks Phase -2 Marks =20
Course Code 18SE2S01 M.Tech (Software Engineering)
Category Seminar Semester: II
Course title SEMINAR - II
Scheme and Credits
No. of Hours/Week
Total hours = 24 L T P S Credits
0 0 2 0 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any): NIL
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49
Scheme of Continuous Internal Evaluation (CIE):
Evaluation would be carried out in TWO phases. The Seminar Evaluation Committee
shall comprise of Chairman of the Department, Faculty/Guide and one more faculty member
nominated by Chairman. The evaluation criteria shall be as per the rubrics given below:
Rubrics for Evaluation:
Topic - Technical Relevance, Sustainability and Societal Concerns : 15%
Review of literature and Technical Content : 35%
Presentation Skills : 25%
Report : 25%
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2 3
CO2 2 3 3
CO3 2
1. Low, 2. Medium, 3. High
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Course Code 18CS2M01 M.Tech (Software Engineering)
Category Audit Course-2
Course title PEDAGOGY STUDIES
Scheme and Credits No. of Hours/Week Semester – II
L T P SS Credits
2 0 - - 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any):
COURSE OBJECTIVES
SThis course will enable students to
1. Understand the Thematic Overview and Pedagogical practices
2. Apply professional classroom practices , curriculum and assessment
3. Analyse methodology for quality assessment of school curriculum teacher
4. Evaluate pedagogic theory and pedagogical approaches
5. Create contexts pedagogy, new curriculum and assessment metrics for future
UNIT- I INTRODUCTION AND METHODOLOGY: 6 Hours Aims and rationale, Policy background, Conceptual framework and terminology Theories of
learning, Curriculum, Teacher education. Conceptual framework, Research questions. Overview of
methodology and Searching.
UNIT- II THEMATIC OVERVIEW: 3 Hours Pedagogical practices are being used by teachers in formal and informal classrooms in developing
countries. Curriculum, Teacher education
UNIT- III PEDAGOGICAL PRACTICES: 6 Hours Evidence on the effectiveness of pedagogical practices Methodology for the in depth stage: quality
assessment of included studies. How can teacher education (curriculum and practicum) and the
school curriculum and guidance materials best support effective pedagogy? Theory of change.
Strength and nature of the body of evidence for effective pedagogical practices. Pedagogic theory
and pedagogical approaches. Teachers‟ attitudes and beliefs and Pedagogic strategies.
UNIT- IV PROFESSIONAL DEVELOPMENT: 6 Hours Professional development: alignment with classroom practices and follow-up support Peer support
Support from the head teacher and the community. Curriculum and assessment Barriers to learning:
limited resources and large class sizes
UNIT- V RESEARCH GAPS AND FUTURE DIRECTIONS: 3 Hours Research design Contexts Pedagogy Teacher education Curriculum and assessment Dissemination
and research impact.
UNIT- VI NEW DEVELOPMENTS IN PEDAGOGY:
REFERENCES
1. Ackers J, Hardman F (2001) Classroom interaction in Kenyan primary schools, Compare, 31
(2): 245-261.
2. Agrawal M (2004) Curricular reform in schools: The importance of evaluation, Journal of
Curriculum Studies, 36 (3): 361-379.
3. Akyeampong K (2003) Teacher training in Ghana - does it count? Multi-site teacher
education research project (MUSTER) country report 1. London: DFID.
4. Akyeampong K, Lussier K, Pryor J, Westbrook J (2013) Improving teaching and learning of
basic maths and reading in Africa: Does teacher preparation count? International Journal
Educational Development, 33 (3): 272–282.
5. Alexander RJ (2001) Culture and pedagogy: International comparisons in primary education.
Oxford and Boston: Blackwell.
6. Chavan M (2003) Read India: A mass scale, rapid, „learning to read‟ campaign
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51
7. www.pratham.org/images/resource%20working%20paper%202.pdf.
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1: What pedagogical practices are being used by teachers in formal and informal
classrooms in developing countries?
CO2: What is the evidence on the effectiveness of these pedagogical practices, in what
conditions, and with what population of learners?
CO3: How can teacher education (curriculum and practicum) and the school curriculum and
guidance materials best support effective pedagogy
CO4: Assess pedagogic theory and pedagogical approaches
CO5: Design new curriculum and assessment metrics for future
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3
CO2 3
CO3 3
CO4 3
CO5 3
1: Low 2: Medium 3:High
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SEMISTER-III
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Course Code 18IT3E1A M.Tech(Software Engineering)
Category Theory-Professional Elective
Course title SOCIAL NETWORK
Scheme and
Credits
No. of Hours/Week Semester – III
L T P SS Credits
4 - - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES This course will enable students to
1. Understand the concept of semantic web and related applications.
2. Construct social network using various representation
3. Understand social web and related communities
4. Build sentiment analysis of social
UNIT-I INTRODUCTION: 9 Hours Introduction to Web - Limitations of current Web – Development of Semantic Web –
Emergence of the Social Web, Evolution in Social Networks , Statistical Properties of
Social Networks -Network analysis - Development of Social Network Analysis - Key
concepts and measures in network analysis - Discussion networks - Blogs and online
communities - Web-based networks
UNIT- II MODELING AND VISUALIZATION: 10 Hours Visualizing Online Social Networks - A Taxonomy of Visualizations - Graph
Representation -Centrality- Clustering - Node-Edge Diagrams - Visualizing Social
Networks with Matrix Based Representations- Node-Link Diagrams - Hybrid
Representations - Modelling and aggregating social network data – Random Walks and
their Applications - Ontological representation of social individuals and relationships
UNIT- III SOCIAL NETWORK ANALYSIS TECHNIQUES: 10 Hours Framework - Tracing Smoothly Evolving Communities - Models and Algorithms for
Social Influence Analysis - Influence Related Statistics - Social Similarity and Influence -
Influence Maximization in Viral Marketing - Algorithms and Systems for Expert Location
in Social Networks - Expert Location without Graph Constraints - with Score Propagation
– Expert Team Formation - Link Prediction in Social Networks -Feature based Link
Prediction - Bayesian Probabilistic Models - Probabilistic Relational Models
UNIT -IV MINING COMMUNITIES: 9 Hours
Aggregating and reasoning with social network data, Advanced Representations -
Extracting evolution of Web Community from a Series of Web Archive - Detecting
Communities in Social Networks - Evaluating Communities – Core Methods for
Community Detection & Mining - Applications of Community Mining Algorithms - Node
Classification in Social Networks.
UNIT- V TEXT AND OPINION MINING: 10 Hours Text Mining in Social Networks -Opinion extraction – Sentiment classification and
clustering - Temporal sentiment analysis - Irony detection in opinion mining - Wish
analysis - Product review mining – Review Classification – Tracking sentiments towards
topics over time
UNIT-VI Recent advances and research being done in the topics mentioned above
units
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REFERENCES
1. Charu C. Aggarwal, “Social Network Data Analytics”, Springer; 2011
2. Peter Mika, “Social Networks and the Semantic Web”, Springer, 1st edition, 2007.
3. Borko Furht, “Handbook of Social Network Technologies and Applications”,
Springer, 1st edition, 2010.
4. Guandong Xu , Yanchun Zhang and Lin Li, “Web Mining and Social Networking –
Techniques and applications”, Springer, 1st edition, 2011.
5. Giles, Mark Smith, John Yen, “Advances in Social Network Mining and Analysis”,
Springer, 2010.
6. Ajith Abraham, Aboul Ella Hassanien, Václav Snášel, “Computational Social
Network Analysis: Trends, Tools and Research Advances”, Springer, 2009.
7. Toby Segaran, “Programming Collective Intelligence”, O‟Reilly, 2012
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1 Develop semantic web related applications.
CO2: Represent knowledge using ontology
CO3: Analysis of models in social network.
CO4: Predict social web and related communities.
CO5: Visualize and sentiment analysis of social networks
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3
CO2 2
CO3 1 3
CO4 1 3
CO5 1 1 3
1: Low 2: Medium 3:High
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Course Code 18SE3E1B M.Tech (Software Engineering)
Category Theory-Professional Elective
Course title BUSINESS INTELLIGENCE
Scheme and
Credits
No. of Hours/Week Semester – III
L T P SS Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVE
The course will enable the student to:
1. Understand the enormous opportunities that currently exists in providing business
intelligence services
2. Apply key data mining methods of classification, prediction, data reduction and
exploration
3. Analyse the strategies of modern enterprise decision makers
4. Evaluate competences in information systems, web science, decision science,
software engineering, and innovation and entrepreneurship.
5. Create BI architecture
UNIT– I INTRODUCTION: 09 Hours
BI Basics – Meeting the BI challenge – BI user models – Basic reporting and querying –
BI Markets - BI and Information Exploitation – Value of BI – BI cycle – Bridging the
analysis gap – BI Technologies – BI Decision Support Initiatives – BI Project Team.
UNIT- II BI BIG PICTURE: 10 Hours Advanced Emerging BI Technologies – Human factors in BI implementations – BI design
and development – OO Approach to BI - BI Environment – BI business process and
information flow – Identifying BI opportunities – Evaluating Alternatives - BI solutions –
BI Project Planning.
UNIT- III BI ARCHITECTURE 10 Hours Components of BI Architecture – BI Design and prototyping – Importance of Data in
Decision Making - Data requirements Analysis - Using OLAP for BI – Data warehouse
and Technical BI Architecture – Business Rules – Data Quality – Data Integration – High
performance BI - BI 2.0 – GoOLAP Fact Retrieval Framework.
UNIT -IV BI TECHNOLOGIES: 10 Hours
Successful BI – LOFT Effect – Importance of BI Tools – BI standardization - Creating
business value through location based intelligence – Technologies enabling BI –
technologies for information integration - Building effective BI Systems – Strategic,
Tactical, Operational and Financial Intelligence.
UNIT -V FUTURE OF BI: 09 Hours
Knowledge Discovery for BI – Markov Logic Networks – BI Search and Text Analytics –
Advanced Visualisation – Semantic Web Technologies for building BI - Service oriented
BI – Collaborative BI - Evaluating BI – Stakeholder model of BI.
UNIT-VI Recent advances and research being done in the topics mentioned above
units
REFERENCES
1. CindiHowson,"Successful Business Intelligence”, Tata McGraw-Hill Education,
2007
2. David Loshin, “Business Intelligence: The Savvy Manager's Guide”, Morgan
Kaufmann, 2nd Edition, Newnes Publishers, 2012
3. Elizabeth Vitt, Michael Luckevich, Stacia Misner, “Business Intelligence”,
O'Reilly Media, Inc., 2010.
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4. Larissa Terpeluk Moss, S. Atre, “Business Intelligence Roadmap: The Complete
Project Lifecycle for Decision-Support Applications, Addison-Wesley Information
Technology Series”, illustrated edition, Addison-Wesley Professional, 2003
5. Marie - Aude Aufaure, Esteban Zimány, “Business Intelligence”, First European
Summer School eBISS, 2011.
6. Murugan Anandarajan, Asokan Anandarajan, Cadambi A. Srinivasan, “Business
Intelligence Techniques: A Perspective from Accounting and Finance”, illustrated
Springer, 2003
7. Rajiv Sabherwal, Irma Becerra-Fernandez, “Business Intelligence”, illustrated
Edition, John Wiley & Sons, 2010
COURSE OUTCOMES
On completion of the course, the student will be able to:
CO1: Explain the business intelligence potential of today data rich environment
CO2: Determine when to use which technique
CO3: Analyse techniques using Excel add-ins
CO4: Assess the intellectual capital required to provide business analytics services.
CO5: Develop BI architecture
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2
CO3 3
CO4 3
CO5 3
1: Low 2: Medium 3:High
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Course Code 18SE3E1C M.Tech (Software Engineering)
Category Theory-Professional Elective
Course title SOFTWARE PROJECT MANAGEMENT
Scheme and
Credits
No. of Hours/Week Semester – III
L T P SS Credits
4 - - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
1. Fundamentals of Software Engineering
COURSE OBJECTIVES
The course will enable the students to:
1. Understand software project management, project life cycle and effort estimation
2. Apply different techniques for software cost estimation and activity planning.
3. Analyse software projects plan and sequencing and scheduling
4. Evaluate a software project and perform project planning.
5. Create the different activity planning models and analyze risk
UNIT -I PROJECT EVALUATION AND PROJECT PLANNING: 9 Hours
Importance Of Software Project Management, Activities Methodologies, Categorization
Of Software Projects, Setting Objectives, Management Principles, Management Control,
Project Portfolio Management, Cost-Benefit Evaluation Technology, Risk Evaluation,
Strategic Program Management, Stepwise Project Planning.
UNIT -II PROJECT LIFE CYCLE AND EFFORT ESTIMATION: 10 Hours Software Process And Process Models, Choice of Process Models, Mental Delivery, Rapid
Application Development, Agile Methods, Extreme Programming, SCRUM, Managing
Interactive Processes, Basics of Software Estimation, Effort and Cost Estimation
Techniques, COSMIC Full Function Points, COCOMO II A Parametric Productivity
Model, Staffing Pattern.
UNIT -III ACTIVITY PLANNING AND RISK MANAGEMENT: 10 Hours
Objectives of Activity Planning, Project Schedules, Activities, Sequencing and Scheduling,
Network Planning Models, Forward Pass and Backward Pass Techniques, Critical Path
(CRM) Method, Risk Identification, Assessment, Monitoring, PERT Technique, Monte
Carlo Simulation, Resource Allocation, Creation of Critical Patterns, Cost Schedules
UNIT -IV PROJECT MANAGEMENT AND CONTROL: 9 Hours
Framework for Management and Control, Collection of Data Project Termination,
Visualizing Progress, Cost Monitoring, Earned Value Analysis, Project Tracking, Change
Control, Software Configuration Management, Managing Contracts, Contract Management
UNIT -V STAFFING IN SOFTWARE PROJECTS: 10 Hours
Managing People, Organizational Behaviour, Best Methods of Staff Selection,
Motivation, The Oldham-Hackman Job Characteristic Model, Ethical and Programmed
Concerns, Working In Teams, Decision Making, Team Structures, Virtual Teams,
Communications Genres, Communication Plans.
UNIT-VI Recent advances and research being done in the topics mentioned above
units
REFERENCES
1. Bob Hughes, Mike Cotterell and Rajib Mall: Software Project Management, Tata
McGraw Hill, V Edition, 2012.
2. Robert K. Wysocki “Effective Software Project Management”, Wiley Publication,
2011.
3. Walker Royce, “Software Project Management”, Addison-Wesley, 1998.
4. Gopalaswamy Ramesh, “Managing Global Software Projects”, McGraw Hill
Education, 2013.
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COURSE OUTCOMES
On completion of the course, the student would be able to:
CO1: Explain project life cycle and effort estimation
CO2: Apply and practice project management principles while developing Software.
CO3: Verify software projects plan and sequencing and scheduling
CO4: Asses a software project and perform project planning.
CO5: Develop the different activity planning models and analyze risk
management techniques.
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3
CO2 3
CO3 3
CO4 3
CO5 3
1: Low 2: Medium 3:High
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Course Code 18CS3P1A M.Tech (Software Engineering)
Category Theory-Professional Open Elective
Course title ARITIFICIAL INTELLIGENCE
Scheme and
Credits
No. of Hours/Week Semester – III
L T P SS Credits
4 - - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
This course will enable students to
1. Understand the various characteristics of Intelligent agents
2. Understand the different search strategies in AI
3. Learn to represent knowledge in solving AI problems
4. Analyse the different ways of designing software agents
5. Evaluate the various reasoning techniques for AI.
UNIT-I INTRODUCTION: 9 Hours Introduction Definition Future of Artificial Intelligence Characteristics and Problem Solving
Approach to Typical AI problems. State Space Search and Heuristic Search Techniques
Defining problems as State Space search, Production systems and characteristics, Hill
Climbing, Breadth first and depth first search, Best first search.
UNIT-II KNOWLEDGE REPRESENTATION ISSUES: 9 Hours Representations and Mappings, Approaches to knowledge representation, Using Predicate
Logic and Representing Knowledge as Rules , Representing simple facts in logic,
Computable functions and predicates, Procedural vs Declarative knowledge, Logic
Programming, Forward vs backward reasoning.
UNIT-III SOFTWARE AGENTS: 10 Hours
Architecture for Intelligent Agents Agent communication Negotiation and Bargaining
Argumentation among Agents Trust and Reputation in Multi-agent systems.
UNIT-IV REASONING I: 10 Hours Symbolic Logic under Uncertainty , Non-monotonic Reasoning, Logics for non-monotonic
reasoning, Statistical Reasoning.
UNIT-V METHODS: 10 Hours
Probability and Bayes Theorem, Certainty factors, Probabilistic Graphical Models, Bayesian
Networks, Markov Networks, Fuzzy Logic.
UNIT-VI Recent advances and research being done in the topics mentioned above
units
REFERENCES:
1. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach", Prentice
Hall, Third Edition, 2009.
2. Rich and Knight, Artificial Intelligence, 2nd Edition, 2013
3. I. Bratko, "Prolog: Programming for Artificial Intelligence", Fourth edition,
Addison-Wesley Educational Publishers Inc., 2011.
4. M. Tim Jones, "Artificial Intelligence: A Systems Approach(Computer Science),
Jones and Bartlett Publishers, Inc.; First Edition, 2008
5. Nils J. Nilsson, "The Quest for Artificial Intelligence", Cambridge University
Press, 2009.
6. William F. Clocksin and Christopher S. Mellish," Programming Using
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COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1: Define and identify various AI concepts
CO2: illustrate different AI strategies
CO3: Sketch various knowledge representation for AI problems
CO4: Analyse agents usage for AI
CO5: Design AI inference techniques
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2
CO3 2
CO4 2
CO5 2 2
1: Low 2: Medium 3:High
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Course Code 18CS3P1B M.Tech(Software Engineering)
Category Theory-Professional Open Elective
Course title BUSINESS ANALYTICS
Scheme and
Credits
No. of Hours/Week Semester – III
L T P SS Credits
4 - - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
The course will enable the students to:
1. Understand the role of business analytics within an organization.
2. Analyze data using statistical and data mining techniques.
3. Distinguish relationships between the underlying business processes of an
organization.
6. Gain an understanding of how managers use business analytics to formulate and
solve business problems and to support managerial decision making.
7. Discuss the uses of decision-making tools and Operations research techniques.
UNIT -I BUSINESS ANALYTICS: 10 Hours Overview of Business analytics, Scope of Business analytics, Business Analytics Process,
Relationship of Business Analytics Process and organisation, competitive advantages of
Business Analytics. Statistical Tools: Statistical Notation, Descriptive Statistical methods,
Review of probability distribution and data modelling, sampling and estimation methods
overview
UNIT -II TRENDINESS AND REGRESSION ANALYSIS: 9 Hours Modelling Relationships and Trends in Data, simple Linear Regression. Important
Resources, Business Analytics Personnel, Data and models for Business analytics, problem
solving, Visualizing and Exploring Data, Business Analytics Technology
UNIT -III ORGANIZATION STRUCTURES OF BUSINESS ANALYTICS:
10 Hours
Team management, Management Issues, Designing Information Policy, Outsourcing,
Ensuring Data Quality, Measuring contribution of Business analytics, Managing Changes.
Descriptive Analytics, predictive analytics, predicative Modelling, Predictive analytics
analysis, Data Mining, Data Mining Methodologies, Prescriptive analytics and its step in
the business analytics Process, Prescriptive Modelling, nonlinear Optimization
UNIT -IV FORECASTING TECHNIQUES: 10 Hours Qualitative and Judgmental Forecasting, Statistical Forecasting Models, Forecasting
Models for Stationary Time Series, Forecasting Models for Time Series with a Linear
Trend, Forecasting Time Series with Seasonality, Regression Forecasting with Casual
Variables, Selecting Appropriate Forecasting Models. Monte Carlo Simulation and Risk
Analysis: Monte Carle Simulation Using Analytic Solver Platform, New-Product
Development Model, Newsvendor Model, Overbooking Model, Cash Budget Model
UNIT- V DECISION ANALYSIS: 9 Hours
Formulating Decision Problems, Decision Strategies with the without Outcome
Probabilities, Decision Trees, The Value of Information, Utility and Decision Making
UNIT-VI Recent advances and research being done in the topics mentioned above
units
REFERENCES
1. Business analytics Principles, Concepts, and Applications by Marc J. Schniederjans,
Dara G. Schniederjans, Christopher M. Starkey, Pearson FT Press
2. Business Analytics by James Evans, persons Education
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COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1. Develop the knowledge of data analytics.
CO2. Demonstrate the ability of think critically in making decisions based
on data and deep analytics
CO3. Discuss the uses of technical skills in predicative and prescriptive
modeling to support business decision-making
CO4. Demonstrate the ability to translate data into clear and actionable insights.
CO5. Evaluate and assess the forecasting techniques.
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 3
CO2 3 3
CO3 3 3
CO4 3 3
CO5 3 3
1: Low 2: Medium 3:High
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Course Code 18CS3P1C M.Tech(Software Engineering)
Category Theory-Professional Open Elective
Course title MODELING AND SIMULATION
Scheme and
Credits
No. of Hours/Week Semester – III
L T P SS Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES:
The course will enable the students to:
1. Understand the system, specify systems using natural models of computation, modelling
techniques
2. Apply natural models of computation, modelling techniques to
understand behaviour of system , and analyse the simulation data
3. Analyse simulation data, simulation tools for simulation, Terminating Simulations –
Steady state simulations.
4. Evaluate the existing simulation models for verification, calibration and validation
5. Design validation, calibration model and decision support
UNIT – I INTRODUCTION TO SIMULATION 09 Hours
Introduction Simulation Terminologies- Application areas – Model Classification Types of
Simulation- Steps in a Simulation study- Concepts in Discrete Event Simulation Example.
UNIT-II MATHEMATICAL MODELS 10 Hours
Statistical Models - Concepts – Discrete Distribution- Continuous Distribution – Poisson
Process- Empirical Distributions- Queueing Models – Characteristics- Notation Queueing
Systems – Markovian Models- Properties of random numbers- Generation of Pseudo Random
numbers- Techniques for generating random numbers-Testing random number generators
Generating Random-Variates- Inverse Transform technique Acceptance- Rejection technique –
Composition & Convolution Method.
UNIT-III ANALYSIS OF SIMULATION DATA 10 Hours
Input Modelling - Data collection - Assessing sample independence – Hypothesizing
distribution family with data - Parameter Estimation - Goodness-of-fit tests – Selecting input
models in absence of data- Output analysis for a Single system – Terminating Simulations –
Steady state simulations.
UNIT -IV VERIFICATION AND VALIDATION 09 Hours
Building – Verification of Simulation Models – Calibration and Validation of Models –
Validation of Model Assumptions – Validating Input – Output Transformations
UNIT-V SIMULATION OF COMPUT ER SYSTEMS 10 Hours
Simulation Tools – Model Input – High level computer system simulation – CPU – Memory
Simulation – Comparison of systems via simulation – Simulation Programming techniques -
Development of Simulation models.
UNIT-VI Recent advances and research being done in the topics mentioned above units
REFERENCES
1. Jerry Banks and John Carson, “Discrete Event System Simulation”, Fourth Edition, PHI,
2005.
2. Geoffrey Gordon, “System Simulation”, Second Edition, PHI, 2006.
3. Frank L. Severance, “System Modelling and Simulation”, Wiley, 2001.
4. Averill M. Law and W. David Kelton, “Simulation Modelling and Analysis, Third
Edition, McGraw Hill, 2006.
5. Jerry Banks, “Handbook of Simulation: Principles, Methodology, Advances,
Applications and Practice”, Wiley-Inter science, 1 edition, 1998.
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COURSE OUTCOMES
On Completion of the course, the student will be able to:
CO1: Explain natural models of computation, modelling techniques
CO2: Determine suitable models of computation, modelling techniques to
understand behaviour of system.
CO3: Distinguish simulation models for verification, calibration and validation
CO4: Assess the performance of different simulation models, statistical models, queuing
Systems and Markovian Models for given problem
CO5: Design goodness-of-fit tests and input models in absence of data
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 05
marks
Unit-VI AAT=15
marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions Total:100 marks
Units which have 09 Hours shall not have
internal choice. 20* 2 = 40 Marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 3
CO3 3
CO4 3
CO5 3 2
1: Low 2: Medium 3:High
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COURSE LEARNING OBJECTIVES:
The objectives of the SEMINAR-III is to prepare the students to learn to:
1. Carry out the literature review of general research area/current topic and analyse the
same effectively.
2. Prepare a technical report, reflecting his/her depth of understanding, on the selected
area/topic and prepare content rich presentation.
3. Acquire communication and time management skills for effective and impactful
presentation. Interact with peers to acquire the qualities of thoughtfulness, friendliness,
adaptability, responsiveness, and politeness in-group discussion. Overcome stage fear
during the presentation.
GUIDE LINES
1. Seminar preparation and presentation is an individual student activity.
2. Topic may be of general/ specific interest to program of engineering or electives not
offered in the semester and to be selected in consultation with the faculty/Guide.
3. Select one pertinent research paper for the seminar presentation.
4. Prepare and submit a detailed technical report of the seminar topic.
COURSE OUTCOMES:
Students shall be able to:
1. Carry out the literature survey of topic of seminar.
2. Prepare a technical report on the selected area/topic.
3. Make an effective presentation with seamless flow of content within the time allocated.
Overcome inhibition in interacting with peers and hence develop the spirit of team
work. Overcome stage fear during the presentation.
SCHEME OF EXAMINATION
CIE – 50
marks
Phase -1 Marks =10 Seminar Report
Marks =20
Total:50
Marks Phase -2 Marks =20
Course Code 18SE3S01 M.Tech (Software Engineering)
Category Seminar Semester: III
Course title SEMINAR - III
Scheme and Credits
No. of Hours/Week
Total hours = 24 L T P S Credits
0 0 2 0 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any): NIL
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Scheme of Continuous Internal Evaluation (CIE):
Evaluation would be carried out in TWO phases. The Seminar Evaluation Committee
shall comprise of Chairman of the Department, Faculty/Guide and one more faculty member
nominated by Chairman. The evaluation criteria shall be as per the rubrics given below:
Rubrics for Evaluation:
Topic - Technical Relevance, Sustainability and Societal Concerns : 15%
Review of literature and Technical Content : 35%
Presentation Skills : 25%
Report : 25%
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2 3
CO2 2 3 3
CO3 2
1. Low, 2. Medium, 3. High
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INTERNSHIP
COURSE LEARNING OBJECTIVES:
Objectives of the internship
1. Provide an opportunity to see how classroom and textbook learning applies to the real
world, and to expose the students to the relevant work experience.
2. Pay close attention to all the steps that go onto completing a job, thereby, help students to
become workforce ready before entering the job market as a graduate. Provide an opportunity to
select the topic of dissertation work by evaluating the requirement of organisation.
3. Prepare and present a technical report of internship.
GUIDELINES
1. Student has to approach the concerned heads of various Industries/organization, which are related
to the field of specialization of the M. Tech program.
2. If any student gets internship, he/she has to submit the internship offer letter duly signed by the
concerned authority of the company to the Chairperson of the Department.
3. The internship on full time basis will be after the examination of II semester and during III
semester for a period of 8 weeks without affects regular class work.
4. The progress has to be reported periodically to the faculty or to the Guide assigned by the
Chairperson as per the format acceptable to the respective industry /organizations and to the
Institution.
5. At the end of the internship the student has to prepare a detailed report and submit.
6. Students are advised to use ICT tools such as Skype to report their progress and submission of
periodic progress reports to the faculty in charge or guide.
7. Duly signed report from internal supervisor (faculty incharge or guide) and external supervisor
from the organization where internship is offered has to be submitted to the Chairperson of the
Department for his/her signature and further processing for evaluation.
The broad format of the internship final report shall contain Cover Page, Certificate from College,
Certificate from Industry / Organization of internship, Acknowledgement, Synopsis, Table of
Contents, chapters of Profile of the Organization - Organizational structure, Products, Services,
Business Partners, Financials, Manpower, Societal Concerns, Professional Practices, Activities of the
Department where internship is done, Tasks Performed and summary of the tasks performed.
specific technical and soft skills that student has acquired during internship, References and
Annexure.
Course Code 18SE3I01 M.Tech (Software Engineering)
Category Internship/ Mini Project Semester: III
Course title INTERNSHIP / MINI PROJECT
Scheme and Credits
No. of Hours/Week
Total hours = 80 L T P S Credits
--- --- 10 --- 5
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs for Batch
of Six(06) students
Prerequisites (if any): NIL
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COURSE OUTCOMES:
The student will be able to:
1. Apply the gained experience along with the theoretical knowledge to solve the real world
problems what engineers ready do.
2. Get equipped with experience required before entering the job market. Explore the possibility of
formulating the dissertation problem.
3. Prepare a technical report and make a presentation of details of internship.
SCHEME OF EXAMINATION
CIE
1.Marks awarded by guide (Internal examiner) = 50 marks
2.Marks awarded by the department internship monitoring
committee = 50 marks
50*
Marks
SEE Presentation of internship in the presence of Guide (Internal examiner) and
external examiner=100 Marks
50**
Marks
Note: *= CIE be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
**= SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
Rubrics for CIE:
1. Topic of internship = 10%
2. Objectives of internship = 10%
3. Specific skills acquired = 20%
4. Document = 40%
5. presentation = 20%
Rubrics for SEE:
1. Topic of internship = 10%
2. Objectives of internship = 10%
3. Specific skills acquired = 20%
4. Document = 40%
5. presentation = 20%
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 2
CO2 2 2
CO3 3
1. Low, 2. Medium, 3. High
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MINI PROJECT
COURSE LEARNING OBJECTIVE:
1. Understand the method of applying engineering knowledge/use application software to solve
specific problems after carrying out literature survey.
2. Apply engineering and management principles while executing the project.
3. Demonstrate the skills for good technical report writing and presentation.
COURSE CONTENT/GUIDELINES
Student shall take up small problems in the field of domain of program as mini project. It can be
related to a solution to an engineering problem, verification and analysis of experimental data
available, conducting experiments on various engineering subjects, material characterisation,
studying a software tool for solution to an engineering problem, etc.
The mini project must be carried out preferably using the resources available in the
department/college and it can be of interdisciplinary also.
COURSE OUTCOMES:
The students shall be able to:
1. Conduct experiments / use the capabilities of relevant application software/ simulation tools
individually to generate data/ solve problems.
2. Assess the available engineering resources available in the institution.
3. Prepare and Present the technical document of mini project.
Rubrics of CIE
Note: * = CIE be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
Sl.
no
Particulars Weightage Marks Total marks
of CIE
1 Selection of the topic & formulation of objectives 10% 10
50*
2 Modelling and simulation/algorithm
development/experiment setup
25% 25
3 Conducting experiments/implementation/testing 25% 25
4 Demonstration & Presentation 15% 15
5 Report writing 25% 25
Total 100% 100
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Rubrics of Scheme for Semester End Evaluation (SEE):
The SEE shall be done by two examiners out of which one examiner is the guide of mini project.
The following weightage would be given for the examination. Evaluation shall be done in batches,
not exceeding 6 students.
Sl.
no
Particulars Weightage Marks Total marks
of SEE
1 Brief write-up about the project 05% 05
50**
2 Presentation/demonstration of the project 20% 20
3 Methodology and Experimental Results &
Discussion
30% 30
4 Report 25% 25
5 Viva Voce 20% 20
Total 100% 100
Note:** = SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 3
CO2 2 3
CO3 2 3
1. Low, 2. Medium, 3. High
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COURSE LEARNING OBJECTIVES:
1. Choose a problem applying relevant knowledge and skills acquired during the course. Formulate the
specifications of the project work, identify the set of feasible solutions, prepare, and execute project
plan considering professional, cultural and societal factors. Identify the problem-solving
methodology using literature survey and present the same.
2. Develop experimental planning and select appropriate techniques and tools to conduct experiments
to Evaluate and critically examine the outcomes followed by concluding the results and identifying
relevant applications. Preparation of synopsis, preliminary report for approval of topic selected
along with literature survey, objectives and methodology.
3. Develop oral and written communication skills to effectively convey the technical content.
GUIDELINES
The Dissertation work will start in III semester and should be a problem with research potential
and should involve scientific research, design, generation/collection and analysis of data, determining
solution and must preferably bring out the individual contribution.
The Dissertation work will have to be done by only one student and the topic of dissertation must
be decided by the guide and the student. The dissertation work shall be carried out, on-campus or in
an industry or in an organisation with prior approval from the Chairperson of the Department. The
student has to be in regular contact with the guide atleast once in a week.
The report of Dissertation work phase I shall contain cover page, certificate from
College/Industry/Organisation, Acknowledgement, List of Figures and Tables Contents,
Nomenclature, Chapters of Introduction including motivation to choose topic, Literature survey,
Conclusion of literature survey, Objectives and Scope of Dissertation, Methodology to be followed,
Experimental requirements, References and Annexure.
The preliminary results (if available) of the problem of Dissertation work may also be discussed
in the report.
Course Code 18SE3D01 M.Tech (Software Engineering)
Category Dissertation Work Semester: III
Course title DISSERTATION WORK PHASE -I
Scheme and Credits
No. of Hours/Week
Total hours = 80 L T P S Credits
0 0 10 0 5
CIE Marks: 50 SEE Marks:50 Total Max. Marks: 100 Duration of SEE: 1Hour
Prerequisites (if any): NIL
SE-80
72
COURSE OUTCOME:
The students will be able to:
1. Self learn various topics relevant to Dissertation work. Survey the literature such as books,
National/International reference journals, articles and contact resource persons for selected topics
of Dissertation.
2. Write and prepare a typical technical report.
3. Present and defend the contents of Dissertation work phase I in front of technically qualified
audience effectively.
SCHEME OF EXAMINATION
CIE 1.Marks awarded by guide (Internal examiner) = 50 marks
2.Marks awarded by the department dissertation monitoring committee = 50 marks 50*
Marks
SEE Presentation of Dissertation work Phase-I in the presence of Guide (Internal
examiner) and external examiner=100 marks
50**
Marks
Note: *= CIE be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
**= SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
Rubrics for CIE: Weightage
1. Introduction and Justification of topic = 10%
2. Literature survey and Conclusion = 30%
3. Objectives and Scope of Dissertation work = 30%
4. Methodology to be adopted = 20%
5. Presentation of contents of Dissertation work Phase-I = 10%
Rubrics for SEE:
1. Introduction and Justification of topic = 10%
2. Literature survey and Conclusion = 30%
3. Objectives and Scope of Dissertation work = 30%
4. Methodology, Experimental /Software = 20%
5. Presentation of Dissertation Phase-I = 10%
Mapping of Course Outcomes (Cos) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 3
CO2 3 3
CO3 3 3
1. Low, 2.Medium, 3. High
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SEMISTER-IV
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COURSE LEARNING OBJECTIVES:
The objectives of the SEMINAR-IV is to prepare the students to learn to:
1. Carry out the literature review of general research area/current topic and analyse the same
effectively.
2. Prepare a technical report, reflecting his/her depth of understanding, on the selected area/topic
and prepare content rich presentation.
3. Acquire communication and time management skills for effective and impactful presentation.
Interact with peers to acquire the qualities of thoughtfulness, friendliness, adaptability,
responsiveness, and politeness in-group discussion. Overcome stage fear during the presentation.
GUIDE LINES
1. Seminar preparation and presentation is an individual student activity.
2. Topic may be of general/ specific interest to program of engineering or electives not offered in
the semester and to be selected in consultation with the faculty/Guide.
3. Select one pertinent research paper for the seminar presentation.
4. Prepare and submit a detailed technical report of the seminar topic.
COURSE OUTCOMES:
Students shall be able to:
1. Carry out the literature survey of topic of seminar.
2. Prepare a technical report on the selected area/topic.
3. Make an effective presentation with seamless flow of content within the time allocated. Overcome
inhibition in interacting with peers and hence develop the spirit of team work. Overcome stage
fear during the presentation.
SCHEME OF EXAMINATION
CIE – 50 marks Phase -1 Marks =10 Seminar Report
Marks =20
Total:50
Marks Phase -2 Marks =20
Course Code 18SE4S01 M.Tech ( Software Engineering )
Category Seminar Semester: IV
Course title SEMINAR - IV
Scheme and Credits
No. of Hours/Week
Total hours = 24 L T P S Credits
0 0 2 0 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any): NIL
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Scheme of Continuous Internal Evaluation (CIE):
Evaluation would be carried out in TWO phases. The Seminar Evaluation Committee shall
comprise of Chairman of the Department, Faculty/Guide and one more faculty member nominated by
Chairman. The evaluation criteria shall be as per the rubrics given below:
Rubrics for CIE:
Topic - Technical Relevance, Sustainability and Societal Concerns : 15%
Review of literature and Technical Content : 35%
Presentation Skills : 25%
Report of seminar : 25%
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2 3
CO2 2 3 3
CO3 2
1. Low, 2. Medium, 3. High
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COURSE LEARNING OBJECTIVES:
1. Apply/Use different experimental techniques, equipments, software/ Computational/ Analytical
/Modelling and Simulation tools required for conducting tests and generate other relevant data.
Students will also be able to design and develop an experimental setup/test rig.
2. Analyse the results of the experiments conducted/models developed.
3. Create a detailed technical document as per format based on the outcome of dissertation work
phase I and II.
GUIDELINES
Dissertation work phase II is the continuation of project work started in III semester. The report of
Dissertation work that includes the details of Dissertation work phase I and phase II should be
presented in a standard format. The candidate shall prepare a detailed report of dissertation that
includes Cover Paper, Certificate from College/Industry/Organisation, Acknowledgement,
Abstract, Table of contents, List of Figures and Table, Nomenclature, Chapter of Introduction,
Literature survey, Conclusion of literature survey, Objectives and Scope of dissertation work,
Methodology, Experimentation, Results, Discussion, Conclusion, Scope for future work,
References, Annexure and full text of the publication (submitted or published).
COURSE OUTCOMES:
Students shall be able to:
1. Conduct experiments/ implement the capabilities of different Software /Computational /
Analytical/Modelling and simulation tools individually and generate data for validation of
hypothesis.
2. Investigate and assess the results obtained within the scope of experiments conducted followed
by conclusions.
3. Prepare detailed technical document present and defend the contents of Dissertation work in
presence of technically qualified audience effectively.
Course Code 18SE4D01 M.Tech ( Software Engineering)
Category Dissertation Work Semester: IV
Course title DISSERTATION WORK PHASE -II
Scheme and Credits
No. of Hours/Week
Total hours = 150 L T P S Credits
--- --- 30 --- 15
CIE Marks: 50 SEE Marks: 50
Total Max. Marks: 100
Prerequisites (if any): NIL
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SCHEME OF EXAMINATION
CIE
1. Marks awarded by guide = 50 marks
2. Marks awarded by the department dissertation monitoring committee
(Guide + Two faculty members )= 50 marks
100
marks
50*
marks
SEE
1. Dissertation evaluation by guide (Internal examiner) = 100 marks
2. Dissertation evaluation by external examiner=100 marks
3. Viva- Voce examination by guide and external examiner who evaluated the
dissertation work =200 marks
300
marks
50**
marks
Note: * = CIE be conducted for 100 marks and the marks obtained shall be reduced for 50 marks.
** = SEE shall be conducted for 300 marks and the marks obtained shall be reduced for 50 marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 2 3
CO2 2 2 3
CO3 3 3 3
1. Low, 2. Medium, 3. High
Rubrics for CIE:
1. Presentation of background of dissertation work = 10%
2. Literature survey, Problem formulation and Objectives = 30%
3. Presentation of methodology and experimentation = 30%
4. Results and Discussion = 20%
5. Questions and Answers = 10%
Rubrics for SEE:
1. Originality = 05%
2. Literature survey = 15%
3. Problem formulation, Objectives and Scope of Work = 10%
4. Methodology, experimentation /Theoretical modelling = 10%
5. Results, Discussion and Conclusion = 20%
6. Questions and Answers = 20%
7. Acceptance/Publication technical paper in Journals/Conference = 20%
SE-86
BANGALORE UNIVERSITY
Department of Computer Science and Engineering
UNIVERSITY VISVESVARAYA COLLEGE OF ENGINEERING
K R Circle, Bengaluru-560 001.
Choice Based Credit System (CBCS)-2018
M.Tech in Computer Science and Engineering
Specialization: Web Technologies
WT1
BANGALORE UNIVERSITY
VISION
―To strive for excellence in education for the realization of a vibrant and
inclusive society through knowledge creation and dissemination‖
MISSION
Impart quality education to meet national and global challenges
Blend theoretical knowledge with practical skills
Pursue academic excellence through high quality research and
publications
Provide access to all sections of society to pursue higher education
Inculcate right values among students while encouraging
competitiveness to promote leadership qualities
Produce socially sensitive citizens
Hasten the process of creating a knowledge society
To contribute to nation building
WT2
Bangalore University
UNIVERSITY VISVESVARAYA COLLEGE OF ENGINEERING
K R Circle, Bengaluru – 560 001.
University Visvesvaraya College of Engineering (UVCE) was started as a School of
Mechanical Engineering by Bharat Ratna Sir. M. Visvesvaraya in the year 1913 to meet the
needs of the State for skilled workers with S V Setty as its Superintendent. Later, it was
converted to a full-fledged Engineering College in the year 1917 under the name Government
Engineering College and was affiliated to the University of Mysore. It is the fifth Engineering
College to be established in the country.
After the formation of Bangalore University in 1964, UVCE became one of the
Constituent Colleges of Bangalore University. This is one of the oldest Institutions in the
country imparting technical education leading to B.E., M.E, B.Arch., M.Sc. (Engineering),
M.Arch. and Ph.D. degrees in various disciplines of Engineering and Architecture. The
Institution currently offers 7 Undergraduate (B.E. / B.Arch.) Full-time, three Undergraduate
(B.E.) Part-time and 24 Postgraduate (M.E. / M.Arch.) Programmes.
VISION
The vision of UVCE is to strive for excellence in advancing engineering education through
path breaking innovations across the frontiers of human knowledge to realize a vibrant,
inclusive and humane society.
MISSION
The mission of UVCE is to prepare human resource and global leaders to achieve the above
vision through discovery, invention and develop friendly technologies to promote scientific
temper for a healthy society. UVCE shapes engineers to respond competently and confidently
to the economic, social and organizational challenges arising from globally advancing
technical needs.
WT3
Bangalore University Bengaluru
Department of Computer Science and Engineering, UVCE, Bengaluru
M. Tech. DEGREE IN COMPUTER SCIENCE AND ENGINEERING under CBCS Scheme -
2K18
Specialization: Web Technologies
Vision of the Department
Strive for Centre of Excellence in advancing Computer Science and Engineering education to
produce highly qualified human resources to meet local and global requirement.
Mission of the Department
CSM1. Implementing effectively, the outcome based education by imparting knowledge of basics
and advances in Computer Science and Engineering and other allied disciplines.
CSM2. Preparing and equipping human resources to become global leaders through innovation,
discovery, sustainable and environment friendly technology.
CSM3. Creatingconducive environment for effective teaching and learning process through
interdisciplinary research, online courses, interaction with institutions of higher learning and
industries, R and D laboratories of national importance, alumni, employers and other internal &
external stake holders.
CSM4. Imbibing awareness of entrepreneurship, ethics, honesty, credibility, social and
environmental consciousness and providing opportunity to the faculty and technical staff for
continuous academic improvement and to equip them with then latest trends in Software
Engineering and thereby inculcate the habit of continuous learning in faculty, staff and
students.
WT4
Program Outcomes
Web Technologies Graduates will be able to:
WTPO1: An ability to independently carry out research/investigate and development work to
solve practical problems
WTPO2: An ability to write and present a substantial technical report/document
WTPO3: Students should be able to demonstrate a degree of mastery over the area as per the
specialization of the problem. The mastery should be at a level higher than the
requirements in the appropriate bachelor degree
Program Educational Objectives:
The post graduates of M.Tech in Computer Networking will provide the knowledge and skill
to:
WTPEO1:Develop core competence in the field of web technologies and develop
themselves as effective professionals by solving real problems with
attention to creativity, Inquisitiveness, critical thinking, effective
communication, and team work.
WTPEO2:Acquire strong knowledge about the principles and concepts of web
technologies and involve in research to analyze, design, and
synthesize data to produce novel solutions.
WTPEO3:Demonstrate ability to adapt to a rapidly changing environment by
having learned and applied new skills and new technologies to
become global leaders in the field of web technologies.
WT5
BANGLORE UNIVERSITY
SCHEME OF STUDIES AND EXAMINATION FOR 24MONTHS COURSE FOR THE AWARD OF
M. Tech. DEGREE IN COMPUTER SCIENCE AND ENGINEERING (WEB TECHNOLOGIES) under CBCS Scheme
– 2K18
Semester I
Sl. No Course Type /
Course Code Course Name
Teaching scheme
Hrs/Week Teaching
DPT
Total
Hrs/week
CIE
Marks
*SEE
Marks Credits
L T P S
1 18CS1C01 Mathematical Foundations of Computer Science 3 1 0 0 CSE 4 50 50 4
2 18CS1C02 Advances in Computer Networks 4 0 0 0 CSE 4 50 50 4
3 18WT1C03 Web Design and Services 4 0 0 0 CSE 4 50 50 4
4
18CS1E1A Cloud Computing
4 0 0 0
CSE
CSE
CSE
4 50 50 4 18WT1E1B Recommender System
18WT1E1C Service Oriented Architecture
5
18IT1E2C Web Engineering 3 0 2 0 CSE
CSE
CSE
4 50 50 4 18WT1E2B Web Intelligence 4 0 0 0
18WT1E2A Ethical Hacking
6 18WT1L01 Web Application Development Lab 0 0 4 0 CSE 4 50 50 2
7 18CS1M01 Research Methodology and IPR. 2 0 0 0 CSE 2 50 50 2
8 18WT1S01 Seminar - I 0 0 2 0 CSE 2 50 -- 1
9 18CS1M02 Audit Course - I ( Technical Paper Writing) 2 0 0 0 English 2 50 -- 1
Total 30 450 350 26
WT6
Semester II
Sl. No Course Type /
Course Code Course Name
Teaching scheme
Hrs/Week Teaching
DPT
Total
Hrs/week
CIE
Marks
*SEE
Marks Credits
L T P S
1 18CS2C01 Advanced Data Structures and Algorithms 4 0 0 0 CSE 4 50 50 4
2 18CS2C02 Advanced Operating Systems 4 0 0 0 CSE 4 50 50 4
3 18WT2C03 Semantic Web 4 0 0 0 CSE 4 50 50 4
4
18WT2E1A Data Warehousing and Web Mining 4 0 0 0 CSE
CSE
CSE
4 50 50 4 18WT2E1B User Interface Design and Evaluation 3 0 2 0
18WT2E1C Trust Management in E-Commerce 4 0 0 0
5
18SE2E2A Software Agents
4 0 0 0
CSE
CSE
CSE
4 50 50 4 18SE2E2B Software Security
18CS2E2C Web Security
6 18CS2L01 Advanced Data Structures and Algorithms Lab 0 0 4 0 CSE 4 50 50 2
7 18WT2S01 Seminar - II 0 0 2 0 CSE 2 50 -- 1
8 18CS2M01 Audit Course - II (Pedagogy Studies) 2 0 0 0 CSE 2 50 -- 1
Total 28 400 300 24
WT7
Semester III
Sl. No Course Type /
Course Code Course Name
Teaching scheme
Hrs/Week Teaching
DPT
Total
Hrs/week
CIE
Marks
*SEE
Marks Credits
L T P S
1
18IT3E1A Social Network 4 0 0 0
CSE 4 50 50 4 18CS3E1B Big Data Analytics 3 0 2 0
18IT3E1C Information Retrieval Systems 4 0 0 0
2 Open Elective 4 0 0 0 CSE 4 50 50 4
3 18WT3S01 Seminar - III 0 0 2 0 CSE 2 50 1
4 18WT3I01 Internship / Mini Project 0 0 10 0 CSE 10 50 50 5
5 18WT3D01 Dissertation Phase - I 0 0 10 0 CSE 10 50 50 5
Total 30 250 200 19
Open Elective
Sl. No Course Type /
Course Code Course Name
Teaching Scheme (No. of hrs per week)
Teaching
Dept.
Total hrs
/ week
CIE
Marks
*See
Marks Credits
L T P S
1
18CS3P1A Artificial Intelligence
4 0 0 0 CSE 4 50 50 4 18CS3P1B Business Analytics
18CS3P1C Modeling and Simulation
2
18CV3P1A Significance of National Building Codes
4 0 0 0 Civil 4 50 50 4 18CV3P1B Water Laws, Rights and Administration
18CV3P1C Waste to Energy
18CV3P1D Remote Sensing and Geographic Information System
3 18ME3P1A Composite and Smart Materials
4 0 0 0 Mech 4 50 50 4 18ME3P1B Industrial Safety
4
18EE3P1A Real Time Embedded Systems
4 0 0 0 EEE 4 50 50 4 18EE3P1B Robotics and Automation
18EE3P1C Solar and Wind Energy
5
18EC3P1A Reliability and Engineering
4 0 0 0 ECE 4 50 50 4 18EC3P1B M-Commerce and Applications
18EC3P1C Optimization Techniques
WT8
COURSE TYPE
Semester IV
Sl. No Course Type /
Course Code Course Name
Teaching scheme
Hrs/Week Teaching
DPT
Total
Hrs/week
CIE
Marks
SEE
Marks Credits
L T P S
1 18CN4S01 Seminar - IV 0 0 2 0 CSE 2 50 1
2 18CN4D01 Dissertation Phase - II 0 0 30 0 CSE 30 50 50 15
Total 32 100 50 16
1 18CSMOOC MOOC Course 0 0 0 0 03
Grand Total of Credits 88
CS: COMPUTER SCIENCE AND ENGINEERING WT: WEB TECHNOLOGY C: PROFESSIONAL CORE
E: PROFESSIONAL ELECTIVE P: OPEN ELECTIVE M: MANDATORY AUDIT
L: LABORATORY S: SEMINAR I: INTERNSHIP/ MINI PROJECT
D: DISSERTATION
L – Theory lecture, T – Tutorial, P – Lab work, S – Self study:
Numbers under teaching scheme indicates contact clock hours.
NOTE:
1) In any course (Program Core or Program Elective), if self study of 4 hrs per week for students is allocated, then the teaching scheme of
such courses will be 3-0-0-4 and the total credits will be 4.
2) * = SEE shall be conducted for 100 marks and the marks obtained shall be reduced to 50 marks.
3) # = The CIE test of the lab component of integrated course shall be conducted with the external examiner for 50 marks and shall be
reduced to 25 marks.
I Semester
WT9
Course Code 18CS1C01 M. Tech (Web Technologies)
Category Professional Core
Course title MATHEMATICAL FOUNDATIONS OF COMPUTER SCIENCE
Scheme and
Credits
No. of Hours/Week Semester – I
L T P S Credits
3 1 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
1. Basics of probability
2. Basics of graph theory
COURSE OBJECTIVES
The course will enable the students to:
1. Understand the concepts of number theory and solve related problems.
2. Apply the concepts of stochastic process and queuing theory required to devise
analytical models for the real problems of computer science.
3. Analyze the various concepts of arranging, selecting and combining objects from a
set.
4. Understand the concept of advanced graph theory that can be used to model any
network, physical or conceptual.
UNIT -I NUMBER THEORY: 10 Hours
The Division algorithm, the Greatest Common Divisor, the Euclidean algorithm. The basic
properties of Congruencies, Binary and decimal representation of integer, linear congruence,
Chinese-Reminder Theorem, Fermat‘s Little theorem, The sum and number of Divisors, The
mobius inversion formula, The Greatest integer function (No theorem proofs).
UNIT -II PROBABILITY AND QUEUING THEORY: 10 Hours
Random Variables, Probability Distribution, Binomial Distribution, Poisson Distribution,
Geometric Distribution, Exponential Distribution, Normal Distribution, Uniform
Distribution. Two Dimensional Random Variables. Introduction to Stochastic Processes,
Markov process, Markov chain, one step and n-step Transition Probability, Chapman
Kolmogorov theorem (Statement only), Transition Probability Matrix, Classification of
States of a Markov chain. Introduction to Markovian queuing models, Single Server Model
with Infinite system capacity, Characteristics of the Model (M/M/1) : (∞/FIFO), Single
Server Model with Finite System Capacity, Characteristics of the Model (M/M/1) :
(K/FIFO).
UNIT -III COMBINATORICS: 10 Hours
Basics of Counting: Permutations, Permutations with Repetitions, Circular Permutations,
Restricted Permutations, Combinations: Restricted Combinations, Generating Functions of
Permutations and Combinations, Binomial and Multinomial Coefficients, Binomial and
Multinomial Theorems, The Principles of Inclusion Exclusion, Pigeonhole Principle and its
Application.
UNIT -IV RECURRENCE RELATIONS: 09 Hours
Generating Functions, Function of Sequences, Partial Fractions, Calculating Coefficient of
Generating Functions, Recurrence Relations, Formulation as Recurrence Relations, Solving
Recurrence Relations by Substitution and Generating Functions, Method of Characteristic
WT10
Roots, Solving Inhomogeneous Recurrence Relations.
UNIT –V GRAPH THEORY: 09 Hours
Basic Concepts of Graphs, Sub graphs, Matrix Representation of Graphs: Adjacency
Matrices, Incidence Matrices, Isomorphic Graphs, Paths and Circuits, Eulerian and
Hamiltonian Graphs, Multi-graphs, Planar Graphs, Euler‗s Formula, Graph Colouring and
Covering, Chromatic Number, Spanning Trees, Algorithms for Spanning Trees (Concepts
and Problems Only, Theorems without Proofs).
UNIT-VI Recent advances and research being done in the topics mentioned above units
REFERENCES
1. David M Burton, ―Elementary Number Theory‖, Allyn and Bacon, 1980.
2. K. S. Trivedi, ―Probability and Statistics with Reliability, Queuing for Computer
Science Applications‖, John Wiley and Sons, II Edition, 2008.
3. Gunter Bolch, Stefan Greiner, Hermann De Meer, K S Trivedi, ―Queuing Networks
and Markov Chains‖, John Wiley and Sons, II Edition, 2006.
4. Richard A Brualdi, Introductory Combinatorics 5th
Edition, Pearson 2009
5. J. A. Bondy and U. S. R. Murty, ―Graph Theory and Applications‖, Macmillan
Press, 1982.
COURSE OUTCOMES
On completion of the course, the student would be able to:
CO1. Solve problems related to number theory.
CO2: Design the analytical models using the concepts of probability and stochastic process.
CO3: Compare the various methods of counting using permutations and combinations.
CO4: Solve the problems of recurrence relations.
CO5: Apply the graph theory concepts in solving problems related to computer science.
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100 Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 1
CO2 2
CO3 1 1
CO4 1
CO5 2
1: Low 2: Medium 3:High
WT11
Course Code 18CS1C02 M. Tech (Web Technologies)
Category Engineering Science Courses
Course title ADVANCES IN COMPUTER NETWORKS
Scheme and
Credits
No. of
Hours/Week
Semester – I
L T P SS Credits
4 0 - - 4
CIE Marks:
50
SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3
Hrs
Prerequisites (if any):
COURSE OBJECTIVE
The course will enable the student to:
1. Understand the requirement of various high speed networks
2. Learn the effect of congestion and its control.
3. Understand Network Traffic Management for reliable delivery.
4. Understand integrated and differentiated architecture and services.
5. Learn the effect of traffic in the networks on various QoS parameters
UNIT I- INTRODUCTION 9 Hours
OSI and TCP/ IP Reference Model, RS-232C, RS-449, Devices used in Physical Layers,
Framing, Flow and Error Control, Error Detection and Correction, Checksum, Sliding
Window Protocols-ARQ.
UNIT II- DATA LINK LAYER 10 Hours
Multiple Access Control Protocols(MAC), ALOHA, CSMA/CD (802.3), Data Link
Protocol- HDLC,PPP, Wired LAN‘s : Fast Ethernet, Gigabit Ethernet, Fibre Channel,
Wireless LAN‘s(802.11), Broadband Wireless(802.16).
UNIT III- ROUTING AND CONGESTION CONTROL 10 Hours
Design issues, Routing Algorithms, Distance Vector Routing, Link State Routing, Routing
in Mobile Host, Broadcast Routing, Reverse Path Forwarding, OSPF, IP Protocols (IPv4 -
ARP, RARP, IPv6), Queuing Analysis- Queuing Models – Single Server Queues –
Effects of Congestion – Congestion Control – Traffic Management – Congestion Control
in Packet Switching Networks.
UNIT IV – TRANSPORT LAYER AND INTEGRATED SERVICES 10 Hours
TCP Header, TCP Flow control – TCP Congestion Control – Retransmission – Timer
Management – Exponential RTO back-off – KARN‘s Algorithm – Window
management. Integrated Services Architecture – Approach, Components, Services-
Queuing Discipline, FQ, PS, BRFQ, GPS, WFQ – Random Early Detection,
Differentiated Services.
UNIT V - PROTOCOLS FOR QOS SUPPORT 09 Hours
RSVP – Goals & Characteristics, Data Flow, RSVP operations, Protocol
Mechanisms – Multiprotocol Label Switching – Operations, Label Stacking, Protocol
details – RTP – Protocol Architecture, Data Transfer Protocol, RTCP.
UNIT VI- To understand latest innovative networks such as Software Defined
Networks(SDN).
WT12
REFERENCES
1. Behrouz A Forouzan and Firouz Mosharraf, ―Computer Networks, A Top-Down
Approach‖, TMH, 2012.
2. Andrew S. Tanenbaum and David J. Wetherall, ―Computer Networks‖, Pearson
Education, 5th Edition,2011.
3. William Stallings, ―High Speed Networks and Internet‖, , Second Edition, 2012.
4. Prakash C Guptha, ―Data Communication and Computer Networks‖, PHI , 6th
printing 2012.
5. Larry L. Peterson and Bruce S Davis , ―Computer Network A System
Approach‖, Elsevier, 5th
edition 2010.
COURSE OUTCOMES
On Completion of the course, the student will be able to:
CO1: Apply the networking principles to manage the network traffic.
CO2: Control the various anomalies in the network to improve the QoS.
CO3: Study the relation and effect of one QoS parameter on the other.
CO4: Apply the efficient techniques to achieve effective and reliable communication.
CO5: Develop new protocols to mitigate emerging problems.
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100 Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COs) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2
CO3 3 2 2
CO4 3 2
CO5 2 2 2
1:Low, 2:Medium, 3:High
WT13
Course Code 18WT1C03 M. Tech (Web Technologies)
Category Professional Core
Course title Web Design and Services
Scheme and Credits No. of Hours/Week Semester – I
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
1. Understand the concept of web, protocols and role of web organizations.
2. Acquire the concept of web design principle
3. Create web page using web tools.
4. Discuss the web service, architecture and protocols.
5. Create web services and UDDI registry.
UNIT I - Web Foundations: 09 Hours
The Evolution of the Web, Internet Applications, Networks, Internet Address Architecture,
IPv6, Higher Level Protocols FTP, Telnet, SMTP, IMAP, MIME, HTTP, Important
Components of the Web, Web Search Engines, Web Servers, Application Server, Internet
Organizations'-Internet Society, Internet Engineering Task Force, Internet Engineering steering
Group, Internet Assigned Numbers Authority, Internet Architecture Board, Internet Research
Task Force,
UNIT II Web Design Principles: 10 hours
Layout and Composition- web page Anatomy, Grid Theory, Balance, unity, Emphasis, Bread
and Butter layout ,Resizing, Screen Resolution, Color- The color psychology, color
Temperature, Chromatic value, Color Theory, color Scheme, color Tools and Resources,
Texture- point, line ,shape ,volume and Depth, pattern, Building Texture, Typography-Text
Image, web fonts, anatomy of letter form.
UNIT III - Web Design Technologies: 09 Hours
HTML, Cascading Style Sheets, XML, XML Schema, XSLT, Xpath.
UNIT IV - Web Services: 10 Hours Introduction, Server side Architecture-Mainframe Architecture, Client/Server Architecture
,Distributed Architecture, Internet and WWW, Client side Architecture-Dumb Terminals,
Browser-based Clients, Service Oriented Architecture and web services, web services
Applications- Supply Chain Management and Logistics, Customer Relations management,
Education,. Simple object Access Protocol- Message Envelope, Encoding Rules, RPC
Connection, Binding with underlying Protocol.
UNIT V - Web Services language and Registry : 10 Hours
Web service Invocation and Web Service Description Language,-Service Creation,
Description, Service Registration, Service Discovery, Service Invocation, Web services
Description and services through WSDL, Universal Description Discovery and Integration-
Business Information and Taxonomy, Specification and Services, Public and Private
Registries, UDDI nomenclature-Node API sets, UDDI Node UDDI Registries, Data Structure,
WT14
Information Model, Core UDDI-Business Entity, Business Service Binding Template tModel,
Service Publication, Service discovery.
UNIT VI
Emerging Trends in web designing and the Service-Oriented Architectures and
Enterprise to support mobility systems, Internet of Things, Ubiquitous Computing,
collaborative and adaptive business processes, Big Data, and Cloud ecosystems.
REFERENCES
1. Web Technology: Theory and Practice, By: M. Srinivasan, Pearson Education India, 2012
2. Web Services: An Introduction, B V Kumar, S V Subramanya, Tata McGRAW Hill, 2008
3. Web Services Essentials, By: Ethan Cerami, Publisher: O'Reilly Media, Inc., 2002
4. Web Design in a Nutshell, Jennifer Niederst Robbins, O‘Reilly, 3rd
Edition, 2001
5. Web Services: Theory and Practice, AnuraGuruge, Digital Press, 2004
COURSE OUTCOMES
On completion of the course, the students will be able to:
CO1: Summarise the web concepts and organization.
CO2: Outline web design principle for layout and composition of web pages.
CO3: Design web pages using web tools.
CO4: Describe the service-oriented architecture and SOAP.
CO5: Construct bind and unbind services in UDDI.
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE –
100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 1
CO2 3
CO3 2 3
CO4 2
CO5 3
1. Low, 2. Medium, 3. High
WT15
Course Code 18CS1E1A M. Tech (Web Technologies)
Category Professional Elective
Course title CLOUD COMPUTING
Scheme and Credits No. of Hours/Week Semester – I
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
1. Operating systems
2. Basics of distributed computing
COURSE OBJECTIVES
The course will enable the students to:
1. Understand the various cloud service providers and cloud interoperability
2. Apply the cloud computing applications and paradigms
3. Analyse the concept of virtualization
4. Acquire the knowledge of cloud resource management and scheduling mechanism
5. Learn various security issues in cloud computing.
UNIT-I CLOUD INFRASTRUCTURE 09 Hours
Cloud computing at Amazon, Cloud computing-the Google perspective, Microsoft Windows
Azure and Online services, Open-Source Software Platforms for Private Clouds Cloud Storage
Diversity and Vendor Lock-in, Cloud Computing Interoperability: The Intercloud, Service- and
Compliance-Level Agreements, Responsibility Sharing Between User and Cloud Service
Provider, User Experience, Software Licensing.
UNIT- II CLOUD COMPUTING: APPLICATIONS AND PARADIGMS 09 Hours
Challenges for Cloud Computing, Existing Cloud Applications and New Application
Opportunities Architectural Styles for Cloud Applications, Workflows: Coordination of Multiple
Activities, Coordination Based on a State Machine Model: The ZooKeeper, The MapReduce
Programming Model, A Case Study: The GrepTheWeb Application, High-Performance
Computing on a Cloud.
UNIT-III CLOUD VIRTUALIZATION 10 Hours
Virtualization, Layering and Virtualization, Virtual Machine Monitors, Virtual Machines,
Performance and Security Isolation, Full Virtualization and Paravirtualization, Hardware Support
for Virtualization, Case Study: Xen, a VMM Based on Paravirtualization, Optimization of
Network Virtualization in Xen 2.0, vBlades: Paravirtualization Targeting an x86-64 Itanium
Processor, A Performance Comparison of Virtual Machines.
UNIT-IV CLOUD RESOURCE MANAGEMENT AND SCHEDULING 10 Hours
Policies and Mechanisms for Resource Management, Applications of Control Theory to Task
Scheduling on a Cloud, Stability of a Two-Level Resource Allocation Architecture, Feedback
Control Based on Dynamic Thresholds, Coordination of Specialized Autonomic Performance
Managers, A Utility-Based Model for Cloud-Based Web Services, Resource Bundling:
Combinatorial Auctions for Cloud Resources, Scheduling Algorithms for Computing Clouds,
Fair Queuing, Start-Time Fair Queuing, Borrowed Virtual Time Cloud Scheduling Subject to
Deadlines, Scheduling MapReduce Applications Subject to Deadlines, Resource Management
and Dynamic Application Scaling.
WT16
UNIT-V CLOUD SECURITY 10 Hours
Cloud Security Risks, Security: The Top Concern for Cloud Users, Privacy and Privacy Impact
Assessment, Trust Operating System Security, Virtual Machine Security, Security of
Virtualization, Security Risks Posed by Shared Images, Security Risks Posed by a Management
OS.
UNIT-VI Recent developments and current research in multi cloud, cloud security, mobile
cloud computing.
REFERENCES
1. Dan C Marinescu, ―Cloud Computing: Theory and Practice‖, Morgan
Kaufmann/Elsevier. 2013.
2. George Reese, ―Cloud Application Architectures: Building Applications and
Infrastructure in the Cloud‖, O‘Reilly, 2009.
3. Rajkumar Buyya, James Broberg and Andrzej M. Goscinski , ―Cloud Computing:
Principles and Paradigms‖, Wiley, 2011.
4. Kai Hwang, Geoffrey C Fox, Jack G Dongarra, ―Distributed and Cloud Computing: From
Parallel Processing to the Internet of Things‖, Morgan Kaufmann Publishers, 2012.
COURSE OUTCOMES
Upon completion of the course, the students would be able to:
CO1: Categorize the architectures, services and delivery models in cloud computing
CO2: Implement the concept of virtualization and its management in cloud computing
CO3: Design the extended functionalities of resource management and scheduling mechanisms
CO4: Analyse the security models in cloud environment
CO5: Investigate recent developments in multi cloud, cloud security and mobile cloud computing
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit VI(AAT)=15
marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks
Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2
CO2 2
CO3 1 2
CO4 2 1
CO5 2 2
1. Low, 2. Medium, 3. High
WT17
Course Code 18WT1E1B M. Tech (Web Technologies)
Category Professional Elective
Course title RECOMMENDER SYSTEM
Scheme and Credits No. of Hours/Week Semester – I
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
Course Objectives: The course will enable the students to:
1. Understand the concepts of recommender system
2. Apply recommendations techniques like non-personalized, content-based, and
collaborative filtering
3. Choose a variety of choice-making strategies with the goal of providing affordable,
personal, and high-quality recommendations.
4. Evaluate the recommender system using evaluation metrics.
5. Compare the different types of recommender systems.
UNIT I - INTRODUCTION: 09 Hours
Overview of Information Retrieval, Retrieval Models, Search and Filtering Techniques:
Relevance Feedback, User Profiles, Recommender system functions, Matrix operations,
covariance matrices, Understanding ratings, Applications of recommendation systems, Issues
with recommender system.
UNIT II - CONTENT-BASED FILTERING: 10 Hours
High level architecture of content-based systems, Advantages and drawbacks of content based
filtering, Item profiles, Discovering features of documents, pre-processing and feature
extraction, Obtaining item features from tags, Methods for learning user profiles, Similarity
based retrieval, Classification algorithms.
UNIT III - COLLABORATIVE FILTERING: 09 Hours User-based recommendation, Item-based recommendation, Model based approaches, Matrix
factorization, Attacks on collaborative recommender systems.
UNIT IV - HYBRID APPROACHES: 10 Hours
Opportunities for hybridization, Monolithic hybridization design: Feature combination, Feature
augmentation, Parallelized hybridization design: Weighted, Switching, Mixed, Pipelined
hybridization design: Cascade Meta-level, Limitations of hybridization strategies.
UNIT V – EVALUATING RECOMMENDER SYSTEM: 10 Hours Introduction, General properties of evaluation research, Evaluation designs: Accuracy,
Coverage, confidence, novelty, diversity, scalability, serendipity, Evaluation on historical
datasets, Offline evaluations. Types of Recommender Systems: Recommender systems in
personalized web search, knowledge-based recommender system, Social tagging recommender
systems, Trust-centric recommendations, Group recommender systems.–
UNIT VI -
Recent Trends in recommender systems and web search.
WT18
REFERENCES
1. Jannach D., Zanker M. and FelFering A., Recommender Systems: An Introduction,
Cambridge University Press, 1st edition, 2011.
2. Charu C. Aggarwal, Recommender Systems: The Textbook, Springer, 1st ed, 2016
3. Ricci F., Rokach L., Shapira D., Kantor B.P., Recommender Systems Handbook,
Springer, 1st Edition , 2011.
4. Manouselis N., Drachsler H., Verbert K., Duval E., Recommender Systems for
Learning, Springer (2013), 1st ed.
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1. Design recommendation system for a particular application domain
CO2. Use non-personalized, content-based, and collaborative filtering recommendations
techniques.
CO3. Apply various choice-making strategies for recommendation.
CO4. Evaluate recommender systems on the basis of metrics such as accuracy, rank accuracy,
diversity, product coverage, and serendipity.
CO5. Differentiate the various recommender systems.
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE –
100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 3
CO2 3 3
CO3 3 3
CO4 3 3
CO5 3 3
1. Low, 2. Medium, 3. High
WT19
Course Code 18WT1E1C M. Tech (Web Technologies)
Category Professional Elective
Course title SERVICE ORIENTED ARCHITECTURE
Scheme and Credits No. of Hours/Week Semester – I
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
Course Objectives: The course will enable the students to:
1. Interpret various architecture for application development
2. Demonstrate the importance of SOA in Application Integration
3. learn web service and SOA related tools and understand various case studies
4. Learn implementation details of SOA.
5. Evaluate SOA through deployment and integration.
UNIT I - SOA BASICS : 09 Hours
Software Architecture – Types of IT Architecture – SOA – Evolution – Key components –
perspective of SOA – Enterprise-wide SOA – Architecture – Enterprise Applications –
Solution Architecture for enterprise application – Software platforms for enterprise
Applications – Patterns for SOA – SOA programming models.
UNIT II - SOA ANALYSIS AND DESIGN: 10 Hours
Service-oriented Analysis and Design – Design of Activity, Data, Client and business
process services – Technologies of SOA – SOAP – WSDL – JAX – WS – XML WS for .NET
– Service integration with ESB – Scenario – Business case for SOA – stakeholder
OBJECTIVES – benefits of SPA – Cost Savings.
UNIT III - SOA GOVERNANCE: 09 Hours SOA implementation and Governance – strategy – SOA development – SOA governance –
trends in SOA – event-driven architecture – software as a service – SOA technologies – proof-
of-concept – process orchestration – SOA best practices
UNIT IV - SOA IMPLEMENTATION: 10 Hours
SOA based integration – integrating existing application – development of web services –
Integration - SOA using REST – RESTful services – RESTful services with and without JWS
– Role of WSDL,SOAP and Java/XML mapping in SOA – JAXB Data binding.
UNIT V - APPLICATION INTEGRATION: 10 hours
JAX –WS 2.0 client side/server side development – Packaging and Deployment of SOA
component – SOA shopper case study –WSDL centric java WS with SOA-J – related software
– integration through service composition (BPEL) – case study - current trends.
UNIT VI
Emerging Trends in the Service-Oriented Architectures and Enterprise
1. Shankar Kambhampaly, ―Service–Oriented Architecture for Enterprise
Applications‖,Wiley 2008.
2. Mark D. Hansen, ―SOA using Java Web Services‖, Practice Hall, 2007.
WT20
3. Waseem Roshen, ―SOA-Based Enterprise Integration‖, Tata McGraw-HILL, 2009.
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1. Compaer different IT architecture.
CO2. Analyze and design of SOA based applications.
CO3. Implement web service and realize of SOA.
CO4. Design and implement of SOA based application.
CO5. Integration using BPEL.
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE –
100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced
for 50 marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2
CO3 2 3
CO4 2 3
CO5 2 3
1. Low, 2. Medium, 3. High
WT21
Course Code 18IT1E2C M. Tech (Web Technologies)
Category Professional Elective - Integrated
Course title WEB ENGINEERING
Scheme and Credits No. of Hours/Week Semester – I
L T P S Credits
3 - 2 - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
Course Objectives: The course will enable the students to:
1. Understand the concepts of Web engineering and requirement engineering.
2. Apply the architecture and models for Web applications.
3. Verify and analyse the Web applications.
4. Provide the knowledge on CGI Programming to implement various Web applications.
5. Design Embedded Web applications using PHP.
UNIT I - INTRODUCTION TO WEB ENGINEERING AND REQUIREMENTS
ENGINEERING: 10 Hours
The need for Web engineering, Categories of Web Applications, Characteristics of Web
Applications. Evolution of Web Engineering, Requirement Engineering and modeling in web
engineering: RE specifics in Web Engineering, principles, modeling requirements. Methods
and Tools for modeling in Web Engineering, Designing a Web application.
UNIT II - WEB APPLICATION ARCHITECTURES AND MODELING WEB
APPLICATIONS: 10 Hours
Introduction- Categorizing Architectures, Specifics of Web Application Architectures,
Components of a Generic Web Application Architecture, Layered Architectures: 2-Layer and
N-Layer Architectures, Data-aspect Architectures, Database-centric Architectures,
Architectures for Web Document Management, Architectures for Multimedia Data. Web
application design, Model based web application development: OOHDM method, W2000
method
UNIT III - TESTING WEB APPLICATIONS: 09 Hours
Introduction, Fundamentals, Test approaches, Test methods and techniques, Test driven
development, Test Automation, Test tools.
UNIT IV - CGI PROGRAMMING: 10 Hours Structural- Apache web server, Apache configuration, MySQL- introduction, Database
independent interface, Loading and Dumping a Database. CGI Programming: Dynamic-
Introduction CGI.pm, Information received by the CGI Program, Form widget Methods, CGI
security considerations.
UNIT V – EMBEDDED WEB APPLICATION 09 Hours Introduction, Security considerations, PHP-introduction, Embedding PHP into HTML,
Configuration, Quick examples, Built-in PHP functions.
UNIT VI
Recent Trends in Web engineering and Web application tools
UNIT – VII (Lab Programs)
1. Write a Perl script to read in a string from the console and print:
(a) The length and reverse of the string
(b) The upper and lower case version of the string
WT22
2. a) Write a Perl program to extract Log file information using regular expression.
b) Write a perl script to compute the nth
power of a given number.
3. a) Write a Perl program to display various Server Information like Server Name, Server
Software, Server protocol, CGI Revision etc.
b) Write a Perl program to accept UNIX command from a HTML form and to display
the output of the command executed.
4. a) Write a Perl Program to check whether the given number is Armstrong number or
not.
b) Write a Perl program to insert name and age information entered by the user into a
table created using MySQL and to display the current contents of this table.
5. Write a Perl program to accept the User Name and display a greeting message
randomly chosen from a list of 4 greeting messages.
6. Write a Perl program to keep track of the number of visitors visiting the web page and
to display this count of visitors, with proper headings.
7. Write a Perl program to display a digital clock which displays the current time of the
server.
8. Write a PHP program to store current date-time in a COOKIE and display the ‗Last
visited on‘ date-time on the web page upon reopening of the same page.
9. Write a PHP program to store page views count in SESSION, to increment the count on
each refresh, and to show the count on web page.
10. Using PHP and MySQL develop a program to accept book information viz. Accession
number, title, authors, edition and publisher from a web page and store the information
in a database and to search for a book with the title specified by the user and to display
the search results with proper headings.
REFERENCES
1. Web Engineering: The Discipline of Systematic Development of Web Applications by
Kappel et al., John Wiley, 2006
2. Web Engineering by Emilia Mendes and Nile Mosley, 1st Edition, Springer, 2006
3. Open Source Web Development with LAMP-using Linux, Apache, MySQL, perl and
PHP by James Lee and Brent Ware, Addison Wesley/Pearson Inc 2003.
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1. Discuss Web engineering and requirement engineering concepts.
CO2. Make use of the Architecture and various modeling techniques for Web applications.
CO3. Discuss design issues involved in Web application development.
CO4. Validate and use testing process specific to Web applications.
CO5. Develop the Web applications using CGI Programming.
SCHEME OF EXAMINATION
CIE -
Practical
Conduction of experiments, performance of student in
every week lab and completion of lab record=50#
Total: Marks
50
Lab Test: Part-A =20, Part-B=20 and Vice-Voce=10
Marks(50##
)
CIE -50 Test I (unit I,II, & III)-15 Quiz/AAT=05 Marks Total: Marks
WT23
Marks Test II (Unit IV & V) -15 Unit-VI(AAT)=15 Marks 50
SEE-100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
# = CIE Marks for laboratory performance is to be evaluated for 50 marks and the
marks obtained shall be reduced for 25 marks
## = Lab test is to be conducted for 50 marks and the marks obtained shall be
reduced for 25 marks. Lab test shall be conducted by two examiners out of which
one examiner is the faculty taught the course. There is no SEE for the practical ‘s
portion of integrated course
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3
CO2 3 3
CO3 2
CO4 2 2
CO5 3 3
1. Low, 2. Medium, 3. High
WT24
Course Code 18WT1E2B M. Tech (Web Technologies)
Category Professional Elective
Course title WEB INTELLIGENCE
Scheme and Credits No. of Hours/Week Semester – I
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
Course Objectives: The course will enable the students to:
1. Acquire the importance of qualitative data, get insights and techniques
2. Develop customer-centric approach in dealing with data
3. Understand the principles, tools and methods of Web intelligence
4. Apply analytics for business situations.
5. Building intelligence in the Web.
UNIT I - WEB ANALYTICS 09 Hours
Basics, Traditional Ways, Expectations, Data Collection, Clickstream Data, Weblogs, Beacons,
JavaScript Tags, Packet Sniffing, Outcomes data, Competitive data, Search Engine Data
UNIT II - QUALITATIVE ANALYSIS 10 Hours
Customer Centricity, Site Visits, Surveys, Questionnaires, Website Surveys, Post visits,
Creating and Running- Benefits of surveys, Critical components of successful strategy
UNIT III - WEB ANALYTIC CONCEPTS 10 Hours
URLS, Cookies, Time on site, Page views, Understand standard reports, Website content
quality, Navigation reports (top pages, top destinations, site overlay), Search Analytics,
Internal search, SEO and PPC, Measuring Email and Multichannel Marketing, Competitive
intelligence and Web 2.0 Analytics, Segmentation, Connectable reports
UNIT IV - GOOGLE ANALYTICS 09 Hours
Analytics, Cookies, Accounts vs Property, Tracking Code, Tracking Unique Visitors,
Demographics, Page Views & Bounce Rate Acquisitions, Custom Reporting
UNIT V 10Hours
Goals & Funnels, Filters, Ecommerce Tracking, Real Time Reports, Customer Data Alert,
Adwords Linking, Adsense Linking, Attribution Modeling, Segmentation, Campaign
Tracking, Multi-Channel Attribution
UNIT VI
Recent in Data warehousing and Web Mining, Web Search and Information Retrieval system
REFERENCES
1. Avinash Kaushik, ―Web Analytics 2.0: The Art of Online Accountability and Science Of
Customer Centricity ―, 1st edition, Sybex, 2009.
2. Michael Beasley, ―Practical Web Analytics for User Experience: How Analytics can help
you Understand your Users‖, Morgan Kaufmann, 2013.
3. Magy Seif El-Nasr, Anders Drachen, Alessandro Canossa, eds., ―Game Analytics:
Maximizing the Value of Player Data‖, Springer, 2013.
4. Bing Liu, ―Web Data Mining: Exploring Hyperlinks, Content, and Usage Data‖, 2 nd
Edition, Springer, 2011.
5. Justin Cutroni, ―Google Analytics‖, O‘Reilly, 2010.
6. Eric Fettman, Shiraz Asif, Feras Alhlou , ―Google Analytics Breakthrough‖, John Wiley &
sons, 2016.
WT25
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1. Explain the concepts and terminologies related to Web analytics.
CO2. Apply various parameters used for Web analytics and their impacts.
CO3. Make use of tools and techniques of Web analytics.
CO4. Get experience on Websites, Web data insights and conversions.
CO5. Design intelligence of Web.
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE –
100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3
CO2 3 2
CO3 3 2
CO4 3
CO5 2 2
1. Low, 2. Medium, 3. High
WT26
Course Code 18WT1E2A M. Tech (Web Technologies)
Category Professional Elective
Course title Ethical Hacking
Scheme and Credits No. of Hours/Week Semester – I
L T P S Credits
4 0 0 - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
Course Objectives:
The course will enable the students to:
1. know the concepts of Ethical Hacking
2. Learn about different tools and techniques in Ethical hacking and security
3. Learn backtrack Linux for ethical hacking.
4. Practically apply using some of the tools.
5. Analyse client side browser exploits and vulnerabilities.
UNIT I – 09 hours
Ethics of Ethical Hacking, Enemy‘s Tactics, Recognizing the Gray Areas in Security,
Vulnerability Assessment, Penetration Testing, The Dual Nature of Tools, Recognizing
Trouble, Emulating the Attack, Proper and Ethical Disclosure, Different Teams and Points of
View, CERT‘s Current Process, Full Disclosure Policy—the RainForest Puppy Policy,
Organization for Internet Safety (OIS), Discovery, Notification, Validation, Resolution,
Release.
UNIT II 09 hours
Physical Penetration Attacks, Conducting a Physical Penetration, Reconnaissance, Mental
Preparation, Common Ways into a Building, The Smokers‘ Door, Manned Checkpoints,
Locked Doors, Physically Defeating Locks, Defending Against Physical Penetrations, Insider
Attacks, Conducting an Insider Attack, Tools and Preparation, Orientation, Gaining Local
Administrator Privileges, Disabling Antivirus, Raising Cain, Defending Against Insider
Attacks.
UNIT III 10 hours
Using the BackTrack Linux Distribution, Using the BackTrack ISO Directly Within a Virtual
Machine, Creating a BackTrack Virtual Machine with VirtualBox, Booting the BackTrack
LiveDVD System, Starting Network Services, Creating a New ISO with Your One-time
Changes, Using a Custom File that Automatically Saves and Restores Changes, Exploring the
BackTrack Boot Menu, Updating BackTrack, Using Metasploit, Metasploit: Getting
Metasploit, Using the Metasploit Console to Launch Exploits, Exploiting Client-Side
Vulnerabilities with Metasploit.
UNIT IV – 10 hours
Passive Analysis, Ethical Reverse Engineering and Considerations, Source Code Analysis,
Auditing , and Utility. Manual Source Code Auditing, Automated Source Code Analysis,
Binary Analysis, Manual Auditing of Binary Code, Automated Binary Analysis Tools,
Advanced Static Analysis with IDA Pro, Static Analysis Challenges, Stripped Binaries,
WT27
Statically Linked Programs and FLAIR, Data Structure Analysis, Extending IDA Pro Scripting
, IDA Pro Plug-In Modules and the IDA Pro SDK.
UNIT V – 10 hours
Client-Side Browser Exploits, Client-Side Vulnerabilities - Bypass Firewall Protections,
Client-Side Applications, Privileges, Targets, Internet Explorer Security Concepts , ActiveX
Controls, Internet Explorer Security Zones, History of attacks and latest Trends, Finding New
Browser-Based Vulnerabilities, Heap Spray to Exploit, Internet Exploiter, Security for
Vulnerabilities, Windows Access Control , Security Identifier, Access Token, Security
Descriptor, The Access Check, Tools for Analyzing Access Control Configurations,
Analyzing Access Control for Elevation of Privilege.
UNIT VI -
Recent trends in ethical hacking, Case study of vulnerability of cloud and mobile platforms.
REFERENCES
1. Shon Harris, Allen Harper, Chris Eagle and Jonathan Ness, Gray Hat Hacking: The Ethical
Hackers' Handbook, TMH Edition
2. Jon Erickson, Hacking: The Art of Exploitation, SPD
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO 1: Appreciate the Cyber Law, ethics and impact of hacking.
CO2 :Understand ethics behind hacking and vulnerability disclosure.
CO3:Understand the core concepts related to malware, hardware and software
vulnerabilities and their causes.
CO4 :Exploit the vulnerabilities related to computer system and networks using state of
the art tools and technologies.
CO5: Differentiate client side and browser based vulneratilities.
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE –
100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
WT28
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 3
CO3 3 2
CO4 3
CO5 2
1. Low, 2. Medium, 3. High
WT29
Course Code 18WT1L01 M. Tech (Web Technologies)
Category Laboratory
Course title Web Application Development Lab
Scheme and
Credits
No. of Hours/Week Semester – I
L T P S Credits
- - 4 - 2
CIE Marks:
50
SEE Marks: 50 Total Max. Marks:
100
Duration of SEE: 3 Hrs
Prerequisites (if any):
1. HTML
COURSE OBJECTIVES
The course will enable the students to:
1. Outline the design of web pages using web tools.
2. Design valid XML web document.
3. Construct web service using web service tools.
4. Understand various operations performed on web applications.
5. Develop an web application using web tools.
PART – A
1. Prepare step-wise snapshot to configure Tomcat Web Server/ Apache Web
Server/ IIS Web Server for an application and configure browser for security
settings
2. Develop a home page of an organization using HTML, CSS and Java Script,
having navigational menus etc.
3. Write an XML file which will display the Book information which includes
the following:
Title of the book
Author Name
ISBN number
Publisher name
Edition
Price
Write a Document Type Definition (DTD) to validate the above XML file and
use XSL and CSS to display the page content.
4. Calendar Creation and Display all months using JavaScript/JSP.
5. Write a program to implement WSDL Service (HelloService.WSDL File)
6. Write a program to implement to create a simple web service that converts the
temperature from Fahrenheit to Celsius (using HTTP Post Protocol)
7. Create a photo slide show using JQuery/Javascript.
8. iMovie Exercise - apply basic video editing concepts like cropping, splitting
clips, adding audio, transitions.
PART – B
Development of an application using Web 2.0/ any relevant Computer Science Tool
Note:
Student should execute one program from Part A and Demonstrate web application
from Part B
WT30
COURSE OUTCOMES
On completion of the course, the student would be able to:
CO1: Develop web pages using web tools.
CO2: Demonstrate valid web document.
CO3: Create the web service using WSDL.
CO4: Demonstrate various operations performed in web applications.
CO5: Device an web application using web tool.
SCHEME OF EXAMINATION
The student has to write and implement two programs selecting ONE from each part
Continuous Internal
Evaluation (CIE) (Lab – 50
Marks)
Marks Semester End Evaluation (SEE)
(Lab – 100 Marks) Marks
Performance of the Student in
the Lab every week
20 Write up 10
Test at the end of the semester 20 Experiment-1 (Part - A) – 35 Marks
Experiment-2 (Part - B) – 35 Marks
70
Viva Voce 10 Viva Voce 20
Total 100
Total (CIE) 50 Total (SEE) 50*
Note. * = SEE shall be conducted for 100 marks for practical and the marks obtained shall be
reduced for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 1 3
CO2 1 3
CO3 2
CO4 1 3
CO5 1 2 3
1. Low, 2. Medium, 3. High
WT31
Course Code 18CS1M01 M. Tech (Web Technologies)
Category Mandatory Audit
Course title RESEARCH METHODOLOGY AND IPR
Scheme and Credits No. of Hours/Week Semester – I
L T P SS Credits
2 0 - - 2
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
This course will enable students to
1. Understand the formulation of research problem, scope and objectives of research problem
2. Use methods for effective technical writing skills
3. Analyse Approaches of investigation of solutions for research problem
4. Evaluate the format of research proposal , intellectual property and patent
5. Create patent, research paper
UNIT -I RESEARCH PROBLEM: 3 Hours
Meaning of research problem, Sources of research problem, Criteria Characteristics of a good
research problem, Errors in selecting a research problem, Scope and objectives of research problem.
Approaches of investigation of solutions for research problem, data collection, analysis,
interpretation, Necessary instrumentations
UNIT- II RESEARCH REQUIREMENTS: 3 Hours
Effective literature studies approaches, analysis Plagiarism, Research ethics,
UNIT- III EFFECTIVE TECHNICAL WRITING: 6 Hours
Effective technical writing, how to write report, Paper Developing a Research Proposal, Format of research
proposal, a presentation and assessment by a review committee
UNIT- IV NATURE OF INTELLECTUAL PROPERTY: 6 Hours
Patents, Designs, Trade and Copyright. Process of Patenting and Development: technological research,
innovation, patenting, development. International Scenario: International cooperation on Intellectual Property.
Procedure for grants of patents, Patenting under PCT.
UNIT- V PATENT RIGHTS: 6 Hours
Scope of Patent Rights. Licensing and transfer of technology. Patent information and databases. Geographical
Indications.
UNIT- VI NEW DEVELOPMENTS IN IPR:
Administration of Patent System. New developments in IPR; IPR of Biological Systems, Computer Software
etc. Traditional knowledge Case Studies, IPR and IITs.
REFERENCES
1. Stuart Melville and Wayne Goddard, ―Research methodology: an introduction for science &
engineering students‘‖
2. Wayne Goddard and Stuart Melville, ―Research Methodology: An Introduction‖
3. Ranjit Kumar, 2nd Edition, ―Research Methodology: A Step by Step Guide for beginners‖
Halbert, ―Resisting Intellectual Property‖, Taylor & Francis Ltd ,2007.
4. Mayall, ―Industrial Design‖, McGraw Hill, 1992.
5. Niebel, ―Product Design‖, McGraw Hill, 1974.
6. Asimov, ―Introduction to Design‖, Prentice Hall, 1962.
WT32
7. Robert P. Merges, Peter S. Menell, Mark A. Lemley, ― Intellectual Property in New
Technological Age‖, 2016.
8. T. Ramappa, ―Intellectual Property Rights Under WTO‖, S. Chand, 2008
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1: Understand research problem formulation. Analyze research related information and
follow research ethics
CO2: Understanding that when IPR would take such important place in growth of
individuals and nation, it is needless to emphasis the need of information about
Intellectual Property Right to be promoted among students in general & engineering
in particular.
CO3: Understand that IPR protection provides an incentive to inventors for further research
work and investment in R & D, which leads to creation of new and better products,
and in turn brings about, economic growth and social benefits.
CO4: Analyze research related information
CO5: Follow research ethics
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 6 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 3 hours shall not have internal
choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3
CO2 3
CO3 3
CO4
CO5 3 3
1: Low 2: Medium 3:High
WT33
COURSE LEARNING OBJECTIVES:
The objectives of the SEMINAR-I is to prepare the students to learn to:
1. Carry out the literature review of general research area/current topic and analyse
the same effectively.
2. Prepare a technical report, reflecting his/her depth of understanding, on the selected
area/topic and prepare content rich presentation.
3. Acquire communication and time management skills for effective and impactful
presentation. Interact with peers to acquire the qualities of thoughtfulness,
friendliness, adaptability, responsiveness, and politeness in-group discussion.
Overcome stage fear during the presentation.
GUIDE LINES
1. Seminar preparation and presentation is an individual student activity.
2. Topic may be of general/ specific interest to program of engineering or electives not
offered in the semester and to be selected in consultation with the faculty/Guide.
3. Select one pertinent research paper for the seminar presentation.
4. Prepare and submit a detailed technical report of the seminar topic.
COURSE OUTCOMES:
Students shall be able to:
1. Carry out the literature survey of topic of seminar.
2. Prepare a technical report on the selected area/topic.
3. Make an effective presentation with seamless flow of content within the time
allocated. Overcome inhibition in interacting with peers and hence develop the spirit
of team work. Overcome stage fear during the presentation.
SCHEME OF EXAMINATION
CIE – 50
marks
Phase -1 Marks =10 Seminar Report
Marks =20
Total:50
Marks Phase -2 Marks =20
Course Code 18WT1S01 M. Tech (Web Technologies)
Category Seminar Semester: I
Course title SEMINAR - I
Scheme and Credits
No. of Hours/Week
Total hours = 24 L T P S Credits
0 0 2 0 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any): NIL
WT34
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2 3
CO2 2 3 3
CO3 2
1. Low, 2. Medium, 3. High
Scheme of Continuous Internal Evaluation (CIE):
Evaluation would be carried out in TWO phases. The Seminar Evaluation Committee
shall comprise of Chairman of the Department, Faculty/Guide and one more faculty member
nominated by Chairman. The evaluation criteria shall be as per the rubrics given below:
Rubrics for Evaluation:
Topic - Technical Relevance, Sustainability and Societal Concerns : 15%
Review of literature and technical content : 35%
Presentation Skills : 25%
Report : 25%
WT35
Course Code 18CS1M02 M. Tech (Web Technologies)
Category Audit Course-I
Course title TECHNICAL PAPER WRITING
Scheme and Credits No. of Hours/Week Semester – I
L T P SS Credits
2 0 - - 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any):
COURSE OBJECTIVES
This course will enable students to
1. Understand the planning section of research paper and preparation of paper writing
2. Apply key skill while writing research paper and know about what to write in each section
3. Analyse literature, methods,
4. Evaluate research paper, paraphrasing paper
5. Create good research paper
UNIT-I PLANNING AND PREPARATION: 6 Hours
Planning and Preparation, Word Order, Breaking up long sentences, Structuring Paragraphs and
Sentences, Being Concise and Removing Redundancy, Avoiding Ambiguity and Vagueness
UNIT- II CLARIFYING: 3 Hours
Clarifying Who Did What, Highlighting Your Findings, Hedging and Criticising, Paraphrasing and
Plagiarism, Sections of a Paper, Abstracts. Introduction
UNIT- III REVIEW OF THE LITERATURE: 6 Hours
Review of the Literature, Methods, Results, Discussion, Conclusions, The Final Check.
UNIT- IV KEY SKILLS: 6 Hours
Key skills are needed when writing a Title, key skills are needed when writing an Abstract, key skills
are needed when writing an Introduction, skills needed when writing a Review of the Literature,
UNIT- V METHODS: 3 Hours
skills are needed when writing the Methods, skills needed when writing the Results, skills are needed
when writing the Discussion, skills are needed when writing the Conclusions.
UNIT- VI NEW DEVELOPMENTS IN RESEARCH PAPER WRITING:
useful phrases, how to ensure paper is as good as it could possibly be the first- time submission
REFERENCES
1. Goldbort R (2006) Writing for Science, Yale University Press (available on Google Books)
2. Day R (2006) How to Write and Publish a Scientific Paper, Cambridge University Press
3. Highman N (1998), Handbook of Writing for the Mathematical Sciences, SIAM.
Highman‘sbook.
4. Adrian Wallwork, English for Writing Research Papers, Springer New York Dordrecht
Heidelberg London, 2011
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1: List of section of research paper and preparation of paper writing
CO2: Determine key skill while writing research paper
WT36
CO3: Analyse literature, methods
CO4: Assess research paper, do paraphrasing paper
CO5: Formulate research paper and results of simulation
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=20 Marks
Total: Marks
50
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3
CO2 3
CO3 3
CO4 3
CO5 3
1: Low 2: Medium 3:High
SEMESTER II
WT37
Course Code 18CS2C01 M. Tech (Web Technologies)
Category Professional Core
Course title ADVANCED DATA STRUCTURES AND ALGORITHMS
Scheme and
Credits
No. of Hours/Week Semester – II
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVE
The course will enable the student to:
1. Learn various data structures and its usage in designing algorithms.
2. Understand to the advanced methods of designing and analysing algorithms.
3. Learn various string matching and graph algorithms.
4. Acquire the knowledge of polynomial, non polynomial and approximation problems.
5. Understand the recent developments in the area of algorithmic design.
UNIT-1 REVIEW OF ANALYSIS TECHNIQUES 09 Hours
Growth of Functions: Asymptotic notations; Standard notations and common functions;
Recurrences -The substitution method, recursion-tree method, the master method,
Probabilistic Analysis and Randomized Algorithms.
UNIT- II BASIC DATA STRUCTURES 09 Hours Stacks and Queues, Vectors, Lists, and Sequences, Trees, Priority Queues and Heaps,
Dictionaries and Hash Tables, Search Trees and Skip lists- Ordered Dictionaries and
Binary Search Trees, AVL Trees, Bounded-Depth Search Trees, Splay Trees, Skip Lists.
UNIT -III DYNAMIC PROGRAMMING 10 Hours
Matrix-Chain multiplication, Elements of dynamic programming, longest common
subsequences. Graph algorithms: Bellman - Ford Algorithm; Single source shortest paths
in a DAG; Johnson‘s Algorithm for sparse graphs; Flow networks and Ford-Fulkerson
method. .
UNIT- IV TRIES AND STRING MACHING ALGORITHMS 10 Hours
Quad trees and K-d trees. String-Matching Algorithms: Naïve string Matching; Rabin -
Karp algorithm; String matching with finite automata; KnuthMorris-Pratt algorithm.
UNIT- V NP-COMPLETENESS 10 Hours
: Polynomial time, Polynomial time verification, NP-Completeness and reducibility, NP-
Complete problems. Approximation Algorithms: vertex cover problem, the set – covering
problem, randomization and linear programming, the subset – sum problem.
UNIT VI
Recent Trends in problem solving paradigms applying recently proposed data
structures
REFERENCES
1. Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein,‖
Introduction to Algorithms‖, Third Edition, Prentice-Hall, 2011.
2. M T Goodrich, Roberto Tamassia, ―Algorithm Design‖, John Wiley, 2002.
3. Mark Allen Weiss, ―Data Structures and Algorithm Analysis in C++‖, 4th
Edition,
Pearson, 2014.
4. Alfred V. Aho, John E. Hopcroft, Jeffrey D. Ullman, ―Data Structures and
Algorithms‖, Pearson Education, Reprint 2006.
5. Ellis Horowitz, Sartaj Sahni, Dinesh Mehta, ―Fundamentals of Data Structures in C‖,
Silicon Pr, 2007.
6. Aho, Hopcroft and Ullman, Data structures and algorithms, 1st edition, Pearson
WT38
Education, India, 2002, ISBN: 8177588265, 978817758826
COURSE OUTCOMES
On completion of the course, the student will be able to:
CO1: Develop and analyze algorithms for red-black trees, B-trees and Splay trees and for
text processing applications.
CO2: Identify suitable data structures and develop algorithms for solving a particular set of
problems
CO3: Analyze the complexity/ performance of different algorithms.
CO4: Categorize the different problems in various classes according to their complexity.
CO5: Use appropriate data structure and algorithms in real time applications.
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for
50 marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2 2
CO3 2 2
CO4 2
CO5 2 2
1. Low, 2. Medium, 3. High
WT39
Course Code 18CS2C02 M. Tech (Web Technologies)
Category Professional Core
Course title ADVANCED OPERATING SYSTEMS
Scheme and
Credits
No. of Hours/Week Semester – II
L T P S Credits
4 - - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
The course will enable the students to:
1. Understand the Design Approaches and Issues related to Advanced Operating Systems.
2. Apply the Knowledge on Distributed Operating Systems Concepts to develop Clocks,
Mutual Exclusion Algorithms.
3. Analyze the Distributed Deadlock Detection Algorithms and Agreement Protocols.
4. Evaluate the Algorithms for Distributed Scheduling, Examine the Commit Protocols
and review Concurrency Control Algorithms.
5. Create Advanced Operating Systems Applications with recent technologies
UNIT- I INTRODUCTION: 09 Hours
Concept of Batch system, Multiprogramming, Time Sharing, Parallel, Distributed and Real-
time System, Process Management: Concept of Process, Synchronization, CPU Scheduling,
IPC, Deadlock: Concept of Deadlock Prevention, Avoidance, Detection and its Recovery.
Memory Management: Contiguous allocation, Paging and Segmentation. Virtual memory:
Demand Paging, Page Replacement Algorithms. Distributed OS- Design Approaches and
Issues in DOS. Message Passing Model and RPC.
UNIT -II CLOCKS AND DISTRIBUTED MUTUAL EXCLUSION: 10 Hours
Concept of Lamport‘s Logical Clock and Vector Clocks, Termination Detection. A simple
solution to distributed mutual exclusion, Non Token based algorithms: Lamport‘s algorithm,
Ricart Agarwala‘s algorithm, Maekawa‘s algorithm, Token based algorithms: Suzuki Kasami‘s
broadcast algorithm, Raymond‘s tree based algorithm.
UNIT -III DISTRIBUTED DEADLOCK DETECTION: 10 Hours
Deadlock Handling, Strategies in Distributed Systems, Issues in Deadlock Detection And
Resolution, Control Organization for Distributed Deadlock Detection, Centralized Deadlock
Detection Algorithm: Ho-Ramamoorthy‘s Algorithm, Distributed Deadlock Detection
Algorithms: A Path- Pushing Algorithm and Edge Chasing Algorithm, Hierarchical Deadlock
Detection Algorithms: Menasce- Muntz Algorithm, Ho-Ramamoorthy‘s Algorithm.
Agreement Protocols: Byzantine Agreement Problem, Solution to The Byzantine Agreement
Problem- Lamport -Shostak- Pease Algorithm, Dolev et al.‘s Algorithm
UNIT –IV DISTRIBUTED SCHEDULING AND FAULT TOLERANCE: 10 Hours
Issues in Load Distribution, Components of a Load Distributing Algorithms, Load Distributing
Algorithms, Performance Comparison, Selecting Suitable Load Sharing Algorithms,
Requirements of Load Sharing Policies. Commit Protocols, Nonblocking Commit Protocols,
Voting Protocols, Dynamic Voting Protocols, The Majority Based Dynamic Voting Protocol,
Dynamic Vote Reassignment Protocols.
UNIT -V DATABASE OS & CONCURRENCY CONTROL 09 Hours
WT40
Requirement of Database OS, A Concurrency Control Model of a Database System, The
Problem of Concurrency Control, Serializability Theory, Concurrency Control Algorithms,
Basic Synchronization Primitives, Lock Based Algorithms, Timestamp Based Algorithms,
Optimistic Algorithms, Concurrency Control Algorithms for Data Replication.
UNIT-VI Recent advances and research being done in the topics mentioned above units
REFERENCES
1. Mukesh Singhal and Niranjan G Shivaratri, Advanced Concepts in Operating Systems, Tata
Mcgraw Hill, 2002.
2. A Silberchatz and Peter Baer Galvin, Operating System Concepts, 10th Edition, John Wiley
and Sons, 2018.
3. William Stallings, Operating System: Internals and Design Principles, 9th Edition, Prentice
Hall India, 2017.
4. Andrew S Tennenbaum and Albert S Woodhull, Operating System Design and
Implementation, 3rd Edition, Pearson Education Inc., 2006.
5. Ceri S and Pelagorthi S, Distributed Databases: Principles and Systems, 1984, McGraw Hill.
COURSE OUTCOMES
On completion of the course, the student would be able to:
CO1: Explain the Fundamentals of Advanced Operating Systems, Design issues.
CO2: Determine the various Clock Synchronization Principles and Implement Mutual
Exclusion Algorithms.
CO3: Differentiate the various Distributed Deadlock Detection Algorithms and Analyze the
Agreement Protocols.
CO4: Select the appropriate Distributed Scheduling Algorithms, Commit Protocols and
Concurrency Control Algorithms.
CO5: Integrate the Concepts of Advanced Operating Systems with recent trends and
technologies to Design and Develop Applications.
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*2=40
Marks
Total:
Marks 100 Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 1
CO2 1 2
CO3 1 2
CO4 1 3
CO5 3 2 2
1: Low 2: Medium 3:High
WT41
Course Code 18WT2C03 M. Tech (Web Technologies)
Category Professional Core
Course title SEMANTIC WEB
Scheme and
Credits
No. of Hours/Week Semester – II
L T P S Credits
4 0 0 - 4
CIE Marks:
50
SEE Marks: 50 Total Max. Marks:
100
Duration of SEE: 3 Hrs
Prerequisites (if any):
Course Objectives: The course will enable the students to:
1. Understand the concept of Ontology and Semantic web
2. Develop web documents by applying semantic web Technologies.
3. Explain stages of Ontology learning
4. Understand the Ontology development methods.
5. Design Ontologies using Ontology tools and tool suites.
UNIT I INTRODUCTION 9 Hours
Philosophical Background ,Component, Types of Ontology , Ontological Commitments
& Categories, Principles for the Design of Ontologies, , Top Level Ontologies,
Linguistic Ontologies, Domain Ontologies , Semantic Web : web to Semantic web with
examples, semantic web technologies, Layers, Architecture.
UNIT II - SEMANTIC WEB AND ONTOLOGY TECHNOLOGIES 09 Hours
Structured Web Documents in XML, Web Resource Description RDF: Overview, XML
based Syntax, RDF-Schema , web Ontology Languages OWL.
UNIT III - ONTOLOGY LEARNING FOR SEMANTIC WEB 10 Hours
Taxonomy for Ontology Learning, Layered Approach, Phases of Ontology Learning,
Importing and Processing Ontologies and Documents, Ontology Learning Algorithms -
Evaluation
UNIT IV ONTOLOGICAL ENGINEERING 10 Hours
Overview, constructing Ontologies manually, reusing, semiautomatic ontology
acquistion, ontology mapping, on-to-knowledge semantic web architecture, ontological
class, constraints. Ontology development methods and methodologies,
METHONTOLOGY- Ontology cross life cycle and conceptual modeling, Comparing
methods and methodology.
UNIT V –. TOOLS AND TOOL SUITES 10 Hours
Evolution, Development of Tools and Tool Suites , Language dependent Ontology-
Ontolingua server, Language independent Ontology- Protege2000, OntoEdit, Ontology
Merge Tools- Prompt plug-in, Chimaera, Glue, FCA-Merge tool set, Ontology based
Annotation Tools- COHSE, SHOE knowledge annotator.
UNIT VI –
Recent Trends in Semantic Web Programming.
WT42
REFERENCES
1. Asuncion Gomez-Perez, Oscar Corcho, Mariano Fernandez-Lopez “Ontological
Engineering: with examples from the areas of Knowledge Management, eCommerce
and the Semantic Web” Springer, 2004
2. Grigoris Antoniou, Frank van Harmelen, “A Semantic Web Primer (Cooperative
Information Systems)”, The MIT Press, 2004.
3. Alexander Maedche, “Ontology Learning for the Semantic Web”, Springer; 1
edition, 2002
4. John Davies, Dieter Fensel, Frank Van Harmelen, “Towards the Semantic Web:
Ontology – Driven Knowledge Management”, John Wiley & Sons Ltd., 2003.
5. John Davies (Editor), Rudi Studer (Co-Editor), Paul Warren (Co-Editor) “Semantic
Web Technologies: Trends and Research in Ontology-based Systems”Wiley
Publications, Jul 2006
6. Dieter Fensel (Editor), Wolfgang Wahlster, Henry Lieberman, James Hendler,
“Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential”,
The MIT Press, 2002
7. Michael C. Daconta, Leo J. Obrst, Kevin T. Smith, “The Semantic Web: A Guide to
the Future of XML, Web Services, and Knowledge Management”, Wiley, 2003
8. Steffen Staab (Editor), Rudi Studer, “Handbook on Ontologies (International
Handbooks on Information Systems)”, Springer 1st edition, 2004
9. Dean Allemang (Author), James Hendler (Author) “Semantic Web for the Working
Ontologist: Effective Modeling in RDFS and OWL” (Paperback), Morgan
Kaufmann, 2008
COURSE OUTCOMES
On Completion of the course, the student will be able to:
CO1: Summarise the concept of Ontology and Semantic web.
CO2: Design web documents using semantic web tools.
CO3: Summarise the stages of Ontology learning.
CO4: Distinguish Ontology development methods and methodology
C05: Develop Ontologies using tools and tool suites.
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for
50 marks.
WT43
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 3
CO3 2
CO4 3
CO5 3
1. Low, 2. Medium, 3. High
WT44
Course Code 18WT2E1A M. Tech (Web Technologies)
Category Professional Elective
Course title DATA WAREHOUSING AND WEB MINING
Scheme and
Credits
No. of Hours/Week Semester – II
L T P S Credits
4 0 - - 4
CIE Marks:
50
SEE Marks: 50 Total Max. Marks:
100
Duration of SEE: 3 Hrs
Prerequisites (if any):
Course Objectives: The course will enable the students to:
1. Understand the concepts of Data warehousing and Web Mining
2. Model the architecture and infrastructure of Data warehouse.
3. Compare the Information access and Delivery to classes of users in Data
Warehouse
4. Evaluate the Web search and Information retrieval system using the performance
metrics
5. Design and develop the various algorithms of clustering and classification
techniques
UNIT I - INTRODUCTION TO DATA WAREHOUSING 09 Hours
Need for data warehousing, Basic elements of data warehousing, Trends in data
warehousing. Planning and Requirements: Project planning and management, Collecting
the requirements –
UNIT II - ARCHITECTURE AND INFRASTRUCTURE 10 Hours
Architectural components, Infrastructure and metadata. Data Design and Data
Representation: Principles of dimensional modeling, Dimensional modeling advanced
topics, data extraction, transformation and loading, data quality.
UNIT III - INFORMATION ACCESS AND DELIVERY 10 Hours
Matching information to classes of users, OLAP in data warehouse, Data warehousing
and the web. Implementation and Maintenance: Physical design process, data warehouse
deployment, growth and maintenance.
UNIT IV - INTRODUCTION TO WEB MINING 09 Hours
Types of Web Mining, Crawling and Indexing, Hyperlink Analysis, Resource Discovery
and Vertical portals, Structured and unstructured data mining. Crawling the Web: Basics,
Engineering large scale crawlers, Putting together a crawler. Web Search and Information
Retrieval: Boolean Queries and the Inverted Index, Relevance Ranking
UNIT V – SIMILARITY AND CLUSTERING 10 Hours
Similarity Search, Introduction to Clustering, Formulations and Approaches, Bottom-up
and top-down partitioning paradigms, Clustering and visualization, Probabilistic
Approaches to clustering, Collaborative Filtering, Supervised Learning: Scenario,
Overview of Classification, Evaluating text classifiers, Nearest neighbor learners, Feature
selections, Bayesian, Discriminator and Hypertext Classification.
WT45
UNIT VI -
Recent Trends in Data warehousing and Web Mining, Web Search and Information
Retrieval system.
REFERENCES
1. Soumen Chakrabarti, Mining the Web, Morgan Kaufmann Publishers, Reprint 2016
2. Bing Liu, Web Data Mining: Exploring Hyperlinks, Contents and Usage Data,
Springer, Second
Edition, 2011
3. Paulraj Ponniah, ―Data Warehousing Fundamentals‖, John Wiley, 2012
4. Jiawei Han and Micheline Kamber, Data Mining, Concepts and Techniques, Elsevier
Publication, 2nd
Edition, 2011
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1. Define the concepts of data warehousing and web mining
CO2. Choose Architecture and Infrastructure od Data Warehouse.
CO3. Usage of Data warehouse to Information access and delivery
CO4. Assess the Web search and Information retrieval using metrics.
CO5. Develop the algorithms of classification and clustering techniques on web data.
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for
50 marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3
CO2 2 1
CO3 1 2
CO4 3
CO5 3
1. Low, 2. Medium, 3. High
WT46
Course Code 18SE2E1B M. Tech (Web Technologies)
Category Professional Elective - Integrated
Course title USER INTERFACE DESIGN AND EVALUVATION
Scheme and
Credits
No. of Hours/Week Semester – II
L T P S Credits
3 - 2 - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
1. Overview of user-centred design field.
2. Describing requirements.
3. Importance of Evaluation.
COURSE OBJECTIVES
The course will enable the students to:
1. Understand the benefits of user centred approach to the design of software,
computer systems and websites.
2. Developing interaction design from user requirements and user interface design
evaluation.
3. Evaluate the user interface design
4. Analyze an established Human computer interaction topics like visibility,
affordance, feedback, metaphors and mental models
5. Apply the design evaluation for the real world applications.
UNIT-I INTRODUCTION: 09 Hours
Overview of the user-interface design. Designing for users, Knowledge needed for UI
designs.
UNIT -II REQUIRMENTS FOR DESIGN EVALUVATION: 10 Hours
How to gather requirements; Users and the domain; Tasks and work; Thinking about and
describing requirements; Case study on requirements;
UNIT -III DESIGN: 10 Hours
Work reengineering and conceptual design; Design rationale and Principles; Interaction
design; Interaction Styles; Choosing interaction devices; Hardware; Choosing interaction
elements; Software components; Case study on design; Style guides; guidelines and user-
centred design; Designing GUI; Designing for web; Design embedded computer systems
and small devices.
UNIT -IV EVALUATIONS: 10 Hours
Why Evaluation?; deciding on what to evaluate, the strategy; Planning; Analysis and
Interpretation of user-observation evaluation data; Inspections of the user Interface;
Variations and More Comprehensive evaluations; .
UNIT -V PERSUVASION: 09Hours
Communication and using findings; Winning and Maintaining support for user-centred
Design.
UNIT-VI Recent advances and research being done in the topics mentioned above
units
UNIT-VII (Practical)- Lab exercise using a suitable modelling and analysis package
of the topics studied in UNIT-III, UNIT-IV and UNIT-V 24 Hours
WT47
REFERENCES
1. Ben Shneiderman and Catherine Plaisant, ―Designing the User Interface: Strategies
for Effective Human-Computer Interaction‖, 5th
Edition, 2014, Pearson
Publications, ISBN:0321537351.
2. Debbie Stone, Caroline Jarrett, Mark woodroffe, Shailey Minocha, ―User Interface
Design and Evaluation‖,1st Edition Elsevier, 2005.
3. Wilbert O Galitz, ――The essential guide to user interface design‖, Wiley, 3rd
Ed,
2007, ISBN:978-0-471-27139-0.
4. Prece, Rogers and Sharps, ―Interaction Design‖, 3rd
Edition, 2011, Wiley,
ISBN:978-1-119-02075-2.
5. Alan Dix, Janet Fincay, GRe Goryd, Abowd, Russel Bealg, ―Human-Computer
Interactio‖, Pearson 3rd Edition, 2004, ISBN 0-13-046109-1.
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1. Identify the benefits of user centred approach to the design field.
CO2: List out the requirements for design evaluvation.
CO3: illustrate the need of user interface design
CO4: Evaluate the importance of evaluvation and user interface design
CO5: Design Case Study on user interface Design.
Scheme of Examination
CIE -
Practical
Conduction of experiments, performance of student in
every week lab and completion of lab record=50#
Total: Marks
50
Lab Test: Part-A =20, Part-B=20 and Vice-Voce=10
Marks(50##
)
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
# = CIE Marks for laboratory performance is to be evaluated for 50 marks and the
marks obtained shall be reduced for 25 marks
## = Lab test is to be conducted for 50 marks and the marks obtained shall be
reduced for 25 marks. Lab test shall be conducted by two examiners out of which
one examiner is the faculty taught the course. There is no SEE for the practical ‘s
portion of integrated course
WT48
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3
CO2 3
CO3 2
CO4 3
CO5 3
1: Low 2: Medium 3:High
WT49
Course Code 18WT2E1C M. Tech (Web Technologies)
Category Professional Elective
Course title TRUST MANAGEMENT IN E-COMMERCE
Scheme and
Credits
No. of Hours/Week Semester – II
L T P S Credits
4 0 - - 4
CIE Marks:
50
SEE Marks: 50 Total Max. Marks:
100
Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
The course will enable the students to:
1. Understand fundamental principles of E-Commerce
2. Illustrate technologies & tools for E-Commerce with emphasis on Security
3. Identify best techniques & practices for different types of legacy & partner
requirements
4. Handle & address risk management
5. Evaluate trusted platform.
UNIT I: 10 Hours
Introduction to E-Commerce: Network and E-Commerce, Types of E-Commerce.
Ecommerce Business Models: B2C, B2B, C2C, P2P and M-commerce business models.
Ecommerce Payment systems: Types of payment system, Credit card E-Commerce
transactions, B2C E-Commerce Digital payment systems, B2B payment system.
UNIT II: 09 Hours
Security and Encryption: E-Commerce Security Environment, Security threats in
Ecommerce environment, Policies, Procedures and Laws.
UNIT III - 10 Hours Inter-organizational trust in E-Commerce: Need, Trading partner trust, Perceived
benefits and risks of E-Commerce, Technology trust mechanism in E-Commerce,
Perspectives of organizational, economic and political theories of inter-organizational
trust, Conceptual model of inter-organizational trust in E-Commerce participation.
UNIT IV - 10 Hours
Introduction to trusted computing platform: Overview, Usage Scenarios, Key
components of trusted platform, Trust mechanisms in a trusted platform.
UNIT V - 09 Hours
Trusted platforms for organizations and individuals: Trust models and the E-Commerce
domain.
UNIT VI -
Recent trends in Trust, Trust management, Business to Business relations.
WT50
REFERENCES
1. Kenneth C. Laudon and Carol Guercio Trave, Study Guide to E-Commerce Business
Technology Society, Pearson Education, 2005.
2. Pauline Ratnasingam, Inter-Organizational Trust for Business-to-Business E-
Commerce,IRM Press, 2005.
3. Siani Pearson, et al, Trusted Computing Platforms: TCPA Technology in Context,
Prentice Hall PTR, 2002.
COURSE OUTCOMES
On completion of the course, the students should be able to:
CO1:Explain the types of E-Commerce, E-Commerce business models and E-commerce
payment systems.
CO2:Illustrate the Policies, Procedures and Laws and Security threats in E-Commerce
environment.
CO3:Analysis issues, risks and challenges in inter-organisational trust in ECommerce
CO4:Explain the Key components and Trust mechanisms of trusted computing platform.
C05:Describe the trusted platform for organizations and individuals.
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for
50 marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 1 2
CO2 1 3
CO3 3 1 3
CO4 2 1 3
CO5 2 1 3
1. Low, 2. Medium, 3. High
WT51
Course Code 18SE2E2A M. Tech (Web Technologies)
Category Professional Elective
Course title SOFTWARE AGENTS
Scheme and
Credits
No. of Hours/Week Semester – II
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
The course will enable the students to:
1. Have an overview of the agent systems and software agents.
2. Understand the basic concepts of intelligent software agents.
3. Explore the use of software agents
4. Analyse and share information to coordinate activities of the agents for the
purpose of group problem solving.
5. Design recurred systems using agents
UNIT - I INTRODUCTION TO AGENTS: 9 hours
Introduction to software agent, Applivations, uses and classification of software agent;
Agent Programming Paradigms, Agent Vs Object, Aglet, Mobile Agents, Agent
Frameworks, Agent Reasoning.
UNIT - II JAVA AGENTS: 9 hours
Processes, Threads, Daemons, Components, Java Beans, ActiveX, Sockets, RPCs,
Distributed Computing, Aglets Programming, Jini Architecture, Actors and Agents, Typed
and proactive messages.
UNIT – III MULTIAGENT SYSTEMS: 10 hours
Interaction between agents, Reactive Agents, Cognitive Agents, Interaction protocols,
Agent oordination, Agent negotiation, Agent Cooperation, Agent Organization, Self-
Interested agents in Electronic Commerce Applications.
UNIT- IV INTELLIGENT SOFTWARE AGENTS: 10 hours
Interface Agents, Agent Communication Languages, Agent Knowledge Representation,
Agent Adaptability, Belief Desire Intension, Mobile Agent Applications.
UNIT- V AGENTS AND SECURITY: 10 hours
Agent Security Issues, Mobile Agents Security, Protecting Agents against Malicious Hosts,
Untrusted Agent, Black Box Security, Authentication for agents, Security issues for
Aglets.
UNIT- VI Recent advances and research being done in the topics mentioned above
units
WT52
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1: Interpret the basics of agents
CO2: Create / develop an agent based system for a particular task.
CO3: Design an application that uses different security issues for intelligent agents.
CO4: Effectively apply agent-based technologies in distributed systems
CO5:Validate the application of distributed information systems that use software agents.
REFERENCES
1. Bradshaw, " Software Agents ", MIT Press, 2010
2. Russel, Norvig, "Artificial Intelligence: A Modern Approach", Second Edition, Pearson
Education, 2003
3. Richard Murch, Tony Johnson, "Intelligent Software Agents", Prentice Hall, 2000
4. Gerhard Weiss, Multi Agent Systems, A Modern Approach to Distributed Artificial
Intelligence, MIT Press, 2000.
5. Bigus&Bigus, " Constructing Intelligent agents with Java ", Wiley, 1997
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3
CO2 1 2
CO3 2
CO4 1 2 2
CO5 2
1: Low 2: Medium 3:High
WT53
Course Code 18SE2E2B M. Tech (Web Technologies)
Category Professional Elective
Course title SOFTWARE SECURITY
Scheme and
Credits
No. of Hours/Week Semester – II
L T P S Credits
4 0 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
The course will enable the students to
1. Understand the basics of secure programming.
2. Describe most frequent programming errors leading to software vulnerabilities.
3. Analyze security problems in software.
4. Evaluate security threats and software vulnerabilities.
5. Effectively design secure software system.
UNIT -I INTRODUCTION TO SECURITY: 9 Hours
Introduction to Security: Need for security, Security approaches, Principles of security,
Types of attacks. Encryption Techniques: Plaintext, Cipher text, Substitution &
Transposition techniques, Encryption & Decryption, Types of attacks, Key range & Size.
Symmetric & Asymmetric Key Cryptography: DES,RSA.
UNIT -II INTRODUCTION TO SOFTWARE SECURITY: 10 Hours Managing software security risk, Selecting software development technologies, An open
source and closed source, Guiding principles for software security, Auditing software,
Buffet overflows, Access control, Race conditions, Input validation, Password
authentication
UNIT-III SECURE RISK MANAGEMENT: 9 Hours Anti-tampering, Protecting against denial of service attack, Copy protection schemes,
Client-side security, Database security, Applied cryptography, Randomness and
determinism
UNIT- IV SECURITY TESTING: 10 Hours Buffer Overrun, Format String Problems, Integer Overflow, and Software Security
Fundamentals SQL Injection, Command Injection, Failure to Handle Errors, and Security
Touchpoints
UNIT- V ADVANCED SOFTWARE SECURITY 10 Hours Cross Site Scripting, Magic URLs, Weak Passwords, Failing to Protect Data, Weak
random numbers, improper use of cryptography Information Leakage, Race Conditions,
Poor usability, Failing to protect network traffic, improper use of PKI, trusting networ
k name resolution
UNIT- VI Recent advances and research being done in the topics mentioned above
units
REFERENCES 1. J. Viega, G. McGraw. Building Secure Software, Addison Wesley -2011
2. Theodor Richardson, Charles N Thies, Secure Software Design, Jones & Bartlett-
2012
3. Kenneth R. van Wyk, Mark G. Graff, Dan S. Peters, Diana L. Burley, Enterprise
Software Security, Addison Wesley. -2010
WT54
COURSE OUTCOMES At the end the student will be able to
CO1: Identify various risk in the softwares.
CO2: illustrate security problems in the open source software.
CO3: Relate security and software engineering.
CO4: Assess real-time software and its vulnerabilities
CO5: Investigate security flaws in software
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3
CO2 2 2
CO3 2
CO4 3
CO5 3
1: Low 2: Medium 3:High
WT55
Course Code 18CS2E2C M. Tech (Web Technologies)
Category Professional Elective
Course title WEB SECURITY
Scheme and
Credits
No. of Hours/Week Semester – II
L T P S Credits
4 0 - - 4
CIE Marks:
50
SEE Marks: 50 Total Max. Marks:
100
Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
The course will enable the students to:
1. Understand web application‘s vulnerability and malicious attacks.
2. Understand basic web technologies used for web application development.
3. Analyse basic concepts of Mapping the application
4. Illustrate different attacking illustrations.
5. Emphasis various basic concepts of Attacking Data Stores.
UNIT I: WEB APPLICATION SECURITY 09 Hours
The Evolution of Web Applications, Common Web Application Functions, Benefits of
Web Applications, Web Application Security.
Core Defense Mechanisms: Handling User Access Authentication, Session
Management, Access Control, Handling User Input, Varieties of Input Approaches to
Input Handling, Boundary Validation.
Multistep Validation and Canonicalization: Handling Attackers, Handling Errors,
Maintaining Audit Logs, Alerting Administrators, Reacting to Attacks.
UNIT II: WEB APPLICATION TECHNOLOGIES 09 Hours
The HTTP Protocol, HTTP Requests, HTTP Responses, HTTP Methods, URLs, REST,
HTTP Headers, Cookies, Status Codes, HTTPS, HTTP Proxies, HTTP Authentication,
Web Functionality, Server-Side Functionality, Client-Side Functionality, State and
Sessions, Encoding Schemes, URL Encoding, Unicode Encoding, HTML Encoding,
Base64 Encoding, Hex Encoding, Remoting and Serialization Frameworks.
UNIT III: MAPPING THE APPLICATION 10 Hours
Enumerating Content and Functionality, Web Spidering, User-Directed Spidering,
Discovering Hidden Content, Application Pages Versus Functional Paths, Discovering
Hidden Parameters, Analyzing the Application, Identifying Entry Points for User Input,
Identifying Server-Side Technologies, Identifying Server-Side Functionality, Mapping
the Attack Surface.
UNIT IV: ATTACKING AUTHENTICATION 10 Hours
Authentication Technologies, Design Flaws in Authentication Mechanisms, Bad
Passwords, Brute-Forcible Login, Verbose Failure Messages, Vulnerable Transmission of
Credentials, Password Change, Functionality, Forgotten Password Functionality, User
Impersonation, Functionality Incomplete, Validation of Credentials, Nonunique
Usernames, Predictable Usernames, Predictable Initial Passwords, Insecure Distribution
of Credentials. Attacking Access Controls.
WT56
UNIT V - ATTACKING DATA STORES 10 Hours
Injecting into Interpreted Contexts, Bypassing a Login, Injecting into SQL, Exploiting a
Basic Vulnerability Injecting into Different Statement Types, Finding SQL Injection
Bugs, Fingerprinting the Database, The UNION Operator, Extracting Useful Data,
Extracting Data with UNION, Bypassing Filters, Second-Order SQL Injection, Advanced
Exploitation Beyond SQL Injection: Escalating the Database Attack, Using SQL
Exploitation Tools, SQL Syntax and Error Reference, Preventing SQL Injection.
UNIT VI
Recent trends in Web Applications and its Security
REFERENCES
1. Defydd Stuttard, Marcus Pinto , The Web Application Hacker's Handbook: Finding
And Exploiting Security, Wiley Publishing, Second Edition.
2.Andres Andreu, Professional Pen Testing for Web application, Wrox Press.
3. Carlos Serrao, Vicente Aguilera, Fabio Cerullo, ―Web Application Security‖ Springer;
1st Edition
4. Joel Scambray, Vincent Liu, Caleb Sima ,―Hacking exposed‖, McGraw-Hill; 3rd
Edition, (October, 2010).
5. OReilly Web Security Privacy and Commerce 2nd Edition 2011.
6. Software Security Theory Programming and Practice, Richard sinn, Cengage Learning.
COURSE OUTCOMES
On completion of the course, the student would be able to:
CO1:Achieve Knowledge of web application‘s vulnerability and malicious attacks.
CO2:Understand the basic web technologies used for web application development
CO3:Understands the basic concepts of Mapping the application.
CO4:Able to illustrate different attacking illustrations
C05:Investigate technique of attacking Data Stores
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks Total:100
marks
Units which have 10 Hours shall have
internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for
50 marks.
WT57
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 1 3
CO2 2 1 3
CO3 1 3
CO4 3 1 3
CO5 1 3
1. Low, 2. Medium, 3. High
WT58
Course Code 18CS2L01 M. Tech (Web Technologies)
Category Laboratory
Course title ADVANCED DATA STRUCTURES AND ALGORITHMS
LAB
Scheme and
Credits
No. of Hours/Week Semester – II
L T P S Credits
0 0 4 0 4
CIE Marks:
50
SEE Marks: 50 Total Max. Marks:
100
Duration of SEE: 3 Hrs
Prerequisites (if any):
1. Data structures and Algorithm
2. Java Programming
Course Objectives: The course will enable the students to:
1. Acquire the knowledge of using advanced data structures
2. Acquire the knowledge of sorting and balancing the tree structure
3. Understand the usage of graph structures and string matching.
4. Understand the implementation of various string matching algorithms.
5. learn to solve the various NP complete problems
Each student has to work individually on assigned lab exercises. Lab sessions could be
scheduled as one contiguous four-hour session per week. It is recommended that all
implementations are carried out in Java. Exercises should be designed to cover the
following topics:
1. Doubly Circular Linked List
2. AVL Tree
3. Efficiency of Heap Sort & Quick Sort
4. Travelling Salesman Problem (Dynamic Programming)
5. N Queens Problem (Backtracking/ Branch & Bound)
6. Bellman-Ford algorithm
7. Shortest paths in a DAG
8. Ford-Fulkerson algorithm
9. Robin-Karp algorithm
10. Knuth-Morris-Pratt algorithms
11. String matching with Finite Automata
12. Vertex Cover problem
13. The Set Covering problem
14. The Subset-Sum problem
15. Maximum Bipartite algorithm
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1: Design and implement basic and advanced data structures extensively.
CO2: Design and apply graph structures for various applications.
CO3: Design and develop efficient algorithms with minimum complexity using design
techniques.
CO4: Design and develop advanced string matching and NP Complete problems.
WT59
CO5: Achieve proficiency in Java programming.
Continuous Internal
Evaluation (CIE) (Lab – 50
Marks)
Marks Semester End Evaluation (SEE)
(Lab – 100 Marks) Marks
Performance of the Student in
the Lab every week
20 Write up 10
Test at the end of the semester 20 Experiment 70
Viva Voce 10 Viva Voce 20
Total 100
Total (CIE) 50 Total (SEE) 50*
Note. * = SEE shall be conducted for 100 marks for practical and the marks obtained shall be
reduced for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2
CO2 2
CO3 2
CO4 2
CO5 2
1. Low, 2. Medium, 3. High
WT60
Course Code 18CS2M01 M. Tech (Web Technologies)
Category Audit Course-2
Course title PEDAGOGY STUDIES
Scheme and Credits No. of Hours/Week Semester – II
L T P SS Credits
2 0 - - 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any):
COURSE OBJECTIVES
SThis course will enable students to
1. Understand the Thematic Overview and Pedagogical practices
2. Apply professional classroom practices , curriculum and assessment
3. Analyse methodology for quality assessment of school curriculum teacher
4. Evaluate pedagogic theory and pedagogical approaches
5. Create contexts pedagogy, new curriculum and assessment metrics for future
UNIT- I INTRODUCTION AND METHODOLOGY: 6 Hours
Aims and rationale, Policy background, Conceptual framework and terminology Theories of
learning, Curriculum, Teacher education. Conceptual framework, Research questions. Overview of
methodology and Searching.
UNIT- II THEMATIC OVERVIEW: 3 Hours
Pedagogical practices are being used by teachers in formal and informal classrooms in developing
countries. Curriculum, Teacher education
UNIT- III PEDAGOGICAL PRACTICES: 6 Hours
Evidence on the effectiveness of pedagogical practices Methodology for the in depth stage: quality
assessment of included studies. How can teacher education (curriculum and practicum) and the
school curriculum and guidance materials best support effective pedagogy? Theory of change.
Strength and nature of the body of evidence for effective pedagogical practices. Pedagogic theory
and pedagogical approaches. Teachers‘ attitudes and beliefs and Pedagogic strategies.
UNIT- IV PROFESSIONAL DEVELOPMENT: 6 Hours
Professional development: alignment with classroom practices and follow-up support Peer support
Support from the head teacher and the community. Curriculum and assessment Barriers to learning:
limited resources and large class sizes
UNIT- V RESEARCH GAPS AND FUTURE DIRECTIONS: 3 Hours
Research design Contexts Pedagogy Teacher education Curriculum and assessment Dissemination
and research impact.
UNIT- VI NEW DEVELOPMENTS IN PEDAGOGY:
REFERENCES
1. Ackers J, Hardman F (2001) Classroom interaction in Kenyan primary schools, Compare, 31
(2): 245-261.
2. Agrawal M (2004) Curricular reform in schools: The importance of evaluation, Journal of
Curriculum Studies, 36 (3): 361-379.
3. Akyeampong K (2003) Teacher training in Ghana - does it count? Multi-site teacher
education research project (MUSTER) country report 1. London: DFID.
WT61
4. Akyeampong K, Lussier K, Pryor J, Westbrook J (2013) Improving teaching and learning of
basic maths and reading in Africa: Does teacher preparation count? International Journal
Educational Development, 33 (3): 272–282.
5. Alexander RJ (2001) Culture and pedagogy: International comparisons in primary education.
Oxford and Boston: Blackwell.
6. Chavan M (2003) Read India: A mass scale, rapid, ‗learning to read‘ campaign
7. www.pratham.org/images/resource%20working%20paper%202.pdf.
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1: What pedagogical practices are being used by teachers in formal and informal
classrooms in developing countries?
CO2: What is the evidence on the effectiveness of these pedagogical practices, in what
conditions, and with what population of learners?
CO3: How can teacher education (curriculum and practicum) and the school curriculum and
guidance materials best support effective pedagogy
CO4: Assess pedagogic theory and pedagogical approaches
CO5: Design new curriculum and assessment metrics for future
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3
CO2 3
CO3 3
CO4 3
CO5 3
1: Low 2: Medium 3:High
WT62
COURSE LEARNING OBJECTIVES:
The objectives of the SEMINAR-II is to prepare the students to learn to:
1. Carry out the literature review of general research area/current topic and analyse
the same effectively.
2. Prepare a technical report, reflecting his/her depth of understanding, on the selected
area/topic and prepare content rich presentation.
3. Acquire communication and time management skills for effective and impactful
presentation. Interact with peers to acquire the qualities of thoughtfulness,
friendliness, adaptability, responsiveness, and politeness in-group discussion.
Overcome stage fear during the presentation.
GUIDE LINES
1. Seminar preparation and presentation is an individual student activity.
2. Topic may be of general/ specific interest to program of engineering or electives not
offered in the semester and to be selected in consultation with the faculty/Guide.
3. Select one pertinent research paper for the seminar presentation.
4. Prepare and submit a detailed technical report of the seminar topic.
COURSE OUTCOMES:
Students shall be able to:
Course Code 18WT2S01 M. Tech (Web Technologies)
Category Seminar Semester: II
Course title SEMINAR - II
Scheme and Credits
No. of Hours/Week
Total hours = 24 L T P S Credits
0 0 2 0 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any): NIL
WT63
1. Carry out the literature survey of topic of seminar.
2. Prepare a technical report on the selected area/topic.
3. Make an effective presentation with seamless flow of content within the time
allocated. Overcome inhibition in interacting with peers and hence develop the spirit
of team work. Overcome stage fear during the presentation.
SCHEME OF EXAMINATION
CIE – 50
marks
Phase -1 Marks =10 Seminar Report
Marks =20
Total:50
Marks Phase -2 Marks =20
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2 3
CO2 2 3 3
CO3 2
1. Low, 2. Medium, 3. High
Scheme of Continuous Internal Evaluation (CIE):
Evaluation would be carried out in TWO phases. The Seminar Evaluation Committee
shall comprise of Chairman of the Department, Faculty/Guide and one more faculty member
nominated by Chairman. The evaluation criteria shall be as per the rubrics given below:
Rubrics for Evaluation:
Topic - Technical Relevance, Sustainability and Societal Concerns : 15%
Review of literature and technical content : 35%
Presentation Skills : 25%
Report : 25%
SEMESTER III
WT64
Course Code 18WT3E1A M. Tech (Web Technologies)
Category Professional Elective
Course title SOCIAL NETWORK
Scheme and
Credits
No. of Hours/Week Semester – III
L T P S Credits
4 - - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
This course will enable students to
1. Understand the concept of semantic web and related applications.
2. Construct social network using various representation
3. Understand social web and related communities
4. Build sentiment analysis of social
UNIT-I INTRODUCTION: 9 Hours
Introduction to Web - Limitations of current Web – Development of Semantic Web –
Emergence of the Social Web, Evolution in Social Networks , Statistical Properties of
Social Networks -Network analysis - Development of Social Network Analysis - Key
concepts and measures in network analysis - Discussion networks - Blogs and online
communities - Web-based networks
UNIT- II MODELING AND VISUALIZATION: 10 Hours
Visualizing Online Social Networks - A Taxonomy of Visualizations - Graph
Representation -Centrality- Clustering - Node-Edge Diagrams - Visualizing Social
Networks with Matrix Based Representations- Node-Link Diagrams - Hybrid
Representations - Modelling and aggregating social network data – Random Walks and
their Applications - Ontological representation of social individuals and relationships
UNIT- III SOCIAL NETWORK ANALYSIS TECHNIQUES: 10 Hours
Framework - Tracing Smoothly Evolving Communities - Models and Algorithms for
Social Influence Analysis - Influence Related Statistics - Social Similarity and Influence -
Influence Maximization in Viral Marketing - Algorithms and Systems for Expert Location
in Social Networks - Expert Location without Graph Constraints - with Score Propagation
– Expert Team Formation - Link Prediction in Social Networks -Feature based Link
Prediction - Bayesian Probabilistic Models - Probabilistic Relational Models
UNIT -IV MINING COMMUNITIES: 9 Hours
Aggregating and reasoning with social network data, Advanced Representations -
Extracting evolution of Web Community from a Series of Web Archive - Detecting
Communities in Social Networks - Evaluating Communities – Core Methods for
Community Detection & Mining - Applications of Community Mining Algorithms - Node
Classification in Social Networks.
UNIT- V TEXT AND OPINION MINING: 10 Hours
Text Mining in Social Networks -Opinion extraction – Sentiment classification and
clustering - Temporal sentiment analysis - Irony detection in opinion mining - Wish
WT65
analysis - Product review mining – Review Classification – Tracking sentiments towards
topics over time
UNIT-VI Recent advances and research being done in the topics mentioned above
units
REFERENCES
1. Charu C. Aggarwal, ―Social Network Data Analytics‖, Springer; 2011
2. Peter Mika, ―Social Networks and the Semantic Web‖, Springer, 1st edition, 2007.
3. Borko Furht, ―Handbook of Social Network Technologies and Applications‖,
Springer, 1st edition, 2010.
4. Guandong Xu , Yanchun Zhang and Lin Li, ―Web Mining and Social Networking –
Techniques and applications‖, Springer, 1st edition, 2011.
5. Giles, Mark Smith, John Yen, ―Advances in Social Network Mining and Analysis‖,
Springer, 2010.
6. Ajith Abraham, Aboul Ella Hassanien, Václav Snášel, ―Computational Social
Network Analysis: Trends, Tools and Research Advances‖, Springer, 2009.
7. Toby Segaran, ―Programming Collective Intelligence‖, O‘Reilly, 2012
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1 Develop semantic web related applications.
CO2: Represent knowledge using ontology
CO3: Analysis of models in social network.
CO4: Predict social web and related communities.
CO5: Visualize and sentiment analysis of social networks
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 3
CO2 2
CO3 1 3
CO4 1 3
CO5 1 1 3
1: Low 2: Medium 3:High
WT66
Course Code 18CS3E1B M. Tech (Web Technologies)
Category Professional Elective - Integrated
Course title BIG DATA ANALYTICS
Scheme and
Credits
No. of Hours/Week Semester – III
L T P S Credits
3 - 2 - 4
CIE Marks:
50
SEE Marks: 50 Total Max. Marks:
100
Duration of SEE: 3 Hrs
Prerequisites (if any):
Data Structures, Computer Architecture and Organization
Course Objectives: The course will enable the students to:
1. Understand big data for business intelligence.
2. Illustrate business case studies for big data analytics.
3. Discuss NoSQL big data management.
4. Demonstrate map-reduce analytics using Hadoop.
5. Compare Hadoop related tools such as HBase, Pig, Cassandra and Hive for big data
analytics.
UNIT I – INTRODUCTION TO BIG DATA 9 Hours Need for big data, convergence of key trends, unstructured data, industry examples of big
data, web analytics, big data and marketing, fraud and big data, risk and big data, credit risk
management, big data and algorithmic trading, big data and healthcare, big data in medicine,
advertising and big data, big data technologies, introduction to Hadoop, open source
technologies, cloud and big data, mobile business intelligence, Crowd sourcing analytics,
inter and trans firewall analytics.
UNIT II - INTRODUCTION TO NoSQL 10 Hours Aggregate data models, aggregates, key-value and document data models, relationships,
graph databases, schemaless databases, materialized views, distribution models, sharding,
master-slave replication, peer peer replication, sharding and replication, consistency,
relaxing consistency, version stamps, map-reduce, partitioning and combining, composing
map-reduce calculations.
UNIT III – HADOOP 10 Hours
Data format, analyzing data with Hadoop, scaling out, Hadoop streaming, Hadoop pipes,
design of Hadoop distributed file system (HDFS), HDFS concepts, Java interface, data flow,
Hadoop I/O, data integrity, compression, serialization, Avro, file-based data structures
UNIT IV – MAPREDUCE 10 Hours MapReduce workflows, unit tests with MRUnit, test data and local tests, anatomy of
MapReduce job run, classic Map-reduce, YARN, failures in classic Map-reduce and YARN,
job scheduling, shuffle and sort, task execution, MapReduce types, input formats, output
formats.
UNIT V – Hbase 9 Hours
Hbase, data model and implementations, Hbase clients, Hbase examples, praxis. Cassandra,
Cassandra data model, Cassandra examples, Cassandra clients, Hadoop integration, Pig,
Grunt, pig data model, Pig Latin, developing and testing Pig Latin scripts. Hive, data types
and file formats, HiveQL data definition, HiveQL data manipulation, HiveQL queries.
UNIT VI -
Recent advances in Big data analytics
WT67
UNIT - VII (Lab Programs)
1. (a) Perform setting up and Installing Hadoop in its two operating modes:
o Pseudo distributed,
o Fully distributed.
(b) Use web based tools to monitor your Hadoop setup.
2. (a) Implement the following file management tasks in Hadoop:
o Adding files and directories
o Retrieving files
o Deleting files
(b) Benchmark and stress test an Apache Hadoop cluster
3. Run a basic Word Count Map Reduce program to understand Map Reduce Paradigm.
(a) Find the number of occurrence of each word appearing in the input file(s)
(b) Performing a MapReduce Job for word search count (look for specific keywords in a
file)
4. Stop word elimination problem:
Input:
o A large textual file containing one sentence per line
o A small file containing a set of stop words (One stop word per line)
Output:
o A textual file containing the same sentences of the large input file without the
words appearing in the small file.
5. Write a Map Reduce program that mines weather data. Weather sensors collecting data
every hour at many locations across the globe gather large volume of log data, which is a
good candidate for analysis with MapReduce, since it is semi structured and record-oriented.
Data available at: https://github.com/tomwhite/hadoopbook/tree/master/input/ncdc/all.
(a) Find average, max and min temperature for each year in NCDC data set?
(b) Filter the readings of a set based on value of the measurement, Output the line of
input files associated with a temperature value greater than 30.0 and store it in a
separate file.
6. Purchases.txt Dataset
(a) Instead of breaking the sales down by store, give us a sales breakdown by
product category across all of our stores
(b) What is the value of total sales for the following categories?
Toys
Consumer Electronics
(c) Find the monetary value for the highest individual sale for each separate store
(d) What are the values for the following stores?
Reno
Toledo
Chandler
(e) Find the total sales value across all the stores, and the total number of sales.
7. Install and Run Pig then write Pig Latin scripts to sort, group, join, project, and filter your
data.
8. Write a Pig Latin scripts for finding TF-IDF value for book dataset (A corpus of eBooks
available at: Project Gutenberg)
9. Install and Run Hive then use Hive to create, alter, and drop databases, tables, views,
functions, and indexes.
10. Install, Deploy & configure Apache Spark Cluster. Run apache spark applications using
Scala.
WT68
REFERENCES
1. Michael Minelli, Michelle Chambers, and Ambiga Dhiraj, "Big Data, Big Analytics:
Emerging Business Intelligence and Analytic Trends for Today's Businesses", Wiley,
2013.
2. P. J. Sadalage and M. Fowler, "NoSQL Distilled: A Brief Guide to the Emerging
World of Polyglot Persistence", Addison-Wesley Professional, 2012.
3. Tom White, "Hadoop: The Definitive Guide", Third Edition, O'Reilley, 2012.
4. Eric Sammer, "Hadoop Operations", O'Reilley, 2012.
5. E. Capriolo, D. Wampler, and J. Rutherglen, "Programming Hive", O'Reilley, 2012.
6. Lars George, "HBase: The Definitive Guide", O'Reilley, 2011.
7. Eben Hewitt, "Cassandra: The Definitive Guide", O'Reilley, 2010.
8. Alan Gates, "Programming Pig", O'Reilley, 2011.
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1. Describe big data and use cases from selected business domains.
CO2. Discuss the business case studies for big data analytics.
CO3. Explain NoSQL big data management.
CO4. Perform map-reduce analytics using Hadoop.
CO5. Use Hadoop related tools such as HBase, Cassandra, Pig, and Hive for big data
analytics.
Scheme of Examination:
CIE -
Practical
Conduction of experiments, performance of student in
every week lab and completion of lab record=50#
Total: Marks
50
Lab Test: Part-A =20, Part-B=20 and Vice-Voce=10
Marks(50##
)
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
# = CIE Marks for laboratory performance is to be evaluated for 50 marks and the
marks obtained shall be reduced for 25 marks
## = Lab test is to be conducted for 50 marks and the marks obtained shall be
reduced for 25 marks. Lab test shall be conducted by two examiners out of which
one examiner is the faculty taught the course. There is no SEE for the practical ‘s
portion of integrated course
WT69
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 1
CO2 2
CO3 3 2
CO4 1 2
CO5 3
1. Low, 2. Medium, 3. High
WT70
Course Code 18IT3E1C M. Tech (Web Technologies)
Category Professional Elective
Course title INFORMATION RETRIEVAL SYSTEMS
Scheme and
Credits
No. of Hours/Week Semester – III
L T P S Credits
4 0 - - 4
CIE Marks:
50
SEE Marks: 50 Total Max. Marks:
100
Duration of SEE: 3 Hrs
Prerequisites (if any):
Course Objectives
This course will enable students to
1. Understand the taxonomy and models of Information retrieval system.
2. Discuss the retrieval evaluation methods.
3. Acquire learning techniques for text classification and clustering.
4. Design the search engine
5. Experiment web content structure searching in search engine.
UNIT I-INTRODUCTION 10 Hours Motivation, Basic concepts, Past, present, and future, The retrieval process. Modelling:
Introduction, A taxonomy of information retrieval models, Retrieval: Adhoc and filtering, A
formal characterization of IR models, Classic information retrieval, Alternative set theoretic
models, Alternative algebraic models, Alternative probabilistic models, Structured text
retrieval models, Models for browsing.
UNIT II- RETRIEVAL EVALUATION 10 Hours Introduction, Retrieval performance evaluation, Reference collections. Query
Languages: Introduction, keyword-based querying, Pattern matching, Structural
queries, Query protocols. Query Operations: Introduction, User relevance feedback,
Automatic local analysis, Automatic global analysis.
UNIT III - TEXT AND MULTIMEDIA LANGUAGES AND PROPERTIES 09 Hours
Introduction, Metadata, Text, Markup languages, Multimedia. Text Operations:
Introduction, Document pre-processing, Document clustering, Text compression,
Comparing text compression techniques
UNIT IV – USER INTERFACES AND VISUALIZATION 10 Hours Introduction, Human-Computer interaction, The information access process, Starting
pints, Query specification, Context, Using relevance judgments, Interface support for
the search process. Searching the Web: Introduction, Challenges, Characterizing the
web, Search engines, Browsing, Meta searchers, Finding the needle in the haystack,
Searching using hyperlinks.
UNIT V - Indexing and Searching 09 Hours Introduction; Inverted Files; Other indices for text;Boolean queries; Sequential searching;
Pattern matching; Structural queries;Compression. Parallel and Distributed IR: Introduction,
Parallel IR, Distributed IR.
UNIT VI -–
Recent trends in information retrieval systems
WT71
REFERENCES
1. Ricardo Baeza-Yates, Berthier Ribeiro-Neto: Modern Information Retrieval, Pearson,
1999.
2. David A. Grossman, Ophir Frieder: Information Retrieval Algorithms and Heuristics, 2nd
Edition, Springer, 2004
COURSE OUTCOMES
Upon completion of this course, the students should be able to:
CO1: Summarize taxonomy and models of information retrieval system.
CO2: Design the various components of an information retrieval system
CO3: Design text classification and clustering applying machine learning technique.
CO4: Demonstrate the functions of search engine.
CO5: Analyse web content structure for efficient information retrieval.
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 15 marks Quiz / AAT = 5
marks
Unit-VI = 15 marks
Total:50
marks Test II (Unit IV & V) – 15 marks
SEE
– 100
marks
Answer FIVE full questions
Units which have 09 Hours shall not
have internal choice. 20* 2 = 40 Marks Total:100
marks
(c) Units which have 10 Hours shall
have internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
Mapping of Course Outcomes (COS) to Program Outcomes
PO1 PO2 PO3
CO1 2
CO2 3
CO3 2
CO4 3
CO5 2 3
1. Low, 2. Medium, 3. High
WT72
Course Code 18CS3P1A M. Tech (Web Technologies)
Category Open Elective
Course title ARITIFICIAL INTELLIGENCE
Scheme and
Credits
No. of Hours/Week Semester – III
L T P S Credits
4 - - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
This course will enable students to
1. Understand the various characteristics of Intelligent agents
2. Understand the different search strategies in AI
3. Learn to represent knowledge in solving AI problems
4. Analyse the different ways of designing software agents
5. Evaluate the various reasoning techniques for AI.
UNIT-I INTRODUCTION: 9 Hours
Introduction Definition Future of Artificial Intelligence Characteristics and Problem Solving
Approach to Typical AI problems. State Space Search and Heuristic Search Techniques
Defining problems as State Space search, Production systems and characteristics, Hill
Climbing, Breadth first and depth first search, Best first search.
UNIT-II KNOWLEDGE REPRESENTATION ISSUES: 9 Hours
Representations and Mappings, Approaches to knowledge representation, Using Predicate
Logic and Representing Knowledge as Rules , Representing simple facts in logic,
Computable functions and predicates, Procedural vs Declarative knowledge, Logic
Programming, Forward vs backward reasoning.
UNIT-III SOFTWARE AGENTS: 10 Hours
Architecture for Intelligent Agents Agent communication Negotiation and Bargaining
Argumentation among Agents Trust and Reputation in Multi-agent systems.
UNIT-IV REASONING I: 10 Hours
Symbolic Logic under Uncertainty , Non-monotonic Reasoning, Logics for non-monotonic
reasoning, Statistical Reasoning.
UNIT-V METHODS: 10 Hours
Probability and Bayes Theorem, Certainty factors, Probabilistic Graphical Models, Bayesian
Networks, Markov Networks, Fuzzy Logic.
UNIT-VI Recent advances and research being done in the topics mentioned above
units
REFERENCES:
1. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach", Prentice
Hall, Third Edition, 2009.
2. Rich and Knight, Artificial Intelligence, 2nd Edition, 2013
3. I. Bratko, "Prolog: Programming for Artificial Intelligence", Fourth edition,
Addison-Wesley Educational Publishers Inc., 2011.
4. M. Tim Jones, "Artificial Intelligence: A Systems Approach(Computer Science),
Jones and Bartlett Publishers, Inc.; First Edition, 2008
WT73
5. Nils J. Nilsson, "The Quest for Artificial Intelligence", Cambridge University
Press, 2009.
6. William F. Clocksin and Christopher S. Mellish," Programming Using
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1: Define and identify various AI concepts
CO2: illustrate different AI strategies
CO3: Sketch various knowledge representation for AI problems
CO4: Analyse agents usage for AI
CO5: Design AI inference techniques
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 2
CO3 2
CO4 2
CO5 2 2
1: Low 2: Medium 3:High
WT74
Course Code 18CS3P1B M. Tech (Web Technologies)
Category Open Elective
Course title BUSINESS ANALYTICS
Scheme and
Credits
No. of Hours/Week Semester – III
L T P S Credits
4 - - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES
The course will enable the students to:
1. Understand the role of business analytics within an organization.
2. Analyze data using statistical and data mining techniques.
3. Distinguish relationships between the underlying business processes of an
organization.
6. Gain an understanding of how managers use business analytics to formulate and
solve business problems and to support managerial decision making.
7. Discuss the uses of decision-making tools and Operations research techniques.
UNIT -I BUSINESS ANALYTICS: 10 Hours
Overview of Business analytics, Scope of Business analytics, Business Analytics Process,
Relationship of Business Analytics Process and organisation, competitive advantages of
Business Analytics. Statistical Tools: Statistical Notation, Descriptive Statistical methods,
Review of probability distribution and data modelling, sampling and estimation methods
overview
UNIT -II TRENDINESS AND REGRESSION ANALYSIS: 9 Hours
Modelling Relationships and Trends in Data, simple Linear Regression. Important
Resources, Business Analytics Personnel, Data and models for Business analytics, problem
solving, Visualizing and Exploring Data, Business Analytics Technology
UNIT -III ORGANIZATION STRUCTURES OF BUSINESS ANALYTICS:
10 Hours
Team management, Management Issues, Designing Information Policy, Outsourcing,
Ensuring Data Quality, Measuring contribution of Business analytics, Managing Changes.
Descriptive Analytics, predictive analytics, predicative Modelling, Predictive analytics
analysis, Data Mining, Data Mining Methodologies, Prescriptive analytics and its step in
the business analytics Process, Prescriptive Modelling, nonlinear Optimization
UNIT -IV FORECASTING TECHNIQUES: 10 Hours
Qualitative and Judgmental Forecasting, Statistical Forecasting Models, Forecasting
Models for Stationary Time Series, Forecasting Models for Time Series with a Linear
Trend, Forecasting Time Series with Seasonality, Regression Forecasting with Casual
Variables, Selecting Appropriate Forecasting Models. Monte Carlo Simulation and Risk
Analysis: Monte Carle Simulation Using Analytic Solver Platform, New-Product
Development Model, Newsvendor Model, Overbooking Model, Cash Budget Model
WT75
UNIT- V DECISION ANALYSIS: 9 Hours
Formulating Decision Problems, Decision Strategies with the without Outcome
Probabilities, Decision Trees, The Value of Information, Utility and Decision Making
UNIT-VI Recent advances and research being done in the topics mentioned above
units
REFERENCES
1. Business analytics Principles, Concepts, and Applications by Marc J. Schniederjans,
Dara G. Schniederjans, Christopher M. Starkey, Pearson FT Press
2. Business Analytics by James Evans, persons Education
COURSE OUTCOMES
At the end of the course, the students will be able to:
CO1. Develop the knowledge of data analytics.
CO2. Demonstrate the ability of think critically in making decisions based
on data and deep analytics
CO3. Discuss the uses of technical skills in predicative and prescriptive
modeling to support business decision-making
CO4. Demonstrate the ability to translate data into clear and actionable insights.
CO5. Evaluate and assess the forecasting techniques.
Scheme of Examination
CIE -50
Marks
Test I (unit I,II, & III)-15
Test II (Unit IV & V) -15
Quiz/AAT=05 Marks
Unit-VI(AAT)=15 Marks
Total: Marks
50
SEE-
100
Marks
Unit which have 10 hours shall have internal
choice
20*3=60
Marks
Total:
Marks 100
Unit which have 09 hours shall not have
internal choice
20*2=40
Marks
Note: SEE shall be conducted for 100 marks and marks obtained shall be reduced
for 50 marks
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 3
CO2 3 3
CO3 3 3
CO4 3 3
CO5 3 3
1: Low 2: Medium 3:High
WT76
Course Code 18CS3P1C M. Tech (Web Technologies)
Category Open Elective
Course title SYSTEM SIMULATION AND MODELING
Scheme and
Credits
No. of Hours/Week Semester – III
L T P S Credits
3 1 - - 4
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs
Prerequisites (if any):
COURSE OBJECTIVES:
The course will enable the students to:
6. Understand the system, specify systems using natural models of computation, modelling
techniques
7. Apply natural models of computation, modelling techniques to
understand behaviour of system , and analyse the simulation data
8. Analyse simulation data, simulation tools for simulation, Terminating Simulations –
Steady state simulations.
9. Evaluate the existing simulation models for verification, calibration and validation
10. Design validation, calibration model and decision support
UNIT – I INTRODUCTION TO SIMULATION 09 Hours
Introduction Simulation Terminologies- Application areas – Model Classification Types of
Simulation- Steps in a Simulation study- Concepts in Discrete Event Simulation Example.
UNIT-II MATHEMATICAL MODELS 10 Hours
Statistical Models - Concepts – Discrete Distribution- Continuous Distribution – Poisson
Process- Empirical Distributions- Queueing Models – Characteristics- Notation Queueing
Systems – Markovian Models- Properties of random numbers- Generation of Pseudo Random
numbers- Techniques for generating random numbers-Testing random number generators
Generating Random-Variates- Inverse Transform technique Acceptance- Rejection technique –
Composition & Convolution Method.
UNIT-III ANALYSIS OF SIMULATION DATA 10 Hours
Input Modelling - Data collection - Assessing sample independence – Hypothesizing
distribution family with data - Parameter Estimation - Goodness-of-fit tests – Selecting input
models in absence of data- Output analysis for a Single system – Terminating Simulations –
Steady state simulations.
UNIT -IV VERIFICATION AND VALIDATION 09 Hours
Building – Verification of Simulation Models – Calibration and Validation of Models –
Validation of Model Assumptions – Validating Input – Output Transformations
UNIT-V SIMULATION OF COMPUT ER SYSTEMS 10 Hours
Simulation Tools – Model Input – High level computer system simulation – CPU – Memory
Simulation – Comparison of systems via simulation – Simulation Programming techniques -
Development of Simulation models.
UNIT-VI Recent advances and research being done in the topics mentioned above units
WT77
REFERENCES
1. Jerry Banks and John Carson, ―Discrete Event System Simulation‖, Fourth Edition, PHI,
2005.
2. Geoffrey Gordon, ―System Simulation‖, Second Edition, PHI, 2006.
3. Frank L. Severance, ―System Modelling and Simulation‖, Wiley, 2001.
4. Averill M. Law and W. David Kelton, ―Simulation Modelling and Analysis, Third
Edition, McGraw Hill, 2006.
5. Jerry Banks, ―Handbook of Simulation: Principles, Methodology, Advances,
Applications and Practice‖, Wiley-Inter science, 1 edition, 1998.
COURSE OUTCOMES
On Completion of the course, the student will be able to:
CO1: Explain natural models of computation, modelling techniques
CO2: Determine suitable models of computation, modelling techniques to
understand behaviour of system.
CO3: Distinguish simulation models for verification, calibration and validation
CO4: Assess the performance of different simulation models, statistical models, queuing
Systems and Markovian Models for given problem
CO5: Design goodness-of-fit tests and input models in absence of data
SCHEME OF EXAMINATION
CIE –
50
marks
Test I (Unit I, II &III)- 20 marks Two Quizzes / AAT
= 10 marks
Total:50
marks Test II (Unit IV & V) – 20 marks
SEE
– 100
marks
Answer FIVE full questions Total:100 marks
Units which have 09 Hours shall not have
internal choice. 20* 2 = 40 Marks
(d) Units which have 10 Hours shall
have internal choice 20*3= 60 Marks
Note: SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2
CO2 3
CO3 3
CO4 3
CO5 3 2
1: Low 2: Medium 3:High
WT78
COURSE LEARNING OBJECTIVES:
The objectives of the SEMINAR-III is to prepare the students to learn to:
1. Carry out the literature review of general research area/current topic and analyse the
same effectively.
2. Prepare a technical report, reflecting his/her depth of understanding, on the selected
area/topic and prepare content rich presentation.
3. Acquire communication and time management skills for effective and impactful
presentation. Interact with peers to acquire the qualities of thoughtfulness, friendliness,
adaptability, responsiveness, and politeness in-group discussion. Overcome stage fear
during the presentation.
GUIDE LINES
1. Seminar preparation and presentation is an individual student activity.
2. Topic may be of general/ specific interest to program of engineering or electives not
offered in the semester and to be selected in consultation with the faculty/Guide.
3. Select one pertinent research paper for the seminar presentation.
4. Prepare and submit a detailed technical report of the seminar topic.
COURSE OUTCOMES:
Students shall be able to:
1. Carry out the literature survey of topic of seminar.
2. Prepare a technical report on the selected area/topic.
3. Make an effective presentation with seamless flow of content within the time allocated.
Overcome inhibition in interacting with peers and hence develop the spirit of team
work. Overcome stage fear during the presentation.
SCHEME OF EXAMINATION
CIE – 50
marks
Phase -1 Marks =10 Seminar Report
Marks =20
Total:50
Marks Phase -2 Marks =20
Course Code 18WT3S01 M. Tech (Web Technologies)
Category Seminar Semester: III
Course title SEMINAR - III
Scheme and Credits
No. of Hours/Week
Total hours = 24 L T P S Credits
0 0 2 0 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any): NIL
WT79
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2 3
CO2 2 3 3
CO3 2
1. Low, 2. Medium, 3. High
Scheme of Continuous Internal Evaluation (CIE):
Evaluation would be carried out in TWO phases. The Seminar Evaluation Committee
shall comprise of Chairman of the Department, Faculty/Guide and one more faculty member
nominated by Chairman. The evaluation criteria shall be as per the rubrics given below:
Rubrics for Evaluation:
Topic - Technical Relevance, Sustainability and Societal Concerns : 15%
Review of literature and technical content : 35%
Presentation Skills : 25%
Report : 25%
WT80
INTERNSHIP
COURSE LEARNING OBJECTIVES:
Objectives of the internship
1. Provide an opportunity to see how classroom and textbook learning applies to the real
world, and to expose the students to the relevant work experience.
2. Pay close attention to all the steps that go onto completing a job, thereby, help students
to become workforce ready before entering the job market as a graduate.Provide an
opportunity to select the topic of dissertation work by evaluating the requirement of
organisation.
3. Prepare and present a technical report of internship.
GUIDELINES
1. Student has to approach the concerned heads of various Industries/organization, which
are related to the field of specialization of the M. Tech program.
2. If any student gets internship, he/she has to submit the internship offer letter duly signed
by the concerned authority of the company to the Chairperson of the Department.
3. The internship on full time basis will be after the examination of II semester and during
III semester for a period of 8 weeks without affects regular class work.
4. The progress has to be reported periodically to the faculty or to the Guide assigned by
the Chairperson as per the format acceptable to the respective industry /organizations
and to the Institution.
5. At the end of the internship the student has to prepare a detailed report and submit.
6. Students are advised to use ICT tools such as Skype to report their progress and
submission of periodic progress reports to the faculty in charge or guide.
7. Duly signed report from internal supervisor (faculty incharge or guide) and external
supervisor from the organization where internship is offered has to be submitted to the
Chairperson of the Department for his/her signature and further processing for
evaluation.
The broad format of the internship final report shall contain Cover Page, Certificate from
College, Certificate from Industry / Organization of internship, Acknowledgement,
Synopsis, Table of Contents, chapters of Profile of the Organization - Organizational
structure, Products, Services, Business Partners, Financials, Manpower, Societal Concerns,
Professional Practices, Activities of the Department where internship is done, Tasks
Performed and summary of the tasks performed. specific technical and soft skills that
Course Code 18WT3I01 M. Tech (Web Technologies)
Category Internship/ Mini Project Semester: III
Course title INTERNSHIP / MINI PROJECT
Scheme and Credits
No. of Hours/Week
Total hours = 80 L T P S Credits
--- --- 10 --- 5
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE: 3 Hrs for a
batch of 6 students
Prerequisites (if any): NIL
WT81
student has acquired during internship, References & Annexure.
COURSE OUTCOMES:
The student will be able to:
1. Apply the gained experience along with the theoretical knowledge to solve the real world
problems what
engineers ready do.
2. Get equipped with experience required before entering the job market.Explore the
possibility of formulating the dissertation problem.
3. Prepare a technical report and make a presentation of details of internship.
SCHEME OF EXAMINATION
CIE 1.Marks awarded by guide (Internal examiner) = 50 marks
2.Marks awarded by the department internship monitoring committee = 50 marks
50*
Marks
SEE Presentation of internship in the presence of Guide (Internal examiner) and external
examiner = 100 marks
50**
Marks
Note: *= CIE be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
**= SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 2
CO2 2 2
CO3 3
1. Low, 2. Medium, 3. High
Rubrics for CIE:
1. Topic of internship = 10%
2. Objectives of internship = 10%
3. Specific skills acquired = 20%
4. Document = 40%
5. presentation = 20%
WT82
Rubrics for SEE:
1. Topic of internship = 10%
2. Objectives of internship = 10%
3. Specific skills acquired = 20%
4. Document = 40%
5. presentation = 20%
MINI PROJECT
COURSE LEARNING OBJECTIVE:
1. Understand the method of applying engineering knowledge/use application software to solve
specific problems after carrying out literature survey.
2. Apply engineering and management principles while executing the project.
3. Demonstrate the skills for good technical report writing and presentation.
COURSE CONTENT/GUIDELINES
Student shall take up small problems in the field of domain of program as mini project. It can be
related to a solution to an engineering problem, verification and analysis of experimental data
available, conducting experiments on various engineering subjects, material characterisation,
studying a software tool for solution to an engineering problem, etc.
The mini project must be carried out preferably using the resources available in the
department/college and it can be of interdisciplinary also.
COURSE OUTCOMES:
The students shall be able to:
1. Conduct experiments / use the capabilities of relevant application software/ simulation tools
individually to generate data/ solve problems.
2. Assess the available engineering resources available in the institution.
3. Prepare and Present the technical document of mini project.
SCHEME OF EXAMINATION
CIE 1.Marks awarded by guide (Internal examiner) = 50 marks
2.Marks awarded by the department internship monitoring committee = 50 marks
50*
Marks
SEE Presentation of internship in the presence of Guide (Internal examiner) and external
examiner = 100 marks
50**
Marks
Note: *= CIE be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
**= SEE shall be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
WT83
Rubrics for CIE
Note: * = CIE be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
Rubrics for SEE:
The SEE shall be done by two examiners out of which one examiner is the guide of mini
project. The following weightage would be given for the examination. Evaluation shall be done
in batches, not exceeding 6 students.
Sl.
no
Particulars Weightage Marks Total
marks of
SEE
1 Brief write-up about the project 05% 05
50**
2 Presentation/demonstration of the project 20% 20
3 Methodology and Experimental Results &
Discussion
30% 30
4 Report 25% 25
5 Viva Voce 20% 20
Total 100% 100
Note: ** = SEE shall be conducted for 100 marks and the marks obtained shall be reduced for
50 marks.
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 3
CO2 2 3
CO3 3
1. Low, 2. Medium, 3. High
Sl.
no
Particulars Weightage Marks Total
marks of
CIE
1 Selection of the topic & formulation of objectives 10% 10
50*
2 Modelling and simulation/algorithm
development/experiment setup
25% 25
3 Conducting experiments/implementation/testing 25% 25
4 Demonstration & Presentation 15% 15
5 Report writing 25% 25
Total 100% 100
WT84
COURSE LEARNING OBJECTIVES:
1. Choose a problem applying relevant knowledge and skills acquired during the course.
Formulate the specifications of the project work, identify the set of feasible solutions,
prepare, and execute project plan considering professional, cultural and societal factors.
Identify the problem-solving methodology using literature survey and present the same.
2. Develop experimental planning and select appropriate techniques and tools to conduct
experiments to Evaluate and critically examine the outcomes followed by concluding the
results and identifying relevant applications. Preparation of synopsis, preliminary report
for approval of topic selected along with literature survey, objectives and methodology.
3. Develop oral and written communication skills to effectively convey the technical content.
GUIDELINES
The Dissertation work will start in III semester and should be a problem with research
potential and should involve scientific research, design, generation/collection and analysis
of data, determining solution and must preferably bring out the individual contribution.
The Dissertation work will have to be done by only one student and the topic of
dissertation must be decided by the guide and the student. The dissertation work shall be
carried out, on-campus or in an industry or in an organisation with prior approval from
the Chairperson of the Department. The student has to be in regular contact with the guide
atleast once in a week.
The report of Dissertation work phase I shall contain cover page, certificate from
College/Industry/Organisation, Acknowledgement, List of Figures and Tables Contents,
Nomenclature, Chapters of Introduction including motivation to choose topic, Literature
survey, Conclusion of literature survey, Objectives and Scope of Dissertation,
Methodology to be followed, Experimental requirements, References and Annexure.
The preliminary results (if available) of the problem of Dissertation work may also be
discussed in the report.
COURSE OUTCOME:
The students will be able to:
1. Self learn various topics relevant to Dissertation work. Survey the literature such as books,
National/International reference journals, articles and contact resource persons for
selected topics of Dissertation.
2. Write and prepare a typical technical report.
Course Code 18WT3D01 M. Tech (Web Technologies)
Category Dissertation Work Semester: III
Course title DISSERTATION WORK PHASE -I
Scheme and Credits
No. of Hours/Week
Total hours = 80 L T P S Credits
0 0 10 0 5
CIE Marks: 50 SEE Marks:50 Total Max. Marks: 100 Duration of SEE: 1Hour
Prerequisites (if any): NIL
WT85
3. Present and defend the contents of Dissertation work phase I in front of technically
qualified audience effectively.
SCHEME OF EXAMINATION
CIE 1.Marks awarded by guide (Internal examiner) = 50 marks
2.Marks awarded by the department dissertation monitoring committee = 50 marks
50*
Marks
SEE Presentation of Dissertation work Phase-I in the presence of Guide (Internal
examiner) and external examiner
50**
Marks
Note: *= CIE be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
**= SEE shall be conducted for 100 marks and the marks obtained shall be reduced for
50 marks.
Rubrics for CIE: Weightage
1. Introduction and Justification of topic = 10%
2. Literature survey and Conclusion = 30%
3. Objectives and Scope of Dissertation work = 30%
4. Methodology to be adopted = 20%
5. Presentation of contents of Dissertation work Phase-I = 10%
Rubrics for SEE:
1. Introduction and Justification of topic = 10%
2. Literature survey and Conclusion = 30%
3. Objectives and Scope of Dissertation work = 30%
4. Methodology, Experimental /Software = 20%
5. Presentation of Dissertation Phase-I = 10%
Mapping of Course Outcomes (Cos) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 3
CO2 3 3
CO3 3 3
1. Low, 2.Medium, 3. High
SEMESTER IV
WT86
COURSE LEARNING OBJECTIVES:
The objectives of the SEMINAR-IV is to prepare the students to learn to:
1. Carry out the literature review of general research area/current topic and analyse the same
effectively.
2. Prepare a technical report, reflecting his/her depth of understanding, on the selected area/topic and
prepare content rich presentation.
3. Acquire communication and time management skills for effective and impactful presentation.
Interact with peers to acquire the qualities of thoughtfulness, friendliness, adaptability,
responsiveness, and politeness in-group discussion. Overcome stage fear during the presentation.
GUIDE LINES
1. Seminar preparation and presentation is an individual student activity.
2. Topic may be of general/ specific interest to program of engineering or electives not offered in the
semester and to be selected in consultation with the faculty/Guide.
3. Select one pertinent research paper for the seminar presentation.
4. Prepare and submit a detailed technical report of the seminar topic.
COURSE OUTCOMES:
Students shall be able to:
1. Carry out the literature survey of topic of seminar.
2. Prepare a technical report on the selected area/topic.
3. Make an effective presentation with seamless flow of content within the time allocated. Overcome
inhibition in interacting with peers and hence develop the spirit of team work. Overcome stage fear
during the presentation.
SCHEME OF EXAMINATION
CIE – 50
marks
Phase -1 Marks =10 Seminar Report
Marks =20
Total:50
Marks Phase -2 Marks =20
Course Code 18WT4S01 M. Tech (Web Technologies)
Category Seminar Semester: IV
Course title SEMINAR - IV
Scheme and Credits
No. of Hours/Week
Total hours = 24 L T P S Credits
0 0 2 0 1
CIE Marks: 50 Total Max. Marks: 50
Prerequisites (if any): NIL
WT87
Scheme of Continuous Internal Evaluation (CIE):
Evaluation would be carried out in TWO phases. The Seminar Evaluation Committee shall comprise
of Chairman of the Department, Faculty/Guide and one more faculty member nominated by Chairman. The
evaluation criteria shall be as per the rubrics given below:
Rubrics for Evaluation:
Topic - Technical Relevance, Sustainability and Societal Concerns : 15%
Review of literature and technical content : 35%
Presentation Skills : 25%
Report : 25%
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 2 2 3
CO2 2 3 3
CO3 2
1. Low, 2. Medium, 3. High
WT88
COURSE LEARNING OBJECTIVES:
1. Apply/Use different experimental techniques, equipments, software/ Computational/
Analytical /Modelling and Simulation tools required for conducting tests and generate other
relevant data. Students will also be able to design and develop an experimental setup/test rig.
2. Analyse the results of the experiments conducted/models developed.
3. Create a detailed technical document as per format based on the outcome of dissertation
work phase I and II.
GUIDELINES
Dissertation work phase II is the continuation of project work started in III semester. The
report of Dissertation work that includes the details of Dissertation work phase I and
phase II should be presented in a standard format. The candidate shall prepare a detailed
report of dissertation that includes Cover Paper, Certificate from
College/Industry/Organisation, Acknowledgement, Abstract, Table of contents, List of
Figures and Table, Nomenclature, Chapter of Introduction, Literature survey, Conclusion
of literature survey, Objectives and Scope of dissertation work, Methodology,
Experimentation, Results, Discussion, Conclusion, Scope for future work, References,
Annexure and full text of the publication (submitted or published)
COURSE OUTCOMES:
Students shall be able to:
1. Conduct experiments/ implement the capabilities of different Software
/Computational / Analytical/
Modelling and simulation tools individually and generate data for validation of
hypothesis.
2. Investigate and assess the results obtained within the scope of experiments conducted
followed by conclusions.
3. Prepare a detailed technical document, Present and defend the contents of Dissertation
work in presence of technically qualified audience effectively.
Course Code 18WT4D01 M. Tech (Web Technologies)
Category Dissertation Work Semester: IV
Course title DISSERTATION WORK PHASE -II
Scheme and Credits
No. of Hours/Week
Total hours = 150 L T P S Credits
--- --- 30 --- 15
CIE Marks: 50 SEE Marks: 50 Total Max. Marks: 100 Duration of SEE:
Prerequisites (if any): NIL
WT89
SCHEME OF EXAMINATION
CIE
1. Marks awarded by guide = 50 marks
2. Marks awarded by the department dissertation monitoring committee
(Guide + Two faculty members )= 50 marks
100
marks
50*
marks
SEE
1. Dissertation evaluation by guide (Internal examiner) = 100 marks
2. Dissertation evaluation by external examiner = 100 marks
3. Viva- Voce examination by guide and external examiner who evaluated the
dissertation work =200 marks
300
marks
50**
marks
Note: * = CIE be conducted for 100 marks and the marks obtained shall be reduced for 50
marks.
** = SEE shall be conducted for 300 marks and the marks obtained shall be reduced for
50 marks.
Rubrics for CIE:
1. Presentation of background of dissertation work = 10%
2. Literature survey, Problem formulation and Objectives = 30%
3. Presentation of methodology and experimentation = 30%
4. Results and Discussion = 20%
5. Questions and Answers = 10%
Rubrics for SEE:
1. Originality = 05%
2. Literature survey = 15%
3. Problem formulation, Objectives and Scope of Work = 10%
4. Methodology, Experimentation/Theoretical modelling = 10%
5. Results, Discussion and Conclusion = 20%
6. Questions and Answers = 20%
7. Acceptance/Publication of technical paper in Journals/Conference = 20%
Mapping of Course Outcomes (COS) to Program Outcomes (POs)
PO1 PO2 PO3
CO1 3 2 3
CO2 2 2 3
CO3 3 3 3
1. Low, 2. Medium, 3. High