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Welcome to Journal of Theoretical and Applied Information Technology

Editorial Board

PATRONProf. Niaz AhmadFCE, MOE, Islamabad, PAKISTAN

EDITOR IN CHIEFDr. Imran BabarHOD SE, APCOMS, Rawalpindi PAKISTAN

EDITORIAL BOARD

Dr. CHRISTOS GRECOSSchool of Computing, Engineering and Physical Sciences, University of CentralLancashire, Preston PR1 2E, UNITED KINGDOM.

Dr. YUSUF PISAN Department of Software Engineering, Faculty of Information Technology, Universityof Technology, Sydney, AUSTRALIA.

Dr. YUXIN MAOSchool Of Computer & Information Engineering Zhejiang Gongshang University,CHINA

Dr. MUHAMMAD SHERFaculty of Basic and Applied Sciences, Department of Computer Science,International Islamic University, Islamabad. PAKISTAN.

Dr. ZARINA SHUKUR Computer Science Dept., Fakulti Teknologi dan Sains Maklumat, UniversityKebangsaan Malaysia, 43600 Bangi, MALAYSIA.

Dr. CHRISTEL BAIER Faculty of Computer Science, Institute for Theoretical Computer Science, TechnicalUniversity Dresden, GERMANY.

Dr. NOR AZAN MAT ZIN Department of Information Science, Faculty of Information Science & Technology,National University of Malaysia (UKM) 43600 UKM BANGI, MALAYSIA.

Dr. KHAIRUDDIN BIN OMAR Faculty of Information Science and Technology,Universiti Kebangsaan Malysia, 43600Bangi Selangor DarulEhsan, MALYSIA.

Dr. TENGKU MOHD. BIN TENGKU SEMBOKFaculty of Information Science and Technology Universiti Kebangsaan,Malaysia, 43600 Bangi Selangor DarulEhsan, MALYSIA.

Dr PRABHAT K. MAHANTIDepartment of Computer Science and Applied Statistics (CSAS), Hazen Hall Room311, University of New Brunswick, Saint John, New Brunswick, CANADA.

Dr. R. PONALAGUSAMYDepartment of Mathematics, National Institute of Technology, Tiruchirappalli, TamilNadu, INDIA.

Dr. NITIN UPADHYAYComputer Science & Information Systems Group, Birla Institute of Technology andScience (BITS), PilaniGoa Campus, NH17B Bypass Road, ZuariNagar, Goa, INDIA.

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Dr. A. SERMET ANAGNEskisehir Osmangazi University, Industrial Engineering Department, BademlikCampus, 26030 Eskisehir, TURKEY.

Dr. YACINE LAFIFIDepartment of Computer Science, University of Guelma, BP 401, Guelma 24000,ALGERIA.

Dr. JAYANTHI RANJANInstitute of Management Technology, Raj Nagar, Ghaziabad, Uttar Pradesh, INDIA

Dr. ADEL M. ALIMINational Engineering School of Sfax (ENIS), University of SFAX, TUNISIA

Dr. SIKANDAR HAYAT KHIYALFaculty of Computer Sciences, Preston University, Islamabad, PAKISTAN

Dr. ADEL MERABETDepartment of Electrical & Computer Engineering, Dalhousie University, Halifax,CANADA

DR. HEMRAJ SAINICE&IT Department, Higher Institute of Electronics, Bani Walid. LIBYA

Dr. MAUMITA BHATTACHARYASOBIT, Charles Sturt University, Albury 2640, NSW, AUSTRALIA

Dr. SEIFEDINE KADRYLebanese International University, LEBONON

Dr. AIJUAN DONGDepartment of Computer Science, Hood College Frederick, MD 21701. USA

Dr. S.S.RIAZ AHAMEDMohamed Sathak Engineering College, Kilakarai, & Sathak Institute of Technology,Ramanathapuram , Tamilnadu, INDIA

Dr. ZURIATI AHMAD ZUKARNAINUniversity Putra Malaysia, MALAYSIA

Dr. CHELLALI BENACHAIBAUniversity of Bechar, ALGERIA

Dr. MOHD NAZRI ISMAILUniversity of Kuala Lumpur (UniKL) MALYSIA

Dr. VITUS SAI WA LAMThe University of Hong Kong, CHINA

Dr. WITCHA CHIMPHLEESuan Dusit Rajabhat University, Bangkok, THAILAND

Dr. SIDDHIVINAYAK KULKARNIUniversity of Ballarat, Ballarat, AUSTRALIA

Dr. S. KARTHIKEYANCaledonian College of Engineering, OMAN

Dr. DRAGAN R. MILIVOJEVIĆMining and Metallurgy Institute Bor Zeleni bulevar 35, 19210 Bor, SERBIA

Dr. ABDUL AZIZProfessor of Computer Science, University of Central Punjab, PAKISTAN



Dr.P.DANANJAYANProfessor, Department of ECE, PEC, Puducherry, INDIA.

Dr. E. SREENIVASA REDDYPrincipal Vasireddy Venkatadri Institute of Technology, Guntur, A.P., INDIA

Dr. SANTOSH DHONDOPANT KHAMITKARRamanand Teerth Marathwada University, Nanded. Maharashtra 431605, INDIA

Dr. M. IQBAL SARIPAN(MIEEE, MInstP, Member IAENG, GradBEM)Dept. of Computer and Communication Systems Engineering, Faculty of Engineering,Universiti Putra MALAYSIA

Dr. E. SREENIVASA REDDYPrincipal Vasireddy Venkatadri Institute of Technology, Guntur, A.P., INDIA

SHAHBAZ GHAYYURFaculty of Basic and Applied Sciences, DCS&SE, International Islamic University,Islamabad. PAKISTAN.

Dr. T.C.MANJUNATH,Professor & Head of the Dept.,Electronicis & Communication Engg. Dept,New Horizon College of Engg.,Bangalore560087, Karnataka, INDIA.

Dr. Nacer Eddine ZAROUR LIRE Laboratory, Computer Science Departement, University Mentouri of Constantine(UMC), ALGERIA

Dr. RIKTESH SRIVASTAVAAssistant Professor, Information Systems, Skyline University P O Box 1797, Sharjah,UAE

Dr. Mohd ZAINAL ABIDIN AB KADIR, PhD, MIEEECentre of Excellence on Lightning Protection (CELP)Dept. of Electrical and Electronics Engineering, Faculty of Engineering, UPM,Selangor, MALAYSIA

Dr. OUSMANE THIAREGaston Berger University, Department of Computer Science, UFR S.A.TBP 234 SaintLouis, SENEGAL

Dr. SIDDHIVINAYAK KULKARNIGraduate School of Information Technology and Mathematics University ofBallart AUSTRALIA

Dr. BONNY BANERJEESenior Scientist Audigence, FL, USA, The Ohio State University, Columbus, OH, USA

Dr. NICKOLAS S. SAPIDISDepartment of Mechanical Engineering, University of Western Macedonia Kozani GR50100, GREECE.

Dr. NAZRI BIN MOHD NAWISoftware Engineering Department, Faculty of Science Computer InformationTechnology, Universiti Tun Hussein Onn MALAYSIA

Dr. JOHN BABALOLA OLADOSULadoke Akintola University of Technology, Ogbomoso, NIGERIA

Dr. ABDELLAH IDRISSIDepartment of Computer Science, Faculty of Science, Mohammed V University Agdal, Rabat, MOROCCO



Dr. AMIT CHAUDHRYUniversity Institute of Engineering and Technology, Panjab University, Sector25,Chandigarh, INDIA

Dr. ASHRAF IMAMAligarh Muslim University, AligarhINDIA

Dr. MUHAMMAD UMER KHANDepartment of Mechatronics, Faculty of Engineering, Air University, Islamabad.PAKISTAN

Dr. MOHAMMED ALI HUSSAINDept. of Computer Science & Engineering, Sri Sai Madhavi Institute of Science &Technology, Mallampudi, Rajahmundry, A.P, INDIA

Dr. KHALID USMANIDepartment of Computer Science, Arid Agriculture University, Rawalpindi,PAKISTAN.

Dr. GUFRAN AHAMD ANSARIQassim University, College of Computer Science, Ministry of Higher Education,Qassim University,KINGDOM OF SAUDI ARABIA

Dr. Defa HuSchool of Information, Hunan University of Commerce, Changsha 410205, Hunan,P. R. of CHINA

Dr. Imran BabarHOD Software Engineering, APCOMS, Rawalpindi Pakistan

Dr. GHADI AbderrahimComputer Sciences Department, Faculty of Sciences and Techniques, Ancienne Routede l’Aéroport, Km 10, Ziaten. BP 416. Tanger MOROCCO

Dr. Hamid Ali Abed AlAsadiHead of Computer Science Department, Faculty of Education for Pure Science, BasraUniversity, Basra, IRAQ.

Shahzad A. Khan (Linguist)Lecturer English IMCB, FDE Islamabad.(Managing Editor/Linguist & Incharge Publishing)Journal of Theoretical and Applied Information Technology

**You can join the elite panel of JATIT as member technical editorial board if youhold a PhD in computing and have atleast 10 publications in InternationalJournals/Conferences. Please drop your CV at managing_editor at jatit.org. Memberslists and requests are reviewed at the end of every year in regional advisory panelmeeting.

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8/4/2016 Journal of Theoretical and Applied Information Technology June 2016 Volume 88 No 3

http://www.jatit.org/volumes/eightyeight3.php 1/15

Journal of Theoretical and Applied Information Technology June 2016 | Vol. 88 No.3

Title: A NEW SOFT SET BASED PRUNING ALGORITHM FOR ENSEMBLE METHOD

Author: MOHD KHALID AWANG, MOKHAIRI MAKHTAR, M NORDIN A RAHMAN, MUSTAFA MATDERIS

Abstract: Ensemble methods have been introduced as a useful and effective solution to improve the performance ofthe classification. Despite having the ability of producing the highest classification accuracy, ensemblemethods have suffered significantly from their large volume of base classifiers. Nevertheless, we couldovercome this problem by pruning some of the classifiers in the ensemble repository. However, only afew researches focused on the ensemble pruning algorithm. Therefore, this paper aims to increaseclassification accuracy and at the same time minimizing ensemble classifiers by constructing a newensemble pruning method (SSPM) based on dimensionality reduction in soft set theory. Ensemblepruning deals with the reduction of predictive models in order to improve its efficiency and predictiveperformance. Soft set theory has been proved to be an effective mathematical tool for dimensionreduction. Thus, we proposed a novel soft set based method to prune the classifiers from heterogeneousensemble committee and select the best subsets of the component classifiers prior to the combinationprocess. The results show that the proposed method not only reduce the number of members of theensemble, but able to produce highest prediction accuracy.

Keywords: Ensemble Pruning, Ensemble Selection, Soft Set, Ensemble Methods

Source: Journal of Theoretical and Applied Information Technology30th June 2016 Vol. 88. No. 3 2016

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Title: FACEBOOKS PUBLIC SOCIAL INTERACTION UTILIZATION TO ASSIST RECOMMENDATIONACROSS SYSTEM DOMAIN

Author: MUHAMMAD MURAD KHAN, IMRAN GHANI, SEUNG RYUL JEONG, ROLIANA IBRAHIM ,KASHIF NASEER QURESHI

Abstract: Social media is most prominent internet transition for this decade and Facebook holds its largest share.Facebook has been utilized by researchers from different perspectives e.g. opinion mining, user moodswing pattern, influential person identification etc. whereas recently Facebooks social interactionswere used for recommendation purposes. Although social interactions assisted recommendation, theseinteractions forced algorithm to work inside Facebooks ecosystem i.e. recommending items existinginside Facebook to Facebook users and these interactions were private in nature, requiring explicitpermission from user before algorithm execution. This study utilize Facebooks public socialinteractions to recommend items across system domain i.e. recommending items to users existingoutside Facebook. For this purpose we propose an algorithm that first identify items on Facebookspublic pages, gather social interactions related to them, generate a rank list and finally recommend it toexternal users. As an experimental case study, whatmobile.pk Facebooks public social page wasscanned for items and respective social interactions. These items were then compared with fanattribute of items existing on GSMARENA.com website in order to show rank similarity. 299 totalitems were found common between Facebooks public page and GSMARENA website. Items wereranked according to social interactions and fans quantity. Then a positive spearman correlation of0.547 was found which was improved to 0.660 by excluding 22 mobile phones.

Keywords: Facebook, Recommendation, Cross Domain, Rank Similarity, Public Social Interaction


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Title: FACTORS AFFECTING THE ADOPTION OF ENTERPRISE RESOURCE PLANNING (ERP) ON

http://www.jatit.org/volumes/Vol88No3/1Vol88No3.pdf




CLOUD AMONG SMALL AND MEDIUM ENTERPRISES (SMES) IN PENANG, MALAYSIA

Author: LEOW YI QIAN, AHMAD SUHAIMI BAHARUDIN, ABDULKARIM KANAANJEBNA

Abstract: The purpose of this research is to investigate the effect of cloud security and data privacy, costeffectiveness, Internet reliability, top management support, and competitive pressure factors on theintention to adopt cloudbased ERP system by Small and Medium Enterprises (SMEs) in Penang,Malaysia. This study employs a survey method where 300 SMEs in both manufacturing and servicesectors were selected from a list taken from the SME Corporation Malaysia (SME Corp) website.Statistical Package for Social Science (SPSS) version 20 was used to analyze the collected data. Therewere 51 valid data records from the manufacturing sector as well as 51 valid data responses from theservice sector. This paper has developed a theoretical model using the Technological OrganizationalEnvironmental (TOE) framework and formulated several hypotheses. The results of this study haverevealed that the top management support factor significantly and positively correlates with theintention to adopt cloudbased ERP system in manufacturing SMEs only. In addition, the analyses havefound that all the factors have no significant impact on the intention to adopt cloudbased ERP systemin both sectors. The practical contribution in this research will be the guidelines for cloudbased ERPproviders, SMEs, as well as the Malaysian government in order to encourage the application of cloudbased ERP systems by SMEs.

Keywords: Smes, ERP, Cloud Computing, CloudBased ERP System, Intention To Adopt, Cloud Security AndData Privacy, Cost Effectiveness, Internet Reliability, Top Management Support, And CompetitivePressure


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Title: EFFICIENT THYROID DISEASE CLASSIFICATION USING DIFFERENTIAL EVOLUTION WITHSVM

Author: K.GEETHA, CAPT. S. SANTHOSH BABOO

Abstract: Thyroid diseases are widespread worldwide. In India too, there is a significant problems caused due tothyroid diseases. Various research studies estimates that about 42 million people in India suffer fromthyroid diseases [4]. There are a number of possible thyroid diseases and disorders, including thyroiditisand thyroid cancer. This paper focuses on the classification of two of the most common thyroiddisorders are hyperthyroidism and hypothyroidism among the public. The National Institutes of Health(NIH) states that about 1% of Americans suffer from Hyperthyroidism and about 5% suffer fromHypothyroidism. From the global perspective also the classification of thyroid plays a significant role.The conditions for the diagnosis of the disease are closely linked; they have several importantdifferences that affect diagnosis and treatment. The data for this research work is collected from the UCIrepository which undergoes preprocessing. The preprocessed data is multivariate in nature. Curse ofDimensionality is followed so that the available 21 attributes is optimized to 10 attributes using HybridDifferential Evolution Kernel Based Navie Based algorithm. The subset of data is now supplied toSupport Vector Machine (SVM) classifier algorithm where Radial Basis Function Kernal(RBF) is used.In order to stabilize the errors this iterative process takes 25 and the data is classified. The accuracy ofclassification is observed to be 99.89%. This result is efficient when compared to our previous workthat used the Kernel based Naive bayes classifier.

Keywords: Classification, Curse of Dimensionality, Differential Evolutionary algorithm, Multivariate Bayesianprediction, Radial Basis Function Kernel, Support Vector Machine, Thyroid disease, Wrapper model


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Title: IMPLEMENTATION OF PACKING METHODS FOR THE ORTHOGONAL PACKING PROBLEMS

Author: CHEKANIN VLADISLAV A., CHEKANIN ALEXANDER V





Abstract: In the paper is considered a multidimensional orthogonal packing problem in general case. Aredescribed packing methods developed for formation of placement schemes during solving therectangular cutting and orthogonal packing problems of arbitrary dimensions. The presented packingmodel allows to describe all existing free spaces in containers. Are offered methods of placing anddeleting of orthogonal objects, application of which in optimization packing algorithms will improvethe quality of packing. Is described a new data structure providing increasing speed of packingformation. Efficiency of application of the packing methods is investigated on standard instances ofthreedimensional orthogonal packing problems. All designed packing methods are realized in a formof an applied software which can be used in solving problems of resources allocation includingcontainer loading, cutting stock, scheduling, knapsack problems and many other practical importantcutting and packing problems.

Keywords: Packing, Orthogonal Packing Problem, Data Structure, ObjectOriented Programming, AppliedSoftware


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Title: ENRICH FRAMEWORK FOR MULTIDOCUMENT SUMMARIZATION USING TEXT FEATURESAND FUZZY LOGIC

Author: SACHIN PATIL, RAHUL JOSHI

Abstract: The rapid growth of Information Technology triggers collection of documents in massive form, so tofind the important information from multiple document is a complex task. The multiple documentssummarization is task of producing assured summary from these document set. There are othersummarization techniques like sentence clustering, term weight etc. However, these techniques useonly two or three feature of text to find the importance of considered sentence. In this paper, we putforward an idea of text summarization which considers multiple extracted features by applying naturallanguage processing (NLP) protocol. The ten feature of text are extracted and these feature classified onthe basis of fuzzy logic to get the best documents summary. The key features are preprocessing, featurescoring, inference engine, and fuzzy logic.

Keywords: Preprocessing, Feature Scoring, Normal Distribution, Inference Engine, Fuzzy Logic.


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Title: PREDICTION OF THE GROUP VELOCITY OF ACOUSTIC CIRCUMFERENTIAL WAVES BYARTIFICIAL NEURAL NETWORK

Author: YOUSSEF NAHRAOUI, EL HOUCEIN AASSIF, GERARD MAZ

Abstract: The present study investigates the use an Artificial Neural Network (ANN) to predict the velocitydispersion curve of the antisymmetric (A1) circumferential waves propagating around an elastic coopercylindrical shell of various radius ratio b/a (a: outer radius and b: inner radius) for an infinite lengthcylindrical shell excited perpendicularly to its axis. The group velocity is determined from the valuescalculated using the eigen mode theory of resonances. These data are used to train and to test theperformances of this model. LevenbergMarquaedt backpropagation training algorithm with tangentsigmoid transfer function and linear transfer function results in best model for prediction of groupvelocity. The overall regression coefficient, mean relative error (MRE), mean absolute error (MAE) andstandard error (SE) are 1, 0.01%, 0.38 and 0.07. It is found that the neural networks are good tools forsimulation and prediction of some parameters that carry most of the information available from theresponse of the shell. Such parameters may be found from the velocity dispersion of the circumferentialwaves, since it is directly related to the geometry and to the physical properties of the target.

Keywords: Artificial Neural Network (ANN), Acoustic Response, Submerged Elastic Shell, Scattering Waves,Circumferential Waves, Phase Velocity, Group Velocity.

Source: Journal of Theoretical and Applied Information Technology






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Title: ANALYSIS STUDY OF A CASCADED HBRIDGE MULTILEVEL INVERTER DEDICATED TOPOWER BANK USAGE

Author: TAJEDDINE KHALILI, ABDELHADI RAIHANI, OMAR BOUATTAN, HASSAN OUAJJI, FOUADAMRI

Abstract: Multilevel inverters have proved their efficiency for usage in variety of renewable energy applications.Therefore Converting DC voltage in a power bank containing multiple units using directly a multilevelinverter is a very powerful converting method. In this paper we present an analysis study of thecascaded Hbridge inverter in different conditions and different states namely 5, 9, and 17 levels. Thearchitecture used trough the entire study is the same topology, the same command type was applied forall the models (SPWM). The study focuses on the output voltage quality and the efficiency of the powerconversion. The study also discusses the influence of the unbalanced units state of charge inside thepower bank on quality of the output voltage and present the most efficient level state configuration inboth cases balanced and unbalanced.

Keywords: Multilevel Inverter, Cascaded Hbridge, SPWM, Unbalanced DC, THD.


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Title: INFORMATION TECHNOLOGY FOR NUMERICAL SIMULATION OF VISCOUSINCOMPRESSIBLE FLOW IN BICONNECTED DOMAINS

Author: NURLAN TEMIRBEKOV, SAYA TOKANOVA, YERZHAN MALGAZHDAROV

Abstract: This paper analyzes the method of supplemented domains for the numerical simulation of viscousincompressible flow in the complex geometrical domain. The problem is considered in a discretedefined biconnected domain with the curved boundary. The spline interpolation of curved boundary isconducted. The NavierStokes equations for viscous incompressible fluid are selected for the numericalsimulation. A stable finitedifference scheme and an algorithm of numerical implementation aredeveloped. The numerical results are obtained with different numbers of grid nodes.

Keywords: NavierStokes Equations, Viscous Incompressible Fluid, Numerical Simulation, Numerical Experiment,Stream Function, Method Of Supplemented Domains, Cube Spline Interpolation, CurvilinearBiconnected Domain.


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Title: IMPLEMENTATION OF IMPROVED LEVENSHTEIN ALGORITHM FOR SPELLINGCORRECTION WORD CANDIDATE LIST GENERATION

Author: HANAN NAJM ABDULKHUDHUR, IMAD QASIM HABEEB, YUHANIS YUSOF, SHAHRULAZMI MOHD YUSOF

Abstract: Candidates list generation in spelling correction is a process of finding words from a lexicon that areclose to the incorrect word. The most widely used algorithm to generate the candidate list is theLevenshtein algorithm. However, the algorithm consumes high computational cost, especially whenthere is a large number of spelling errors. The reason is that calculating Levenshtein algorithm includesoperations that create an array and fill the cells of this array by comparing the characters of an incorrectword with the characters of a word from a lexicon. Since most lexicons contain millions of words, suchoperations will be repeated millions of times for each incorrect word in order to generate its candidates






list. This study proposes an improved Levenshtein algorithm that reduces the operation steps incomparing characters between the query and lexicon words. Experimental results show that theproposed algorithm outperformed the Levenshtein algorithm in terms of processing time by having32.43% percentage decrease.

Keywords: Levenshtein Algorithm, Processing Time, Word Candidate List Generation, Spelling Correction, EditDistance


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Title: A SNSINTEGRATED COLLABORATIVE LEARNING SYSTEM TO SUPPORT PROGRAMMINGLANGUAGE LEARNING

Author: FANGFANG CHUA, KEKLUN LIEW

Abstract: Today, using and accessing to Social Network Services (SNS) are part of our daily activities and havegained popularity to attract learners in the socialization engagement. This motivates the adoption ofSocial Network Services into collaborative learning environment which strongly encouragecommunication and interaction between learners. The engagement of learners to use the learning systemis always a challenge to improve as they are easily distracted, lack of motivation and interest. Thefactors which contribute to the problems are mainly the availability of the learning resources, variety ofthe communication mode within the system, and also lack of real time interaction. With theimplementation of Social Network Services (i.e. Facebook services and Twitter services) intocollaborative learning environment, learners will feel motivated and engage more eagerly with thelearning process as reflected from the heavy usage of social media recently. Real time communicationand interaction are being promoted and learners can express and share real thoughts and feelings withthe help of SNS while going through the learning process. In this proposed work, we design andimplement a SNSintegrated collaborative learning system that allows learners to collaborate and learnanytime and anywhere. We have chosen the subject domain of learning programming language torealize our proposed solution.

Keywords: Social Network Services, collaborative learning, Facebook, communication, collaboration, interaction


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Title: SOCIAL NETWORK SITE (SNS) APPROPRIATION PROCESS IN FAMILY PERSPECTIVE BASEDON FAMILY TYPES

Author: YUZI MAHMUD, NOR ZAIRAH AB. RAHIM, SURAYA MISKON

Abstract: Many previous researchers have highlighted the positive and negative impacts of SNS usage in familyenvironment such as improving family communications and bonding or worsen the familyrelationships. These impacts would varies depending on the family relationships. However, studies onSNS adoption, adaptation and use according to different types of family relationships have receivedlittle research attention. Eleven actual case studies which involved 31 respondents were selected. Thedata collection were conducted through interviews, observations and content analysis to achieve themain research objectives of why and how do family members adopt, adapt and use SNS according todifferent types of family groups. Results from the data collection were used in the development ofFamily Appropriation Process of Social Network Site (FAPSNS) framework which also facilitated in theunderstanding of SNS appropriation process criteria in family, individual, technical and extrafamilialperspectives. The current level of SNS appropriation according to family groups namely Modern,Chummy and Mixed families are also identified. However, this paper is focusing on the SNSappropriation process in family perspective only. The results highlighted that Modern family hassuccessfully appropriated Facebook at Level 3 of family perspectives. Whereas, Chummy and Mixedfamilies have disappropriated Facebook at Level 2 in family perspective.

Keywords: Facebook, Model of Technology Appropriation (MTA), SocioTechnical Theory, Family System Theory






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Title: THE ANTECEDANTS OF BROADBAND INTERNET ADOPTION AND CONTINUANCE USAGEIN MALAYSIAN HOUSEHOLD CONTEXT

Author: SARAVANAN NATHAN LURUDUSAMY, T. RAMAYAH

Abstract: The Internet is simply a series of worldwide computer networks linked together, communicating almostinstantly by using various access technologies. It is playing a major role in many areas of our lives, suchas communication, entertainment and information which is supported by newer innovations andtechnology evolvement. While the broadband Internet penetration rate is encouraging in manycountries, its adoption is still a notable issue in Malaysia. Therefore this research is focused onidentification of two relevant research streams covering broadband Internet, which are adoption andcontinuance of usage (after initial adoption) in Malaysia. The theoretical framework which is utilized inthis study is an integrated model of Unified Theory of Acceptance and Use of Technology (UTAUT)and IS Continuance Model which has been further extended by integrating another 2 IndependentVariables, namely Perceived Innovativeness and Perceived Playfulness. This study is trying todetermine the relationship among the independent variables that influences the adoption andcontinuance usage of Broadband Internet technology among Malaysian individuals. Survey was usedas the research instrument and the unit of analysis are existing broadband Internet subscribers inMalaysia. Data obtained from the survey was analyzed using Partial Least Square (PLSSEM) andindicate that intention to adopt broadband have a significant positive influence on initial usage,Intention to continue using broadband have a significant positive influence on actual usagecontinuance and Initial broadband usage have a significant positive influence on usage continuance ofbroadband. This paper is concluded by some recommendations, limitations and directions for futurestudy.

Keywords: Internet Broadband, Technology Adoption, UTAUT, Continuance Usage Of Technology


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Title: TOWARDS EXPLORING FACTORS THAT INFLUENCE SOCIAL MEDIABASED KNOWLEDGESHARING INTENTIONS IN DISASTER MANAGEMENT

Author: YUNIS ALI AHMED, MOHAMMAD NAZIR AHMAD, NOR HIDAYATI ZAKARIA

Abstract: Knowledge sharing is considered vitally important for the success of disaster management initiatives.Within the process of disaster management, a growing number of users have started to utilize socialmedia as a means of knowledge sharing. Specifically, social media empowers individuals to take part inknowledge sharing activities, which will in turn encourage more people to join in disaster reliefactivities. Encouraging online knowledge sharing behaviors among employees is a prominent researchtopic. However, to date, little empirical research has been undertaken to examine social media basedknowledge sharing behaviors within the disaster management domain. This study explores the factorsthat facilitate voluntary social mediabased knowledge sharing intentions, for use within disastermanagement. The study offers a conceptual model for assessing these factors. In this paper, the threedependent variables of individual attitude, subject norms and perceived behavioral control are definedas related to social mediabased knowledge sharing intention. In addition, the three groups oforganizational factors, individual factors and technology factors, with seven subset variables ofmanagement support, organizational reward, knowledge selfefficacy, interpersonal trust, enjoyment inhelping others, perceived usefulness and perceived ease of use, are identified as independent variablesin this study. This study reviews the existing literature both in the field of social mediabasedknowledge sharing in general and in the disaster management domain in particular. Comparing thisresearch with other studies, the main difference is that this study proposes a full set of factors thatinfluence social mediabased knowledge sharing behavior. Concluding remarks and suggestions forfurther statistical study work are provided, particularly in relation to the implications for disaster relief





organizations in Somalia.

Keywords: Knowledge Sharing, Social Media, Social MediaBased Knowledge Sharing, Disaster Management.


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Title: MULTIPARTY PROTOCOL WITH ACCESS CONTROL ON SYMMETRIC FULLYHOMOMORPHIC ENCRYPTION SCHEME

Author: WAMDA NAGMELDIN, SITI MARIYAM SHAMSUDDIN

Abstract: Homomorphic encryption is a particular class of encryption presented by Rivest, Adleman et al. in 1978that permits mathematical operations on the encrypted data without decrypting it [1], in fact, withoutknowing the decryption key. In the last few years, homomorphic encryption techniques have beenstudied deeply since they have become more and more vital and important in several differentcryptographic applications such as lottery protocols, voting protocols, anonymity, privacy, andelectronic auctions.This paper introduces the symmetric fully homomorphic scheme (SymFHE) andmultiparty protocol with access control to allow many users access and manipulate the data in thecloud without violating the confidentiality of sensitive data.

Keywords: Homomorphic Encryption, Cloud Computing, Multiparty.


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Title: HEALTH CARE ANALYSIS FOR PROCESS DEVIATION USING ALPHAFITNESS ALGORITHMIN PROCESS MINING

Author: GANESHA K, RASHMI NAGARAJ, NAYANA MD

Abstract: Health care sectors are continuously exploring new and innovative way to improve operationalefficiencies. This research study investigate a way to find potential efficiency gains in healthcare sectorsby observing how they are carried out in the past and then investigating better ways of implementingthem by considering the factors like time, cost and resource utilization. To achieve competitiveadvantage, healthcare centers try and contour their processes. Process mining can be enforced to extractdata from recorded event. The aim of the system is to propose effective process models by applyingdataset for each model which indeed identifies the deviation from the actual process with help the ofanalytical tool ProM. In this paper several blood tests are considered as the baseline scenario whereineffective process models are generated and checked for the efficiency using alphafitness algorithm. Oneof the major parts involved in process improvement is process modeling which can be optimized andanalyzed.

Keywords: Process Deviation, Event Log, Information Management Systems, Prom, Alpha.


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Title: THE IMPLEMENTATION OF INFORMATION TECHNOLOGIES IN HIGHER EDUCATION: ACASE OF KAZAKHSTAN AND TURKEY

Author: MERUYERT TLEBALDIYEVA, TILEGEN SADIKOV, GULMIRA KAMIYEVA, ZULKIYAMOLDAKHMETOVA

Abstract: The purpose of this research is to analyze the issues related to implementing information technologiesinto an educational system by the example of Turkey and Kazakhstan. Information technologies havebeen increasingly used in educational institutions for refining the quality of service and achieving the






efficient organizational outcomes in the context of a competitive international environment. Theintegration of computer technologies into an educational system depends on its successful elaborationand application, which is an expensive and challenging process. This study also reveals the cooperationbetween Kazakhstan and Turkey in the sphere of information technologies and science. The results andrecommendations can be applied in the educational, scientific and economic system developmentstrategies and are of significant interest to Kazakh and Turkish scientific and educational thought.

Keywords: Educational Technology, Information And Communication Technologies, Integration, HigherEducation.


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Title: METHOD FOR STUDYING THE TUBULAR SOLAR COLLECTOR TESTING IN ALABORATORY

Author: MURAT KUNELBAYEV, NURBAPA MEKEBAEYEV, ASSEM KABDOLDINA, ASHIRGULSEIDILDAYEVA, DMITRY SERGEEVICH SILNOV

Abstract: This paper considers the technique for studying the testing of tubular solar collector in a lab. Today,there are several ways methods of using solar collectors. When using the first method, the followingvalues are measured: coolant flow, difference between the temperatures of the collector coolant fluid atthe collector inlet and outlet and the density of the incident solar radiation flux. Here, all these valuesare measured simultaneously and under quasistationary mode. Much of the research is related to testingof collectors in field conditions using the instant method. At the end of the tests, the product of the totalheat loss coefficient of the collector and efficiency coefficient of the absorption panel was determined.The heat output of the collector was also measured. As is seen from experimental methods of testingtubular solar collector, the tubular collector with an absorbing screen decreases from 0.8 to 0.17 whenwater is supplied at the inlet 20 C, 30 C, 40 C and 50 C, while the efficiency of the tubular collectorwith a reflector increases from 0.17 to 0.68 when water is supplied at the inlet 20 C, 30 C, 40 C, andthen decreases to 0.4 at t150 C. It is obvious that the efficiency of heat absorption and transfer as aresult of thermal conductivity is much higher than the capturing and reflection of sunlight by theabsorbing pipe. However, both the cost and labor input involved in the manufacture of the abovetubular collector with an absorbing screen is higher.

Keywords: Tubular Solar Collectors, Heat Losses, Efficiency Factor, Optical Efficiency Product, Absorption PanelEfficiency Coefficient


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Title: DESIGN OF SINGLE FEED CIRCULARLY POLARIZED HARMONIC SUPPRESSED MICROSTRIP PATCH ANTENNA FOR XBAND APPLICATIONS

Author: P.POORNA PRIYA, HABIBULLA KHAN, CH.ANUSHA, G.SAI TEJASWI, CH.SIVA RAMAKRISHNA

Abstract: Introduction of a symmetrical slot near feed point for a symmetrical radiation patch of micro strip patchantenna realize both circular polarization and higher order mode suppression. Simulated andexperimental results shows that application of symmetrical slot near feed point for asymmetrical patchcan remarkably suppress the harmonic frequencies. Measured return loss and VSWR results shows thatthe proposed antenna suppress the higher order harmonics by maintaining circular polarization in Xband applications.

Keywords: Circular Polarization, Harmonics Suppression, Micro Strip Antennas, Antenna Radiation Patterns






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Title: EXTRACTION OF RETINAL BLOOD VESSEL USING ARTIFICIAL BEECOLONYOPTIMIZATION

Author: KAVYA K, DECHAMMA M.G, SANTHOSH KUMAR B.J

Abstract: Retinal blood vessel Extraction in retinal images allows early diagnosis of disease and is useful indetecting ocular disorders and helps in laser surgery. Automating this process provides several benefitsincluding minimizing subjectivity and eliminating a painstaking. This paper proposes an automatedretinal blood vessel segmentation approach based on Fuzzy CMeans (FCM) clustering and thenperformed extraction using Artificial Beecolony (ABC) to improve the accuracy of segmented image.FCM allocate the values of membership to the pixels instead of separating the pixels as in hardclustering problem and the clustering is optimized using ABC swarm based optimization algorithm,finally the system classify the images according to the level of damage in blood vessel using supportvector machine (SVM). The performance was evaluated on DRIVE database and an accuracy of 96.35%was obtained.

Keywords: Fundus Camera, Clustering, Fuzzy CMeans, Artificial BeeColony, Support Vector Machine.


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Title: THE EFFECT OF CIVIL CONFLICTS AND NET BENEFITS ON MGOVERNMENT SUCCESS OFDEVELOPING COUNTRIES: A CASE STUDY OF IRAQ

Author: SALIH HAJEM GLOOD, WAN ROZAINI SHIEK OSMAN, MASLINDA MOHD NADZIR

Abstract: Information and communication technologies (ICTs) are playing an important role in the advancementof society. ICTs served as one of the main resources for promoting products and services, for deliveringand broadcasting information, and also for connecting organizations and communities together in termsof better interaction and better communicational possibilities. Therefore, several governments seekingto establish IS projects by exploiting the modern of ICTs. The mobile government (mG) system is one ofthe important IS projects provided by governments to improve the quality of life, through enhancingthe delivery of information or services to citizens. The ratio of use of mG services in developingcountries, especially in rural areas, is still quite low and Iraq is not an exception. Despite of Iraq is thehighest mobile penetration rate amongst 34 countries, the use of mG services amongst citizens in Iraq islower than expected compared to the amount of money spent on this projects. Moreover, providingmobile government (mG) services alone did not guarantee success of mG without releasing the benefitsof using mG services, especially in rural areas. Net benefits are considered a critical phenomenon for thesuccess of any IS, and mG is not far from this issue. Thus, this study aims to investigate the contributingfactors mG success in the Iraqi context, where literature in this field of research is lacking. Quantitativedata were collected from Iraqi citizens in rural areas. Structural equation modeling was used to test therelationships between constructs. Results show that information quality has appositive effect on the useof mG, whereas the use of mG has a strong effect on net benefits of mG services. The moderating effectof civil conflicts between the use and net benefits of mG is supported negatively. The results imply thatservice providers need to deliver quality information and quality service to facilitate the users postadoption usage of mG services under stable environment.

Keywords: Civil Conflicts, Mobile Government, Net Benefits, Rural Areas, Evaluation IS Success


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Title: SECURING DATA STORED IN CLOUDS USING MULTI KEYS AND PROXY INJECTIONSCHEMES

Author: GIRISHMA.V, Dr. K.V.V. SATYANARAYANA






Abstract: A new robust control scheme for Multi key distribution scheme that supports secured data storage andaccess in clouds along with anonymous upload feature to protect user privacy is proposed.Userauthenticity is established by the cloud through proper registration procedures and in turn dataauthenticity with multi key sharing authenticity and support for anonymous sharing is established byregistered users. Access control is being implemented where the stored information can be decrypted byusers who are valid.Replay attacks are prevented through Proxy injection based schemes and they arealso helpful in containing Cloud Services Provider (CSP) from knowing whereabout of uploaderthemselves. User revocation is addressed and creating, reading and modifying information in cloud isalso supported.

Keywords: Cloud Data, Attribute Based Encryption, Anonymity Based Uploads and Key Distributions


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Title: COMPARING WINDOWING METHODS ON FINITE IMPULSE RESPONSE (FIR) FILTERALGORITHM IN ELECTROENCEPHALOGRAPHY (EEG) DATA PROCESSING

Author: NOVA EKA DIANA, UMI KALSUM, AHMAD SABIQ, WISNU JATMIKO, PETRUS MURSANTO

Abstract: Electroencephalography (EEG) data contains electric signal activities on a cerebral cortex to recordbrain electrical activities. EEG signal has some characteristics such as nonperiodic, nonstandardizedpattern, and small voltage amplitude. Hence, it is lightly heaped up to noise and difficult to recognizeand extract meaningful information from EEG data. Finite Impulse Response (FIR) with variouswindowing methods has been widely used to mitigate noise and distortions. This paper compares andanalyzes the windowing techniques in resulting the most optimal results in EEG filtration process. Theexperiment results show that Blackman Window gives the best result in term of the most negative valuein stopband attenuation, the widest transition bandwidth, and the highest cutoff frequency compares toRectangular Window, Hamming Window, and Hann Window.

Keywords: Electroencephalography (EEG), Finite Impulse Response, Windowing Methods, Signal Filtering,Blackman Window


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Title: A MACHINE LEARNING APPROACH FOR IDENTIFYING DISEASE TREATMENT RELATIONSIN SHORT TEXTS

Author: T.V.M. SAIRAM, DR. G. RAMA KRISHNA

Abstract: The Machine learning (ML) region has proved its power in almost every industry and is currently areliable technology in health care industry. Computerized study of the clinical industry includessuitable care choice guide, healthcare photo and DNA connections. ML is recognized as a toolemploying computer systems integrating health care mechanisms resulting in more appropriate care andattention patients and further study or research on a disease. This paper provides powerful algorithmsand techniques used in diagnosing illness using remedy associated phrases from brief published writtentext launched in healthcare documents. The objective of this work is to show how Natural LanguageProcessing (NLP) and Machine Learning strategies can be used for reflection of information and whatclass strategies are appropriate for determining & figuring out suitable care information in briefpublished written textual content. This paper additionally focuses on suitable care analysis therapy &prevention of contamination, infection harm in human. The system found out some assignment ofclinical suitable care statistics, healthcare control, and man or woman health data and so forth. Theproposed method may be incorporated with any healthcare management software to make bettersuitable care selection. The inpatient management application can instantly mine biomedical data fromvirtual databases.

Keywords: HealthCare, System Mastering, Natural Language Processing, Aid Vector Machine, Choice Aid





System.


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Title: INDIAN SIGN LANGUAGE RECOGNITION SYSTEM USING NEW FUSION BASED EDGEOPERATOR

Author: M. V. D. Prasad, P. V. V. Kishore, E. Kiran Kumar, D. Anil Kumar

Abstract: The objective is to generate a basis for sign language recognizer under simple backgrounds.Complications arise in extracting shapes of hands and head using traditional segmentation models dueto nonuniform lighting. This paper proposes a wavelet based fusion of two weak edge detectionmodels. One is morphological subtraction model and the other is gradient based canny edge operator.Elliptical Fourier descriptors provide shape models with optimized number of shape descriptors.Principle components determined keep the feature vector to a minimum to accommodate all the framesin the video sequence. Classification of the signs is achieved by training a neural network trained withback propagation algorithm. The proposed method is exclusively tested many times with differentexamples for correct recognition sequence. Finally, the recognition rate stands at 92.34% whencompared to similar model using discrete cosine transform based features at 81.48%.

Keywords: Artificial Neural Network (ANN), Canny Segmentation, Elliptical Fourier Descriptors, MorphologySegmentation, Principle Component Analysis.


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Title: LEXICAL BASED METHOD FOR PHISHING URLS DETECTION

Author: AMMAR YAHYA DAEEF, R. BADLISHAH AHMAD, YASMIN YACOB

Abstract: Phishing is a social engineering attack that exploits users ignorance during system processing has animpact on commercial and banking sectors. Numerous techniques are developed in the last years todetect phishing attacks such as authentication, security toolbars, blacklists, phishing emails, phishingwebsites, and URL analysis. Regrettably, nowadays detection system implemented for specific attackvectors such as email which make developing wide scope detection is much needed. Previous studiesshow that analysis of URLs proved to be a good option to detect malicious activities where this methodmostly based on features of lexical, host information, and other complex method which requires a longprocessing time. In this paper, we present phishing detection system using features extracted from URLslexical only to meet two important goals which are wide scope of protection and applicability in a realtime system. The system provides accuracy of 94% and can classify single URL in average time of 0.12second.

Keywords: Phishing, Classifiers, Machine Learning, Lexical Features.


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Title: ERHREFFICIENT AND RELIABLE HETEROGENEOUS ROUTING PROTOCOL FOR SENSORNETWORKS

Author: THOTA SIVA RATNA SAI, DR. SRIKANTHVEMURI

Abstract: A WSN is a collection of various nodes having the capability to sense the information namely sensornodes and organized over a distributed region, in that region all nodes are communicated with eachother and forms the sensor network. The nodes of sensor network have limited communication interface,resources and computational resources. Moreover, sensor network are used in real life application.






Mainly, each application requires different capabilities of sensor devices such as capability of sensingand range of propagation. Consequently, heterogeneous sensor networks are came into existence.Previously, various routing protocols are exist but most of them are concentrating on single issue. Thoseare datacentric, hierarchical, location based and quality of service. In this article we intend a newrouting protocol it will address the heterogeneity of nodes and QOS issues. This protocol isimplemented with NS2 and performance of the protocol is compared with standard sensor routingprotocol AODV.

Keywords: Heterogeneous wireless sensor networks, Routing, Wireless, DataCentric, Hierarchical.


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Title: A DECISION SUPPORT SYSTEM FOR PREDICTING DIABETIC RETINOPATHY USINGNEURAL NETWORKS

Author: K CHANDANA, DR.Y.PRASANTH, J.PRABHU DAS

Abstract: Diabetic retinopathy (DR) is an eye fixed ill complete by the impairment of polygenic disorder and thatwe purchased to acknowledge it before of calendar for sensible treatment. As polygenic disorderadvances, the vision of a patient might begin to interrupt down and incite diabetic retinopathy. on theselines, 2 social occasions were perceived, specifically nonproliferative diabetic retinopathy (NPDR) ,proliferative diabetic retinopathy (PDR).during this paper, to dissect diabetic retinopathy, 3 models likeProbabilistic Neural framework (PNN), Bayesian Classification and Support vector machine (SVM)square measure pictured and their displays square measure thoughtabout. The live of the unwellnessunfold within the membrane are often recognized by analytic the elements of the membrane. Theelements like veins, hemorrhages of NPDR image and exudates of PDR image square measure off fromthe unrefined photos victimization the icon prepare techniques, fed to the classifier for gathering. acomplete of 350 structure photos were used, out of that100 were used for designing and 250 pictureswere used for testing. Exploratory results show that PNN has an accuracy of 89.6 % Bayes Classifierincorporates a exactness of 94.4% and SVM has an exactitude of 97.6%. This determines the SVMmodel beats one other model. What is more our system is equally continue running on 130 picturesopen from "DIARETDB0: Evaluation Database and Procedure for Diabetic Retinopathy" and also theresults show that PNN incorporates a exactness of 87.69% Bayes Classifier has an accuracy of 90.76%and SVM has a precision of 95.38%.

Keywords: Probabilistic Neural Network Bayesian Classification, Support Vector Machine, Sensitivity, Specificity,Accuracy


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Title: RECOGNIZING GENDER THROUGH FACIAL IMAGE USING SUPPORT VECTOR MACHINE

Author: FITRI DAMAYANTI, AERI RACHMAD

Abstract: The face is one part of the human body that has special characteristics, which is often used todistinguish the identity of one individual and another. Facial recognition is very important to bedeveloped since this application is applied in the security system. The recognition of sex is one part ofthe face recognition. Gender plays an important role in our interactions in the community and with thecomputer. Classification gender of the face image can be applied in the field of demographic datacollection, humancomputer interface (customize the behavior of software in connection with the sex ofthe user) and others. The purpose of this study is to make implementation of the system in recognizingthe gender on facial image or filling the form with the Gender Recognition face image that is able torecognize a person s sex quickly and accurately, and run well. This study used methods of TwoDimensional Linear Discriminant Analysis (TDLDA) for feature extraction, which directly assesswithinclass scatter matrix of the transformation matrix without any image into a vector image, and thisresolves the singular problem withinclass scatter matrix. To obtain optimal recognition results of the





classification method, it used the classification Support Vector Machine. This study integrates TDLDAand SVM methods for the introduction of gender based on facial image. The combination of bothmethods proves the optimal results with an accuracy of 74% to 92% with a test that uses a database offaces taken from http://www.advancedsourcecode.com.

Keywords: Support Vector Machine, Two Dimensional Linear Discriminant Analysis, Gender


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Title: HUMAN EMOTION DETECTION THROUGH FACIAL EXPRESSIONS

Author: KRISHNA MOHAN KUDIRI, ABAS MD SAID, M YUNUS NAYAN

Abstract: Human to human social communication in reallife is possible through different modalities like facialexpressions, speech and body poses. However, facial expressions plays important role while dealingwith human emotions in reallife than the other modalities. It is because facial expression provides nonverbal data towards emotions. And also gives emotion of a person towards his goal. On the other hand,speech and body poses are mostly language and culture dependent respectively which creates problemwhile detection emotions of a person. Thus in order to deal with the above issues, this research workfocused on facial expressions instead other modalities. To improve detection performance of the system,proposed Relative SubImage Based features is used. Support Vector Machine with radial basis kernel isused for classification. Total six basic emotions (angry, sad, happy, disgust, boredom and surprise) aretested. From experimental results, the proposed Relative SubImage Based features enhanced theclassification rates than the conventional features.

Keywords: Relative SubImage Based (RSB), Support Vector Machine (SVM), Human Computer Interaction (HCI).


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Title: AN APPROACH FOR EFFICIENT AND SECURE COOPERATIVE WIRELESS NETWORKSUSING TRANSMISSION RELIABILITY PROTOCOL

Author: APOORVA P, NAGARJUN S, PARVATHI T V

Abstract: Energy efficiency and security is the major problems identified in wireless sensor networks. This workintroduces the Secured Cooperative communication protocol in wireless sensor networks forestablishment of cooperative clusters during transmission of data in a collective way. In the cooperationprocess using cooperative transmission protocol, recruitment policy helps the nodes to cooperate eachother. Cluster head on one node thick path recruit neighbouring nodes to assist in communication.Proposed method aimed to build security between the intermediate cluster nodes and also minimize theoverall energy consumption and increase the transmission reliability of packet delivery between asource and a sink in an unreliable wireless network by giving some level of cooperation among them.Cooperative Transmission Protocol that uses any wireless networks communications between anysource node and sink node can be with optimal energy and not compromising with the reliability oftransmission to decrease packet loss. In order to bring the security among the nodes inside the clusterthe method called Rijndael algorithm is used as an Advanced Encryption Standard (AES). AESprovides flexibility and security between the systems when compared with other cryptographicalgorithms. To enhance efficiency of sensors, the existing algorithm found inefficient. Hence with allaccounting of the existing systems, this work concentrates on reducing energy consumption byselecting only few/optimal node and also maintains a data cache until an acknowledgement is receivedfrom receiving cluster head upon successful transmission.

Keywords: Rijndael Algorithm, Rijndael Managed Objects (RMO), Crypto Stream Object, CooperativeTransmission, Salt Data, Initialization Vector(IV), Cooperative Caching.






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Title: EXTRACTION AND PROCESSING OF SITUATION SPATIOTEMPORAL TRAFFIC USING SVMALGORITHM WITH BIG DATA

Author: S HEMA LATHA, K SUBRAHMANYAM

Abstract: With the wide variety of motion sensors that traffic information can come from many research has beenreserved for the development of traffic forecast, which in turn increases the shipping routes, trafficmanagement, urban planning, etc. The most important challenge is to predict how traffic based onpredictive models based on historical data traffic in real time, which may differ from historical data andchange over time. In this system can learn new context of the current online traffic situation (or context)in real time, most effectively formed using a predictive historical data traffic model is intended topredict the future of the current situation. If traffic in real time, distributed environment enters thebloodstream space efficiently adapt to assess the effectiveness of each significant predictor differentsituations. We can show you the way, and shortterm and longterm performance guarantees (STEP), ouralgorithm is designed in accordance with the algorithm works well in situations where there are no realsigns (for ex. Traffic Ready) or later. We proposed an algorithm called Extraction and Processing ofsituation Spatiotemporal traffic using SVM algorithm with Big data By using the proposed framework,a context in which the most important is to predict the traffic by monitoring the movement of vehicles,which can further reduce the complexity of the request and inform the tradepolicy. Our experiencewith real data in realtime circumstances indicates that the proposed approach is superior to existingsolutions.

Keywords: Big data, Spatiotemporal, GPS, Traffic.


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Title: DIAGNOSIS OF HEART DISEASE USING NEURAL NETWORKSCOMPARATIVE STUDY OFBAYESIAN REGULARIZATION WITH MULTIPLE REGRESSION MODEL

Author: K.SAI KRISHNASREE, M.R.NARASINGA RAO

Abstract: Heart disease is one of the significant reasons of death and the progress of which is rampant all over theglobe. Blood vessels carry blood with oxygen to all the cells in the body. It is a common reason that,Cholesterol and other substances can be deposited in blood vessels which block blood vessels and thatno blood and oxygen can get to heart. This leads to heart disease. Several works have been made topredict the heart disease in different methods. The main aim of this paper is to predict heart diseaseusing Multiple Regression and Bayesian Regularization methods and compare the results of thesemodels. Multiple Regression is one of the strong model used for prediction and it shows the associationbetween input variables and output variable. It predicts the output variable based on the relationshipbetween one or more input variables and target variable. Bayesian regularization is a statistical modelwhich process nonlinear dataset. It increases the generalization capability and decreases squared errors.Bayesian regularization works on with large inputs efficiently. The results are calculated using MultipleRegression and Bayesian Regularization methods and predicted the heart disease. The results ofMultiple Regression and Bayesian Regularization are compared and it is observed that the resultsgenerated from Bayesian Regularization are more accurate than multiple regression model.

Keywords: Bayesian Regularization, Multiple regression, Heart disease, Artificial Neural Network (ANN),Prediction.


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Title: FUSION OF COLOR AND STATISTIC FEATURES FOR ENHANCING CONTENTBASED IMAGERETRIEVAL SYSTEMS






Author: AHMAD B. A. HASSANAT, AHMAD S. TARAWNEH

Abstract: Contentbased image retrieval is one of most debated topics in computer vision research, and hasreceived a great deal of interest recently. It aims to retrieve similar images from a huge unlabelled imagedatabase. In this work we propose a method that reduces the error rate and retrieves relevant imagesearly in the process, with the ability to work on both color and grayscale images. The proposed methodscans an image using 8x8 overlapping blocks, extracting a set of probability density functions of themost discriminative statistical features. Our experiments, conducted on several image databases, showthe robustness of the proposed method, outperforming some of the most popular methods described inthe literature.

Keywords: CBIR, Color Features, Statistical Features, Image Analysis, Computer Vision, Face Searching


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Title: MORPH SYNTACTIC ANALYSIS OF ARABIC WORDS BY DETERMINISTIC FINITEAUTOMATON (DFA)

Author: HAMMAD BALLAOUI, EL HABIB BEN LAHMAR, NASSER LABANI

Abstract: In this article we introduce a technical analysis that permits; on the one hand, to help the user todiscriminate optimally the morphological results of a word in an Arabic text and to identify its nature(noun or a verb) on the basis of these prefixes, suffixes and its particles of attributions. On the otherhand, we can determine the syntactic results of each analyzed word on the basis of the context. In thishumble work, the approach has two steps: In the first stage, the study focuses on a broad analysis ofwords on the basis of Arabic rules. Then, in the second stage, we can clarify a technique based on adeterministic finite automaton (DFA), which is designed to treat the chosen words character bycharacter in the sense of a suitable transition. In the final output and via different labels, the systemdetermines the nature, the gender and number for each automated word.

Keywords: Arabic Language, Automatic Natural Language Processing, Deterministic Finite Automaton (DFA),Disambiguation, Labeling, Morphsyntactic Analysis.


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Journal of Theoretical and Applied Information Technology30th June 2016. Vol.88. No.3

© 2005 - 2016 JATIT & LLS. All rights reserved.

ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195

607

RECOGNIZING GENDER THROUGH FACIAL IMAGE USINGSUPPORT VECTOR MACHINE

1FITRI DAMAYANTI, 2AERI RACHMAD1,2Faculty of Engineering, University of Trunojoyo Madura, Indonesia

E-mail: [email protected]

ABSTRACT

The face is one part of the human body that has special characteristics, which is often used to distinguishthe identity of one individual and another. Facial recognition is very important to be developed since thisapplication is applied in the security system. The recognition of sex is one part of the face recognition.Gender plays an important role in our interactions in the community and with the computer. Classificationgender of the face image can be applied in the field of demographic data collection, human-computerinterface (customize the behavior of software in connection with the sex of the user) and others. Thepurpose of this study is to make implementation of the system in recognizing the gender on facial image orfilling the form with the Gender Recognition face image that is able to recognize a person's sex quickly andaccurately, and run well. This study used methods of Two Dimensional Linear Discriminant Analysis(TDLDA) for feature extraction, which directly assess within-class scatter matrix of the transformationmatrix without any image into a vector image, and this resolves the singular problem within-class scattermatrix. To obtain optimal recognition results of the classification method, it used the classification SupportVector Machine. This study integrates TDLDA and SVM methods for the introduction of gender based onfacial image. The combination of both methods proves the optimal results with an accuracy of 74% to 92%with a test that uses a database of faces taken from http://www.advancedsourcecode.com.

Keywords: Support Vector Machine, Two Dimensional Linear Discriminant Analysis, Gender

1. INTRODUCTION

The face is one of the easiest physiologicalmeasure and often used to distinguish the identityof one individual to another. The human brain hasthe ability to recognize and distinguish betweenthose which face each other with a relatively quickand easy. Face recognition man is one field that isdeveloping today. Application of face recognitioncan be applied in the field of security system suchas room access permission.One part of face recognition that has beendeveloped is the recognition of sex (genderrecognition). It has similarities between genderrecognition and face recognition that lies in itsextraction process. However, there is still a littledifference in the classification process. All of theserecognitions which are used to calculate how manypeople are male or female who are coming to astore or a public agency are still processedmanually, so it takes a longer time. To facilitatewhat advertisements are displayed on electronicbillboards in public places or roadside, it can beadjusted with the sex of the person who passed theadvertisements. That is why, this software for theintroduction of gender based on facial image was

created to facilitate and speed up the processingtime.The difficulties in the process of gender recognitionis mainly because of the complexities of thecondition of the face, such as the position of theimage, lighting and expression of different imagesthat have a high dimension that must go through theprocess of the compression / extraction prior to thedata processed by the method of classification.The previous research which was associated withthis research is the research conducted by BurhanErgen and Serdar Abut entitled "GenderRecognition Using Facial Images". In that study, itconducted the recognition of gender-based facialimage using GLCM. The trial results have anaccuracy rate of 60% by using Face FEI databaseconsisting of 100 female face images and the facialimage 100 men [1].A research which was conducted by VladimirKhryashchev, Andrey Priorov, Lev Shmaglit andMaxim Golubev entitled "Gender Recognition ViaFace Area Analysis", provides the recognition ofgender-based facial image using Adaptive Featureand SVM method. The trial results have anaccuracy rate of 90.8% using ferrets database [2].




608

Another research which was conducted by NafinFenanda, Rima Tri Wahyuningrum, FitriDamayanti entitled "Introduction to Gender BasedFacial Image Method Using Local Binary Pattern(LBP) and Fisherface", presented the recognition ofgender-based facial image using LBP andFisherface. The trial results have the highest degreeof accuracy by 75% by using a database drawnfrom http://www.advancedsourcecode.com [3].The last research which was conducted by FitriDamayanti entitled "Introduction to Gender BasedFacial Image Method Using Two-DimensionalLinear Discriminant Analysis", proved therecognition of gender-based facial image usingTDLDA and ED. The trial results have the highestdegree of accuracy by 89% by using a databasetaken from http://www.advancedsourcecode.com[4].

This study integrates LDA and SVM for theintroduction of gender-based facial image. TDLDAis used as a feature extraction method that directlyassess within-class scatter matrix of images withoutimage transformation matrix into a vector.Moreover, it is used to overcome the problem ofsingular matrices within-class scatter. TDLDAwears fisher criterion to find the optimaldiscriminating projections which are obtained fromall the features of the selected faces which arelooking eigen-values and the greatest eigenvectors.Classification method which is used is the classifierSupport Vector Machine (SVM). SVM classifieruses a function or hyperplane to separate the twoclasses of patterns. SVM will try to find the optimalhyperplane in which two classes of patterns can beseparated to the maximum.

2. SYSTEM DESIGN

Broadly speaking, this system consists of twoparts, namely the image of the training process andthe testing process. In Figure 1, it is an outline ofthe picture recognition system based on the sex ofthe face image. In the training process TDLDAthere is a process used to extract features, thefeatures that are selected during the training processused in the classification process and is also used toget features that are selected in the trial data. Eachface data base used is divided into two, partly usedfor training process and the rest was used for thetesting process.

Figure 1. System Introduction to Gender BasedFacial Image.2.1 Feature extractionExtraction feature in the training process is doneusing Two-Dimensional Linear DiscriminantAnalysis. This stage aims to get the features that areselected from the data enter training. These featuresare selected to obtain from all the facial features,look for eigen-values and eigen-vectors greatest.Features that are selected will be used for theclassification process is used for training andtesting data feature extraction.Extraction feature in the testing process is done bytaking the feature extraction results on the trainingprocess which was applied to the test data. Featureextraction results on this test data is used as input tothe classification process testing.

2.2 Algorithm Design TDLDAHere are the steps in the process TDLDA against adatabase of training images [5]:1. If a database of facial images are a set of ntraining image Ai = [A1, A2, ..., An] (i = 1,2, ..., n)with dimensions of image (RXC), then the total setof all the image matrix are:

An =

rcnrnrn

cnnn

cnnn

AAA

AAAAAA

)(2)(1)(

2)(22)(21)(

1)(12)(11)(

...............

...

...(1)

2. Determining the value 1 (dimension projectionlines) and 2 (dimensional projection column). ≤value ≤ r and c.3. The next step is the calculation of the averageimage of classroom training to i:

iXi

i Xn

M 1 (2)

4. Calculate the average of all training image:




609

M =n1

k

i X iX

1(3)

5. Establish the transformation matrix R size (c,2 )

which is obtained from a combination of theidentity matrix size ( 2 ,

2 ) with zero matrix size(c- 2 , 2 ).6. Calculate the between class scatter matrix R inaccordance with equation (4).S R

b = Ti

Tk

iii MMRRMMn )()(

1

the size of the

matrix (r x r) (4)7. Counting within class scatter matrix R inaccordance with equation (5).S R

W = ,)()(1

Ti

Ti

k

i xMXRRMX

i

the size of the

matrix (r x r) (5)8. Calculate the generalized eigenvalue ( i ) of SRb and S R

W using SVD in accordance with equation(6)J4(L) = maxtrace((LTS R

W L)-1(LTS Rb L)), maxtrace

size of the matrix (r x r) (6)9. Take as much eigenvector 1 from step 8 as a

transformation matrix of rows (L). L = [L

1 , ...,L1

] as the size of the matrix (r x 1 ).10. Calculate the range class scatter matrix Laccording to equation (7).S L

b = )()(1

MMLLMMn iTT

k

iii

,the size of the

matrix (c x c). (7)11. Counting within class scatter matrix Laccording to equation (8).S L

W = ),()(1

iTT

i

k

i xMXLLMX

i

the size of

the matrix (c x c). (8)12. Calculate the generalized eigenvalue ( i ) of SLb andS L

W using SVD in accordance with equation(9).J5(R)=maxtrace((RTS L

W R)-1(RTS Lb R)),the size of the

matrix (c x c). (9)13. Take as much eigenvector 2 of step 12 as thetransformation matrix column (R). R = [ R

1 , ...,R

2 ], the size of the matrix (c x 2 ).14. Calculate the extraction feature matrixBi=LTAiR, the size of the matrix (

1 x2 )

15. Output: Bi extraction feature matrix, linematrix transformation L, and the transformationmatrix column R.

2.3 ClassificationThe classification of SVM is divided into

two processes, namely the processes of training andtesting process. In the SVM, training process usesthe feature matrix which is generated in theextraction process as input features. While in testSVM, utilizing the projection matrix is generated inthe process of feature extraction which is thenmultiplied by the test data (test samples) as input.The classification of SVM for Multiclass OneAgainst All will build a number of binary SVM k(k is the number of classes). The decision has afunction that has a maximum value, indicating thatthe data xd is the members of the class of functionsof that decision. Block diagram SVM training andtesting process is shown in Figure 2 [6].

Figure 2. Block diagram of the process of trainingand classification using SVM.

Training data that has been projected by TDLDA,then became the SVM training data. If thedistribution of the data generated in the processTDLDA have a linear distribution, then one of themethods used SVM is used to classify these datawhich is used to transform data into dimensionalfeature space, so it can be separated linearly on thefeature space. Because the feature space in practice




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usually has a higher dimension of the input vector(input space). This resulted in computing thefeature space may be very large, because there isthe possibility of feature space can have a numberof features that are not infinite. So on SVM used"kernel trick". Kernel functions used in this study isa Gaussian shown in equation 10. [7]

K(x,y)=exp ))2(||( 2

2

yx

. (10)

A number of support vector at each training data tolook for to get the best solution interface. The issueof the best interface solutions can be formulated inequation 11.

l

iijijiji

l

ii xxyyQ

1,1 21)( , (11)

Where : 0),...,2,1(01

i

l

iii yli .

Data ix were correlated with αi> 0 iscalled a support vector. Thus, it can be obtained thevalue which will be used to find w. Solutioninterface obtained by the formula: w =Σαiyixi; b= yk- wTxkfor every xk, withαk0.The testing process or classification is carried outon every binary SVM use the value w, b, and xigenerated in the training process in any binarySVM. The resulting function for the testing processis shown in Equation 12.

,),( iidii bwxxKf (12)where: i = 1 to k; xi = support vector; xd = test data.The output is in the form of an index i with thegreatest fi which is a class of test data.

2.4 Data UsedTesting the system in this study uses the

test data a 200x200 pixel image 400 of 200 imagesof 200 male and female image. Trial data are takenfrom http://www.advancedsourcecode.com and hasbeen used in previous studies. The data is the dataof these trials tested testing using SVMclassification with training data. Figure 3 showsseveral examples of facial images that are used as adata testing and training data.

Figure 3. Example Of Facial Imagery Used For DataTrial

Scenario experiments performed in thisstudy are divided into eight scenarios, the scenario1, scenario 2 scenario 3, scenario 4, 5 scenario,scenario 6, 7 scenario, the scenario 8. Thedifference of those eight scenarios lie in the amountof training data and data testing used. More detailscan be seen in Table 1.

Table 1 Simulation Scenarios In SystemScenario Data Training Data Testing

1 300images

150 Female150 Male

100images

50 Female50 Male

2 280image

140 Female140 Male

120images

60 Female60 Male

3 260image

130 Female130 Male

140images

70 Female70 Male

4 240images

120 Female120 Male

160images

80 Female80 Male

5 160images

80 Female80 Male

240images

120 Female120 Male

6 140images

70 Female70 Male

260images

130 Female130 Male

7 120images

60 Female60 Male

280images

140 Female140 Male

8 100images

50 Female50 Male

300images

150 Female150 Male

3. EXPERIMENTS AND RESULTS

The methods which are used in this test aredivided into three groups. The first group uses theLocal Binary Pattern (LBP) for preprocessing, it isFisherface method for feature extraction andclassification methods Euclidean Distance (ED) asthe research done by Nafin Fenanda, Rima TriWahyuningrum, Damayanti Fitr [3]. The secondgroup uses methods TDLDA as feature extractionand classification methods Euclidean Distance as[4]. The third group uses methods TDLDA asfeature extraction and SVM as the classificationmethod. The third group was a study done byresearchers. Table 2 shows the comparison of thetest results from previous studies and researchundertaken by researchers.

Table 2 Comparison of Results of Testing




611

Table 2 shows that the percentageintroduction TDLDA - SVM is higher than themethod TDLDA-ED and methods LBP-Fisherface-ED. It would be easier to see the differencebetween the trial results TDLDA-SVM methodwith other methods by using bar charts. Figure 4shows the results of testing against a databasewhich is taken fromhttp://www.advancedsourcecode.com to see thedifferences of TDLDA-SVM method to the othermethods.

Figure 4. Graph recognition success rate for each testvariations on Database taken from

http://www.advancedsourcecode.comu using TDLDA-SVM method and other methods.

The excellence of TDLDA method – SVMis compared to other methods like follows:

i. TDLDA - SVM compared with TDLDA - ED.In the ED, it did not concern to the distribution ofthe data which is only based on the distance of thenew data into multiple data / nearest neighbor. Itcould be data / nearest neighbor which was not agroup, so that the resulting classification is wrong.In SVM, the distribution of data is trying to find afunction separator (classifier) which is optimumthat can separate the two sets of data from twodifferent classes. Each class has a different patternand is separated by a dividing function, so that ifthere is new data that will be known, the classes areclassified according to the new data. Thus theresulting classification is more perfect than theother classification methods.

ii. TDLDA - SVM compared with Fisherface.In Fisherface pre-processing procedures to reducethe dimension using PCA may cause the loss ofsome important discriminant information for LDAalgorithm is applied after PCA.

In TDLDA take full advantage of the informationthat is discriminatory on the scope of the face (facespace), and did not throw some subspace whichmay be useful for the introduction.From the results of experiments which wereconducted, there were some incorrect recognition,like women were recognized as men in therecognition results, and vice versa. Somerecognition that were caused by several factors,namely the shape of the head, the hair shape andexpression between the image of women and men.

4. CONCLUSION

From the experiments that have been done can bedrawn conclusions as follows:1. Method TDLDA - SVM was able to show that

the optimal recognition accuracy which iscompared to other methods (TDLDA - ED, LBP -Fisherface - ED). This is because the singularTDLDA is able to overcome the problem, tomaintain the existence of discriminatoryinformation, and to maximize the distancebetween classes and minimizes inter-classdistance. While SVM has the ability to discoverthe function of separator (classifier) which isoptimum.

2. There are three important variables that affect thesuccess rate of introduction, that is the use ofsequence variations of training samples per class,the use of the number of training samples perclass, and the number of dimensions ofprojection.

3. From the test results using TDLDA-SVMmethod, it obtained recognition accuracy rate ofbetween 74% to 92%.

4. Incorrect classification of the trials was caused bythe head shape, the hair style and faceexpressions between the image of women andmen.

5. SUGGESTIONThis research will be continued to recognizephrases that will be applied in the form of multiface images. In addition to the field of digitalimage processing.

REFERENCES:

[1]. Ergen B, Abut S. Gender Recognition UsiangFacial Images. International Conference onAgriculture and Biotechnology. 2013. Vol 60 :112-117.

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20

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age

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[2]. Khryashchev V, Priorov A, Shmaglit L, andGolubev M. Gender Recognition Via FaceArea Analysis. Proceedings of The WorldCongress on Engineering and CompuerScience. 2012. Vol 1 .

[3]. Fenanda N, Wahyuningrum RT, Damayanti F.Introduction of Gender Based Facial ImageMethod Using Local Binary Pattern (LBP) andFisherface. Bangkalan. InformaticsEngineering University of Trunojoyo Madura.2015.

[4]. Damayanti F. Introduction of Gender BasedFacial Image Method Using Two-DimensionalLinear Discriminant Analysis. NationalConference Nasional System & Informatics.2015.

[5]. Quan XG, Lei Z, and David Z. FaceRecognition Using FLDA With SingleTraining Image Per Person. AppliedMathematics and Computation. 2008. Vol 205: 726-734.

[6]. Burges JC. A Toturial on Support VectorMachines for Pattern Recognition, DataMining and Knowledge Discovery. 2 (2) : 955-974. 1998.

[7]. Hsu, Chih-Wei,Chih-Jen Lin. A Comparison ofMethods for Multi-class Support VectorMachines. IEEETransactions on Neuralnetwork. 13(2) : 415-425. 2002.

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