International Journal of Innovative Technology and Exploring … · 2020. 7. 6. · International...
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Transcript of International Journal of Innovative Technology and Exploring … · 2020. 7. 6. · International...
Editor-In-Chief Dr. Shiv Kumar
Ph.D. (CSE), M.Tech. (IT, Honors), B.Tech. (IT), Senior Member of IEEE, Member of the Elsevier Advisory Panel
Blue Eyes Intelligence Engineering and Sciences Publication, Bhopal (MP), India
Associate Editor-In-Chief Chair Dr. Hitesh Kumar
Ph.D.(ME), M.E.(ME), B.E. (ME)
Professor and Head, Department of Mechanical Engineering, Technocrats Institute of Technology, Bhopal (MP), India
Dr. Anil Singh Yadav
Ph.D(ME), ME(ME), BE(ME)
Professor, Department of Mechanical Engineering, LNCT Group of Colleges, Bhopal (M.P.), India
Dr. Gamal Abd El-Nasser Ahmed Mohamed Said
Ph.D(CSE), MS(CSE), BSc(EE)
Department of Computer and Information Technology, Port Training Institute, Arab Academy for Science, Technology and Maritime
Transport, Egypt
Members of Associate Editor-In-Chief Chair Dr. Mayank Singh
PDF (Purs), Ph.D(CSE), ME(Software Engineering), BE(CSE), SMACM, MIEEE, LMCSI, SMIACSIT
Department of Electrical, Electronic and Computer Engineering, School of Engineering, Howard College, University of KwaZulu-
Natal, Durban, South Africa.
Scientific Editors Prof. (Dr.) Hamid Saremi
Vice Chancellor of Islamic Azad University of Iran, Quchan Branch, Quchan-Iran
Dr. Moinuddin Sarker
Vice President of Research & Development, Head of Science Team, Natural State Research, Inc., 37 Brown House Road (2nd Floor)
Stamford, USA.
Prof. (Dr.) Nishakant Ojha
Principal Advisor (Information &Technology) His Excellency Ambassador Republic of Sudan& Head of Mission in New Delhi, India
Dr. Shanmugha Priya. Pon
Principal, Department of Commerce and Management, St. Joseph College of Management and Finance, Makambako, Tanzania, East
Africa, Tanzania
Dr. Veronica Mc Gowan
Associate Professor, Department of Computer and Business Information Systems,Delaware Valley College, Doylestown, PA, Allman,
China.
Dr. Fadiya Samson Oluwaseun
Assistant Professor, Girne American University, as a Lecturer & International Admission Officer (African Region) Girne, Northern
Cyprus, Turkey.
Dr. Robert Brian Smith
International Development Assistance Consultant, Department of AEC Consultants Pty Ltd, AEC Consultants Pty Ltd, Macquarie
Centre, North Ryde, New South Wales, Australia
Dr. Durgesh Mishra
Professor (CSE) and Director, Microsoft Innovation Centre, Sri Aurobindo Institute of Technology, Indore, Madhya Pradesh India
Prof. MPS Chawla
Member of IEEE, Professor-Incharge (head)-Library, Associate Professor in Electrical Engineering, G.S. Institute of Technology &
Science Indore, Madhya Pradesh, India, Chairman, IEEE MP Sub-Section, India
Dr. Vinod Kumar Singh
Associate Professor and Head, Department of Electrical Engineering, S.R.Group of Institutions, Jhansi (U.P.), India
Dr. Rachana Dubey
Ph.D.(CSE), MTech(CSE), B.E(CSE)
Professor, Department of Computer Science & Engineering, Lakshmi Narain College of Technology Excellence (LNCTE), Bhopal
(M.P.), India
Executive Editor Chair Dr. Deepak Garg
Professor, Department Of Computer Science And Engineering, Bennett University, Times Group, Greater Noida (UP), India
Members of Executive Editor Chair Dr. Vahid Nourani
Professor, Faculty of Civil Engineering, University of Tabriz, Iran.
Dr. Saber Mohamed Abd-Allah
Associate Professor, Department of Biochemistry, Shanghai Institute of Biochemistry and Cell Biology, Shanghai, China.
Dr. Xiaoguang Yue
Associate Professor, Department of Computer and Information, Southwest Forestry University, Kunming (Yunnan), China.
Dr. Labib Francis Gergis Rofaiel
Associate Professor, Department of Digital Communications and Electronics, Misr Academy for Engineering and Technology,
Mansoura, Egypt.
Dr. Hugo A.F.A. Santos
ICES, Institute for Computational Engineering and Sciences, The University of Texas, Austin, USA.
Dr. Sunandan Bhunia
Associate Professor & Head, Department of Electronics & Communication Engineering, Haldia Institute of Technology, Haldia
(Bengal), India.
Dr. Awatif Mohammed Ali Elsiddieg
Assistant Professor, Department of Mathematics, Faculty of Science and Humatarian Studies, Elnielain University, Khartoum Sudan,
Saudi Arabia.
Technical Program Committee Chair Dr. Mohd. Nazri Ismail
Associate Professor, Department of System and Networking, University of Kuala (UniKL), Kuala Lumpur, Malaysia.
Members of Technical Program Committee Chair Dr. Haw Su Cheng
Faculty of Information Technology, Multimedia University (MMU), Jalan Multimedia (Cyberjaya), Malaysia.
Dr. Hasan. A. M Al Dabbas
Chairperson, Vice Dean Faculty of Engineering, Department of Mechanical Engineering, Philadelphia University, Amman, Jordan.
Dr. Gabil Adilov
Professor, Department of Mathematics, Akdeniz University, Konyaaltı/Antalya, Turkey.
Dr.Ch.V. Raghavendran
Professor, Department of Computer Science & Engineering, Ideal College of Arts and Sciences Kakinada (Andhra Pradesh), India.
Dr. Thanhtrung Dang
Associate Professor & Vice-Dean, Department of Vehicle and Energy Engineering, HCMC University of Technology and Education,
Hochiminh, Vietnam.
Dr. Wilson Udo Udofia
Associate Professor, Department of Technical Education, State College of Education, Afaha Nsit, Akwa Ibom, Nigeria.
Dr. Ch. Ravi Kumar
Dean and Professor, Department of Electronics and Communication Engineering, Prakasam Engineering College, Kandukur (Andhra
Pradesh), India.
Dr. Sanjay Pande MB
FIE Dip. CSE., B.E, CSE., M.Tech.(BMI), Ph.D.,MBA (HR)
Professor, Department of Computer Science and Engineering, G M Institute of Technology, Visvesvaraya Technological University
Belgaum (Karnataka), India.
Dr. Hany Elazab
Assistant Professor and Program Director, Faculty of Engineering, Department of Chemical Engineering, British University, Egypt.
Dr. M.Varatha Vijayan
Principal, Department of Mechanical Engineering, Mother Terasa College of Engineering and Technology, Pudukkottai (Tamil Nadu)
India.
Dr. S. Balamurugan
Director, Research and Development, Intelligent Research Consultancy Services (IRCS), Coimbatore (Tamil Nadu), India.
Dr. Rajalakshmi Rahul
FIE Dip. CSE., B.E, CSE., M.Tech.(BMI), Ph.D.,MBA (HR)
Founder and CEO Talaash Research Consultants, Chennai (Tamil Nadu), India.
Editorial Chair Dr. Arun Murlidhar Ingle
Director, Padmashree Dr. Vithalrao Vikhe Patil Foundation’s Institute of Business Management and Rural Development, Ahmednagar
(Maharashtra) India.
Members of Editorial Chair Dr. J. Gladson Maria Britto
Professor, Department of Computer Science & Engineering, Malla Reddy College of Engineering, Secunderabad (Telangana), India.
Dr. Wameedh Riyadh Abdul-Adheem
Academic Lecturer, Almamoon University College/Engineering of Electrical Power Techniques, Baghdad, Iraq
Dr. T. Sheela
Associate Professor, Department of Electronics and Communication Engineering, Vinayaka Mission’s Kirupananda Variyar
Engineering College, Periyaseeragapadi (Tamil Nadu), India
Dr. Manavalan Ilakkuvan
Veteran in Engineering Industry & Academics, Influence & Educator, Tamil University, Thanjavur, India
Dr. Shivanna S.
Associate Professor, Department of Civil Engineering, Sir M.Visvesvaraya Institute of Technology, Bengaluru (Karnataka), India
Dr. H. Ravi Kumar
Associate Professor, Department of Civil Engineering, Sir M.Visvesvaraya Institute of Technology, Bengaluru (Karnataka), India
Dr. Pratik Gite
Assistant Professor, Department of Computer Science and Engineering, Institute of Engineering and Science (IES-IPS), Indore (M.P),
India
Dr. S. Murugan
Professor, Department of Computer Science and Engineering, Alagappa University, Karaikudi (Tamil Nadu), India
Dr. S. Brilly Sangeetha
Associate Professor & Principal, Department of Computer Science and Engineering, IES College of Engineering, Thrissur (Kerala),
India
Dr. P. Malyadri
Professor, ICSSR Senior Fellow Centre for Economic and Social Studies (CESS) Begumpet, Hyderabad (Telangana), India
Dr. K. Prabha
Assistant Professor, Department of English, Kongu Arts and Science College, Coimbatore (Tamil Nadu), India
Dr. Liladhar R. Rewatkar
Assistant Professor, Department of Computer Science, Prerna College of Commerce, Nagpur (Maharashtra), India
Dr. Raja Praveen.N
Assistant Professor, Department of Computer Science and Engineering, Jain University, Bengaluru (Karnataka), India
Dr. Issa Atoum
Assistant Professor, Chairman of Software Engineering, Faculty of Information Technology, The World Islamic Sciences & Education
University, Amman- Jordan
Dr. Balachander K
Assistant Professor, Department of Electrical and Electronics Engineering, Karpagam Academy of Higher Education, Pollachi
(Coimbatore), India
Dr. Sudhan M.B
Associate Professor & HOD, Department of Electronics and Communication Engineering, Vins Christian College of Engineering,
Anna University, (Tamilnadu), India
Dr. T. Velumani
Assistant Professor, Department of Computer Science, Kongu Arts and Science College, Erode (Tamilnadu), India
Dr. Subramanya.G.Bhagwath
Professor and Coordinator, Department of Computer Science & Engineering, Anjuman Institute of Technology & Management
Bhatkal (Karnataka), India
Dr. Mohan P. Thakre
Assistant Professor, Department of Electrical Engineering, K. K. Wagh Institute of Engineering Education & Research Hirabai
Haridas Vidyanagari, Amrutdham, Panchavati, Nashik (Maharashtra), India
Dr. P Venkata Subbareddy
Professor, Department of Computer Science and Engineering, Annamalai University (Tamil Nadu), India.
Dr. Muttipati Appala Srinuvasu
Professor, Department of Computer Science and Engineering, Gitam Deemed To Be University, Gandhi Nagar, Rushikonda
Visakhapatnam (Andhra Pradesh), India.
Dr. Namita Gupta
Professor, Department of Economics, MG Kashi Vidyapeeth, Varanasi (Uttar Pradesh), India.
Dr. Chandan Medatwal
Professor, Department of Management, University Of Kota, MBS Marg, Kota (Rajasthan), India.
Dr. Narasimhan D
Professor, Department of Mathematics, Srinivasa Ramanujan Centre Sastra Deemed University Kumbakonam (Tamil Nadu), India.
Dr. Yuriy Pyvovar
Professor, Department of Constitutional and Administrative Law, National Aviation University, Kiev, Ukraine.
Dr. Asim K. Mandal
Professor, Department of Agriculture, Bidhan Chandra Krishi Viswavidyalaya (BCKV), Mohanpur, Nadia (West Bengal), India.
Dr. Lokesh P Gagnani
Professor, Department of Computer Science and Engineering, C U Shah University, Nr. Kothariya Village, Dist. Surendranagar,
Wadhwan (Gujarat), India.
Dr. Trilochan Jena
Professor, Department of Civil Engineering, Siksha O Anusandhan (Deemed to be University), ITER, Bhubaneswar (Odisha), India.
Dr. S. Ismail Kalilulah
Professor, Department of Computer Science and Engineering, St. Peter’s Engineering College, Avadi, Chennai (Tamil Nadu), India.
Dr. S Vijayakumar
Professor, Department of Computer Science and Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
Dr. Serhii Kozlovskyi
Professor, Department of Economics, Vasyl’ Stus Donetsk National University, Vinnytsia, Ukraine.
Dr. V. Jaiganesh
Professor, Department of Mechanical Engineering, Anna University Chennai (Tamil Nadu), India.
Dr. Mohankumar Namdeorao Bajad
Professor, Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Surat (Gujarat), India.
Dr. G. Purushotham
Professor, Department of Mechanical Engineering Sciences, Visvesvaraya Technological University, Belagavi (Karnataka), India.
Dr. Rajendiran Muthusamy
Professor, Department of Computer Science and Engineering, Sathyabama University, Chennai (Tamil Nadu), India.
Dr. S Madhava Reddy
Professor, Department of Mechanical Engineering, Osmania University, Hyderabad (Telangana), India.
Dr. Siddhartha Choubey
Professor, Department of Computer Science and Engineering, MATS University, Aarang, Raipur (Chhattisgarh), India.
Dr. Ebissa
Professor, Department of Civil Engineering, IIT Roorkee, Roorkee (Uttarakhand), India.
Dr. R. Dhanasekaran
Professor, Department of Mechanical Engineering, Anna University, Chennai (Tamil Nadu), India.
Dr. Kajal Chaudhary
Professor, Department of Commerce, Chaudhary Charan Singh University, Meerut (Uttar Pradesh), India.
Dr. Sivasankari
Assistant Professor, Department of Chemistry, Cauvery College for Women, Tiruchirappalli (Tamil Nadu), India.
Dr. K. S. Meenakshisundaram
Former Director, Cresent School of Business, Crescent University, Chennai (Tamil Nadu), India.
S. NoVolume-8 Issue-12S3, October 2019, ISSN: 2278-3075 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Page No.
1. Authors: Akhilesh Sharma, Namrata Prakash, Vishal Sagar, Abhishek Singh Chauhan
Paper Title: Financial Technology (Fin-Tech): Revolutionizing the Indian Agrarian Sector
Abstract: Agriculture is the largest employer of India which constitutes 50% of its workforce and also acontributor to 17-18% in its GDP. Still, it is one of the most disorganized and disjointed sector.Somewhere thissector has not been given due attention and itcan be proven with the fact that the GDP contribution of this sectorhas fallen from 43% to 18% (1970- 2018).Though the Indian Government is digitally driving to providefinancial inclusion to more than 145 million households that are not having access to banking services but stillthe farmers aremajorlyusing traditional credit for their basic and main two factors; Production & Consumption(Distribution). The financial segment has an important role to make agriculture aprime contributorto theeconomic growth of the country and also in reducing poverty. A fast-evolving technological landscape isbringing up new potential to focus&provide credit, risk-sharing, and to explore technology to enhanceagricultural productivity. Our paper firstly examines agricultural finance in the Indian context and then discusseshow financial technology (Fin-Tech) can drive new products in credit and risk markets in India. We evaluate therole of mobile banking, financial literacy, digital financial services, digital financial technology, and block-chaintechnology. The paper is concluded with a discussion of policy takeaways for Fin-Tech in agriculture to promoteagricultural growth, enhance financial inclusion, and improve regional economic integration through agriculture.
Keywords: Fin-Tech, Digital Technology, Block-chain Technology, Financial Literacy, Digital Finance,Agriculture, Indian Economy.
References:
1. Ajit Kumar Mishra and UpasanaMohapatra(2017)- IJESRT;“Agricultural Finance In India- An Overview” 2. Arner, D. W. – Barberis, J. N. – Buckley, R. P. (2015)-SSRN 2676553; “The Evolution of Fin-Tech: A New Post-Crisis Para-
digm” 3. Anshari, M., & Lim, S.A. (2017)-International Journal of Public Administration, 40:13, 1143-1158; “E-Government with Big
Data Enabled through Smartphone for Public Services: Possibilities and Challenges”. 4. Anshari, M., & Alas, Y. (2015). Smartphone Habits, Necessities, and Big Data Challenges.Journal of High Technology
Management Research. 5. "Banking and Finance on the Internet," edited by Mary J. Cronin. Retrieved 2008-07-10. 6. Da Silva, C. A. (2009). Agro-industries for development.CABI. 7. Durai ,T&Stella ,G.(2019) - JETIR Volume 6, Issue 1; “Digital Finance And Its Impact On Financial Inclusion”. 8. Ernst and Young implementation(2016). The Learning Organization.Vol. 17. 9. "Fin-Tech Investments Skyrocket in (2016– Report)". redherring.com. Retrieved July 12, 2016. 10. Gandy, T. (1995): "Banking in e-space", The banker, 145 (838), pp. 74–76. 11. Hinson, R. , Lensink, R. & Mueller, A. (2019)- Current Opinion in Environmental Sustainability 2019, 41:1–9; “Transforming
agribusiness in developing countries: SDGs and the role of FinTech”. 12. Infinite Financial Intermediation, 50 Wake Forest Law Review 643 (2015). 13. Kim, Y. – Park, Y. J. – Choi, J. (2016): The Adoption of Mobile Payment Services for “Fin-Tech”. International Journal of
Applied Engineering Research, 11(2), p. 1058-1061. 14. McAuley, D. ( 2015): What is Fin-Tech? Wharton Fin-Tech, 22.10.2015 15. Mishra, A. R. (2019, January 10). World Bank pegs India’s fiscal 2019 growth at 7.3%. Livemint. Retrieved from
https://www.livemint.com/Politics/NnjV6Sc0SSxfluBbEoKmLM/Indias-GDP-expected-to-grow-at-73-in-201819-World-Bank.html.
16. Muhammad Anshari, Mohammad Nabil Almunawar, MasairolMasri&MahaniHamdan- CPESE (2018), 19–21 September 2018,Nagoya, Japan.;“Digital Marketplace and Fin-Tech to support Agriculture Sustainability.”
17. NasscomAndZinnov Management Consulting (2018); Indian Startup Ecosystem 2018 – Approaching escape velocity.NASSCOM.
18. Nga, D., & Siebert, J. W. (2009). Toward better defining the field of agribusiness management.International Food andAgribusiness Management Review,
19. Ojha, N.P. &Ingilizian, Z. (2019, January). How India will consume in 2030:10 megatrends.World Economic Forum. Retrievedfrom: https://www.weforum.org/agenda/2019/01/10-mega-trends-for-india-in-2030-the-future-of-consumption-in-one-of-the-fastest-growing-consumer-markets.
20. Omidyar Network and The Boston Consultancy Group (2018, November 20). Credit Disrupted Digital MSME Lending in India.Omidyar Network.
21. PwC (2016): Global Fin-Tech Report, Blurred lines: How Fin-Tech is shaping the financial world? London: PwC. 22. Rivza1 B., Vasilevska D., Rivza1 P(2019).- Engineering For Rural Development Jelgava, 22.-24.05.2019; “Impact Of Digital
Innovation On Development Of Agriculture In Latvia”. 23. "Safe Internet Banking". Go Banking Rates. FDIC. 2016-01-11. Retrieved 2016-07-20. 24. SaurabhAhluwalia, Raj V. Mahtob, Maribel &Guerreroc(2019) - Technological Forecasting & Social Change
(www.elsevier.com/locate/techfore) 2019; “Blockchain technology and startup financing: A transaction cost economicsperspective”.
25. Samantha Sharf (November 7, 2016). "The Fin-Tech 50: The Complete List 2016". Forbes.Retrieved 2017-04-30. 26. Saxena, D. & Joshi, N. (2018) South Asian Journal of Business and Management Cases; “Digitally Empowered Village: Case of
Akodara in Gujarat, India” 27. Shah, R., Kayal, G.M., Chaudhury, R.R., Gurtoo, P., &Sreyasi, S. (October 2017). “The battle for the Indian consumer- Fin-Tech
companies transform the financial services landscape in India.” 28. Srivastav, S., Gupta, A., Garg, V.,(2019)- International Conference on Automation, Computational and Technology Management
(ICACTM); “Improving Performance Analysis Of Indian Farmers Fertilizers Co-Operative Limited (IFFCO) ThroughTechnology Management”.
29. VinayKandpal&RajatMehrotra (2019)-Indian Journal of Economics & Business, Vol. 19, No.1 (2019) : 85-93); “FinancialInclusion: The Role Of Fin-Tech And Digital Financial Services In India”.
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2.
Authors: Bhawnesh Kumar, Umesh Kumar Tiwari, Santosh Kumar, Vikas Tomer, Jasmeet Kalra
Paper Title: Comparison and Performance Evaluation of Boundary Fill and Flood Fill Algorithm
Abstract: In computer graphics, there are many polygon filling algorithms available like as inside and outsidetest, scan line method, boundary fill, flood fill, edge fill and fence fill algorithm. In this paper we focus on theflood fill and boundary fill algorithms for both four connected as well as eight connected pixels. This researchpaper shows the comparison and performance evaluation of boundary fill and flood fill algorithms withconsideration of running time in C-language. And also shows the how stack affects the performance of thesystem due to overflow of buffer later on JAVA implementation with queue improves the system performancefor large polygon. It also shows the literature review of various papers use the fill algorithm on differentapplications, some proposed the new and enhanced polygon fill algorithms.
Keywords: Boundary fill, Flood fill, Four connected, Eight connected, Seed fill.
References:
1. [N. Nisha and S. Varshney, “A Review: Polygon Filling Algorithms Using Inside-Outside Test,” Int. J. Adv. Eng. Res. Sci., vol.4, no. 2, pp. 73–75, 2017.
2. I. Al-rawi, “Implementation of an Efficient Scan-Line Polygon Fill Algorithm,” vol. 5, no. 4, pp. 22–29, 2014. 3. J. Pineda, “A Parallel Algorithm for Polygon Rasterization,” vol. 22, no. 4, pp. 17–20, 1988. 4. E. M. Nosal, “Flood-fill algorithms used for passive acoustic detection and tracking,” New Trends Environ. Monit. Using Passiv.
Syst. Passiv. 2008, vol. 321, pp. 2–6, 2008. 5. M. R. Dunlavey, “Efficient Polygon-Filling Algorithms for Raster Displays,” ACM Trans. Graph., vol. 2, no. 4, pp. 264–273,
1983. 6. H. Li, “Research and Implementation the Fundamental Algorithms of Computer Graphics Based on VC,” no. Meici, pp. 588–593,
2016. 7. Vipul Aggarwal, “Optimization of Flood Fill Algorithm Using Iterative Look-Ahead and Directional Technique,” Int. J. Comput.
Sci. Eng., vol. 2, no. 5, pp. 89–94, 2013. 8. S. Salunke, “Modified Boundary Fill for Complete Surface Coverage by Robotic Agents,” Int. J. Comput. Appl., vol. 73, no. 13,
pp. 8–11, 2013. 9. W. G. M. Geraets, A. N. Van Daatselaar, and J. G. C. Verheij, “An efficient filling algorithm for counting regions,” Comput.
Methods Programs Biomed., vol. 76, no. 1, pp. 1–11, 2004. 10. H. C. Liu, M. H. Chen, S. Y. Hsu, C. Chien, T. F. Kuo, and Y. F. Wang, “A new polygon based algorithm for filling regions,”
Tamkang J. Sci. Eng., vol. 2, no. 4, pp. 175–186, 2000. 11. K. Kallio, “Scanline edge-flag algorithm for antialiasing,” Theory Pract. Comput. Graph. 2007, TPCG 2007 - Eurographics UK
Chapter Proc. Celebr. 25 Years Eurographics UK Chapter, pp. 81–88, 2007. 12. D. Henrich, “Space-efficient Region Filling in Raster Graphics,” vol. 10, no. 4, pp. 205–215, 1994. 13. J. Arvo, M. Hirvikorpi, and J. Tyystjärvi, “Approximate soft shadows with an image-space flood-fill algorithm,” Comput. Graph.
Forum, vol. 23, no. 3 SPEC. ISS., pp. 271–279, 2004. 14. K. Muthukumar, S. Poorani, and S. Sindhu, “Color Image segmentation using Similarity based Region merging and Flood Fill
Algorithm,” vol. 5, no. 06, pp. 40–46, 2016. 15. A. Javeed, “a Novel Region Filling Algorithm for Discontinuous Contours,” Int. J. Res. Eng. Technol., vol. 06, no. 12, pp. 18–23,
2017. 16. C. Bond, “An Efficient and Versatile Flood Fill Algorithm for Raster Scan Displays,” 2011.
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3. Authors: NS Bohra, Girish Lakhera, Poonam Verma
Paper Title: Dimensions of Investment Behavior: A Case of Kids Higher Education
Abstract: Making excellent investments decisions is difficult for most of the peoples, we all have hopes andfears regarding our investments, yet unknown to us, these same hopes and fears can damage our investmentperformance. Neuroscience research demonstrates how these emotions originate in the brain's center ofemotional processing, the limbic system, in various financial situations. We can learn to identify these emotionaldisturbances in our financial reasoning. Distorted reasoning is often characterized by intense feeling. However,unless we make a conscious effort to become aware of our feelings, we can easily succumb to their destructivetendencies, based on this understanding researchers in this study is focusing on some of few core issues, whichare main constraints at the time of basic financial planning of an individual. These issues are why do somepeople take a lot of risk in financial and other people don’t? Why do some people save very little for retirementand some save a lot more? Why do some people become overconfident when they invest? In this study,education is treated as a form of investment and the focus of the study is to understand individual’s investmentbehavior at the time of investing in child higher education.
Keyword: Investment Behavior, Higher Education.
References:
1. Gounden, A. N. (1967). Investment in education in India. Journal of Human Resources, 347-358. 2. Shalini Kalra Sahi "Neurofinance and investment behaviour", Studies in Economics and Finance, Vol. 29 Iss: 4, 2012, pp.246 –
267. 3. “Behavioral Finance and NeuroFinance and Research Conducted in This Area” Interdisciplinary Journal of Contemporary
Research in Business, April 2013 Vol 4, No 12, pp.793 – 799 [Online] Available at http://journal-archieves31.webs.com/793-801.pdf
4. Mohammed Z. Shariff , Jamal Al-Khasawneh, Musab Al-Mutawa “Risk and Reward: A Neurofinance Perspective”International Review of Business Research Papers Vol. 8. No.6. September 2012. pp. 126 – 133. [Online] Available at
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http://www.bizresearchpapers.com/8.%20Shariff.pdf 5. Balleine W. Bernard – “The Neural Basis of Choice and Decision Making”, The Journal of Neuroscience, August, 1. 2007,
27(31): pp8159-8160 6. Kuhnen, Camelia M., and Brian Knutson. "The neural basis of financial risk taking." Neuron 47.5 (2005): pp763-770. 7. K.C. Tseng “Behavioral Finance, Bounded Rationality, Neuro-Finance, and Traditional Finance” Investment Management and
Financial Innovations, Volume 3, Issue 4, 2006, pp 7-18 [Online] Available at http://businessperspectives.org/journals_free/imfi/2006/imfi_en_2006_04_Tseng.pdf
4.
Authors: Priti Sharma, Himani Upreti, Mohit Kumar Ojha, Shipra Gupta
Paper Title:Role of Government, Private and Cooperative Stakeholders in Development and promotion ofFinancial Products: A Study of Farmers Producers Organisations (FPOs)
Abstract: The study is an attempt to assess the role of Central and State Government and Co-operative Societiesin promoting FPOs in Maharashtra, India. Study also explores Financial Products and their development in orderto meet financial needs of FPOs. Finally, study provides measures for promotion and development of FPOs so asto make Agri Finance viable option even for poors’. Study follows descriptive research method based onsecondary data and information collected from reports of government agencies, institutions and bodies. In orderto ensure the quality and authenticity of results, only reliable websites of central and state government have beenaccessed. Initiatives have been taken at both Central and Stale level for promotion of FPOs and to developfinancial products for financing FPOs. A lot has been done still a long way to go so as to make environmentconducive for FPOs. Study concludes that FPOs play a positive role and leads to enhanced income for farmersby providing them with access to institutional credit, informed and better decisions, access to better andimproved inputs, effectiveness &efficiency in farming operations and better marketing facilities; there stillremains challenges and policy gaps that are unaddressed. Few of the major challenges faced by institutions andgovernment agencies in building strong and sustainable FPOs include inadequacies related to professionalmanagement, access to credit, risk mitigation mechanism, accessibility to market, alongside weak financials andlack of technical skill and awareness among users of FPOs.
Keywords: Central Government, State Government, Co-operative Societies, Financial Products, FPOs, AgriFinance.
References: 1. NABARD (2018). National Paper - PLP 2019-20. NABARD. Accessed from
https://www.NABARD.org/auth/writereaddata/CareerNotices/2708183505Paper%20on%20FPOs%20-%20Status%20&%20%20Issues.pdf.
2. NABARD (2018). National Paper - PLP 2019-20. NABARD. Accessed fromhttps://www.NABARD.org/auth/writereaddata/CareerNotices/2708183550Policy%20Initiatives%20-%20NABARD.pdf
3. Department of Agriculture Cooperation & Farmers Welfare (2013). Policy & Process Guidelines for Farmer ProducerOrganisations, Ministry of Agriculture, Government of India. Accessed from http://sfacindia.com/UploadFile/Statistics/Farmer%20Producer%20Organizations%20Scheme.pdf.
4. Krishi Maharashtra (2017). District Wise List of Farmer Producer Companies Registered in Maharashtra State up to December.Accessed from http://krishi.maharashtra.gov.in/Site/Upload/Pdf/FPC%20Data%20Updated%20upto%2031-12-2017.pdf Websites
5. www.NABARD.org 6. www.sfacindia.com
7. www.rbi.org 8. krishi.maharashtra.gov.in
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5. Authors: Kapil Ghai, Navadha Bhatt, Brij Bhushan, Arunima Nayak
Paper Title: Phneological Studies on Trewia Nudiflora
Abstract: Trewia nudiflora Linn belongs to plant genus of the spurge family Euphorbiaceae, sub-familyAcalyphoideae and is one of the important medicinal plants in Indian systems of medicine like Ayurveda,Siddha, etc. It has numerous phytochemical and pharmacological significance. The whole plant is alternative,stomachic and efficacious in swellings. The root decoction is beneficial in flatulence, stomachic, applied locallyin form of poultice for the cure of gout rheumatism.Trewia nudiflora is distributed from Kumaon (Himalaya)region of Garwhal up to eastward to Assam and fruits were collected from Dehradun. Trewia nudiflora plants areunisexual. The phenological observations made showed that in the male and female plants from the monthMarch to July.
Keywords: Trewia nudiflora, Phenology, Phytochemical applications, Pharmacological significance
References:
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5. Du, Z. Z., He, H. P., Wu, B., Shen, Y. M., & Hao, X. J. (2004). Chemical constituents from the pericarp of Trewia nudiflora.Helvetica chimica acta, 87(3), 758-763.
6. Ghai, K., Gupta, P. K., & Gupta, A. K. (2016). Physiochemical behaviour changes during ripening in fruits of Trewia nudiflora
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Linn. Perspectives in Science, 8, 596-598. 7. Jackson, J. K. (1994). Manual of afforestation in Nepal. Vol. 2. Kathmandu, Nepal, Forest Research and Survey Centre, Ministry
of Forests and Soil Conservation. 8. Kang, Q. J., Yang, X. W., Wu, S. H., Ma, Y. L., Li, L., & Shen, Y. M. (2008). Chemical constituents from the stem bark of
Trewia nudiflora L. and their antioxidant activities. Planta medica, 74(04), 445-448. 9. Kumar, K. P., & Sastry, V. G. (2012). Protective effect of Trewia nudiflora against Ischemic Stroke in Experimental Rats.
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C. R. (1982). Chemotherapeutically active maytansinoids from Trewia nudiflora. U.S. Patent No. 4,313,946. Washington, DC:U.S. Patent and Trademark Office.
13. Powell, R. G., Smith Jr, C. R., Plattner, R. D., & Jones, B. E. (1983). Additional new maytansinoids from Trewia nudiflora: 10-Epitrewiasine and nortrewiasine. Journal of Natural products, 46(5), 660-666.
14. Powell, R. G., Weisleder, D., & Smith Jr, C. R. (1981). Novel maytansinoids tumor inhibitors from Trewia nudiflora: trewiasine,dehydrotrewiasine, and demethyltrewiasine. The Journal of Organic Chemistry, 46(22), 4398-4403.
15. Powell, R. G., Weisleder, D., Smith Jr, C. R., Kozlowski, J., & Rohwedder, W. K. (1982). Treflorine, trenudine, and N-methyltrenudone: novel maytansinoids tumor inhibitors containing two fused macrocyclic rings. Journal of the AmericanChemical Society, 104(18), 4929-4934.
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6. Authors: Poonam Verma, Charu Negi, Nisha Chandran, N. S. Bohra
Paper Title:Comparison between the Naïve Bayes and Hierarchical Clustering to Classify The Global LandslideCatalog for the Prediction of the Landslide.
Abstract: Machine Learning has been used since long to identify the features of a given datasets that areimportant for the prediction. Landslides are complex events taking place in the various regions of the world. It isthe movement of the debris, soil or rocks from an upper plane in downward direction. Identification of thefeatures that are used for the Landslide involves consideration of various categories of parameters. Present paperstudies about the performance comparison between a supervised algorithm Naïve Bayes and unsupervisedalgorithm Hierarchical Clustering. Naïve Bayes is a non parametric supervised algorithm that can be used for theforecasting purposes in the field of Agriculture, Economics, Aviation etc, whereas Hierarchical Clustering isused to partition the available instances of a dataset into optimal homogeneous groups on the basis of thesimilarities between the datapoints. The present paper draws a comparison between the accuracy of the NaïveBayes and Hierarchical Clustering for the prediction of the Landslide dataset. The dataset used is the GlobalLandslide Catalog that has important parameters like date, location coordinates, country, trigger of the event,continent etc. Before the implementation of both the algorithms, reduction of the parameters is carried out usingsubset evaluation of the parameters and considering only the most important.
Keywords: Landslide prediction, GLC, Hierarchical Clustering, Naïve Bayes, Multinomial Text, MachineLearning
References:
1. Kirschbaum, D. B., Adler, R., Hong, Y., Hill, S., & Lerner-Lam, A. (2010). A global landslide catalog for hazard applications:method, results, and limitations. Natural Hazards, 52(3), 561–575. doi:10.1007/s11069-009-9401-4.
2. Kirschbaum, D.B., T. Stanley, Y. Zhou (In press, 2015). 3. Spatial and Temporal Analysis of a Global Landslide Catalog. Geomorphology. doi:10.1016/j.geomorph.2015.03.016. 4. Kirschbaum, D. and Stanley, T. (2018). Satellite-Based Assessment of Rainfall-Triggered Landslide Hazard for Situational
Awareness. Earth's Future. doi:10.1002/2017EF000715. 5. "New NASA Model Finds Landslide Threats in Near Real-Time During Heavy Rains", NASA, Greenbelt, MD (March 22, 2018).6. Stanley, T., & Kirschbaum, D. B. (2017). A heuristic approach to global landslide susceptibility mapping. Natural Hazards, 87(1),
145-164. doi:10.1007/s11069-017-2757-y 7. Kirschbaum, D., Stanley, T., & Yatheendradas, S. (2016). Modeling landslide susceptibility over large regions with fuzzy
overlay. Landslides, 13(3), 485-496. doi:10.1007/s10346-015-0577-2 8. "A Global View of Landslide Susceptibility", NASA Earth Observatory, Greenbelt, MD (March 30, 2017) 9. Juang.C.S, et.al., “Using citizen science to expand the global map of landslides: Introducing the Cooperative Open Online
Landslide Repository (COOLR)”, PLOS. 10. Chen,W., Yan,X., Zhao,Z. et al. Spatial prediction of landslide susceptibility using data mining based kernel logistic regression,
naïve Bayes and RBF Network models for the Long County area (China).Bull Eng Geol Environ 78, 247-266 (2019) 11. Tien Bui, D.; Shahabi, H.; Omidvar, E.; Shirzadi, A.; Geertsema, M.; Clague, J.J.; Khosravi, K.; Pradhan, B.; Pham, B.T.; Chapi,
K.; Barati, Z.; Bin Ahmad, B.; Rahmani, H.; Gróf, G.; Lee, S. Shallow Landslide Prediction Using a Novel Hybrid FunctionalMachine Learning Algorithm. Remote Sens. 2019, 11, 931.
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13. C. N. Madawala, B. T. G. S. Kumara and L. Indrathilaka, "Novel machine learning ensemble approach for landslide prediction,"2019 International Research Conference on Smart Computing and Systems Engineering (SCSE) , Colombo, Sri Lanka, 2019, pp.78-84.
14. Husam A. H. Al-Najjar, Bahareh Kalantar, Biswajeet Pradhan, and Vahideh Saeidi "Conditioning factor determination for
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mapping and prediction of landslide susceptibility using machine learning algorithms", Proc. SPIE 11156, Earth Resources and Environmental Remote Sensing/GIS Applications X, 111560K (3 October 2019);
15. Dang, V., Dieu, T.B., Tran, X. et al. Enhancing the accuracy of rainfall-induced landslide prediction along mountain roads with aGIS-based random forest classifier. Bull Eng Geol Environ 78, 2835–2849 (2019).
16. Basu, T., Pal, S. A GIS-based factor clustering and landslide susceptibility analysis using AHP for Gish River Basin, India.Environ Dev Sustain (2019).
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anomaly maps. Earth Surf Process Landf 34:16–25. 21. Tang, R., Kulatilake, P.H.S.W., Yan, E. et al. Evaluating landslide susceptibility based on cluster analysis, probabilistic methods,
and artificial neural networks. Bull Eng Geol Environ (2020).
7. Authors: Umesh Kumar Tiwari, Santosh Kumar, Kamlesh Purohit, Vijay Kumar
Paper Title: Test-Case Minimization Technique for Large Scale Software using Link-Matrix
Abstract: In today’s software development environment, testing commences just after the finalization ofsystems requirements. Testing process is executed to make the underlying software error free, to enhance andassure the development of reliable and quality software product. This paper explores and analyses some ofblack-box as well as white-box testing methods according to their key findings, measures and metrics andconsiderations of testing factors. Further this paper proposes an efficient test case minimization technique. Thistechnique uses Link-Matrix to identify the number of actual parameters used between components. We haveused Boundary Value Analysis (BVA) method to compute the count of tests cases for individual modules orcomponents. We have compared the proposed technique with all three cases of Boundary Value Analysismethod including normal case, robust case and worst case. Results achieved during comparison shows that thisproposed technique is effective to minimize the total count of test-cases especially in case of robust and worstscenarios.
Keywords: Testing, Link-Matrix, Large-scale software, black-box, white-box, Boundary value analysis.
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8. Authors: Rajesh Kumar Upadhyay, Namrata Prakash, Abhishek Negi
Paper Title:Organizational Virtues and Psychological Capital as Positive Predictors of Job Satisfaction andPerformance
Abstract: In this paper we analyzed the incremental validity of the organizational virtues over the individualpsychological capital (IPC) in terms of predicting performance and job satisfaction. The sample was made up of459 Indian employees (232 men, 227 women); average age: 36.43 years (SD = 11.56); Belonging to public(17.4%, n = 80) and private (82.6%, n = 379) companies, the majority resided in and around Dehradun &Haridwar city (96.8%, n = 443). For data collection, an inventory of organizational virtues –IVO, PsychologicalCapital Scale, and some ad-hoc designed surveys were used. Sociodemographic, organizational, jobdissatisfaction, and job performance surveys. in relation to the dimensions of the IPC, regarding the prediction ofsatisfaction and job performance.
Keywords: Organizational Virtues, Psychological Capital, Job Satisfaction, Performance.
References:
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9. Authors: Varsha Mittal, Durgaprasad Gangodkar Bhaskar Pant, Nisha Chandran
Paper Title: A Deep Insight in Challenges of Natural Language Processing and Usage of Deep Learning
Abstract: Natural Language Processing (NLP) using the power of artificial intelligence has empowered theunderstanding of the language used by human. It has also enhanced the effectiveness of the communicationbetween human and computers. The complexity and diversity of the huge datasets have raised the requirementfor automatic analysis of the linguistic data by using data-driven approaches. The performance of the data-drivenapproaches is improved after the usage of different deep learning techniques in various application areas of NLPlike Automatic Speech Recognition, POS tagging etc. The paper addresses the challenges faced in NLP and theuse of deep learning techniques in different application areas of NLP.
Keywords: Artificial Intelligence, Deep Learning, Natural Language Processing; Machine Learning.
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17. L. Pang, Y. Lan, J. Guo, J. Xu, S. Wan, and X. Cheng, “Text Matching as Image Recognition,” in 30th AAAI Conference onArtificial Intelligence, Feb. 2016.
18. J. Guo, Y. Fan, Q. Ai, and W. B. Croft, “A deep relevance matching model for ad-hoc retrieval,” in Proceedings of the 25th ACMInternational on Conference on Information and Knowledge Management. ACM, pp. 55–64 2016.
19. S. MacAvaney, A. Yates, A. Cohan, and N. Goharian, “CEDR: Contextualized Embeddings for Document Ranking,” inProceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Paris,France, pp. 1101–1104,July 2018.
20. J. Hammerton, “Named entity recognition with long short-term memory,” in Proceedings of the seventh conference on Naturallanguage learning at HLT-NAACL 2003 - Volume 4, Edmonton, Canada, pp. 172–175, May 2003.
21. G. Lample, M. Ballesteros, S. Subramanian, K. Kawakami, and C. Dyer, “Neural Architectures for Named Entity Recognition,”in Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: HumanLanguage Technologies, San Diego, California, pp. 260–270,Jun 2016.
22. A. Akbik, D. Blythe, and R. Vollgraf, “Contextual String Embeddings for Sequence Labeling,” in Proceedings of the 27thInternational Conference on Computational Linguistics, Santa Fe, New Mexico, USA, pp. 1638–1649, Aug 2018.
23. Y. Chen, L. Xu, K. Liu, D. Zeng, and J. Zhao, “Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks,” inProceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International JointConference on Natural Language Processing Beijing, China, pp. 167–176, July 2015..
24. T. H. Nguyen, K. Cho, and R. Grishman, “Joint Event Extraction via Recurrent Neural Networks,” in Proceedings of the 2016Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,
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San Diego, California,pp. 300–309, Jun 2016. 25. Y. Kim, “Convolutional Neural Networks for Sentence Classification”, Proc, of 14th International Conference on Empirical
Methods of Natural Language Processing, Doha, Qatar, pp. 1746-1751, July 2014. 26. A. Conneau, H. Schwenk, L. Barrault, and Y. Lecun, “Very Deep Convolutional Networks for Text Classification,” in
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, LongPapers, Valencia, Spain, Apr. 2017, pp. 1107–1116.
27. N. Kalchbrenner, E. Grefenstette, and P. Blunsom, “A Convolutional Neural Network for Modelling Sentences,” ,Proc. of 52thAnnual Meeting of Association of Computer Linguistic, Baltimore, Maryland, pp. 655-665,June 2014.
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29. C. Zhou, C. Sun, Z 33. Liu, and F. C. M. Lau, “A C-LSTM Neural Network for Text Classification,” ArXiv151108630 Cs, Nov.2015, [Online]. Available: http://arxiv.org/abs/1511.08630.
10.
Authors: Yash Kumar Arora, Santosh Kumar, Umesh Kumar Tiwari, Shubhank Singhal , Vijay Kumar
Paper Title: Accident Severity in India
Abstract: Among all the transportation services available, road transport is one of the most important servicesavailable. It acts as a feeder for the other services. According to the Ministry of Road Transport and Highways,the road transport amounts to the traffic of about 87% related to the passenger and 60% related to the freight.Now, there is another field, where road transport is among the top list and that field is of road accidents. In 2016,about 150785 people died in road accidents. And as the population is increasing, there is also an increase in therate of road accidents. So, it is vital to analyze the data of road accidents for future predictions and therebydeveloping proper measures for this increasing rate. Many factorsresult in accidents and many cases might nothave been recorded. So, the available data may not be consistent, but the data is gathered mainly from theMinistry of Road Transport and Highways and then the information was extracted from that data. Thisinformation is used for the statistical analysis for the prediction of a future road accident or accident severity.
Keywords: Polynomial Regression, R2 Score,Road Accidents.
References:
1. Abdalla IM, Raeside R, Barker D, McGuigan DR (1997) An investigation into the relationships between area socialcharacteristics and road accident casualties. Accid Anal Prev 29:583–593
2. Mussone L, Ferrari A, Oneta M (1991) An analysis of urban collisions using an artificial intelligence model. Accid Anal Prev31:705–718
3. Arora, Yash & Kumar, Santosh. (2020). Statistical Approach to Predict Road Accidents in India. 10.1007/978-981-32-9515-5_18 4. Poch M and Mannering F (1996) Negative binomial analysis of intersection-accident frequencies. J TranspEng 122 5. Miaou SP (1994) The relationship between truck accidents and geometric design of road sections–poisson versus negative
binomial regressions. Accid Anal Prev 26 6. Karlaftis M, Tarko A (1998) Heterogeneity considerations in accident modeling. Accid Anal Prev 30:425–433 7. Jones B, Janssen L and Mannering F (1991) Analysis of the frequency and duration of freeway accidents in Seattle. Accid Anal
Prev 23 8. J. Ma, K. Kockelman (2006) Crash frequency and severity modeling using clustered data from Washington state. In: IEEE
Intelligent Transportation Systems Conference. Toronto Canadá 9. Chen W, Jovanis P (2002) Method of identifying factors contributing to driver-injury severity in traffic crashes. Transp Res Rec.
1717 10. Abdel-Aty MA and Radwan AE (2000) Modeling traffic accident occurrence and involvement. Accid Anal Prev 32 11. Maher MJ and Summersgill IA (1996) Comprehensive methodology for the fitting of predictive accident models. Accid Anal
Prev 28 12. Joshua SC and Garber NJ (1990) Estimating truck accident rate and involvements using linear and poisson regression models.
Transp Plan Technol 15 13. Miaou SP and Lum H (1993) Modeling vehicle accidents and highway geometric design relationships, Accid Anal Prev 25 14. MORTH Road Accidents in India 2016. New Delhi: Ministry of Road Transport and Highways, Transport Research Wing,
Government of India, August 2018.
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11. Authors: Yash Kumar Arora, Santosh Kumar,Umesh Kumar Tiwari, Aditi Goswami and Jasmeet Kalra
Paper Title: Statistical Method to predict Agricultural CPI
Abstract: The growth of any country depends on its economy and economic growth is nothing but an increasein the inflation i.e. adjusted market value of the goods and services produced by an economy over time.Statisticians conventionally measure such inflation using the price indices. They are mainly WPI (WholesalePrice Index and CPI (Consumer Price Index). WPI is now known to be an older method of computation becausethe main focus has to be on consumer prices.CPI is a measure of consumer prices over a certain period. Changesin the CPI are used to assess price changes associated with the cost of living. It can be calculated for rural, urbanareas as well as for both. In CPI rural, the workers and labourers are benefitted as their daily wages can bepredicted by this approach. The CPI by state data represents the inflation of each of the states giving a conciseview of the country. The data is collected and analysed using a mathematical approach called linear regression infuture prediction for rural labours based on previous data.
Keywords: CPIInflation, Linear Regression, RMS.
References:
1. Arora, Yash & Kumar, Santosh. (2020). Statistical Approach to Predict Road Accidents in India. 10.1007/978-981-32-9515-5_18
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2. Dennis R. Starleaf, The Impact of Inflation on farmers and agriculture, IOWA State University, ISU Economic Report Series. 3. Goel, D. (2018). Measures of Inflation in India. Journal of BusinessThought, 9, 24–45. https://doi.org/10.18311/jbt/2018/21171 4. Aheibam, M., & Singh, R. (2017). Inflationary Trends and Agricultural Production in India: An Empirical Analysis. Indian
Journal of Economics and Development, 13(3), 533. https://doi.org/10.5958/2322-0430.2017.00212.8 5. IIBM Management review, measuring the contribution of mark-up shock in Food Inflation in India, Volume 31, June 2019 6. Sasmal, J. (2015). Food price inflation in India: The growing economy with sluggish agriculture. Journal of Economics, Finance
and Administrative Science, 20(38), 30–40. https://doi.org/10.1016/j.jefas.2015.01.005 7. Pollak, R. A. (1998). The Consumer Price Index: A Research Agenda and Three Proposals. Journal of Economic Perspectives,
12(1), 69–78. https://doi.org/10.1257/jep.12.1.69 8. Labor Bureau, Annual Report, Consumer Price Index Numbers for Agricultural and Rural Laborers, 2011-2012 9. Gupta K and Siddiqui F (2014), “Salient Features of Measuring, Interpreting and Addressing Indian Inflation”, Indian Council for
Research on International Economic Relations, Working Paper No. 279. 10. Malhotra, Akash &Maloo, Mayank. (2017). Understanding Food Inflation in India: A Machine Learning Approach. SSRN
Electronic Journal. 10.2139/ssrn.2908354. 11. Sonna, T., H. Joshi, A. Sebastian, and U. Sharma. 2014. Analytics of food inflation in India. RBI Working Paper, October,
Reserve Bank of India, Mumbai.
12.
Authors: Amit Uniyal, Rupa Khanna, Ranjit Mukherji, Shipra Gupta
Paper Title:Implementation of Cashless Economy: Measuring the Impact of Awareness and Advantages withProblems with Reference to North India
Abstract: A new flagship programme was propelled by the government of India called “The Digital IndiaProgramme” to enhance and with a vision to transform the economy of the country into a “Faceless, Paperless,Cashless” economy. RBI is having a major role to play and support the government in achieving the goals. Inthis study we had tried to study the impact of various factors like awareness, advantages and problems in thecashless economy in India and the impact of awareness and advantages on the problems .The empirical studyfound that all the three factors have their own importance and impact on the cashless economy. There are otherfactors also which have an impact on cashless economy. In India it is at its adolescent stage.
Keywords: Demonetization; Cashless; IMPS; NEFT; RTGS; AEPS; point of sale.
References:
1. Dr. P. Abirami and S. Senthil Kumar, Electronic Payment SystemsTechnical and Strategic Issues. (2017)International Journal ofManagement, 8 (2), pp. 77–80.
2. Dr. Subramanian.S, Paper- Free Payment Systems In India-An Analytical Study, Volume 5, Issue 1, January (2014), pp. 80-87,International Journal of Management (IJM).
3. Goel R. et al., Moving from cash to cashless economy: A digital study of consumer perception towards digital transactions.IJRTE (international journal of recent technology and engineering) ISSN:2277-3878, volume-8, Issue: 1, May 19. file:///C:/Users/admin/Downloads/cash%20to%20cashless%20economy.pdf
4. Garg P., and Panchal. M ,- Study on Introduction of Cashless Economy in India (2016). Benefits & Challenge’s .Journal ofBusiness and Management (IOSR) , e-ISSN: 2278-487X, p-ISSN: 2319-7668. Volume 19, Issue 4. Ver. II , Apr. 2017, PP 116-120.
5. Hoehle H, Scornavacca E, Huff S (2012) Three decades of research on consumer adoption and utilization of electronic bankingchannels: A literature analysis. Decision Support Systems 54: 122-132.
6. Moses-Ashike, H- Cashless Economic can Reduce Risk of Carrying Huge Cash, [Online] (2011).Available:http://www.businessdayonline.com/…/22217.
7. Manpreet Kaur, DEMONETIZATION: IMPACT ON CASHLESS PAYEMNT SYSTEM, (2017). 6th International Conferenceon Recent Trends in Engineering, Science and Management, ISBN: 978-93-86171-21-4.
8. Martin Anane Felix Oppong Asamoah, Customers’ Satisfaction and Attitude towards Electronic Banking Services in Ghana: ACase Study of Selected Banks in Kumasi
9. Metropolis, (2015). European Journal of Business and Management , ISSN 2222-1905 (Paper) ISSN 2222-2839 (Online), Vol.7,No.32.
10. Marco, A. & L. Bandiera, -Monetary Policy, Monetary Areas and Financial Development with Electronic Money, IMF WorkingStudy, IMF., (2004).
11. Roth, B. L.- The Future of Money: The Cashless Economy – Part 1‖. [Online](2010).Available: https://www.x.com/.../future-money-cashless-economy—part-i.
12. Woodford M. ―Interest & Price: Foundation of a Theory of Monetary Policy‖(2003). Princeton University Press. 13. Yogesh Kumawat, Customers Attitude towards E-Banking System in Rajasthan, IOSR. (2014). Journal of Business and
Management (IOSR-JBM) e-ISSN: 2278-487X, p-ISSN: 2319-7668. Volume 16, Issue 9.Ver. III, PP 08-14. 14. http://economictimes.indiatimes.com/wealth/spend/ready-to-go-cashless/articleshow/56269830.cms 15. http://economictimes.indiatimes.com/topic/cashless-transactions. 16. https://www.sarkariyojna.co.in/10-cashless-digital-payments-methods-cashless-india/ 17. http://moneyexcel.com/15775/10-best-cashless-payment-methods
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13. Authors: Vivekanand kuriyal, Vikas Tripathi, Devesh Pratap Singh, Bhaskar Pant, Vijay Kumar
Paper Title: A Comprehensive Analysis and Solution of Cyber Attacks using Machine Learning Techniques
Abstract: Cyber security is a major problem of modern society so that Vulnerabilities of computer Network isbecome easy with the help of technologies and human skills. Now day’s difference type of attacks occurred forexample DOS attack, Probing, R2U, R2L virus, port scans, buffer overflow, CGI Attack and flooding etc. Weneed a platform where a system can be developed for recognition and prevention of these attacks. In This paper,most of the latest methods are summarised to implement IDS for cyber security. Intrusion Detection Systems is amost suitable solution for cyber attacks. Machine learning based Intrusion Detection Systems have highaccuracy, in rapidly changing environment. This paper discusses which type of ML techniques has low accuracy,so it explore some research area for researcher.
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Keywords: -Machine Learning, Intrusion Detection Systems, Cyber Security, Attacks.
References:
1. S. Larson. 10 biggest hacks of 2017. 2017, December 20. Retrieved: November 3, 2018. 2. Dinil Mon, Divakaran, et al. “Evidence gathering for network security and forensics,” Digital Investigation ,2017, pp.56–65. 3. S. Dolev and S. Lodha, “Cyber Security Cryptography and Machine Learning“, In Proceedings of the First International
Conference, CSCML 2017, Beer-Sheva, Israel, June 29-30, 2017. 4. Ayon Dey Department of CSE, Gautam Buddha University, Greater Noida, Uttar Pradesh, India. “Machine Learning Algorithms:
A Review”. Vol, 7, 1174-1179, 2016. 5. I. Zaharakis, S. B. Kotsiantis and P. Pintelas. “Supervised machine learning: Emerging artificial intelligence applications in
computer engineering”, 160, 3-24, 2007. 6. Gozde Karatas, Onder Demir, Ozgur Koray Sahingoz “Deep Learning in Intrusion Detection Systems”, International Congress on
Big Data, Deep Learning and Fighting Cyber Terrorism, Dec, 2018. 7. R. Kiruthiga, D. Akila,” Phishing Websites Detection Using Machine Learning”, International Journal of Recent Technology and
Engineering (IJRTE) ISSN: 2277-3878, Volume-8, Issue-2S11, September 2019. 8. Jibi Mariam Biju1, Neethu Gopal2, Anju J Prakash3,” CYBER ATTACKS AND ITS DIFFERENT TYPES”, International
Research Journal of Engineering and Technology (IRJET), Volume: 06 Issue: 03 | Mar 2019. 9. Richa Adlakha, Shobhit Sharma, Aman Rawat, Kamlesh Sharma,” Cyber Security Goal’s, Issue’s, Categorization & Data
Breaches” , 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (Com-IT-Con), India,2019.
14. Authors: Chandrakala Arya, Manoj Diwakar, KajalAggarwal
Paper Title: IoT based Road Traffic Management System for Metropolitan Cities
Abstract: Many cities in the world face jamming problems in road traffic, particularly in metropolitan cities. Atpresent the traffic controlling systems aresemiautomatic in nature. With the introduction of IoT in road trafficmanagement systems, it revolutionizes the field of road traffic management system and improves the road trafficcongestion problem.This paper proposes an IoT-based road traffic management system for metropolitan cities.The proposed system provides the hassle free movement of the vehicles to avoid inconvenience and reroute thehigher priority vehicles. Experimental results show that the proposed system gives higher success rate for thelow traffic density in the lane.
Keywords: Traffic management system, road trafficReferences:
1. Saravanan, S.: Implementation of efficient automatic traffic surveillance using digital image processing. In: IEEE InternationalConference on Computational Intelligence and Computing Research. (2014)
2. Roy, A.B., Halder, A., Sharma, R., Hegde, V.: A Novel concept of smart headphones using active noise cancellation and speechrecognition. In: International Conference on Smart Technologies and Management for Computing, Communication, Controls,Energy and Materials (ICSTM), pp. 366–371. (2015)
3. F. Su, H. Dong, L. Jia, and X. Sun, ``On urban road traffic state evaluation index system and method,'' Modern Phys. Lett. B, vol.31, no. 01, Jan. 2017, Art. no. 1650428
4. L.-A. Gille and C. Marquis-Favre, ``Testing of urban road traffic noise annoyance modelsBased on psychoacoustic indicesUsingin situ socio-acoustic survey,'' J. Acoust. Soc. Amer., vol. 141, no. 5, p. 3802, May 2017.
5. Stojmenovic I (2014) Fog computing: a cloud to the ground support for smart things and machine-to-machine networks.Australasian telecommunication networks and applications conference. IEEE, pp 118–122.
6. Pan B, Zheng Y, Wilkie D, Shahabi C (2013) Crowd sensing of traffic anomalies based on human mobility and social media. In:Proceedings of the 21st ACM SIGSPATIAL international conference on advances in geographic information systems, pp 344–353.https://doi.org/10.1145/2525314.2525343
7. Pinheiro CAR (2014) Human mobility behavior and predicting amount of trips based on mobile data records. 8. R. Al Mallah, A. Quintero, and B. Farooq, ``Cooperative evaluation of the cause of urban traffic congestion via connected
vehicles,'' IEEE Trans. Intell. Transp. Syst., vol. 21, no. 1, pp. 5967, Jan. 2020. 9. Stojmenovic I (2014) Fog computing: a cloud to the ground support for smart things and machine-to-machine networks.
Australasian telecommunication networks and applications conference. IEEE, pp 118–122 10. Djahel S, Salehie Met al (2013) Adaptive traffic management for secure and efficient emergency services in smart cities. IEEE,
pp 340–343 11. L.P.J. Rani, M.K. Kumar, K.S. Naresh, S. Vignesh, Dynamic traffic management system usinginfrared (IR) and the internet of
things (IoT), in2017 Third International Conference on ScienceTechnology Engineering & Management (ICONSTEM),Chennai(2017), pp. 353–357
12. Pangbourne, K., Stead, D., Mladenović, M., & Milakis, D. (2018). The case of mobility as a service: A critical reflection onchallenges for urban transport and mobility governance. Governance of the smart mobility transition, 33-48.
13. Schimbinschi F, Nguyen XV, Bailey J, Leckie C, Vu H, Kotagiri R (2015) Traffic forecasting in complex urban networks:Leveraging big data and machine learning. In: 2015 IEEE international conference on big data (big data), pp1019–1024.https://doi.org/ 10.1109/BigData.2015.7363854
14. Pinheiro CAR (2014) Human mobility behavior and predicting amount of trips based on mobile data records. 15. Peng C, Wong JX, Shi K-C, Lio` M (2012) Collective human mobility pattern from taxi trips in urban area. PLoS ONE
7(4):34487.https://doi.org/10.1371/journal.pone.0034487 16. Wang M, Yang S, Sun Y, Gao J (2017) Human mobility prediction from region functions with taxi trajectories. PLoS ONE
12(11):e0188735.https://doi.org/10.1371/journal.pone.0188735 17. L. Lin, C. Jiang, X.-D. Xu, Y.-L. Guo, and T. Chen, ``Study on evaluation for crowd degree of trafc ow at urban tunnel access
based on acceleration noise,'' J. Highway Transp. Res. Develop. (English Ed.), vol. 11, no. 1, pp. 7076, Mar. 2017. 18. W. Pei, Y. Wu, S. Wang, L. Xiao, H. Jiang, and A. Qayoom, ``BVis: Urban traffic visual analysis based on bus sparse
trajectories,'' J. Vis., vol. 21, no. 5, pp. 873883, Oct. 2018. 19. R. Armas, H. Aguirre, and K. Tanaka, ``Bi-objective evolutionary optimization of level of service in urban transportation based
on trafc density,'' Cogent Eng., vol. 5, no. 1, pp. 121, 2018. 20. W. Xiangxue, X. Lunhui, and C. Kaixun, ``Data-driven short-term forecasting for urban road network trafc based on data
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processing and LSTMRNN,'' Arabian J. for Sci. Eng., vol. 44, no. 4, pp. 30433060, Apr. 2019. 21. Djahel S, Salehie Met al (2013) Adaptive traffic management for secure and efficient emergency services in smart cities. IEEE,
pp 340–343. 22. Joshi, A., Jain, N., & Pandey, A. (2020). IoT-Based Traffic Management System Including Emergency Vehicle Priority. In
International Conference on Intelligent Computing and Smart Communication 2019 (pp. 1501-1507). Springer, Singapore.
15.
Authors: Neelam Singh, Shivanshi Tripathi, Devesh Pratap Singh, Bhasker Pant, Vijay Kumar
Paper Title: Recommendation Systems in the Big Data Era
Abstract: Rapid progression in technology and increasing use of social media platforms like Facebook,Instagram and Twitter has altered the way of articulating people’s judgment, observation and sentiments aboutspecific product, services, and more. This leads to the production and accumulation of massive amount of data.Recommendation systems are getting impetus when it comes to find insights from this data to make decisionsthat can be represented in various statistical and graphical forms. They have proven useful in predicting orrecommending products ranging from food, movies, restaurants etc. This paper presents an overview aboutrecommendation systems and a review of generation of recommendation methods based on categories likecontent-based, collaborative, and hybrid approaches. The paper will enlist the limitations which the presentrecommendation system faces and the possible improvements required in their capabilities to fit into a widerrange of application areas.
Keywords: Recommender system, Recommendation, Models, Data analytics.
References:
1. Lu, Ji et al. "Recommender system application developments: a survey." Decision Support Systems, vol. 74 pp. 12-32, 2015 2. Burke, Robin. "Hybrid web recommender systems." The adaptive web. Springer, Berlin, Heidelberg. pp. 377-408, 2007 3. Babu, Maddali Surendra Prasad, and Boddu Raja Sarath Kumar. "An Implementation of the User-based Collaborative Filtering
Algorithm." IJCSIT) International Journal of Computer Science and Information Technologies vol. 2 no.3, pp. 1283-1286, 2011. 4. Chen, Rui, et al. "A survey of collaborative filtering-based recommender systems: From traditional methods to hybrid methods
based on social networks." IEEE Access, vol. 6, pp. 64301-64320, 2018. 5. Sorde, Roshni K., and Sachin N. Deshmukh. "Comparative study on approaches of recommendation system." International
Journal of Computer Applications, vol. 118 no. 2, 2015. 6. P. Nagarnaik and A. Thomas, "Survey on recommendation system methods," 2015 2nd International Conference on Electronics
and Communication Systems (ICECS), Coimbatore, pp. 1603-1608, 2015. doi: 10.1109/ECS.2015. 7124857. 7. Choi, Keunho, et al. "A hybrid online-product recommendation system: Combining implicit rating-based collaborative filtering
and sequential pattern analysis." Electronic Commerce Research and Applications vol.11, no.4, pp. 309-317, 2012. 8. Mu, Ruihui. "A survey of recommender systems based on deep learning." IEEE Access, vol. 6, pp. 69009-69022. 2018. 9. Zhang, Y., 2016. GroRec: a group-centric intelligent recommender system integrating social, mobile and big data technologies.
IEEE Transactions on Services Computing, 9(5), pp.786-795. 10. Wang, Y., Wang, M. and Xu, W., 2018. A sentiment-enhanced hybrid recommender system for movie recommendation: a big
data analytics framework. Wireless Communications and Mobile Computing, 2018. 11. Bai, Xiaomei, et al. "Scientific paper recommendation: A survey." IEEE Access 7 (2019): 9324-9339. 12. Misale, Mohini, and Pankaj Vanwari. "A survey on recommendation system for technical paper reviewer assignment." 2017
International conference of Electronics, Communication and Aerospace Technology (ICECA). Vol. 2. IEEE, 2017. 13. Chakraborty, Jayeeta, and Vijay Verma. "A survey of diversification techniques in Recommendation Systems." 2016
International Conference on Data Mining and Advanced Computing (SAPIENCE). IEEE, 2016. 14. Akhil, P. V., and Shelbi Joseph. "A Survey Of Recommender System Types And Its Classification." International Journal of
Advanced Research in Computer Science 8.9 (2017). 15. Nagarnaik, Paritosh & Thomas, A. (2015). Survey on recommendation system methods. 2nd International Conference on
Electronics and Communication Systems, ICECS 2015. 1603-1608. 10.1109/ECS.2015.7124857. 16. Sarwar, B., Karypis, G., Konstan, J. and Riedl, J., 2002, December. Incremental singular value decomposition algorithms for
highly scalable recommender systems. In Fifth international conference on computer and information science (Vol. 1, No.012002, pp. 27-8).
17. Takács, G., Pilászy, I., Németh, B. and Tikk, D., 2009. Scalable collaborative filtering approaches for large recommendersystems. The Journal of Machine Learning Research, 10, pp.623-656.
18. Mohamed, Marwa Hussien, Mohamed Helmy Khafagy, and Mohamed Hasan Ibrahim. "Recommender systems challenges andsolutions survey." In 2019 International Conference on Innovative Trends in Computer Engineering (ITCE), pp. 149-155. IEEE,2019
19. Sarwar, B.M., Karypis, G., Konstan, J. and Riedl, J., 2002, December. Recommender systems for large-scale e-commerce:Scalable neighborhood formation using clustering. In Proceedings of the fifth international conference on computer andinformation technology (Vol. 1, pp. 291-324). Almazro, Dhoha, et al. "A survey paper on recommender systems." arXiv preprintarXiv:1006.5278 (2010).
20. De Gemmis, M., Lops, P., Semeraro, G. and Musto, C., 2015. An investigation on the serendipity problem in recommendersystems. Information Processing & Management, 51(5), pp.695-717.
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16. Authors: Madhulika Esther Prasad, Ishita Joshi, Navin Kumar, Pankaj Gautam, Jyoti Chhabra
Paper Title: Proximate Composition of Minor Millets from Cold Semi-Arid Regions
Abstract: The nutritional importance of minor millets growing in geographically and environmentally isolatedsemi-arid regions remains largely unexplored, which has led to it being under-utilized for diet diversification. Inthis study, the proximate composition of three species of minor millets, namely, Barnyard millet (Echinochloafrumentacea),Finger millet (Eleusine coracana) and Foxtail millet(Setaria italica), grown in traditional milletcultivating regions (cold semi-arid) of the Himalayan range, have been analyzed. Two high altitude locations ofcontrasting cold temperatures in this region were selected for analysis. Dehradun which exhibits a temperature of
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25-27℃, is located at 640 masl. (Meters Above Sea Level) in Uttarakhand West (UW), whereas, the secondlocation, Pithoragarh which exhibits a temperature of 15-17℃ is located at 1514 masl. in Uttarakhand East (UE).The results of this study record a 30.75 percent increase in average protein content of Barnyard millet grainswhen the same seed stock was grown at the second region of lower temperature, i.e. Pithoragarh (15-17℃), ascompared to Dehradun (15-17℃). A 42.66 percent increase in average fat content was also recorded forBarnyard millet grains when grown at Pithoragarh (15-17℃). The two other millet species, Finger millet andFoxtail millet, did not record significant differences in protein and fat contents, however, Foxtail milletdisplayed marginally increased levels of sodium and potassium. In contrast to the other components analyzed,Total Dietary Fiber (TDF) was found to decrease with growth at the comparatively colder location ofPithoragarh. A 36.71 percent decrease in TDF content was recorded for Barnyard millet, whereas, a 19.25percent decrease was recorded for Finger millet. Foxtail millet displayed a marginal decrease of only 5.3 percentin TDF content with growth at Pithoragarh. Starch concentration and moisture content for all three species wasalso studied, but did not record any notable differences due to growth at the colder location of Pithoragarh. Theresults here indicate an important role of cold temperature and high altitude in regulating the proximatecomposition of minor millet grains. Studies which explore the proximate composition of millet cultivars in suchgeographically and environmentally distinct millet growing regions, may reveal new information regarding thenutritional importance of minor millets, and the ideal conditions of growth for maximum nutritional benefit.
Keywords: Millets, Proximate Composition, Cold Temperature, High Altitude.
References:
1. B. Dayakar Rao, K. Bhaskarachary, G. D. Arlene Christina, G. Sudha Devi, A. T. Vilas and A. Tonapi, “Nutritional and healthbenefits of millets,” ICAR_Indian Institute of Millets Research (IIMR), Rajendranagar, Hyderabad, p. 112, 2017.
2. R. B. Singh, S. Khan, A. K. Chauhan, M. Singh, P. Jaglan, P. Yadav and L. R. Juneja, “Millets as functional food, a gift fromAsia to Western World,” In The Role of Functional Food Security in Global Health, Academic Press, pp. 457-468, 2019.
3. S. Venkatesan and K. Sujatha, “Characterization of Barnyard Millet Cultivars using Seed Image Analysis,” Seed Research, vol.45(2), pp. 1-3, 2018.
4. J. R. N. Taylor and J. Kruger, “Millets. Encyclopedia of Food and Health,” pp. 748-757, 2016.http://dx.doi.org/10.1016/B978-0-12-384947-2.00466-9
5. P. B. Devi, R. Vijayabharathi, S. Sathyabama, N. G. Malleshi and V. B. Priyadarisini, “Health benefits of finger millet (Eleusinecoracana L.) polyphenols and dietary fiber: a review,” Journal of food science and technology, vol. 51(6), pp. 1021-1040, 2014.
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9. G. Ravindran, “Studies on millets: Proximate composition, mineral composition, and phytate and oxalate contents,” FoodChemistry, vol. 39(1), pp. 99-107, 1991.
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Eleusine coracana (finger millet),” International Journal of Advanced Chemistry, vol. 2(2), pp. 171-174, 2014. 19. D. L. Morris, “Quantitative Determination of Carbohydrates With Dreywood's Anthrone Reagent,” Science (Washington), vol.
107, pp. 254-255, 1948. 20. AOAC, “Official Methods of Analysis,” Official Method 935.29, 1997. 21. Vinoth and R. Ravindhran, “Biofortification in millets: a sustainable approach for nutritional security,” Frontiers in plant science,
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AACC International Press, pp. 85-129, 2019. 24. R. Ugare, B. Chimmad, R. Naik, P. Bharati and S. Itagi, “Glycemic index and significance of barnyard millet ( Echinochloa
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17. Authors: Shipra Gupta, Preeti Sharma
Paper Title: Ayurvedic Companies on Consumer Behaviour
Abstract: The Indian FMCG is the fourth largest sector in the economy. In India, raw materials are cheaper andavailability of labor are easy than other place. India may be got benefit in this worldwide competitive Era. Many
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numbers of ayurvedic companies are available in India. Few companies are maintaining their loyalty and believein customers. Maximum countries of whole world appreciate the ayurvedic medicine of Indian companies. Inthis manuscript a questionnaire is filled by 50 respondents to know about the consumers’ perception regardingmainly three Ayurveda company and their common brand. This questionnaire is fulfilled by different variables.After the analysis of this study it has been concluded that mostly consumers like the company Dabur and itsproduct Chyawanprash. This manuscript will be very fruitful for the consumers and researchers.
Keywords: Ayurvedic companies, comparative study, product chyawanprash, consumers buying behavior.
References:
1. Philip Kotler, Marketing Management ; Analysis Planning & Control; Prentice Hall, 9th Edition. 2. Saxena Ranjan, Marketing Management; TATA Mcgraw Hill, 4th Edition, 2000. 3. Dr. R.L. Varshney & Dr. S.L. Gupta, Marketing Management; An Indian Perspective; Sultan Chand & Sons Education
Publishers, New Delhi; 2nbd Ed. 2001.4. Consumers and Their Brands: Developing Relationship Theory in Consumer Research, 1998, Susan Fournier. 5. Assessing Measurement Invariance in Cross-National Consumer Research, 1998, Jan-Benedict E. M. Steen amp and Hans
Baumgartner. 6. Constructive Consumer Choice Processes, 1998, James R. Bettman, Mary Frances Luce, and John W. Payne. 7. A Model of Customer Satisfaction With Service Encounters Involving Failure and Recovery, 1999, Amy K. Smith, Ruth N.
Bolton, and Janet Wagner 8. Satisfaction, Repurchase Intent, and Repurchase Behavior: Investigating the Moderating Effect of Customer Characteristics,
2001, Vikas Mittal and Wagner A. Kamakura 9. A Dynamic Model of Customers, Usage of Services: Usage as an Antecedent and Consequence of Satisfaction, 1999, Ruth N.
Bolton and Katherine N. Lemon. 10. Building Brand Image Through Event Sponsorship: The Role of Image Transfer, 1999, K. P. Gwinner and J. Eaton. 11. The Impact of Corporate Credibility and Celebrity Credibility on Consumer Reaction to Advertisements and Brands, 2000, R. E.
Goldsmith, B. A. Lafferty, and S. J. Newell. 12. Impact of 3-D Advertising on Product Knowledge, Brand Attitude, and Purchase Intention: The Mediating Role of Presence,
2002, Hairong Li, Terry Daugherty, and Frank Biocca. 13. Buboltz, W. C., Jr., Miller, M., & Williams, D. J. (1999). Content analysis of research in the Journal of Counseling Psychology. 14. Howard, J., & Sheth, J. N. (1968). The theory of buyer behavior. New York, NY: John Wiley. 15. Kassarjian, H. H. (1977). Content analysis in consumer research. Journal of Consumer Research, 4, 8-18. 16. www.google.com 17. Wikipedia 18. www.dabur.com
18. Authors: Vijay Kumar, Deepak Kotnala, J. S. Kalra, Bhaskar Pant
Paper Title: Effects of Computer/Laptop Screen Radiation on Human Beings
Abstract: In offices, many people work continuously in front of the screen of computer and laptop. Screen ofsuch type of equipments continuously produce a radiations. These radiations are incident on the face of workers.It affect to the eye and other organs of the body. The radiations enter inside the body and electromagnetic energyis absorbed by the cells and tissues. In this manuscript, it is observed that the radiation of screen ofcomputer/laptop may be harmful for the human beings after a long exposure. It is suggested that people shouldwork for a short duration and if is mandatory to work for a long time in front of screen, the duration of workingmay be divided into parts.
Keywords: Computer/laptop screen, Electromagnetic radiation, Health effects,
References:
1. Vijay Kumar, R. P. Vats and P. P. Pathak, Harmful effects of 41 and 202 MHz radiations on some body parts and tissues, IndianJ. of Biochemistry and Biophysics, 45(4), 269-274, 2008
2. Vijay Kumar, R. P. Vats, S. Kumar and P. P. Pathak, Interaction of EMW with human body, Ind. J. Radio & Space Physics, 37,131-134, 2008.
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4. Anisimov VN, Arutiunian AV, Burmistrov SO, Zabezhinskiĭ MA, Muratov EI, Oparina TI, Popovich IG, Prokopenko VM,Frolova EV. (1997). Effects of radiation from video display terminals of personal computers on free radical processes in rats.Biull Eksp Biol Med.; 124:192–4.
5. International Agency for Research on Cancer Non- Ionizing Radiation, Part 1: Static and extremely low-frequency (ELF) electricand magnetic fields. IARC Monographs on the Evaluation of carcinogenic Risks to Humans, (IARC, 2002), Volume 80, Lyon:IARC Press.
6. Anisimov VN, Arutiunian AV, Burmistrov SO, Zabezhinskiĭ MA, Muratov EI, Oparina TI, Popovich IG, Prokopenko VM,Frolova EV. (1997). Effects of radiation from video display terminals of personal computers on free radical processes in rats.Biull Eksp Biol Med.; 124:192–4.
7. Briggs, R. (1991). “Safety and health effects of visual display terminals”. A chapter in GD Claytonand FE Clayton (eds), Patty'sIndustrial hygiene and toxicology, fourth edition, vol. 1,John Wiley & Sons, Inc. FRN (2006). National Population CensusReport, Federal Republic of Nigeria. web.archieve.org. Retrieved 15 October 2017.
8. IARC (2002). “Non-ionizing Radiation, Part 1: Static and extremely low-frequency (ELF) electricand magnetic fields”.International Agency for Research on Cancer, Monographs onthe Evaluation of carcinogenic Risks to Humans: Volume 80.Lyon: IARC Press.
9. Mortazavi SM, Ahmadi J, Shariati M. (2007). Prevalence of subjective poor health symptoms associated with exposure toelectromagnetic fields among university students. Bioelectromagnetics; 28:326–30.
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10. WHO (1986). “Provisional statements of WHO working group on occupational healthaspects inthe use of visual display units”.World Health Organization. VDT News, vol.3(1):13.
11. M. R. Usikalu, T. T. Ikeh and C. O. Olawole (2014). Safe Distance to Extremely Low Frequency Radiation Associated withPower Transmission Lines Located in Ota, Southwest, Nigeria, International Journal of Engineering Technology, IJENS14(2):118-121
12. International Agency for Research on Cancer Non- Ionizing Radiation, Part 1: Static and extremely low-frequency (ELF) electricand magnetic fields. IARC Monographs on the Evaluation of carcinogenic Risks to Humans, (IARC, 2002), Volume 80, Lyon:IARC Press
13. M. R. Usikalu, T. T. Ikeh and C. O. Olawole (2014). Safe Distance to Extremely Low Frequency Radiation Associated withPower Transmission Lines Located in Ota, Southwest, Nigeria, International Journal of Engineering Technology, IJENS14(2):118-121
14. Radiation Protection Program Radiation from Computer Monitors. Retrieved from Environment, Health & Safety:https://ehs.mit.edu/site/content/radiation-computer-monitors , (2010).
15. M. L. Akinyemi, J. S. Kayode and M. R. Usikalu Investigation of Extremely Low Frequency (ELF) Hot Spots in the College ofScience and Technology, Covenant University, Ota, Turkish Journal of Physics, (2011), 35(3): 359-361.
16. International Agency for Research on Cancer Non- Ionizing Radiation, Part 1: Static and extremely low-frequency (ELF) electricand magnetic fields. IARC Monographs on the Evaluation of carcinogenic Risks to Humans, (IARC, 2002), Volume 80, Lyon:IARC Press
17. Perez-Vega, C., Zamanillo, J. M., and Ipina, J. S. (2000). Assessment of Ionization Radiation from PC Monitors and TVReceivers. IEEE, 1048-1051. 14.
18. Radha, R., and Gurupranesh, P. (2014). Electromagnetic Radiation from Electronic Appliances. Journal of Mechanical and CivilEngineering, 41-46.
19. Usikalu M. R and Akinyemi M. L (2012). Analysis of Radiation Dose around some Base Stations in Ota and Lagos Environ,International Journal of Basic and Applied Sciences, IJENS 12(5): 7-12.
20. Usikalu M. R, Babarimisa I. O, Akinwumi S. A, Akinyemi M. L, Adagunodo T. A and Ayara W. A (2018) Radiation from VisualDisplay Unit, IOP Conference Series: Earth and Environmental Science, Volume 173 (1): 012- 039.
21. Usikalu M. R. and Akinyemi M. L. (2007) Monitoring of radiofrequency radiation from selected mobile phones, Journal ofApplied Sciences Research, 3(12): 1701-1704.
19. Authors: Noor Mohd, Ankur Dumka, Vijay Kumar
Paper Title: Dynamic Range Method using Hiddenmarkov Models in RSA and DSA Algorithm
Abstract: Dynamic range approach has become a highest level of research field from last some year. Theinitiative beyond this process researched field is the reality that some particularrange are used in currentenvironment and also which is used or deploy in an in effectivemanner. Theoverall wireless various range areallocated and fixed, but it is not need to be used. So, at this time the requirement for research in various wirelesstechnologies expanding and there is no space for the wireless various rangesto assignimportant frequencybandsrelated to the future wireless technologies. Therefore, this is a reason to increase the use of the variousranges. To target this, we must be calculating the method for sharing the range. Our purpose to find out thewireless sensors’ techniques, artificial Intelligencetechniques, cloud computing techniques for updatingverification time of dynamic range approach. So, we used in this paper the Hidden Markov Model for definingthe various types of range characteristics and forecast therangesample that will be obtained in future relatedmethods. This forecast is used to define the RSA and DSA in better its verification time. The method forutilization HMM technique for minimizing the verification time of RSA and DSA will be executed in NS2 andMatlab simulator.
Keywords: Cognitive Radio, Dynamic Range Approach, Hidden Markov Model, RSA Algorithm, DSAAlgorithm.
References:
1. Morales-Jimenez, David, Raymond HY Louie, Matthew R. McKay, and Yang Chen. "Multiple-Antenna Signal Detection inCognitive Radio Networks with Multiple Primary User Signals." arXiv preprint arXiv:1405.6408 (2014).
2. Liu, Yang, Zhangdui Zhong, Gongpu Wang, and Dan Hu. "Cyclostationary Detection Based Spectrum Sensing for CognitiveRadio Networks." Journal of Communications 10, no. 1 (2015).
3. Yucek, Tevfik, and Hüseyin Arslan. "A survey of spectrum sensing algorithms for cognitive radio applications."Communications Surveys & Tutorials, IEEE 11, no. 1 (2009): 116-130.
4. Lu, Xiao, Ping Wang, Dusit Niyato, and Ekram Hossain. "Dynamic spectrum access in cognitive radio networks with RF energyharvesting." Wireless Communications, IEEE 21, no. 3 (2014): 102- 110.
5. Wang, Jianfeng, Monisha Ghosh, and Kiran Challapali. "Emerging cognitive radio applications: A survey." CommunicationsMagazine, IEEE 49, no. 3 (2011): 74-81.
6. Bkassiny, M.; Yang Li; Jayaweera, S.K.,"A Survey on Machine- Learning Techniques in CognitiveRadios",IEEE,Communications Surveys & Tutorials, IEEE,2013
7. Salami, G.; Durowoju, O.; Attar, A.; Holland, O.; Tafazolli, R.; Aghvami, H.,"A Comparison Between the Centralized andDistributed Approaches for Spectrum Management",IEEE,Communications Surveys & Tutorials, IEEE,2011
8. Gronsund, P.; MacKenzie, R.; Lehne, P.H.; Briggs, K.; Grondalen, O.; Engelstad, P.E.; Tjelta, T.,"Towards spectrum micro-trading",IEEE,Future Network & Mobile Summit (FutureNetw), 2012,2012
9. Lili Cao; Haitao Zheng,"Distributed Rule-Regulated Spectrum Sharing",IEEE,Selected Areas in Communications, IEEE Journalon,2008
10. Mangold, S.; Jarosch, A.; Monney, C.,"Operator Assisted Cognitive Radio and Dynamic Spectrum Assignment with DualBeacons - Detailed Evaluation",IEEE,Communication System Software and Middleware, 2006. Comsware 2006. FirstInternational Conference on,2006
11. Wang Fan; Lu Fang; Geng Xuan,"Researching on future spectrum management based on cognitive radio",IEEE,ComputerApplication and System Modeling (ICCASM), 2010 International Conference on,2010
12. Chung-Wei Wang; Li-Chun Wang,"Analysis of Reactive Spectrum Handoff in Cognitive Radio 13. Networks",IEEE,Selected Areas in Communications, IEEE Journal on,2012 14. Axell, E.; Leus, G.; Larsson, E.G.; Poor, H.V.,"Spectrum Sensing for Cognitive Radio : State-of-the-Art and Recent
Advances",IEEE,Signal Processing Magazine, IEEE,2012 15. Abadie, A.; Wijesekera, D.,"Leveraging an Inventory of the Cognitive Radio Attack Surface",IEEE,Cyber Security
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(CyberSecurity), 2012 International Conference on,2012 16. Brown, T.X.; Jaroonvanichkul, S.,"Policy-based radios for UAS operations",IEEE,Globecom Workshops (GC Wkshps), 2012
IEEE,2012 17. Wysocki, T.; Jamalipour, A.,"Pricing of Cognitive Radio Rights to Maintain the Risk-Reward of Primary User Spectrum
Investment",IEEE,New Frontiers in Dynamic Spectrum, 2010 IEEE Symposium on,2010 18. Li-Chun Wang; Chung-Wei Wang; Adachi, F.,"Load-Balancing Spectrum Decision for Cognitive Radio
Networks",IEEE,Selected Areas in Communications, IEEE Journal on,2011 19. Sheng-Yuan Tu; Kwang-Cheng Chen; Prasad, R.,"Spectrum Sensing of OFDMA Systems for Cognitive Radio
Networks",IEEE,Vehicular Technology, IEEE Transactions on,2009 20. Peng Jia; Mai Vu; Tho Le-Ngoc; Seung-Chul Hong; Tarokh, Vahid,"Capacity- and Bayesian-Based Cognitive Sensing with
Location Side Information",IEEE,Selected Areas in Communications, IEEE Journal on,2011 21. Lingjie Duan; Jianwei Huang; Biying Shou,"Investment and Pricing with Spectrum Uncertainty: A Cognitive Operator's
Perspective",IEEE,Mobile Computing, IEEE Transactions on,2011 22. Wei Feng; Jiannong Cao; Chisheng Zhang; Liu, C.,"Joint Optimization of Spectrum Handoff Scheduling and Routing in Multi-
hop Multi-radio Cognitive Networks",IEEE,Distributed Computing Systems, 2009. ICDCS '09. 29th IEEE InternationalConference on,2009.
20.
Authors: Ishita Verma, Urvi Marhatta, Sachin Sharma, Vijay Kumar
Paper Title: Age Prediction using Image Dataset using Machine Learning
Abstract: Gender is a central feature of our personality still. In our social life it is also an significant element.Artificial intelligence age predictions can be used in many fields, such as smart human-machine interface growth, health, cosmetics, electronic commerce etc. The prediction of people's sex and age from their facial images isan ongoing and active problem of research. The researchers suggested a number of methods to resolve thisproblem, but the criteria and actual performance are still inadequate. A statistical pattern recognition approachfor solving this problem is proposed in this project.Convolutionary Neural Network (ConvNet / CNN), a DeepLearning algorithm, is used as an extractor of features in the proposed solution. CNN takes input images andassigns value to different aspects / objects (learnable weights and biases) of the image and can differentiatebetween them. ConvNet requires much less preprocessing than other classification algorithms. While the filtersare hand-made in primitive methods, ConvNets can learn these filters / features with adequate training.In thisresearch, face images of individuals have been trained with convolutionary neural networks, and age and sexwith a high rate of success have been predicted. More than 20,000 images are containing age, gender andethnicity annotations. The images cover a wide range of poses, facial expression, lighting, occlusion, andresolution.
Keywords: Facial Images; Gender Prediction; Age Prediction; Convolutional Neural Network; Deep Learning.
References:
1. Dataset downloaded from Kaggle website:- https://www.kaggle.com/age-groupclassification- with-cnn 2. S. U. Rehman, S. Tu, Y. Huang, and Z. Yang, Face recognition: A Novel Un-supervised Convolutional Neural Network Method,
IEEE International Conference of Online Analysis and Computing Science (ICOACS), 2016. 3. N. Srinivas, H. Atwal, D. C. Rose, G. Mahalingam, K. Ricanek, and D. S. Bolme, Age, Gender, and Fine-Grained Ethnicity
Prediction Using Convolutional Neural Networks for the East Asian Face Dataset, 12th IEEE International Conference onAutomatic Face and Gesture Recognition (FG 2017), 2017.
4. N. Jain, S. Kumar, A. Kumar, P. Shamsolmoali, and M. Zareapoor, Hybrid Deep Neural Networks for Face Emotion recognition,Pattern Recognition Letters, 2018.
5. G. Levi, and T. Hassner,‖ Age and Gender Classification Using Convolutional Neural Networks,‖ IEEE Workshop on Analysisand Modeling of Faces and Gestures (AMFG), IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Boston, 2015.
6. S. Turabzadeh, H. Meng, R. M. Swash, M. Pleva, and J. Juhar, Realtime Emotional State Detection From Facial Expression OnEmbedded Devices, Seventh International Conference on Innovative Computing Technology (INTECH), 2017.
7. A. Dehghan, E. G. Ortiz, G. Shu, and S. Z. Masood, Dager: Deep Age, Gender and Emotion Recognition Using ConvolutionalNeural Network, arXiv preprint arXiv: 1702.04280, 2017.
8. A. Krizhevsky, I. Sutskever, and G. E. Hinton, ImageNet classification with deep convolutional neural networks,Communications of the ACM, vol. 60, no. 6, pp. 8490, 2017
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