Volume-7 Issue-5

S. No

Page No.

1.

Authors:

Paper Title:

Abstract: Block chain technology in today’s time changing the world of transactions and documentations. Mainly it gives a transparency to the numerous fields like electronic voting, cost analysis of product manufacturing, paying employees, cloud storage and smart contracts (the economist). Or we can say that these applications are the major pillors of a country which can be handled very efficiently with the help of blockchain technology. This paper explains the role of blockchain in bitcoin and will give the applications of blockchain In the field of transactions, governance, productions and documentation. The use of blockchain will reduce the use of third party and third party databases. The paper will give the relation of bitcoin and blockchain technology for improving the political aspects of country and reducing the dominating behaviour of the powerful persons and frauds in various fields by giving the transparency.

Keywords: Transactions; Governance; Productions Component; Blockchain.

References:

1. Pilkington, M ,Blockchain technology: principles and applications. Browser Download This Paper, 2015.
2. Atzori, M ,Blockchain technology and decentralized governance: Is the state still necessary?,2015.
3. https://www.finextra.com/blogposting/13068/5-ways-blockchain-will- transform-financial-services
4. Yli-Huumo, J., Ko, D., Choi, S., Park, S., & Smolander, K. (2016). Where is current research on blockchain technology?—a systematic review. PloS one, 11(10), e0163477.
5. Mattila, J. (2016). The blockchain phenomenon–the disruptive potential of distributed consensus architectures (No. 38). The Research Institute of the Finnish Economy.
6. Ekblaw, A., Azaria, A., Halamka, J. D., & Lippman, A. (2016, August). A Case Study for Blockchain in Healthcare:“MedRec” prototype for electronic health records and medical research data. In Proceedings of IEEE open & big data conference (Vol. 13, p. 13).

1-7

2.

Authors:

Paper Title:

Abstract: Big data analytics will revolutionize the health care sector. It provides us the power to assemble, handle, analyze, and understand massive amount of different, organized and unorganized data generated by the health care sector regularly. Consultants have known the requirement for analytics to enhance the standard of health care and improve care coordination for patients. It will improve operational efficiencies, facilitate predict and arrange responses to malady epidemics, improve the standard of observance of clinical trials, and optimize health care defrayment in the least levels from patients to hospital systems to governments. This paper, provides a summary of massive knowledge, relevancy of it in health care, a number of the add progress and a future outlook on however huge data analytics will improve overall quality in health care systems.

Keywords: Big Data Analytics, Assemble, Handle, Analyze, and Understand Massive Amount of Different,

References:

1. http://www.nature.com/nature/journal/v498/n 7453/full/498255a.html
2. Brenda software information is taken from https://en.wikipedia.org/wiki/BRENDA
3. http://www.futurescience.com/doi/full/10.415 5/fmc-2016-0264
4. http://www.intelligentpharma.com/blog.php?i d=65
5. All images are taken from http://www.google.co.in
6. Data-driven medicinal chemistry in the era of big data by Scott J. Lusher , Ross McGuire , Rene´ C. van Schaik , C. David Nicholson and Jacob de Vlieg
7. Open PHACTS: semantic interoperability for drug discovery by Antony J. Williams, Lee Harland, Paul Groth, Stephen Pettifer, Christine Chichester, Egon L. Willighagen ,volume 17, 2012
8. The Journal of Antibiotics: Where we are now and where we are heading by Jason Berdy, 2012 Japan Antibiotics Research Association
9. Role of open chemical data in aiding drug discovery and design by Anna Gaulton and John P Overington , 2010
10. IBM Watson    https://www-01.ibm.com/common/ssi/cgi- bin/ssialias?htmlfid=HLW03045USEN&
11. Uses of             big           data https://www.sas.com/en_us/insights/analytics/bi g-data-analytics.html

8-12

3.

Authors:

Paper Title:

Abstract: Network traffic modelling is the process of describing the dynamic behavior of network by random processes. The issue that it is hard to fully predict the demand on any network from service provider point of view, so it is important to find an accurate traffic model to maintain the quality of service. This paper focus on analyzing the internet traffic in the University of Jordan network as a case study. The reading and monitoring of the traffic was done with appreciated support from the service provider (JU Net Co.).

Keywords: Network traffic, Traffic model Quality of Service (QoS), Internet Service Provider (ISP), Markov Traffic Models, Poisson Traffic Model, Long-tail traffic models. 

References:

1. Lee, H., Jeon, D. (2015). A Mobile Ad-Hoc network multipath routing protocol based on biological attractor selection for disaster recovery communication, The Korean Institute of Communications Information Sciences. ICT Express 1 (2015) 86–89.
2. https://doi.org/10.1016/j.icte.2015.10.001
3. Manaseer, S., Alawneh, A. (2017). A New Mobility Model for Ad Hoc Networks in Disaster Recovery Areas. International Association of Online Engineering. V. 13, No.3.
4. Chen, T. (2007). Network Traffic Modeling. Hossein Bidgoli (ed.), Wiley.
5. Floyd and V. Jacobson, Link-sharing and resource management models for packet networks, IEEE Trans. Networking, vol. 3, 1995.
6. Keshav. (1997). An engineering approach to computer networking: ATM networks, the Internet, and the telephone network. Addison-Wesley Longman Publishing Co.
7. SB .A. Mohammed, S.M Sani, D.D. DAJAB. (2013). Network Traffic Analysis: A Case Study of ABU Network. Computer engineering and intelligent systems journal, Vol 4, No 4, 2013.
8. Thompson, K., Miller, G., and Wilder, R. (1997). Wide-area Internet traffic patterns and characteristics. IEEE Network, 11.
9. Flickenger, R.; Belcher, M.; Canessa, E.; Zennaro, M. How To Accelerate Your Internet: A practical guide to Bandwidth Management and Optimisation using Open Source Software. (2006) ISBN 0-9778093-1-5. Accessed in May/2018.
10. Chandrasekaran, B. (2009). “Survey of Network Traffic Models”. Technical report accessed in May/2018, available online: < http://www.cse.wustl.edu/~jain/cse567-06/traffic_models3.htm >.

13-16

4.

Authors:

Paper Title:

Abstract: The present work proposes the Image processing plays a vital role in medical diagnosis system. Out of various processing tools, image segmentation is very crucial in identifying the exact reason of disease. Image segmentation clusters the pixels into silent image regions i.e. regions corresponding to individual surfaces, objects or any part of objects. Various algorithms have been proposed for image segmentation. We have analyzed the various systems that have been developed to medical diagnosis analysis. Reviewing of these frameworks will be dependent upon level set strategies from claiming segmenting pictures. The theme, merits, faults from claiming Different frameworks will be talked about in this paper. Dependent upon that, another framework need been suggested to segmenting those MRI picture utilizing variety level situated calculation without reinitialisation for MRI image. Those framework could be used both to recreated and also genuine im-ages.

Keywords: MRI, Segmentation Level set, Image Processing.

References:

1. Aubert and P. Kornprobst, Mathematical Problems in Image  Processing: Partial Differential Equations and the Calculus of Variations. New York: Springer-Verlag, 2002.
2. Caselles, F. Catte, T. Coll, and F. Dibos, “A geometric model for active contours in image processing,” Numer. Math., vol. 66, no. 1, pp. 1–31, Dec. 1993.
3. Caselles, R. Kimmel, and G. Sapiro, “Geodesic active contours,” Int. J. Comput. Vis., vol. 22, no. 1, pp. 61–79, Feb. 1997.
4. Chan and L. Vese, “Active contours without edges,” IEEE Trans. Image. Process, vol. 10, no. 2, pp. 266–277, Feb. 2001.
5. Cremers, “A multiphase levelset framework for variational motion segmentation,” in Proc. Scale Space Meth. Comput. Vis., Isle of Skye, U.K., Jun. 2003, pp. 599–614.
6. Bhavana Godavarthi , M Lakshmi Raviteja, Paparao Nalajala,” Pressure Monitoring by capturing IR Image,” International Journal of Emerging Trends in Engineering Research, Vol. No.4, Issue IV, pp.32-35, Jan 2016. ISSN: 2347 - 3983, (Impact Factor: 0.987)
7. Kichenassamy, A. Kumar, P. Olver, A. Tannenbaum, and A. Yezzi, “Gradient flows and geometric active contour models,” in Proc. 5th Int. Conf. Comput. Vis., 1995, pp. 810–815.
8. Kimmel, A. Amir, and A. Bruckstein, “Finding shortest paths on surfaces using level set propagation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 17, no. 6, pp. 635–640, Jun. 1995.
9. Li, R. Huang, Z. Ding, C. Gatenby, D. Metaxas, and J. Gore, “A variational level set approach to segmentation and bias correction of medical images with intensity inhomogeneity,” in Proc. Med. Image Comput. Comput. Aided Intervention, 2008, vol. LNCS 5242, pp. 1083–1091, Part II.
10. Li, C. Kao, J. C. Gore, and Z. Ding, “Minimization of region-scalable fitting energy for image segmentation,” IEEE Trans. Image Process., vol. 17, no. 10, pp. 1940–1949, Oct. 2008.
11. Li, C. Xu, C. Gui, and M. D. Fox, “Distance regularized level set evolution and its application to image segmentation,” IEEE Trans. Image Process., vol. 19, no. 12, pp. 3243–3254, Dec. 2010.
12. Malladi, J. A. Sethian, and B. C. Vemuri, “Shape modeling with front propagation: A level set approach,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 17, no. 2, pp. 158–175, Feb. 1995.
13. Mumford and J. Shah, “Optimal approximations by piecewise smooth functions and associated variational problems,” Commun. Pure Appl. Math., vol. 42, no. 5, pp. 577–685, 1989.
14. Osher and J. Sethian, “Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations,” J. Comp. Phys., vol. 79, no. 1, pp. 12–49, Nov. 1988.
15. Paragios and R. Deriche, “Geodesic active contours and level sets for detection and tracking of moving objects,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 3, pp. 266–280, Mar. 2000.
16. Paragios and R. Deriche, “Geodesic active regions and level set methods for supervised texture segmentation,” Int. J. Comput. Vis., vol. 46, no. 3, pp. 223–247, Feb. 2002.
17. Ronfard, “Region-based strategies for active contour models,” Int. J. Comput. Vis., vol. 13, no. 2, pp. 229–251, Oct. 1994.
18. Samson, L. Blanc-Feraud, G. Aubert, and J. Zerubia, “A variational model for image classification and restoration,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 5, pp. 460–472, May 2000.
19. An efficient algorithm for image compression" paper published in IJCIET journal with V-8,I-8, Aug-2017. 2."An efficient motion estimation multiple reference frames algorithm" paper published in IJMET journal with V-8, I-8, Aug, 2017
20. P.Bachan,Samit Kumar Ghosh, Shelesh Krishna Saraswat, "Comparative Error Rate Analysis of Cooperative Spectrum Sensing in Non-Fading and Fading Environment”, IEEE International Conference on Communication Control and Intelligent Systems, GLA University. Mathura.Pages:124-127, ISBN: 978-1-4673-7540-5, DOI: 10.1109/CCIntelS.2015.7437891, 2015. (IEEE Xplore) 

17-21

5.

Authors:

Paper Title:

Abstract: This research paper proposes the Cloud computing platforms would spread very quickly the standout amongst the principle aspects of cloud computing will be the Part under a number layers. Starting with specialized fault tolerance a large portion cloud computing platforms misuse virtualization, this intimates that they need a part under 3 layers such as hosts, virtual machines and requisitions. Starting with an organization purpose from claiming view, they need aid part under 2 layers: the cloud supplier who manages those facilitating focal point and the client who manages as much provision in the cloud. This structuring for cloud makes it challenging to actualize all the viable management arrangements. This paper concentrates for deficiency tolerance over cloud Computing platforms for more that's only the tip of the iceberg decisively once autonomic repair shed in the event that about faults. It examines the meanings from claiming this Part in the usage about issue tolerance. Clinched alongside The majority for current approaches, faults line tolerance will be only took care of toward the supplier alternately that customer, which prompts fractional or wasteful results. Solutions, which include a coordinated effort the middle of the supplier and the client, need aid substantially guaranteeing. We show this talk for analyses the place elite Also community oriented deficiency tolerance results are actualized to an autonomic cloud foundation that we prototyped.

Keywords: Cloud Computing, Fault Tolerance, Faulty Node, Proactive Technique.

References:

1. Design and Implementation of Effective Check pointing for Multithreaded Applications on Future Clouds. IEEE-Cloud Computing, 2013.
2. Jiajun Cao, Matthieu Simonin. Checkpointing as a Service in Heterogeneous Cloud HAL Archives-2014.
3. Ghoshal and L. Ramakrishnan, “Frieda: Flexible robust intelligent elastic data managementin cloud environments,” in High Performance Computing, Networking, Storage andAnalysis (SCC), 2012 SC Companion:. IEEE, 2012, pp. 1096–1105.
4. D, Singh, J., "High Availability of Clouds: Failover Strategies for Cloud Computing Using Integrated Checkpointing Algorithms" IEEE-CSNT, 2012.
5. Jangjaimon, "Design and Implementation of Effective Checkpointing for Multithreaded Applications on Future Clouds", IEEE-Cloud Computing (CLOUD), 2013
6. Ghoshal and L. Ramakrishnan, “Frieda: Flexible robust intelligent elastic data managementin cloud environments,” in High Performance Computing, Networking, Storage andAnalysis (SCC), 2012 SC Companion:. IEEE, 2012, pp. 1096–1105.
7. Di, Y. Robert, F. Vivien, D. Kondo, C.-L. Wang, and F. Cappello, “Optimization of cloudtask processing with checkpoint-restart mechanism,” in Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, ser. SC’13. New York, NY, USA: ACM, 2013, pp. 64:1–64:12.

22-24

6.

Authors:

Paper Title:

Abstract: To improve students’ learning outcomes (SLO’s), it requires efforts on many aspects, out of which effective learning techniques helps and motivate students to achieve their learning goals. Learning conditions depends on prior knowledge, learning environments and the nature of the area in which the techniques are implemented. There are certain rubrics which have been decided to measure SLO’s. To achieve the target, it is important to meet the introduced methodology in all respect of Course Learning Outcomes(CLO’s) in National Qualification Framework (NQF) Domains of Learning and Alignment with Assessment Methods and Teaching Strategies: Knowledge, Cognitive Skills, Interpersonal Skills & Responsibility, Communication, Information Technology, Numerical and Psycho-motor.

Keywords: National Qualification Framework (NQF), Course Learning Outcomes (CLO’s), Students Learning outcomes (SLO’s). 

References:

1. NILOA (The National Institute for Learning Outcomes Assessment, 2015). Measuring Quality in Higher Education: An Inventory of Instruments, Tools and Resources. 2015-12-12.
2. Jianxin Zhang, Research on the Assessment of Student Learning Outcomes, 2017 Council for Higher Education Accreditation/CHEA International Quality Group.
3. Jennifer E. R. “Methods for Assessing Student Learning Outcomes,” Coordinator of Academic Assessment Office of Institutional Research, Planning, and Assessment Northern Virginia Community College 2008.
4. Yusuf A. Al-Turki, Anwar L. Bilgrami, “A case study of key performance indicators in scienctific research in a middle eastern university, International journal of latest research in science and technology. Volume 4, issue 6: Page No.21-28, Nov-Dec 2015
5. Dick M. Carpenter II Æ Lindy Crawford Æ Ron Walden, “Testing the efficacy of team teaching” Springer Science+Business Media B.V. 2007, Learning Environ Res (2007) 10:53–65 DOI 10.1007/s10984-007-9019.
6. Tomasz NIEDOBA* , Paulina PIĘTA, “Application of Anova in mineral Processing” Mining Science, vol. 23, 2016, 43−54, ISSN 2084-4735
7. Kwaku F. Darkwah, Richard Tawiah1 , Maxwell Adu-Gyamfi, “Two-way ANOVA for the Study of Revenue Mobilization Inequalities” Lithuanian Statistical Association, Statistics Lithuania Lietuvos statistikų sąjunga, Lietuvos statistikos departamentas ISSN 2029-7262, 2015, vol. 54, No 1, pp. 45–51 2015, 54 t., Nr. 1, 45–51 p,
8. Jean Ashby,” Comparing student success between developmental math courses offered online, blended, and face-to-face” Volume 10, Number 3, Winter 2011 ISSN: 1541-4914, Journal of Interactive Online Learning
9. Ramona Lile, Camelia Bran CESC 2013, “The assessment of learning outcomes”, Procedia - Social and Behavioral Sciences 163 ( 2014 ) 125 – 131. Published by Elsevier Ltd.
10. Berger, J. B., & Milem, J. F. (1999). The role of student involvement and perceptions of integration in a causal model of student persistence. Research in Higher Education, 40, 641–664.
11. Sylvia Encheva, Evaluation of Learning Outcomes, ICWL 2010: Advances in Web-Based Learning – ICWL 2010 pp 72-80. Springer Link

25-33

7.

Authors:

Paper Title:

Abstract: The Simplex method is the most popular and successful method for solving linear programs. The objective function of linear programming problem (LPP) involves in the maximization and minimization problem with the set of linear equalities and inequalities constraints. There are different methods to solve LPP, such as simplex, dual-simplex, Big-M and two phase method. In this paper, an approach is presented to solve LPP with new seven steps process by choosing “key element rule” which is still widely used and remains important in practice. We propose a new technique i.e. seven step process in LPP for the simplex, dual-simplex, Big-M and two phase methods to get the solution with complexity reduction. The complexity reduction is done by eliminating the number of elementary row transformation operation in simplex tableau of identity matrix. By the proposed technique elementary transformation of operation has completely avoided and we can achieve the results in considerable duration.

Keywords: Linear programming problem (LPP), Key element (KE), Key column (KC), Key row (KR), Profit per unit (PPU), Random variables (RV), Linear Gaussian Random variables (LGRV), standard deviation (SD), Probability Density Function (PDF)

References:

1. T. Kitahara and S. Mizuno: A Bound for the Number of Different Basic Solutions Generated by the Simplex Method, Technical Report, October 15, 2010, to appear in Mathematical Programming (available online at http://www.springerlink.com/content/103081/)
2. T. Kitahara and S. Mizuno: An Upper Bound for the Number of Different Solutions Generated by the Primal Simplex Method with Any Selection Rule of Entering Variables.
3. T. Kitahara and S. Mizuno: New Evaluation of Computational Amount of the Simplex Method (Japanese), Technical Report No. 2011-8, August, 2011.
4. Divya K.Nadar : Some Applications of Simplex Method, International Journal of Engineering Research and Reviews ISSN 2348-697X (Online) Vol. 4, Issue 1, pp: (60-63), Month: January - March 2016, Available at: www.researchpublish.com
5. Dr. R.G. Kedia: A New Variant of Simplex Method, Volume-3, Issue-6, December-2013, ISSN No.: 2250-0758 International Journal of Engineering and Management Research Available at: www.ijemr.net Page Number: 73-75.
6. Barry Cipra: The Best of the 20th Century: Editors Name Top 10 Algorithms SIAM News, Volume 33, Number 4, page 1, 2000.
7. Benichou, M., J. M. Gauthier, G. Hentges, G. Ribi`ere. 1977. The efficient solution of large scale linear programming problems. Some algorithmic techniques and computational results. Math. Programming 13 280–322.
8. Achterberg, T. 2009. SCIP: solving constraint integer programs. Math. Programming Computation, 1 (1) 1–41.
9. Bartels, R. H., G. H. Golub. 1969. The simplex method of linear programming using LU decomposition. Communications of the Association for Computing Machinery 12 266–268.
10. Harris, P. J. J. 1974. Pivot selection methods of the devex LP code. Math. Programming 5 1–28.
11. Hoffman, A., A. Mannos, D. Sokolowsky, D. Wiegmann. 1953. Computational experience in solving linear prog. SIAM J.11–33.
12. Karmarkar, N. 1984. A new polynomial-time algorithm for linear programming, Combinatorica 4 373–395.
13. Van Roy, T. J., L. A., Wolsey. 1987. Solving mixed integer programming problems with automatic reformulation. Operations Research 35 (1) 45– 57.
14. Mihai Banciu: Dual Simplex, School of Management Bucknell Univ. Lewisburg, PA 17837 mmb018@bucknell.edu Jan 2010.
15. Dimitris Bertsimas and John Tsitsiklis. Introduction to Linear Optimization. Athena Scientific, Belmont, MA, 1st edition, 1997.
16. George B. Dantzig. Maximization of a linear function of variables subject to linear inequalities. In T. C. Koopmans, editor, Activity Analysis of Production and Allocation, pages 359–373. John Wiley and Sons, Inc., New York, NY, 1951.
17. V. Klee and G. J. Minty. How good is the simplex algorithm? In O. Shisha, editor, Inequalities - III, pages 159–175. Academic Press, New York, NY, 1972.
18. William S. Jewell: A Primal-Dual Multi-Commodity Flow Algorithm, Operations Research Center University of California, Berkeley, September 1966, ORC 66-24
19. Beale, E.M.L., "An Alternative Method for Linear Programming," Proo. Cambridge Philos. Soc. 50 (l954), 513-523.
20. Charnes, A., Cooper, W.W., and Farr, D. and Staff, "Linear Programming and Profit Preference Scheduling for a Manufacturing Firm," Jour. Oper. Res. Soc. Amer., _1, no. 3 (1953), 114-129.
21. Dautzig, G.B., "Maximizing of a Linear Function of Variables Subject to Linear Inequalities," Chapter XXI of Activity Analysis of Production and Allocation, edited by Koopmans, T-C, Wiley, N.Y., 1951.
22. Dantzig, G.B., "Computational Algorithm of the Simplex Method", the RAND Corporation, Paper 394, 10 April 1953. (RM-1266, Part Xll, 26 October, 1953)
23. Ford, L.R., and Fulkerson, D.R., "A Simple Algorithm for Finding Maximal Netv.-ork Flows and an Application to the Hitchcock Problem," The RAND Corporation, Paper 743, 26 Sept. 1955j RIJ-1604, 29 Dec. 1956.
24. Ford, L.R., and Fulkerson, D.R., "A Primal Dual Algorithm for the Capacitated Hitchcock Problem," The RAITO Corporation, RI.i-1798 Part XXXIV, 25 Sept. 1956. (Naval Res. Legist. Quarto, 4, no. 1 (l957), 47-54, RAND Conference on Computational Aspects of Linear Programming, 30-31 Aug. 1956.)
25. Goldman, A. J., and Tucker, A.W. , "Theory of Linear programming," Paper 4 in Linear Inequalities and Related Systems, Princeton Univ. Press, Princeton, 1956, 53-98.
26. S.Mittal,APande, LWang, PKumar (2011) Design Exploration and Implementation ofSimplex Algorithm over Reconfigurable Computing Platforms In: IEEE International Conference on Digital Convergence 204-209
27. Cui, T. Ho, T. Tellambura, C. Linear Programming Detection and Decoding for MIMO Systems IEEE International Symposium on Information Theory, pp. 1783-1787, July 2006.
28. Majumdar, A. FPGA Implementation of Integer Linear Programming Accelerator International Conference on Systemics, Cybernetics and Informatics, (ICSCI), Jan 2006.
29. R.G. Bland, D. Goldfarb, and M.J. Todd, “The ellipsoid method: a survey,” Operations Research 29, 1039-1091 (1981
30. Karmarkar, N. A new polynomial-time algorithm for linear programming Combinatorica 4, 373−395 (1984)
31. Borgwardt, K. H. Some distribution independent results about the asymptotic order of the average number of pivot steps in the simplex method Mathematics of Operations Research, vol.7, no.3, pp.441-462, 1982.

34-43

8.

Authors:

Paper Title:

Abstract: In this paper, we proposed a novel method based on coupling of SVM (Support Vector Machine) and WWO (Water Wave Optimization) for detection of metamorphic malware. The working of SVM model depends upon the proper selection of SVM parameters. Malware signatures have been taken from G2, MWOR, MPCGEN and NGVCK (Next Generation Virus Creation Kit).Benign signatures have been taken from Gygwin, GCC, TASM, MingW and Clang .ClustalW and T-Coffee are used for signature alignment during primary pairwise alignment and secondary multiple alignment in order to avoid the problem of variable length of code. In this study WWO has been employed for determining the parameters of SVM. The performance of SVM-WWO method has been compared with LAD Tree, Naive Bayes, SVM and ANN(Artificial Neural Network). Furthermore, The results obtained show that the newly proposed approach provides significant accuracy. Satisfactory experimental results show the efficiency of proposed method for metamorphic malware detection. Further, it has been recommended that this method can be used to facilitate commercial antivirus engines.

Keywords: metamorphic malware detection, support vector machine (SVM), water wave optimization (WWO).

References:

1. Bayer, A. Moser, C. Kruegel and E. Kirda, " Dynamic Analysis of Malicious Codes," Journal of Computer Virology and Hacking Techniques, vol. 2, no.1, pp. 67-77, 2006.
2. Cohen, "Computer Viruses: Theory and Experiments." Computers & Security," vol. 6, no. 1, pp. 22-35, 1985.
3. Fu, and J. Blum, "Controlling for Cybersecurity Risks of Medical Device Software," Communications of the ACM," vol. 56, no. 10, pp.35-37, 2013.
4. Zhou, Q. Zhu and M. Zhou," On The Time Complexity of Computer Viruses," IEEE Transaction of Information Theory," vol. 51, no. 8, pp. 2962-2966, 2005.
5. Bilge and T. Dumitras, " Before We Knew it: An Empirical Study of Zero-Day Attacks in The Real World,"In: Proceedings of The ACM Conference on Computer and Communication Security," pp. 833-844, 2012.
6. Bilar," Opcodes are Predictor for Malware," IJESDF, vol. 1, no. 2, pp. 156-168, 2007.
7. Szor," The Art of Computer Virus Research and Defense,"Pearson Education, 2005.
8. S. Bist, "Detection of Metamorphic Viruses: A Survey,"In Advances in Computing, Communications and Informatics (ICACCI, International Conference on,  pp. 1559-1565, 2014.
9. P. Nair, H. Jain, Y. K. Golecha, M. S.Gaur, & V. Laxmi, " MEDUSA: MEtamorphic Malware Dynamic Analysis Using Signature from API," In Proceedings of the 3rd International Conference on Security of Information and Networks, pp. 263-269.
10. Annachhatre, T. H. Austin, and M. Stamp, "Hidden Markov models for malware classification," Journal of Computer Virology and Hacking Techniques, vol. 11, no. 2, pp.59-73, 2015.
11. Wong, and M. Stamp, "Hunting for metamorphic engines," Journal in Computer Virology, vol. 2, no. 3 , pp. 211-229, 2006.
12. Srinivasan, "SSCT Score for Malware Detection," SJSU Master Thesis, pp. 10-40, 2015.
13. Rajeswaran, "Function Call Graph Score for Malware Detection." SJSU Master Thesis, pp. 11-47, 2015.
14. K. Jidigam, T. H. Austin & M. Stamp, "Singular value decomposition and metamorphic detection," Journal of Computer Virology and Hacking Techniques, vol. 11, no. 4, pp. 203-216, 2015.
15. Narra, F. D. Troia, V. A. Corrado, T.H. Austin, and M. Stamp, "Clustering versus SVM for malware detection," Journal of Computer Virology and Hacking Techniques, pp.1-12. 2015.
16. Mangesh, T. H. Austin, and M. Stamp. "Hunting for Metamorphic JavaScript malware," Journal of Computer Virology and Hacking Techniques, vol. 11, no. 2, pp. 89-102, 2015.
17. Sayali, Y. Park, and M. Stamp. "Eigenvalue analysis for metamorphic detection," Journal of computer virology and hacking techniques, vol.10, no. 1 , pp. 53-65, 2014.
18. Gayathri, R. M. Low, and M. Stamp, "Simple substitution distance and metamorphic detection," Journal of Computer Virology and Hacking Techniques, vol. 9, no. 3 , pp. 159-170, 2013.
19. Donabelle, R. M. Low, and M. Stamp. "Structural entropy and metamorphic malware," Journal of computer virology and hacking techniques, vol. 9, no. 4 , pp. 179-192, 2013.
20. Sayali, Y. Park, and M. Stamp. "Eigenvalue analysis for metamorphic detection," Journal of computer virology and hacking techniques10, no. 1, pp. 53-65, 2014.
21. Neha, R. M. Low, and M. Stamp. "Opcode graph similarity and metamorphic detection," Journal in Computer Virology, vol. 8, no. 1-2 , pp. 37-52, 2012.
22. Ronak. "METAMORPHIC VIRUSES BUFFER OVER." PhD diss., San Jose State University, 2010.
23. P., Mila, R. Giacobazzi, A. Lakhotia, and I. Mastroeni, "Abstract symbolic automata: mixed syntactic/semantic similarity analysis of executables," In ACM SIGPLAN Notices, vol. 50, no. 1, pp. 329-341, 2015.
24. Lakhotia, A. Walenstein, C. Miles, and A. Singh, "VILO: a rapid learning nearest-neighbor classifier for malware triage," Journal of Computer Virology and Hacking Techniques, vol. 9, no. 3, pp.109-123, 2013.
25. Shahid, R. Nigel Horspool, and I. Traore, "MAIL: Malware Analysis Intermediate Language: a step towards automating and optimizing malware detection," In Proceedings of the 6th International Conference on Security of Information and Networks, pp. 233-240, 2013.
26. Parvez, V. Laxmi, M. S. Gaur, and P. Vinod, "Mining control flow graph as API call-grams to detect portable executable malware," In Proceedings of the Fifth International Conference on Security of Information and Networks, pp. 130-137, 2012.
27. Ivan, A. Erwin, and A. S. Nugroho. "Analysis of machine learning techniques used in behavior-based malware detection," In Advances in Computing, Control and Telecommunication Technologies (ACT), Second International Conference on, pp. 201-203, 2010.
28. V. Shijo, and A. Salim. "Integrated static and dynamic analysis for malware detection, " Procedia Computer Science, vol. 46. pp. 804-811, 2015.
29. Neha, R. M. Low, and M. Stamp. "Opcode graph similarity and metamorphic detection," Journal in Computer Virology, vol.8, no. 1, pp.37-52, 2012.
30. H. Annie, and M. Stamp. "Chi-squared distance and metamorphic virus detection," Journal of Computer Virology and Hacking Techniques , vol. 9, no. 1 ,pp. 1-14, 2013.
31. Hardikkumar, and M. Stamp "Hunting for Pirated Software Using Metamorphic Analysis." Information Security Journal: A Global Perspective, vol. 23, no. 3 ,pp. 68-85, 204.
32. J. Zheng, "Water wave optimization: a new nature-inspired metaheuristic. Computers & Operations Research", 55, 1-11,2015.
33. M. Sridhara, & M. Stamp, (2013). "Metamorphic worm that carries its own morphing engine". Journal of Computer Virology and Hacking Techniques, 9(2), 49-58.
34. VX Heavens, Retrieved from: http://vxheaven.org/vx.php?id=tn02
35. VX Heavens, Retrieved from: http://vxheaven.org/vx.php?id=tn02
36. VX Heavens. Retrieved from:http://download.adamas.ai/dlbase/Stuff/VX%20Heavens%20Library/static/vdat/creatrs1.htm

37. Retrieved from http://clang.llvm.org/.
38. Retrieved from: http://www.cygwin.com/
39. Retrieved from http://gcc.gnu.org/.
40. Taken from: http://www.mingw.org/.
41. Retrieved from:
42. http://trimtab.ca/2010/tech/tasm-5-intel-8086-turbo-assemblerdownload
43. Kisi , & K. S. Parmar, "Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution". Journal of Hydrology, 534, 104-112.
44. C. Mojumder, J. C. Ong, W. T. Chong, & S. Shamshirband, "Application of support vector machine for prediction of electrical and thermal performance in PV/T system". Energy and Buildings, 111, 267-277. 2016.
45. D. Fisher, T. K. Camp & V. V. Krzhizhanovskaya, "Crack detection in earth dam and levee passive seismic data using support vector machines". Procedia Computer Science, 80, 577-586.2016.
46. Ch, S. K. Sohani, D. Kumar, A. Malik, B. R. Chahar, A. K.Nema, ... & R. C. Dhiman, "A support vector machine-firefly algorithm based forecasting model to determine malaria transmission". Neurocomputing, 129, 279-288.2014.
47. Huda, J. Abawajy, M. Alazab, M. Abdollalihian, R. Islam, & J. Yearwood, "Hybrids of support vector machine wrapper and filter based framework for malware detection". Future Generation Computer Systems, 55, 376-390.2016.
48. Sahs, & L. Khan,"A machine learning approach to android malware detection". In Intelligence and security informatics conference (eisic), 2012 european(pp. 141-147). IEEE. 2012.
49. D. Schmidt, R. Bye, H. G. Schmidt, J. Clausen , O. Kiraz, K. A. Yuksel, ... & S. Albayrak, "Static analysis of executables for collaborative malware detection on android". In Communications, ICC'09. IEEE International Conference on(pp. 1-5). IEEE.2009.
50. K. A. Mirza, I. Awan, & M. Younas, "CloudIntell: An intelligent malware detection system" Future Generation Computer Systems.2017.
51. Peddabachigari, A. Abraham, C. Grosan, & J. Thomas, "Modeling intrusion detection system using hybrid intelligent systems" Journal of network and computer applications, 30(1), 114-132.2007.
52. J. Vidmar. (1992, August). On the use of atmospheric plasmas as electromagnetic reflectors. IEEE Trans. Plasma Sci. [Online]. 21(3). pp. 876—880.Available:http://www.halcyon.com/pub/journals/21ps03-vidmar

44-50

9.

Authors:

Paper Title:

Abstract: Removal of shadows from one image could be a difficult drawback. Manufacturing a high-quality shadow-free image that is indistinguishable from a replica of a real shadow-free scene is even tougher Shadows in pictures area unit generally full of many phenomena within the scene, as well as physical phenomena like lighting conditions, kind and behavior of shadowy surfaces, occluding objects, etc. Additionally, shadow regions might endure post acquisition, image process transformations, e.g., distinction sweetening, which can introduce noticeable artifacts within the shadow-free pictures. We dispute that the assumptions introduced in most studies arise from the quality of the matter of shadow removal from one image and limit the category of shadow pictures which might be handled by employing a Modified Multi-exposure image fusion (MMEF) technique. Experimental results showing definitively the capabilities of our algorithmic rule are given. The difference is that HDR reconstruction works in the radiance domain where the value is linear w.r.t. the exposure, while MMEF works in the intensity based domain. Compared with object motion, camera motion is relatively easy to tackle via either setting a tripod or employing some registration techniques.

Keywords: SPD-MMEF, Image fusion, Ghost Removal Algorithm, Pixel - level based image Fusion. Image enhancement.. 

References:

1. Kede Ma, Hui Li, Hongwei Yong &Zhou Wang (2017) ‘Robust Multi-Exposure Image Fusion: A Structural Patch Decomposition Approach’IEEE Transactions On Image Processing, Vol. 26, No. 5,pp.2519-2532.
2. Ma and Z. Wang, “Multi-exposure image fusion: A patch-wise approach,” in Proc. IEEE Int. Conf. Imag. Process., Sep. 2015,
3. 1717–1721.
4. B. Kang, M. S. Winder, and R. Szeliski, “High dynamic range video,” ACM Trans. Graph., vol. 22, no. 3, pp. 319–325, 2009.
5. Gu, B. Li, W. Wong, J. Zhu, M. and Wang, M. (2012) ‘Gradient field Multi-exposure images fusion for high dynamic range image   Visualization’,J.Vis.Comm.image  23,  no. 4, pp. 604–610.
6. Hu, J. Gallo, O. and Pulli, K.(2012) ‘Exposure stacks of live scenes with     Cameras’, in Proc. Eur. Conf. Compute. Vis., 2012, pp. 499–   512.
7. Hu, J. Gallo, O. Pulli, K. and Sun, X. (2013)  ‘HDR deghosting How
8. to deal with saturation?’, in Proc. IEEE Conf. Compute.  Pattern Recognit. pp. 1163–1170.
9. Lee, J-Y. Matsushita, Y. Shi, B. Kweon, I. S. (2013) Mach
10. Radiometric calibration by rank minimization’, IEEE Trans. Pattern Anal.   In tell. vol. 35, no. 1, pp. 144–156.
11. Li, S. Kang, X. and Hu, J. (2013) ‘Image    fusion with guided filtering’,
12. IEEE Trans. Image Process, vol. 22, no. 7, pp. 2864–2875.
13. Zitová, B. and Flusser, J. (2003) ‘Image registration methods: A
14. survey’, Image Vis. Compute., vol. 21, pp. 977–1000.
15. Li, S. and Kang, X. (2012) ‘Fast multi- exposure image fusion with
16. median Filter and recursive filter’, IEEE Trans.  Electron. vol. 58, no. 2, pp. 626–632.
17. Lowe, D. G. (2004) ‘Distinctive image features from scale-invariant
18. key points’ Int. J.Comp,Vis. vol. 60, no.2,pp.91-110.
19. Ma, K. and Wang, Z. (2015) ‘Multi-Exposure image fusion:
20. Apatch-wise Approach’, in .Proc. IEEE Int. Conf. Image. Process, pp.1717–1721.
21. Ma, K. Zeng, K. and Wang, Z. (2015) ‘Objective quality assessment
22. for Color to-gray image conversion’, IEEE Image Pro.vol.11,pp 3345-3356.
23. Ma, K. Yeganeh, H.  Zeng, K. and Wang,  Z.(2015) ‘High dynamic
24. Range image compression by optimizing tone mapped image quality  Index’, IEEE Trans. Image   vol. 24, no. 10, pp. 3086–3097.
25. Mertens, T. Kautz, J. and Van Reeth, F. (2009) ‘Exposure fusion: A
26. Simple and practical alternative to high dynamic range photography’, Compute. Graph. Forum, vol. 28, no. 1, pp. 161–171
27. Raman, S. and Chaudhuri, S. (2009) ‘Bilateral filter based
28. ompositing For variable exposure photography’, in Proc. Euro graphics, pp. 1–4.
29. Sen, P. Kalantari, N. K. Yaesoubi, M. Goldman, D. B. & Shechtman,
30. E (2012)‘Robust patch-based HDR reconstruction of Dynamic scenes’, ACM Trans. Graph., vol. 31, no. 6, pp. 203-213 
31. Wang, Z.  Bovik, A. C. Sheikh, H. R. and  Simon cell, E. Pi, (2004)
32. ‘Image quality assessment: From error visibility to structural  milarity’, IEEE Trans. Image Process. vol. 13, no. 4, pp. 600–612.
33. A. A. Goshtasby, “Fusion of multi-exposure  images,” Image Vis.
34. ,vol. 23, no.6 pp. 611–618, Jun. 2005.
35. S. Li, X. Kang, and J. Hu,(2013) “Image Fusion with guided
36. filtering,” IEEE Trans. Image.Proc.,vol. 22, no. 7, pp.
37. 2864–2875.
38. Enfuse HDR webpage. [Online]. Available:
39. http://www.photographerstoolbox. com/products/lrenfuse.php, 2016.
40. J. Wang, S. Wang, K. and Z. Wang,(2017) “Perceptual depth quality
41. in distorted stereoscopic images,” IEEE Trans. Image  , vol. 26, no. 3, pp. 1202–1215

51-59

10.

Authors:

Paper Title:

Abstract: In order to improve the wear and corrosion behavior for the alloys, coating is found as the most suitable method. Mg base alloys have a wide range of industrial application. These alloys shows a high specific strength but poor wear and corrosion resistance. An ordinary coating of Cu, Ni & Zn etc. provide a physical barrier against the wear rate and corrosion attack of magnesium substrate. In the present investigation, Ni-P plating was done on AZ91 composite by immersing samples into Nickel sulphate bath in presence of surfactants. The mechanism of Ni-P deposits was studied by using SEM. Ni-P coating was coated uniformly in the presence of surfactants. Effect of surfactant and Effect of Nano-additives Al2O3, ZnO, and SiO with different quantities were studied. 0.5 g/l Nano Al2O3 additive enhanced the deposition of Ni-P on AZ91 magnesium composite and the same results have been observed in case of SiO addition. Influence of ZnO was also observed. So is very clear that Ni-P coating is very effective to reduce the corrosion and increase the wear behaviour if it is used along with Nano additive and the surfactants. 

Keywords: Coating, Nano-additives, Scanning Electron Microscope, surfactants.

References:

1. Yamashita, Z. Horita, and T. G. Langdon, "Improving the mechanical properties of magnesium and a magnesium alloy through severe plastic deformation," Materials Science and Engineering: A, vol. 300, pp. 142-147, 2001.
2. Singh and S. P. Harimkar, "Laser surface engineering of magnesium alloys: a review," Jom, vol. 64, pp. 716-733, 2012.
3. Kasprzak, F. Czerwinski, M. Niewczas, and D. Chen, "Correlating hardness retention and phase transformations of Al and Mg cast alloys for aerospace applications," Journal of Materials Engineering and Performance, vol. 24, pp. 1365-1378, 2015.
4. Cisar, Y. Yoshida, S. Kamado, Y. Kojima, and F. Watanabe, "Development of High Strength and Ductile Magnesium Alloys for Automobile Applications," Materials Science Forum, vol. 419-422, pp. 249-254, 2003.
5. Tan and M. Tan, "Dynamic continuous recrystallization characteristics in two stage deformation of Mg–3Al–1Zn alloy sheet," Materials Science and Engineering: A, vol. 339, pp. 124-132, 2003.
6. J. Blau and M. Walukas, "Sliding friction and wear of magnesium alloy AZ91D produced by two different methods," Tribology International, vol. 33, pp. 573-579, 2000.
7. K. Pancrecious, S. B. Ulaeto, R. Ramya, T. P. D. Rajan, and B. C. Pai, "Metallic composite coatings by electroless technique – a critical review," International Materials Reviews, pp. 1-25, 2018.
8. Xu, S. Kamado, N. Matsumoto, T. Honma, and Y. Kojima, "Recrystallization mechanism of as-cast AZ91 magnesium alloy during hot compressive deformation," Materials Science and Engineering: A, vol. 527, pp. 52-60, 2009.
9. -h. Sun, R.-c. Wang, C.-q. Peng, Y. Feng, and M. Yang, "Corrosion behavior and surface treatment of superlight Mg–Li alloys," Transactions of Nonferrous Metals Society of China, vol. 27, pp. 1455-1475, 2017/07/01/ 2017.
10. K. Lee, "Corrosion and wear-corrosion resistance properties of electroless Ni–P coatings on GFRP composite in wind turbine blades," Surface and Coatings Technology, vol. 202, pp. 4868-4874, 2008/06/25/ 2008.
11. Sribalaji, P. Arunkumar, K. S. Babu, and A. K. Keshri, "Crystallization mechanism and corrosion property of electroless nickel phosphorus coating during intermediate temperature oxidation," Applied Surface Science, vol. 355, pp. 112-120, 2015/11/15/ 2015.
12. Araghi and M. H. Paydar, "Wear and corrosion characteristics of electroless Ni–W–P–B4C and Ni–P–B4C coatings," Tribology - Materials, Surfaces & Interfaces, vol. 8, pp. 146-153, 2014/09/01 2014.
13. Mimani and S. M. Mayanna, "The effect of microstructure on the corrosion behaviour of electroless NiP alloys in acidic media," Surface and Coatings Technology, vol. 79, pp. 246-251, 1996/02/01/ 1996.
14. L. Ge, D. Wei, C. J. Wang, B. Zeng, and Z. C. Chen, "A study on wear resistance of the Ni-P-SiC coating of Magnesium Alloy," in Applied Mechanics and Materials, 2011, pp. 1078-1083.
15. Choi, C. Lee, Y. Hwang, M. Park, J. Lee, C. Choi, et al., "Tribological behavior of copper nanoparticles as additives in oil," Current Applied Physics, vol. 9, pp. e124-e127, 2009/03/01/ 2009.
16. Saeedi Heydari, H. R. Baharvandi, and S. R. Allahkaram, "Electroless nickel-boron coating on B4C-Nano TiB2 composite powders," International Journal of Refractory Metals and Hard Materials, vol. 76, pp. 58-71, 2018/11/01/ 2018.
17. Gholizadeh-Gheshlaghi, D. Seifzadeh, P. Shoghi, and A. Habibi-Yangjeh, "Electroless Ni-P/nano-WO3 coating and its mechanical and corrosion protection properties," Journal of Alloys and Compounds, vol. 769, pp. 149-160, 2018/11/15/ 2018.
18. Bonin, V. Vitry, and F. Delaunois, "The tin stabilization effect on the microstructure, corrosion and wear resistance of electroless NiB coatings," Surface and Coatings Technology, vol. 357, pp. 353-363, 2019/01/15/ 2019.

60-66

11.

Authors:

Paper Title:

Abstract: This paper presents a solution to those who forget to water the indoor potted plants because of the busy schedule. It presents a system that is fully autonomous and cost-effective.
This autonomous system consists of a mobile robot with RFID detector and a temperature-humidity sensor and uses wireless communication between the mobile robot and sensing module. Thisautonomous system is adaptive to any kind of weather condition and addresses the watering needs of the plants with the help of temperature-humidity sensor. The gardening robot used is portable and contains an RFID module, Controller, awater reservoir and a water pump. Without human intervention thisautonomousrobot can sense the watering need of a plantlocatesthe plant following a predefined path and then waters the plant.An RFID tag attached to the potted plant helps for identification. In addition this paper describes the implementation of the system in detail along with the complete circuitry. The paper is concluded with the analysis of water carrying capacity and time needed to water a set of potted plants.

Keywords: Atmega16 micro controller, RFID reader, Mobile robot, RFID Tag, motor drivers, DC motors.

References:

1. C. Wolverton, Anne Johnson, and Keith Bounds, “Interior Landscape Plants for Indoor Air Pollution Abatement: Final Report”, National Aeronautics and Space Administration ( NASA-TM-101768) Science and TechnologyLaboratory, Stennis Space Center, 1989.
2. J. Van Henten, J. Hemming, B.A.J. Van Tuijl, J.G. Kornet, J. Meuleman, J. Bontsema and E.A. Van Os; “An Autonomous Robot for Harvesting Cucumbers in Greenhouses”; Autonomous Robots; Volume 13 Issue 3, November 2002.
3. Kevin Sikorski, “Thesis- A Robotic PlantCare System”, University of Washington, Intel Research, 2003.
4. Ayumi Kawakami, Koji Tsukada, Keisuke Kambara and ItiroSiio, “Pot Pet: Pet-like Flowerpot Robot”, Tangible and Embedded Interaction 2011, Pages 263-264 ACM New York, NY, USA, 2011.
5. ConstantinosMarios Angelopoulos, Sotiris Nikoletseas, GeorgiosConstantinosTheofanopoulos, “A Smart System for Garden Watering using Wireless Sensor Networks”, MobiWac '11 Proceedings of the 9th ACM internationalsymposium on Mobility management and wireless access Pages 167-170 ACM New York, NY, USA, 2011.
6. C.Manjunath, Ph.D. ( IIT Bombay ) & Fellow IETE, Ashok Kusagur , Shruthi Sanjay, SarithaSindushree, C. Ardil, “Design, Development & Implementation of a Temperature Sensor using Zigbee Concepts”, InternationalJournal of Electrical and Computer Engineering 3:12 2008.
7. Rafael Muñoz-Carpena and Michael D. Dukes, “Automatic Irrigation Based on Soil Moisture for Vegetable Crops”, Applied Engineering in Agriculture (2005).

67-69

12.

Authors:

Paper Title:

Abstract: The principle objective of the proposed framework is to outline a convenient, versatile and low cost farmer friendly intelligent system to accomplish efficient use of water supply and motor control. It senses on field data like climate temperature and soil moisture level, rainfall, with the assistance of sensors used in the system. And also check for 3-ϕ supply availability, no load condition of water pump, intruder detection (humans, animals etc.). farmer receives all the parameters data sensed on field, for further decision making about the need for watering. Also need for motor Turn ON/OFF based on sensing rain fall and the same will be sent to the farmer, who might be away from the field. If any intruder is detected alarm gets enabled and the same is notified to the farmer via SMS.
In the proposed system “SMS on demand service” is provided to get the status of all parameters like water resource availability, soil’s moisture content, 3-ϕ power supply availability and the intruder detection. The system helps the farmer in switching the motor according to his need i.e., whether the water is required for the crop or not. A user friendly mobile application and normal SMS service will enable the farmer to monitor and control the land parameters from the remote place efficiently. Hence the proposed method shows satisfactory performance to measure and monitor the land parameters and moreover 3- ϕ supply availability based motor operation saves from motor failure.

Keywords: Ultra-Low power MSP430, 3-Phase system, GSM, Sensors, Intruder detector, Motor etc.. 

References:

1. Roy, Sanku Kumar, Arijit Roy, Sudip Misra,Narendra S. Raghuwanshi, and Mohammad S.Obaidat. "AID: A prototype for Agricultural Intrusion Detection using Wireless Sensor Network", 2015 IEEE International Conference on Communications (ICC), 2015.
2. “Design of Remote Monitoring and Control System with Automatic Irrigation System using GSM-Bluetooth” International Journal of Computer Applications (0975 – 888) Volume 47– No.12, June 2012.
3. Natural Capitalism Solutions, prepared for boulder country parks and open spaces by: “Sustainable Agriculture Literature Review”. March 2011.
4. India Country Overview 2008". World Bank. 2008 Siebert et al. (2010), Groundwater use for irrigation – a global inventory, Hydrol. Earth Syst. Sci., 14, pp. 1863–1880.
5. Global map of irrigated areas: India FAO-United Nations and Bonn University, Germany (2013)
6. Agricultural irrigated land (% of total agricultural land) The World Bank (2013)
7. Bush, E. D. (2010). An overview of the estimation of kimberlite diamond deposits. Southern African Institute of Mining and Metallurgy: Diamonds—source to use 2010 (pp. 73–84). Johannesburg, S Africa: The Southern African Institute of Mining and Metallurgy.
8. Castrignanò, A., Buttafuoco, G., Quarto, R., Parisi, D., Viscarra Rossel, R. A., Terribile, F., et al. (2018). A geostatistical sensor data fusion approach for delineating homogeneous management zones in precision agriculture. Catena, 167, 293–304.
9. Castrignanò, A., Buttafuoco, G., Quarto, R., Vitti, C., Langella, G., Terribile, F., et al. (2017). A combined approach of sensor data fusion and multivariate geostatistics for delineation of homogeneous zones in an agricultural field. Sensors, 17(12), 2794. https://doi.org/10.3390/s17122794.
10. Castrignanò, A., Giugliarini, L., Risaliti, R., & Martinelli, N. (2000). Study of spatial relationships among some soil physico-chemical properties of a field in central Italy using multivariate geostatistics. Geoderma, 97(1–2), 39–60. https://doi.org/10.1016/S0016-7061(00)00025-2.
11. Corwin, D. L., & Lesch, S. M. (2010). Geostatistical applications for precision agriculture. In M. A. Oliver (Ed.), Geostatistical applications for precision agriculture (pp. 139–165). Berlin, Heidelberg, Germany: Springer. https://doi.org/10.1007/978-90-481-9133-8.
12. Gebbers, R., & Adamchuk, V. I. (2010). Precision agriculture and food security. Science, 327(5967), 828–831. https://doi.org/10.1126/science.1183899.
13. Mulla, D. J. (2017). Spatial variability in precision agriculture. In S. Shashi, H. Xiong, & X. Zhou (Eds.), Encyclopedia of GIS (pp. 2118–2125). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-23519-6_1652-1.
14. McBratney, A. B., Minasny, B., & Whelan, B. (2011). Defining proximal soil sensing. In V. I. Adamchuk & R. A. ViscarraRossel (Eds.), The second global workshop on proximal soil sensing (pp. 144–146). Montreal, Canada: McGill University.
15. Mzuku, M., Khosla, R., Reich, R., Inman, D., Smith, F., & MacDonald, L. (2005). Spatial variability of measured soil properties across site-specific management zones. Soil Science Society of America Journal, 69(5), 1572–1579. https://doi.org/10.2136/sssaj2005.0062.
16. R Karthik, Dharma Reddy Tetali, Susmitha Valli Gogula, G Manisha - Enhancement of Disciples Cognition levels using Bloom's Taxonomy in Data Mining, Journal of Advanced Research in Dynamical and Control Systems, Vol. 3S, pp. 1225-1237, (2018).
17. Design of low threshold Full Adder cell using CNTFET – P Chandrashekar, R Karthik,        O Koteswara Sai Krishna, Ardhi Bhavana, International Journal of Applied Engineering Research, Vol 12, No 1, pp. 3411-3415, (2017).
18. Samit Kumar Ghosh, P.B. Natarajan, Tapan Kumar Dey, J. Nagaraju, R. Karthik and T.S. Arulananth, “Energy Aware Multi-hop Routing Protocol for Internet of Things based Wireless Sensor Network”, Journal of Engineering and Applied Sciences, Vol. 12, pp. 5307-5311, (2017). 

70-73

13.

Authors:

Paper Title:

Abstract: Retrieving the data is the major aspect of concern in CMOS technology. At present lower power consumption is the primary objective. The lower power consumption the SRAM cells will be used in the near future extensively. The existing models do not give stability in reading operation because of which a correct logic decision at the output cannot be made. In this paper SRAM cell is designed using FinFET technology and is compared with existing CMOS 45nm technology, and a new SRAM cell structure is proposed which enhances the read stability and write stability with reduction in noise. The transient analysis is done for both CMOS 45nm and FinFET technology based SRAM cell. This proposed model is designed with 8 transistors where 6 transistors are used for data writing and another two are for data reading. The present design increases the read stability. 

Keywords: Read stability, 8T SRAM, CMOS, FinFET.

References:

1. ParidhiAthe, S. Dasgupta “A Comparative Study of 6T, 8T and 9T Decanano SRAM cell”, 2009 IEEE Symposium on Industrial Electronics and Applications (ISIEA 2009), October 4-6, 2009, Kuala Lumpur, Malaysia.
2. NahidRahman, B. P. Singh “Design and Verification of Low Power SRAM using 8T SRAM Cell Approach”, International Journal of Computer Applications (0975 – 8887) Volume 67– No.18, April 2013.
3. Grossar, “Read Stability and Write-Ability Analysis of SRAM Cells for Nanometer Technologies”, IEEE Journal of Solid-State Circuits, vol.41, no.11, pp. 2577-2588, Nov.2006.
4. Budhaditya Majumdar, Sumana Basu, “Low Power Single Bit line 6T SRAM Cell With High Read Stability”, IEEE 2011 International Conference on Recent Trends in Information Systems.
5. Takeda et al., “A Read-Static-Noise-Margin- Free SRAM Cell for Low-VDD and High-Speed Applications,” IEEE Journal of Solid-State Circuits, vol.41, no.1 pp.113-121, Jan., 2006.
6. Birlaeta., “Static Noise Margin Analysis of Various SRAM Topologies”, IACSIT, pp.304309, vol.3, No.3, June2011.
7. Aly, R. E.,Bayoumi, M. A., “Low-Power Cache Design Using 7T SRAM Cell ”, IEEE Transaction on Circuit and Systems II, April 2007, pp. 318-322.
8. Sil, S. Ghosh and M. Bayoumi, “A novel 8T SRAM cell with improved read-SNM,” IEEE Northeast workshop on circuit and system, 2007, pp.1289-1292.
9. Khare, N. Khare, V. Kulhade and P. Deshpande, “VLSI Design And Analysis Of Low Power 6T SRAM Cell Using Cadence Tool”, leSE, lohorBahru, Malaysia, 2008.
10. Premalatha, “A Comparative Analysis of 6T, 7T, 8T and 9T SRAM Cells in 90nm Technology” 2015 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT).
11. Kirti Bushan Bawa, “A Comparative Study of 6T, 8T and 9T SRAM Cell” Volume 3 Issue 6, June 2015.

74-76

14.

Authors:

Paper Title:

Abstract: Big Data refers to that huge amount of data which cannot be analyzed by using traditional analytics methods. With the increase of web content at a rapid rate, only analyzing data is not enough rather managing it with that great pace and efficiency is needed. A new framework Hadoop was implemented in order to perform parallel distributed computing. Hadoop is supported by various frameworks. In this paper, a performance comparison of Pig, Hive and Map Reduce over Big Data is analyzed.

Keywords: Pig, Hive, Map Reduce, Hadoop, Big Data

References:

1. Prof R A Fadnavis & Sannudhi Tabhane, "Big Data Processing using Hadoop", in IJCSIT, Vol I, 2015
2. Ashish Thusoo, Joydeep Sen Sarma, Namit Jain, Zheng Shao, Prasad Chakka, Ning Zhang, Suresh Antony, Hao Liu and Raghotham Murthy, “Hive – A Petabyte Scale Data warehouse using Hadoop”, IEEE, 2010
3. Alan F. Gates, Olga Natkovich, Shubham Chopra, Pradeep Kamath, “Building a high level data flow system on top of Map Reduce : the Pig Experience”, Proceedings of VLDB Endowment, Vol 2, Issue 2, August, 2009
4. Jeffery Dean & Sanjay Ghemawat, “Map Reduce : Simplified Data Processing on Large Clusters”, 6^th Symposium on Operating System Design and Implementation, Dec 2004
5. Laxmi Lydia & Dr. M. Ben Swarup, “ Big Data analysis using Hadoop components like Flume, Map Reduce, Pig and Hive”, IJCSET, Vol 5, Issue 11, Nov 2015
6. Bichitra Mandai, Ramesh Kumar Sahoo and Srinivas Sethi "Architecture of efficient word processing using Hadoop for Big Data Applications", in International Conference on Man and Machine Interfaccing",IEEE 2015
7. Poonam Vashisht and Vishal Gupta, “Big Data Analytics Techniques: A survey” , in IEEE 2015

77-81

15.

Authors:

Paper Title:

Abstract:  Wireless Ad hoc networks are experiencing a rapid increase in its applicability as well as in security threats. The wireless communication medium makes them highly prone to security attacks. Key management plays a vital role in secured communication. Power efficient and secure key management is one of its major requirements. Group key management is a promising approach for efficient cryptographic key management for MANETs. In this paper, we proposed a weight clustering algorithm for a decentralized group key management. The whole network is divided into smaller subgroups called clusters. The cluster is locally managed by the cluster head (CH). The CHs mutually manage the security key process. All nodes have equal opportunities to take part in CH selection. The CHs are selected by a weight clustering algorithm based on the computational power and the neighbor count of the node. The elected CH selects next CH from its neighbor by comparing their computational power, neighbor nodes and their distance from it. This eliminates the need of gateway nodes for inter-cluster communications. The size of the cluster is directly proportional to the weight of the cluster head that is the cluster head with high weight will manage the large cluster. Therefore the group key management activities are proportionally divided among the cluster heads according to their power. This eliminates the risk of frequent drowning of cluster heads. The performance of our algorithm is assessed through stimulation and compare with two popular weight clustering algorithms. 

Keywords: Cluster, Decentralized group key management, weight clustering algorithm, Wireless ad hoc ntwork

References:

1. Basagni, S., Conti, , Giordano, S.,  Stojmenovic, I.: Mobile Ad Hoc Networking: Cutting Edge Directions, Second Edition, Chap-1, John Wiley and Sons (2013)
2. Zhang, Y., Lee, W.: Security in Mobile Ad-Hoc Networks. In: Ad Hoc Networks Techologies and Protocols, Springer (2005)
3. Rafaeli, S., Hutchison, D.: A survey of Key Management for Secure Group Communication, ACM Computing Surveys, pp. 309-329, Vol. 35, No. 3, September (2003)
4. Kuroiwa, J.,Yamauchi,Y., Sun,W., Ito,M.: A self-stabilizing algorithm for stable clustering in mobile ad-hoc networks. IEEE (2011)
5. Tao, Y., Wang, J., Wang, Y. L., Sun, T.,: An enhanced maximum stability weighted clustering algorithm in ad hoc network. In: Proc. 4^th Conf. Wireless Commun. Netw. Mobile Comput. Pp. 1-4 (2008)
6. Anitha, V. S., Seastian, M. P.,: (k,r)-dominating set-based, weighted and adaptive clustering algorithms for mobile ad hoc networks, IET Commun., vol. 5, no. 13, pp. 1836-1853 (2011)
7. Wang, X., Cheng, H., Huang, H.,: Constructing a MANET based on clusters. Wireless Pers. Commun., vol. 75, no. 2, pp. 1489-1510 (2014)
8. Sathiamoorthy, J., Ramakrishnan, B.,: Energy and delay efficient dynamic cluster formation using hybrid AGA with FACO in EAACK MANETs. Wireless Netw., vol. 23, no. 2, pp. 371-385 (2017)
9. Cai, M., Rui, L., Liu, D., Huang, H., Qiu, X.,: Group mobility based clustering algorithm for mobile ad hoc networks. In: proc. APNOMS, pp. 340-343, August (2015)
10. Maragatham, T., Karthik, S., Bhavadharini, R. M.,: TCACWCA: transmission and collusion aware clustering with enhanced weight clustering algorithm for mobile ad hoc networks, Cluster Computing, https://doi.org/10.1007/s10586-017-1574-0 (2018)
11. Salma, B. U., Lawrence, A. ,: Improved group key management region based cluster protocol in cloud. Cluster Computing. https://doi.org/10.1007/s10586-017-1455-6 (2017)
12. Aftab, F., Zhang, Z., Ahmad, A.,: Self-Organization Based Clustering in MANETs Using Zone Based Group Mobility. IEEE Access https://doi.org/1109/ACCESS.2017.2778019 (2017)
13. Farkas, K., Hossmann, T., Plattner, B., Ruf, L.,: NWC: node weight computation in MANETs. In: Int. Conf. on Computer Commun. And Netw, pp. 1059-1064 (2007)

82-87

16.

Authors:

Paper Title:

Abstract: In geotechnical engineering field, the behaviour of soil does rely much on the shear strength for design purpose. Previously, findings show that the change of grained size in soil will change the structure (microstructure) and behaviour of the soil; consequently, affected the strength. To date, limited study focused on the effect of grading on the behaviour of sand fine mixtures. This study aims to investigate the effect of coarse sand on undrained strength behaviour of sand matrix soils in comparison with clean sand. A series of test on reconstituted sand matrix soils had been carried out by conducting consolidated undrained (CU) triaxial test using GDS ELDYN® triaxial machine. Coarse sand (retain within 2.0 mm to 0.6 mm) was mixed with 0%, 10 %, 20%, 30%, and 40% of fine particles (kaolin) independently by weight to prepare reconstituted samples. Triaxial samples of 50 mm diameter and 100 mm height were prepared using wet tamping technique (5% of moisture content) with targeted relative density at 15% (loose state). Each reconstituted sample was sheared at two effective confining pressures of 100 kPa and 200 kPa, respectively. Results show that the gradation contributed to the behaviour of the sand matrix soils. Increasing percentage of coarse sand in sand matrix soils exhibited higher effective friction angle. Similar trends were also observed on the angularity effect on undrained shear strength parameters.

Keywords: Sand Matrix Soils, Coarse Sand, Consolidated Undrained, Cohesion, Friction Angles. 

References:

1. Yokoi, 1968. Relationship between soil cohesion and shear strength. Soil Science and Plant Nutrition, Vol. 14, No. 3.
2. M. Rahman, S.R. Lo, 2008. Effect of Sand Gradation and Fines Type on Liquefaction Behaviour of Sand-finesMixture. Geotechnical Earthquake and Engineering and Soil Dynamics IV Congress 2008. Page 1-11.
3. V. Dinesh, G. Mahesh Kumar, Muttana S. Balreddy, B.C. Swamy, 2011. Liquefaction Potential of Sabarmati-River Sand. ISET Journal of Earthquake Technology, Paper No. 516, Vol. 48, No. 2-4, June-Dec. 2011, pp. 61–71.
4. T. Phan, D. Hsiao, P.T. Nguyen, 2016. Critical State Line and State Parameter of Sand-Fines Mixtures. Procedia Engineering 142 (2016) 299-306.
5. J.N. Azeiteiro, P.A.L.F. Coelho, D.M.G. Taborda, J.C.D. Grazina. J., 2017. Critical State–Based Interpretation of the Monotonic Behavior of Hostun Sand. Geotech. Geoenviron. Eng., (2017), 143(5):04017004.
6. D. Nik Norsyahariati, K.R. Hui, A.G.A. Juliana, 2016. The Effect of Soil Particle Arrangement on Shear Strength Behavior of Silty Sand. MATEC Web of Conferences 47, 03022.
7. Marto, C.S. Tan, A.M. Makhtar, N.Z. Mohd Yunus, A. Amaluddin, 2013. Undrained Shear Strength of Sand With Plastic Fines Mixtures. Malaysian Journal of Civil Engineering 25(2) :189-199
8. S. Tan, 2015. Effect of Fines Content and Plasticity on Liquefaction Susceptibility of Sand Matrix Soils. PhD Thesis, Universiti Teknologi Malaysia.
9. W. Boulanger, M.W. Meyers, L.H. Mejia, I.M. Idriss, 1998. Behavior of a fine-grained soil during Loma Prieta earthquake. Canadian Geotechnical Journal, 35(1), 146-158.
10. Batilas, P. Pelekis, V. Vlachakis, G. Athanasopoulos, 2013. International Journal of Geoengineering Case Histories, 2(4), 270-287.
11. P. Orense, T. Kiyota, S. Yamada, M. Cubrinovski, Y. Hosono, M. Okamura, S. Yasuda, 2011. Comparison of liquefaction features observed during the 2010 and 2011 Canterbury earthquakes. Seismological Research Letters, 82(6), 905-918.
12. Fontana, S. Lugli, S.Marchetti Dori, R. Caputo, M. Stefani, 2015. Sedimentology and composition of sands injected during the seismic crisis ofMay 2012 (Emilia, Italy): clues for source layer identification and liquefaction regime. Sedimentary Geology 325 (2015) 158–167.
13. Gautam, F. Santucci de Magistris, G. Fabbrocino, 2017. Soil liquefaction in Kathmandu valley due to 25 April 2015 Gorkha, Nepal earthquake. Soil Dynamics and Earthquake Engineering 97 (2017) 37–47
14. A. Othman, A. Marto, 2018. Laboratory test on maximum and minimum void ratio of tropical sand matrix soils. IOP Conf. Ser.: Earth Environ. Sci. 140 012084
15. Marto, C.S. Tan, A.M. Makhtar, N.J. Jusoh, 2016. Cyclic Behaviour of Johor Sand. International Journal of GEOMATE. Vol. 10, Issue 21, pp, 1891-1898
16. A. Yamamuro, P.V. Lade, 1997. Static liquefaction of very loose sands. Can. Geotech. J. 34:905-917.
17. Chang, M. Hong, 2008. Effects of Clay Content on Liquefaction Characteristics of Gap-Graded Clayey Sands. SOILS AND FOUNDATIONS Vol. 48, No. 1, 101–114.
18. Yilmaz, M. Mollamahmutoglu, V.Ozaydin, K.Kayabali, 2008. Experimental investigation of the effect of grading characteristics on the liquefaction resistance of various graded sands. Engineering Geology 100 (2008) 91-100.
19. Juneja, M.E. Raghunandan, 2010. Effect of Sample Preparation on Strength of Sands. Indian Geotechnical Conference – 2010, GEOtrendz, 327-330.
20. Ibraim, A. Diambra, D. Muir Wood, A.R. Russell, 2010. Static liquefaction of fibre reinforced sand under monotonic loading. Geotextiles and Geomembranes 28 (2010) 374–385.
21. Jafarian, R. Vakili, A. Sadeghi Abdollahi, 2013. Prediction of cyclic resistance ratio for silty sands and its applications in the simplified liquefaction analysis. Computers and Geotechnics 52 (2013) 54–62.
22. Mohammadi, A. Qadimi, 2015. Characterizing the process of liquefaction initiation in Anzali shore sand through critical state soil mechanics. Soil Dynamics and Earthquake Engineering 77 (2015) 152–163.
23. E. Takch, A. Sadrekarimi, H.E. Naggar, 2016. Cyclic resistance and liquefaction behavior of silt and sandy silt soils. Soil Dynamics and Earthquake Engineering 83 (2016) 98–109
24. Rees, 2013.What is Triaxial Testing? Part 1of 3. Published on the GDS website w ww.gdsinstruments.com (2013).
25. H. Head, R.J. Epps, 2014. Manual of Soil Laboratory Testing, Volume 3 : Effective Stress Tests, ISBN 978-184995-054-1.
26. A. Bareither, T.B. Edil, C.H. Benson, D.M. Mickelson, 2008. Geological and Physical Factors Affecting the Friction Angle of Compacted Sands. Journal of Geotechnical and Geoenvironmental Engineering, Vol. 134, No. 10, October 1.
27. BS 1377-2: 1990, 1990. Methods of test for soils for civil engineering purposes - Part 2: Classification tests.
28. BS1377-8: 1990, 1990. Methods of test for soils for civil engineering purposes – Part 8 : Shear strength tests (effective stress).

88-92

17.

Authors:

Paper Title:

Abstract: Federated Cloud Model referred as the interconnection of two or more providers with some guidelines prescribed in Service Level Agreement to address the uncertainty such as SLA Violation for the specific service. Most well-known models use the concept of either probability or fuzzy set theory in managing the Quality of Service (QoS) required by the Cloud user, application and tool. In this paper, Deep Learning is applied to predict the SLA Violation and manage the uncertainty. SLA violation is defined as the failure to meet the requirement prescribed for the user and application. In addition to that, banker’s algorithm is modified and used as prediction algorithm to find the possible safe state computation of the tasks and avoid wastage of resources in federated cloud. Random forest data mining technique is applied to rank the trust based provider and top provider may be considered for the service. The simulation results reveal that the proposed model helps to avoid uncertainty to about 78% and recognized that it is one of the most appropriate model needed in federated cloud architecture.

Keywords: Cloud Model, SLA, Fuzzy Logic, Deep Learning. 

References:

1. Rodrigo N. Calheiros, Rajiv Ranjan , Anton Beloglazov , César A. F. De Rose and RajkumarBuyya, “CloudSim: a toolkit for modelling and simulation of cloud computing environments and evaluation of resource provisioning algorithms”, 24 August 2010, https://doi.org/10.1002/spe.995.
2. RajkumarBuyya ;Saurabh Kumar Garg ; Rodrigo N. Calheiros, ”SLA-oriented resource provisioning for cloud computing: Challenges, architecture, and solutions” in: 2011 IEEE International Conference on Cloud and Service Computing, 12-14 Dec. 2011
3. Saurabh Kumar Garg, Steve Versteeg and RajkumarBuyya,” SMICloud: A Framework for Comparing and Ranking Cloud Services” in 2011 Fourth IEEE International Conference on Utility and Cloud Computing, 5-8 Dec 2011.
4. Udayakumar, M. Manikkam, and A. Arun, "Cloud-SLA: Service Level Agreement for Cloud Computing."
5. Mohammed, T. Dillon, E. Chang, SLA-based trust model for cloud computing, in: Proceedings of 2010 13^th International Conference on Network-Based Information Systems (NBiS), 2010, pp. 321–324.
6. Maheswari, R. Sanjana, S. Sowmiya, SudhirShenai& G. Prabhakaran, “An Efficient Cloud Security System Using Double Secret Key Decryption Process for Secure Cloud Environments”, International Journal of Advanced Scientific Research & Development (IJASRD), 3 (1/II), pp. 134 – 139.
7. Sudip Chakraborty, Krishnendu Roy, An SLA-based framework for estimating trustworthiness of a cloud, in: Proceedings of 2012 IEEE 11^th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 2012, pp. 937–942.
8. Serrano, S. Bouchenak, Y. Kouki, T. Ledoux, J. Lejeune, J. Sopena, L. Arantes, and P. Sens, "Towards QoS-oriented SLA guarantees for online cloud services," in Cluster, Cloud and Grid Computing (CCGrid), 2013 13^th IEEE/ACM International Symposium on, 2013, pp. 50-57.
9. Wu, S. K. Garg, and R. Buyya, "SLA-based resource allocation for software as a service provider (SaaS) in cloud computing environments," in Cluster, Cloud and Grid Computing (CCGrid), 2011 11^th IEEE/ACM International Symposium on, 2011, pp. 195-204.
10. Xiaoyong, L. Ying, J. Tong, L. Tiancheng, and W. Zhonghai, "An Analysis on Availability Commitment and Penalty in Cloud SLA," in Computer Software and Applications Conference (COMPSAC), 2015 IEEE 39^th Annual, 2015, pp. 914-919.
11. Abawajy, Determining service trustworthiness in inter-cloud computing environments, in: Proceedings of 2009 10^th International Symposium on Pervasive Systems, Algorithms, and Networks (ISPAN), 2009, pp. 784–788.
12. Abawajy, Establishing trust in hybrid cloud computing environments, in: Proceedings of 2011 IEEE 10^th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 2011, pp. 118–125.
13. Bernstein, D. Vij, Inter-cloud security considerations, in: Proceedings of 2010 IEEE Second International Conference on Cloud Computing Technology and Science (CloudCom), 2010, pp.
14. MukeshSinghal, Chandrasekhar Santosh, GeTingjian, Sandhu Ravi, Krishnan Ram, Ahn Gail-Joon, Bertino Elisa, Collaboration in multi cloud computing environments: framework and security issues, Computer 46 (2) (2013).
15. Lu, H.Z. Wu, “Research of Trust Valuation and Decision-making Based on Cloud Model in Grid Environment,” Journal of System Simulation, Vol. 21, Jan. 2009, pp. 421 – 426.
16. Y.J. Hsu, K.J. Lin, T.H. Chang, C.J. Ho, H.S. Huang, W.R. Jih, Parameter learning of personalized trust models in broker-based distributed trust management, Inform. Syst. Front. 8 (4) (2010) 321–333.
17. J. Lin, H. Lu, T. Yu, C.E. Tai, A reputation and trust management broker framework for web applications, in: Proceedings of the IEEE International Conference on e-Technology, e-Commerce, and e-Service, 2005, pp. 262–269.

93-97

18.

Authors:

Paper Title:

Abstract: In olden Days Farmers used to figure the ripeness of soil and influenced suspicions to develop which to kind of yield. They didn't think about the humidity, level of water and especially climate condition which terrible a farmer increasingly The Internet of things (IOT) is remodeling the agribusiness empowering the agriculturists through the extensive range of strategies, for example, accuracy as well as practical farming to deal with challenges in the field. IOT modernization helps in assembly information on circumstances like climate, dampness, temperature and fruitfulness of soil, Crop web based examination empowers discovery of wild plant, level of water, bug location, creature interruption in to the field, trim development, horticulture. IOT utilize farmers to get related with his residence from wherever and at whatever point. Remote sensor structures are utilized for watching the homestead conditions and tinier scale controllers are utilized to control and mechanize the home shapes. To see remotely the conditions as picture and video, remote cameras have been used. IOT development can diminish the cost and update the productivity of standard developing.

Keywords: Soil moisture sensor, Water level sensor, Humidity sensor, Temperature sensor, IOT

References:

1. lakshmisudha, swathi hegde, neha cole, shruti iyer, " good particularity most stationed cultivation spinning sensors", state-of-the-art weekly going from microcomputer applications (0975-8887), number 146-no.11, july 2011
2. nikesh gondchawar, dr. r.complexion.kawitkar, "iot based agriculture", all-embracing almanac consisting of contemporary analysis smart minicomputer additionally conversation planning (ijarcce), vol.5, affair 6, june 2016. Overall Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169 Volume: 5 Issue: 2 177 – 181
3. K.Gayatri, J.Jayasakthi, Dr.G.S.Anandhamala, "Giving Smart Agriculture Solutions to Farmers for Better Yielding Using IoT", IEEE International Conference on Technological Innovations in ICT for Agriculture and Rural
4. Lustiness r. nandurkar, slant. r. thool, r. tumor. thool, "plan together with situation coming from rigor horticulture technique executing trans-missions sensor network", ieee world consultation toward telemechanics, regulate, intensity also wiring (aces), 2014. Development (TIAR 2015).
5. Paparao Nalajala, D. Hemanth Kumar, P. Ramesh and Bhavana Godavarthi, 2017. Design and Implementation of Modern Automated Real Time Monitoring System for Agriculture using Internet of Things (IoT). Journal of Engineering and Applied Sciences, 12: 9389-9393.
6. Joaquín Gutiérrez, Juan Francisco Villa-Medina, Alejandra Nieto-Garibay, and Miguel Ángel PortaGándara, "Computerized Irrigation System Using a Wireless Sensor Network and GPRS Module", IEEE Transactions on Instrumentation and Measurements, 0018-9456,2013
7. Paparao Nalajala, P Sambasiva Rao, Y Sangeetha, Ootla Balaji, K Navya,” Design of a Smart Mobile Case Framework Based on the Internet of Things”, Advances in Intelligent Systems and Computing,Volume 815, Pp. 657-666, 2019.
8. vidya devi,lockup. meena kumari, "continuous mechanization along with patrol process under the authority of most aerodynamic agriculture" ,universal newspaper made from appraisal furthermore probe contemporary scientific knowledge together with structures (ijrrase) vol3 no.1. pp 7-12, 2013.
9. Meonghun Lee, Jeonghwan Hwang, Hyun Yoe, "Agrarian Protection System Based on IoT", IEEE sixteenth International Conference on Computational Science and Engineering, 2013.

98-102

19.

Authors:

Paper Title:

Abstract: Text summarization is way of reducing the text content of a document without the losing any information. People are likely to look multiple documents on a single topic because a one document may not include all the major details. The abstract/summary of multiple documents connected to a text will conserve the effort and time. Automatic text summarization is one of the area of natural language processing. Sentiment analysis is a machine learning method in which machine study and inspect the sentiments, opinions, etc about reviews about movies or products. This is extremely hard summarize by human. effective data from the very large document. In this research, we propose a novel method for multiple document summarizations using extractive method of summarization and sentiment analysis from online sources. At first, various document’s URLs are fetched as input relate to a text and generate individual summaries. The sentiment analysis is tried on these generated separate summaries. The sentiment analysis says that whether these input documents have any dissimilar opinion about the topic. Lastly, a unique summary is generated from all these first level summaries. The performance of our proposed method evaluated by ROUGE metric. 

Keywords: Text Summarization, Sentence Extraction, Sentiment Analysis, Natural Language Processing, ROUGE 

References:

1. P. Addala, "Text Summarization A Literature Survey". Available:https://www.scribd.com/document/235008952/Text- Summarization-Literature-Survey. [Accessed 09 April 2017].
2. Sharockman, "PunditFact checks in on the cable news channels", PolitiFact, 2015. Available: http://www.politifact.com/truth-o- meter/article/2015/jan/29/punditfact-checks-cable-news-channels/. [Accessed: 09- Apr- 2017].
3. Ansari, "Sentiment Polarity Classification Using Structural Features," IEEE International Conference on Data Mining Workshop 2015 (ICDMW), Atlantic City, NJ, 2015, pp. 1270-1273.
4. Krishnaprasad, A. Sooryanarayanan and A. Ramanujan, "Malayalam text summarization: An extractive approach," International Conference on Next Generation Intelligent Systems (ICNGIS) 2016, Kottayam, 2016, pp. 1-4.
5. Feng Li, Yan Chen and Zhoujun Li, "Learning from the past: Improving news summarization with past news articles," International Conference on Asian Language Processing (IALP), Suzhou, 2015, pp. 140-143.
6. Jassem and Ł. Pawluczuk, "Automatic summarization of Polish news articles by sentence selection," Federated Conference on Computer Science and Information Systems (FedCSIS), Lodz, 2015, pp. 337-341.
7. Agarwal, , Xie, B., Vovsha, I., Rambow, O., and Passonneau, R,
8. “Sentiment Analysis of Twitter Data,” in Proc of ACL HLT Conf, 2011.
9. B. Mirani and S. Sasi, "Sentiment Analysis of ISIS Related Tweets Using Absolute Location," International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, 2016, pp. 1140-1145.
10. Jackson, P. and Moulinier, Natural Language Processing for Online Applications: Text Retrieval, Extraction, and Categorization. John Benjamins Publishing Co., 2002.
11. Manning, P. Raghavan and H. Schü tze, Introduction to information New York: Cambridge University Press, 2008.
12. Loria, "TextBlob: Simplified Text Processing", Textblob.readthedocs.io, 2013.       [Online].Available: https://textblob.readthedocs.io/en/dev/index.html. [Accessed: 09- Apr- 2017].
13. "Python", Python.org, 2001. Available:
14. https://www.python.org/about/. [Accessed: 09- Apr- 2017].
15. Chin Yew Lin, “ROUGE: A package for automatic evaluation of sum- maries,” In Proceedings of Workshop on  Text Summarization BranchesOut, Post-Conference Workshop of ACL, Barcelona, Spain, 2004.
16. Ebert, "Titanic Movie Review & Film Summary", Roger ebert, 1997. Available: http://www.rogerebert.com/reviews/titanic-1997. [Accessed: 21- Mar- 2017].
17. Tarun B Mirani, sreela Sasi, “Two-level Text Summarization from Online News Sources with Sentiment Analysis”, International Conference on Networks & Advances in Computational Technologies (NetACT) |20-22 July 2017| Trivandrum

103-107

20.

Authors:

Paper Title:

Abstract: The key to a web application is the information and a variety of facilities and features present according to the needs of the user’s requirements. For the superior quality and maintenance of the website, new methodologies and tools are developing day by day. In this paper, we propose an algorithm using the concept of existing A* algorithm to get the shortest path from the home page to the target page. Before reaching the target page, the user visits other pages. Our motto here is to reduce the longest shortest path of a site, which is an indicator of better structure. For this, we propose a web model. A web model is an intermediate representation of a given web application, which is designed based on pages, frames and links of the application. The A* algorithm is not competent to get the Unreachable pages on the website. We create our sample HTML web page (a case study) to observe Unreachable pages (broken links), frame count and, the order of the frames. The web page is embedded in various websites such as www.learn-automation.com, www.seleniumhq.org, and www.html.com. Frame testing is conducted on the proposed web model using a well-known tool, Selenium web driver. The Selenium tool shows how the frames are moving from one frame to other (order of frame execution), frame count, confirm the broken links or unreachable pages and the ghost pages in our sample HTML web page. These methodologies are very useful for the implementation and maintenance of websites.

Keywords: frame testing, Selenium web driver, static analysis, web application, web browser, web model

References:

1. Montgomery, D. C. Design and analysis of experiments. John Wiley & Sons, 2017.
2. Marchetto, A., Tonella, P., & Ricca, F. Testing techniques applied to Ajax web applications. In Proceedings of the Workshop on Web Quality, Verification and Validation, WQVV’07, 2007.
3. Costa, M., Gomes, D., & Silva, M. J. The evolution of web archiving. International Journal on Digital Libraries, 18(3), 2017, pp. 191-205.
4. Felke-Morris, T. Basics of web design: HTML5 & CSS3. Pearson, 2014.
5. Conallen, J. Building Web applications with UML. Addison-Wesley Longman Publishing Co., Inc, 2002.
6. Gojare, S., Joshi, R., & Gaigaware, D. Analysis and Design of Selenium WebDriver Automation Testing Framework. Procedia Computer Science, 50, 2015, pp. 341-346.
7. Panthi, V., & Mohapatra, D. P. An approach for dynamic web application testing using MBT. International Journal of System Assurance Engineering and Management, 8(2), 2017, pp. 1704-1716.
8. Hall, M., Brown, L., & Chaikin, Y. Core Servlets and JavaServer Pages: Advanced Technologies. Pearson Education, 2, 2007.
9. Brown, D., Pandya, A., Mulgrew, Z., Smith, J., Miller, A., & Kusuma, A. Dynamic loading of routes in a single-page application. U.S. Patent No. 9,967,309. Washington, DC: U.S. Patent and Trademark Office, 2018.
10. Andrews, A. A., Offutt, J., & Alexander, R. T. Testing web applications by modelling with FSMs. Software and Systems Modeling, 4(3), 2005, pp. 326-345.
11. Bellettini, C., Marchetto, A., & Trentini, A. TestUml: user-metrics driven web applications testing. In Proceedings of the 2005 ACM symposium on applied computing, 2005, pp. 1694-1698.
12. Ricca, F., & Tonella, P. Building a tool for the analysis and testing of web applications: Problems and solutions. Tools and Algorithms for the Construction and Analysis of Systems, 2001, pp. 373-388.
13. Tonella, P., & Ricca, F. A 2-layer model for the white-box testing of web applications. In Web Site Evolution, Sixth IEEE International Workshop on (WSE'04), 2004, pp. 11-19.
14. Hoffmann, M. R., Brock, J., & Mandrikov, E. Eclemma-java code coverage for eclipse, 2009.
15. Kessis, M., Ledru, Y., & Vandome, G. Experiences in coverage testing of a Java middleware. In Proceedings of the 5th international workshop on Software engineering and middleware, ACM, 2005, pp. 39-45.
16. Bellettini, C., Marchetto, A., & Trentini, A. Dynamical extraction of web applications models via mutation analysis. INFORMATION-YAMAGUCHI-, 8(5), 673, 2005.
17. Tonella, P., & Ricca, F. Dynamic model extraction and statistical analysis of web applications. In Web Site Evolution, 2002. Proceedings. Fourth International Workshop on IEEE, 2002, pp. 43-52.
18. Ricca, F., & Tonella, P. Web Site Analysis: Structure and Evolution. In icsm, 76, 2000.
19. Di Sciascio, E., Donini, F. M., Mongiello, M., & Piscitelli, G. An Web: a system for automatic support to web application verification. In Proceedings of the 14th international conference on Software engineering and knowledge engineering, ACM, 2002, pp. 609-616.
20. Ricca, F., & Tonella, P. Understanding and restructuring Web sites with ReWeb. IEEE MultiMedia, 8(2), 2001, pp. 40-51.
21. Di Lucca, G. A., & Fasolino, A. R. Testing Web-based applications: The state of the art and future trends. Information and Software Technology, 48(12), 2006, pp. 1172-1186.
22. Aho, A. V., Sethi, R., & Ullman, J. D. Compilers, Principles, Techniques. Addison Wesley, 7(8), 9, 1986.

108-115

21.

Authors:

Paper Title:

Abstract: Interlinking physical and human sciences in a seamless continuum is a task yet to be achieved. The difficulty however arises largely due to our inability to understand the mind-body connections. With two centuries of research to reduce individual sensations to neural mechanisms having remarkably failed, this article puts forward an alternative view. A plain acceptance of the creative ability of the universe to bring up fundamental sets of mutually relatable fields and features such as quarks and electrons and at a more evolved state visual and auditory sensations does in fact prepare us to see the whole of reality not merely as norms of Nature but as norms built on purposeful selection and fine-tuning. Under this singular axiom of inextricable interconnection between intelligence and matter, the rare occurrence of life in planet earth is no longer an anomaly but a renewed pattern of additional selection and lawful expansion. The complex societal structures that hinge on the choices, rules and regulations of individuals, now turn out to be a repeating natural development extremely similar to the complex biological functions built on the quantum fields. The total edifice of human and physical complexities easily reduce into a simple pattern of one plus one rising over yet another one plus one and the marvel of this pattern is bound to capture the attention of any intelligent being. One single overarching idea, indeed, subsumes every little turn of event that has ever occurred or will ever occur in this large universe. 

Keywords: Sensation, Mind-body relation, Neural Firing, Quantum fields, Physical and Human Sciences, Reductive Approach.

References:

1. Chalmers, D. The Conscious Mind. New York: Oxford University Press, (1996)
2. Wilczek, F. A Beautiful Question: Finding Nature's Deep Design. New York: Penguin Books Ltd. (2015)
3. Hawking, S., & Mlodinow, L. The Grand Design. London: Transworld Publishers (2010).
4. Freud, S. Introduction to Psychoanalysis, (1917)
5. Croswell, K. The Cosmic Origin of Carbon, (2006, January 11).Retrieved January 30, 2017, from kencroswell.com: http://kencroswell.com/OriginOfCarbon.html
6. Denton, M. J. Nature's Destiny: How the Laws of Biology Reveal Purpose in the Universe. New York: The Free Press, (1998).
7. Horgan, J. The End of Science: Facing the Limits of Knowledge in the Twilight of the Scientific Age. Helix Books (1996).
8. Orwig, J. The Two Most Dangerous Numbers in the universe could signal the end of Physics. (2016, January 15). Retrieved from www.businessinsider.in
9. Carroll, S. The Big Picture: On the origins of Life, Meaning and the Universe itself, (2016). (Kindle Edition ed.). Retrieved from www.amazon.com
10. Mohrhoff, U. (2001, May 21). The World According to Quantum Mechanics (Or, The 18 errors of Henry P.Stapp). Retrieved from arXiv:quant-ph/0105097V1
11. Chardin, T. d. The Phenomenon of Man, (1959). (Kindle Edition ed.) Retrieved from www.amazon.com
12. Mach, E. Contributions to the Analysis of the Sensations. (C.M.Williams, Trans.) Chicago: The Open Court Publishing Company, (1897).
13. Hume, D. An Enquiry Concerning Human Understanding (Second ed.) (1748). (L. Bigge, Ed.) doi:January, 2006

116-120

22.

Authors:

Paper Title:

Abstract: The PV array generating power is always directly affected by various conditions such as angle of inclination, temperature, irradiation of sun, shading effect, and solar array configuration. In practice, PV arrays are commonly partially shaded by trees, clouds, nearby buildings, bird droppings and other utilities which leads to multiple peaks appear in the P-V curve, a global maximum and one or several local peaks. In this paper, the "perturb and observe" (P&O) maximum power point tracking (MPPT) algorithm employed for tracking the maximum power point under uniform and non-uniform irradiation conditions. Initially, this paper presents the P and O algorithm operation, later the boost converter performance details and finally the combination of a boost converter with P and O algorithm. The evaluation process has been carried systematically for the uniform and non-uniform solar irradiance and finally, the results are analyzed.

Keywords: Solar Photovoltaic array (PV); Uniform and non Uniform irradiation; Partial shading; Maximum Power Point tracking (MPPT); Perturb and Observe (P&O); Boost Converter.

References: 

1. G.Villalva, J.R.Gazoli, “Comprehensive approach to modeling and simulation of photovoltaic arrays,” IEEE Trans. Power Electron.,, vol. 24, no. 5, pp. 1198–1208,2009.
2. F.Murtaza et.al., “Comparative Anlaysis of Maximum Power Point Tracking Techniques,” IEEE, vol. 12, pp. 83-88, 2013.
3. Soubhagya Kumar Dash et al., “ Comparative Analysis of MPP for Solar PV Application using MATLAB/simulink,” vol.14, May 2014.
4. Esram and P.L. Chapman, “Comparison of photovoltaic array maximum power point tracking techniques,” IEEE Trans. on Energy conversion., vol. 22, no. 2, pp. 439–449, 2007.
5. Nicola Femia, Member IEEE, “Optimization of Perturb and Observe Maximum Power Point Tracking Method”, IEEE 2005.
6. Scarpa, S. Buso, and G. Spiazzi, “Low-complexity MPPT technique exploiting the PV module MPP locus characterization,”IEEE Trans.Ind.Electron.,vol.56.no.5,pp. 1531-1538, May 2009
7. L. Chen, Y. Wang, J. S. Lai, Y. S. Lee, and D. Martin, “Design of parallel inverters  for smooth mode transfer micro-grid applications,” IEEE Trans. Power Electron, vol. 25, no. 1, pp. 6-15, Jan.2010.
8. S. Yang, T. J. Liang, and J. F. Chen, “Transformer less dc-dc converters with high voltage gain,” IEEE Trans.Ind.Electron., vol.56, no.8, pp.3144-3152, Aug.2009.
9. L. Luo, “Six self-lift dc-dc converters, voltage lift technique,” IEEE Trans. Ind. Electron., vol. 48, no. 6, pp. 1268–1272, Dec. 2001.
10. L. Luo and H. Ye, “Positive output super-lift converters,” IEEE Trans.Power Electron., vol. 18, no. 1, pp. 105–113, Jan. 2003.
11. L. Luo and H. Ye, “Positive output multiple-lift push-pull switchedcapacitor Luo-converters,” IEEE Trans. Ind. Electron., vol. 51, no. 3,pp. 594–602, Jun. 2004.
12. Jun and A. Ioinovici, “Switching-mode dc-dc converter with switched capacitor based resonant circuit,” IEEE Trans. Circuits Syst. I, Fundam.
13. Axelrod,Y. Berkovich and A. Ioinovici, “Switched –capacitor/switched inductor structures for getting transformerless hybrid dc-dc PWM converters,” IEEE Trans.  circuits Syst. I, Reg.Papers, vol.55, no.2,pp.687-696, Mar. 2008.
14. Xuefeng Hu and Chunying Gong, “A High Voltage Gain DC–DC Converter Integrating Coupled-Inductor and Diode–Capacitor Techniques,” ,” IEEE Trans.Power Electron., vol. 29, no. 2, pp.789-800, Feb. 2014.
15. Zhao and F. C. Lee, “High-efficiency, high step-up dc-dc converters,”IEEE Trans. Power Electron., vol. 18, no. 1, pp. 65–73, Jan. 2003.
16. Prudente, L.Pfitscher, G.Emmendoerfer, E.Romaneli, and R.Gules,"Voltage multiplier cells applied to non-isolated DC-DC converters,"IEEE Trans. Power Electron., vol. 23, no.2, March 2008.
17. Viraj Savakhande, C.L.Bhattar and Pctejasvi L.Bhattar, “A Voltage-Lift DC-DC converter using Modular Voltage Multiplier Cell For Photovoltaic Application” International conference on circuit, power and cumputing technologies(ICCPCT),April 2017.
18. B.Savakhande,C.L.Bhattar and P L.Bhattar, “Voltage-lift DC-DC Converters For Photovoltaic Application-A Review” International Conference of Data Management, Analytics and Innovation(ICDMAI),Feb.2017.
19. A.Chwale,V.B.Savakhande and H.T.Jadhav, “An interleaved flyback inverter for grid connected photovoltaic systems” International conference on circuit, power and cumputating technologies (ICCPCT), April 2017.
20. Bunyamin Tamyurek, and Bilgehan Kirimer, “An Interleaved High-Power Flyback inverter for Photovoltaic Applications,” IEEE Trans. Power Electron., vol. 30, no. 6,  3228–3241, Jun. 2015.
21. Sreenivasa Reddy, T. Bramhananda Reddy, M.Vijaya Kumar. "A MATLAB based PV Module Models analysis under Conditions of Nonuniform Irradiance", Energy Procedia, 117 (2017)  pp. 974–983.

121-126

23.

Authors:

Paper Title:

Abstract: In basic Block Truncation Coding, the given data sequence is encoded into a two level quantized approximation. This results in two segments one of which is comprised of the high level value and the other one, the low level value. In the proposed scheme, the basic Block Truncation Coding is refined by further sub segmenting the present segments. This increases the granularity of the data and reduces the truncation error. 

Keywords: Two stage Block Truncation Coding, Binarization.

References:

1. . J.Delp, O. R. Mitchell, “Image Compression using Block Truncation Coding,” IEEETransactions on Communications Vol. 27, no.9, pp.1335-1342, Sep. 1979.
2. Healy and O. R. Mitchell, “Digital Video Bandwidth Compression Using Block Truncation Coding,” IEEETransactionsonCommunications, Vol. 29, no. 12, pp. 1809-1817, Dec. 1981.
3. Harjito and H. Prasetyo, "Image forensics using EDBTC feature," 2017 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW), Taipei, 2017, pp. 167-168.
4. Yadav, R. Gupta and S. Kumar, "Content based image retrieval using Dither Block Truncation coding with similarity comparison algorithm," 2017 International Conference on Computer, Communications and Electronics (Comptelix), Jaipur, 2017, pp. 489-493.
5. Elsayed, M. Mahmuddin, A. Badawy, T. Elfouly, A. Mohamed and K. Abualsaud, "Walsh transform with moving average filtering for data compression in wireless sensor networks," 2017 IEEE 13th International Colloquium on Signal Processing & its Applications (CSPA), Batu Feringgi, 2017, pp. 270-274.
6. Patel and J. Chaudhary, "Energy efficient WMSN using image compression: A survey," 2017 International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), Bangalore, 2017, pp. 124-128.
7. S. Chen and Y. T. Tsou, "Compressive Sensing-Based Adaptive Top-k Query over Compression Domain in Wireless Sensor Networks," 2017 IEEE Wireless Communications and Networking Conference (WCNC), San Francisco, CA, 2017, pp. 1-6.
8. Rekha and P. Samundiswary, "Image compression using multilevel thresholding based Absolute Moment Block Truncation Coding for WSN," 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, 2016, pp. 396-400.
9. J. Yang, C. Y. Lien, P. Y. Chen and C. L. Hsu, "An Efficient Quadtree-Based Block Truncation Coding for Digital Image Compression," 2016 30th International Conference on Advanced Information Networking and Applications Workshops (WAINA), Crans-Montana, 2016, pp. 939-942.
10. Lema M.D and Mitchell O.R, “ Absolute Moment BlockTruncation Coding and Application to Color Image”, IEEETransactions on Communications, Vol. COM-32, 1984,pp. 1148-1157.
11. Halverson D, Griswold N.C and Wise G.L, “A generalized blocktruncation coding algorithm for image compression”, IEEETransactions on Acoustics, Speech and Signal Processing, Vol. 32,1984.
12. Udpikar V.R and Raina J.P, “BTC image coding using vectorquantization,” IEEE Transactions on Communications., vol. 35, no.3,1987,,pp. 352–356.
13. Yan Yanga, b, Qiqiang Chena, Yi Wana,“A fast near-optimum block truncation coding method using a truncated K-means algorithm and inter-block correlation,” AEU - International Journal of Electronics and Communications.,Volume 65, Issue 6,2011, pp. 576–581.
14. PasiFränti, Olli Nevalainen and TimoKaukoranta,” Compression of Digital Images by Block Truncation Coding: A Survey”. The Computer Journal, 37 (4), 1994,pp. 308-332.
15. Doaa Mohammed, FatmaAbou-Chadi, “Image Compression Using Block Truncation Coding”, Cyber Journals: Multidisciplinary Journals inScience and Technology, Journal of Selected Areas in Telecommunications(JSAT), February Edition, 2011.
16. Wu and D. C. Coll, "Multilevel block truncation coding using a minimax error criterion for high-fidelity compression of digital images," in IEEE Transactions on Communications, vol. 41, no. 8, 1993,pp. 1179-1191.
17. Cho, Moonki& Yoon, Yungsup. (2013). BTC Algorithm Utilizing Multi-Level Quantization Method for Image Compression. Journal of the Institute of Electronics and Information Engineers. 50. 10.5573/ieek.2013.50.6.114.
18. Chandravadhana, S &Nithiyanandam, N. (2017). Least mean square error-based image compression using block truncation coding. International Journal of Information and Communication Technology. 11. 25. 10.1504/IJICT.2017.10006381.
19. Vimala, S., Uma, P., and Abidha, B. Improved adaptive block truncation coding for image compression. International Journal of Computer Applications (0975–8887) Volume (2011).
20. Chan, K. W. and Chan, K. L. Optimization of multilevel block truncation coding. Signal Process.: Image Communication., vol 16, 2001, 445–459.
21. Hui, "An adaptive block truncation coding algorithm for image compression," International Conference on Acoustics, Speech, and Signal Processing, Albuquerque, NM, vol 4,1990, pp. 2233-2236.
22. Mathews and M. S. Nair, “Adaptive block truncation coding technique using edge-based quantization approach”, Computers & Electrical Engineering, vol.43,2015, pp.169-179.
23. F. Liu, J. M. Guo and Y. Cheng, “Adaptive block truncation coding image compression technique using optimized dot diffusion”, 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, 2016, pp. 2137-2141.
24. Nandini K S and S.A.HariPrasad, “Optimal Spectrum Sensor Assignment in Multi-channel Multi-user Cognitive Radio Networks”, International Journal of Telecommunications & Information Technology (JTIT), no. 4, 2018 (doi: 10.26636/jtit.2018.124017).

127-131

24.

Authors:

Paper Title:

Abstract: With the intense need of security, a reliable authentication system can be attained using multimodal biometrics. Predominately vein patterns are attracting the researchers for developing authentication system. Multimodal biometric system not only aims at combining traits but also on fusion at various levels. Proposed approach fuses invariant iris features and finger vein shape features. The fusion at feature level framework is evaluated to perceive classification accuracy of biometric authentication system. Algorithm prioritizes on reducing high dimension features by considering iris Hu moments and finger vein shape features to accomplish a secured and convenient authentication system. SVM Classifier results prove that multimodal biometric outperforms compared to Uni-modal system.

Keywords: Biometrics, Feature Fusion, Hu moment, Multimodal, Shape Features

References:

1. Jain AK, Ross A, Nandakuma K (2011) Introduction to biometrics. Springer, Boston.
2. Punam Bedi, Roli Bansal, Priti Sehgal, Multimodal Biometric Authentication using PSO based Watermarking, Procedia Technology, Volume 4, 2012, Pages 612-618, ISSN 2212-0173.
3. Quang Duc Tran., Panos Liatsis., Improving Fusion with One-Class Classification and Boosting in Multimodal Biometric Authentication, intelligent Computing in Bioinformatics Lecture Notes in Computer Science Volume 8590, 2014, pp 438-444.
4. Long, Tran Binh., Thai, LeHoang., Hanh, Tran., Multimodal Biometric Person Authentication Using Fingerprint, Face Features, PRICAI 2012: Trends in Artificial Intelligence Lecture Notes in Computer Science Volume 7458, 2012, pp 613-624.
5. Jucheng Yang., Yanbin Jiao., Chao Wang., Chao Wu., Yarui Chen.,Multimodal Biometrics Recognition Based on Image Latent Semantic Analysis and Extreme Learning Machine, Biometric Recognition Lecture Notes in Computer Science Volume 8232, 2013, pp 433-440.
6. Qing Zhang ; Yilong Yin ; De-Chuan Zhan ; Jingliang Peng, Novel Serial Multimodal Biometrics Framework Based onSemisupervised Learning Technique, IEEE Transactions on Information Forensics and Security, Volume: 9 , Issue: 10 2014 , pp 1681 - 1694 .
7. Gyaourova, A., Ross, A., Index Codes for Multibiometric Pattern Retrieval,  IEEE Transactions on Information Forensics and Security, Volume: 7 , Issue: 2, 2012 , pp 518 – 529.
8. Nagar, A. ; Nandakumar, K. ; Jain, A.K. , Multibiometric Cryptosystems Based on Feature Level Fusion, IEEE Transactions on Information Forensics and Security, Volume: 7 , Issue: 1 , Part: 2, 2012 , Page(s): 255 – 268.
9. Norman Poh, Josef Kittler, Thirimachos Bourlai., Quality-Based Score Normalization With Device Qualitative Information for Multimodal Biometric Fusion, IEEE Transactions On Systems, Man, And Cybernetics—Part A: Systems And Humans, VOL. 40, NO. 3,  2010, pp 539-554.
10. Kien Nguyen, Student Member, IEEE, Simon Denman, Member, IEEE, Sridha Sridharan, Senior Member, IEEE, and Clinton Fookes, Member, IEEE, Score-Level Multibiometric Fusion Based on Dempster–Shafer Theory Incorporating Uncertainty Factors, IEEE Transactions On Human-Machine Systems, Vol. 45, No. 1,2015, 132-140.
11. Mohammad Imran, Ashok Rao, G. Hemantha Kumar, Multibiometric systems: A comparative study of multi-algorithmic and multimodal approaches, Procedia Computer Science, Volume 2, 2010, Pages 207-212, ISSN 1877-0509.
12. N, Saini., A, Sinha., Face and Palmprint multimodal biometric systems using Gabor–Wigner transform as feature extraction, Pattern Analysis and Applications,2014,pp 1-12.
13. N, Saini., A, Sinha., Face and Palmprint multimodal biometric systems using Gabor–Wigner transform as feature extraction, Pattern Analysis and Applications, 2014,pp 1-12.
14. Zengxi Huang., Yiguang Liu.,Ronggang Huang., Menglong Yang., Frameworks for Multimodal Biometric Using Sparse Coding, intelligent Science and Intelligent Data Engineering Lecture Notes in Computer Science Volume 7751, 2013, pp 433-440.
15. Fridman, A. ; Stolerman, A. ; Acharya, S. ; Brennan, P. ; Juola, P. ;Greenstadt, R. ; Kam, M., Decision Fusion for Multimodal Active Authentication, in IT Professional 2013, vol.15  Issue No.04 , 2013 , pp 29 – 33.
16. McLaughlin, N., Ming, J., & Crookes, D., Robust Multimodal Person Identification With Limited Training Data. Human-Machine Systems, IEEE Transactions on, 43(2), 2013, pp 214-224.
17. Conti,V. , Militello,C. , Sorbello,F. , VitabileS. , Frequencybased Approach for Features Fusion inFingerprint and Iris Multimodal Biometric Identification Systems, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, Volume: 40 , Issue: 4 , 2010 , Page(s): 384-395.
18. Shekhar, S. ,Patel, V.M. , Nasrabadi, N.M., Chellappa, R., Joint Sparse Representation for Robust Multimodal Biometrics Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume: 36 , Issue: 1 2014 , pp 113 – 126.
19. Rao, Sreenivasa T., Reddy ,Sreenivasa E., Multimodal Biometric Authentication Based on Score Normalization Technique , Intelligent Informatics, Intelligent Informatics Advances in Intelligent Systems and Computing Volume 182, 2013, pp 425-434.
20. Michal Szczepanik., Ireneusz Jóźwiak., Reliability and Error Probability for Multimodal Biometric System, Intelligent Systems in Technical and Medical Diagnostics Advances in Intelligent Systems and Computing Volume 230, 2014, pp 325-332

21. Al-Osaimi, Faisal R., Mohammed Bennamoun, and Ajmal Mian. "Spatially optimized data-level fusion of texture and shape for face recognition." Image Processing, IEEE Transactions on 21, no. 2 (2012): 859-872.
22. Maleika Heenaye, Mamode Khan, A Multimodal Hand Vein Biometric based on Score Level Fusion, Procedia Engineering, Volume 41, 2012, Pages 897-903, ISSN 1877-7058.
23. Ross, Arun.,Jain, Anil K.,Fusion Techniques in Multibiometric Systems,in Face Biometrics for Personal Identification Signals and Communication Technology, 2007, pp 185-212.
24. Tao Wu, Xiao-Jun Wu, Xing Liu, Xiao-Qing Luo, New Method using Feature Level Image Fusion and Entropy Component Analysis for Multimodal Human Face Recognition, Procedia Engineering, Volume 29, 2012, Pages 3991-3995, ISSN 1877-7058.
25. Morteza Modarresi., Iman Sheikh Oveisi., A Contourlet Transform Based for Features Fusion in Retina and Iris Multimodal Biometric System, Biometric Authentication Lecture Notes in Computer Science 2014, pp 75-90.
26. Madeena Sultana., A Novel Content Based Methodology for a Large Scale Multimodal Biometric System , Advances in Artificial Intelligence Lecture Notes in Computer Science Volume 7884, 2013, pp 364-369.
27. Paul,P.P., Gavrilova,M.L.,Alhajj,R.,Decision Fusion for Multimodal Biometrics Using SocialNetwork Analysis IEEE Transactions on Systems, Man, and Cybernetics: Systems, Volume: 44, Issue: 11, 2014, pp 1522-1533.
28. Sriram Pavan Tankasala, Plamen Doynov, Reza Derakhshani, Visible Spectrum, Bi-Modal Ocular Biometrics, Procedia Technology, Volume 6, 2012, Pages 564-573, ISSN 2212-0173.
29. Shan Juan Xie., Bin Zhou., Jucheng Yang., Yu Lu., Yuliang Pan., Novel Hierarchical Structure Based Finger Vein Image Quality Assessment, Biometric Recognition ,Lecture Notes in Computer Science Volume 8232, 2013, pp 266-273.
30. Yu Lu, Sook Yoon, Dong Sun Park, “Finger Vein Recognition based on Matching Score-Level Fusion of Gabor Features”, Ministry of Education, Science Technology (MEST) and National Research Foundation of Korea (NRF), 38A(2).
31. Randa Boukhris Trabelsi, Alima Damak Masmoudi, and Dorra Sellami Masmoudi ,A New Multimodal Biometric System Based on Finger Vein and Hand Vein Recognition, International Journal of Engineering and Technology (IJET), ISSN : 0975-4024, Vol 5, No 4, 2013,pp 3175-3183.
32. S.Brindha, Dr.Ila.Vennila, Ms.B.Nivedetha, Performance Analysis of Fused Eye Vein and Finger Vein Multimodal Biometric System, International Journal of Engineering Research and Development, e-ISSN: 2278-067X, p-ISSN: 2278-800X, www.ijerd.com,Volume 10, Issue 7, July 2014, PP.69-75.
33. Feifei CUI, Gongping YANG ,Score Level Fusion of Fingerprint and Finger Vein Recognition Journal of Computational Information Systems 7: 16 (2011) 5723-5731.
34. Alima Damak Masmoudi, Randa Boukhris Trabelsi, Mohamed Krid and Dorra Sellami Masmoudi,  Implementation of a Fingervein Recognition System based on Improved Gaussian      Matched Filter,   MAGNT Research Report, ISSN. 1444 - 8939, Vol.2 (4). PP: 251-260. Wencheng Yang, Jiankun Hu, Song Wang, A Finger-Vein Based Cancellable Bio-cryptosystem, LNCS 7873, pp. 784–790, 2013.

35. Arun Ross and Rohin Govindarajan, “Feature Level Fusion Using Hand and Face Biometrics”, Appeared in Proc. Of SPIE Conference on Biometric Technology for Human Identification II, Vol. 5779, pp. 196-204 (Orlando, USA), March 2005.
36. Daugman, How iris recognition works, IEEE Trans. on Circuits and Systems for Video Technology, Vol. 14, Issue 1, 2004.
37. Miura, N.; Nagasaka, A.; Miyatake, T. Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification. Mach. Vis. Appl. 2004, 15, 194–203.
38. Miura, N.; Nagasaka, A.; Miyatake, T. Extraction of Finger-Vein Patterns Using Maximum Curvature Points in Image Profiles. In Proceedings of the IAPR Conference on Machine Vision Applications, Tsukuba Science City, Japan, 16–18 May 2005; pp. 347–350.

39. Wu, J.D.; Ye, S.H. Driver identification using finger-vein patterns with Radon transform and neural network. Expert. Syst. Appl. 2009, 36, 5793–5799.
40. Qin, H.F.; Qin, L.; Yu, C.B. Region growth–based feature extraction method for finger-vein recognition. Opt. Eng. 2011, 50, 057208.
41. Huang, B.N.; Dai, Y.G.; Li, R.F.; Tang, D.R.; Li, W.X. Finger-Vein Authentication based on Wide Line Detector and Pattern Normalization. In Proceedings of the 20th International Conference on Pattern Recognition, Istanbul, Turkey, 23–26 August 2010; pp. 1269–1272.
42. Song, W.; Kim, T.; Kim, H.C.; Choi, J.H.; Lee, S.R.; Kong, H.J. A finger-vein verification system using mean curvature. Pattern Recognit. Lett. 2011, 32, 1514–1547.
43. Harsha, P., Kanimozhi,R., Subashini,C., A Real Time Embedded System Of Vein Used For Authentication In Teller Machine, in International Journal of Emerging Technology and Advanced Engineering ,ISSN 2250-2459 (Online), Volume 3, Special Issue 1, 2013, pp 400-405.
44. Ramya, V., Vijaya Kumar, P. Palaniappan ,B., A Novel Design Of Finger Vein Recognition For Personal Authentication And Vehicle Security, Journal of Theoretical and Applied Information Technology, Vol. 65 No.1, 1992-8645, 2014. , ISSN: 1992-8645.
45. Shandong University Machine Learning and Applications - Homologous Multi-modal Traits (SDUMLA-HMT), Shadong University,China. http://mla.sdu.edu.cn/sdumla-hmt.html
46. Ming-Kuei, "Visual pattern recognition by moment invariants," Information Theory, IRE Transactions, vol. 8, pp. 179-187, 1962.
47. Hong L., Jain A.K. Multimodal Biometrics. In: Jain A.K., Bolle R., Pankanti S. (eds) Biometrics. Springer, Boston, MA , 1996.

132-139

25.

Authors:

Paper Title:

Abstract: In wireless networks, throughput can be enhanced via network coding, which can also extend network lifespan in the case of devices running on battery, such as wireless sensor nodes. The number of transmissions needed for broadcasting a certain message over the network is also reduced through network coding, which thereby improves energy usage. On the other hand, network lifespan can be negatively impacted if network coding is applied too extensively.In addition to being expensive, network coding is associated with significant overhead as regards control message transmissions and can cause substantial delays. Hence, the performance advantages of network coding are somewhat diminished by its limitations. A key objective of the present study is to identify regions (i.e. nodes) that could benefit from network coding by undertaking network characterisation. Two aims are hoped to be achieved in this way, namely, prevalence of performance improvement over latency and overhead problems provided that a network is compatible with coding application, and the development of coding-aware routing protocols that can successfully direct packets within the network based on topology information.This study seeks to explore opportunities for coding at a node by creating a neighbourhood map for that node and extracting a graph (i.e. transformed graph) from that map. In theory, this should help to demonstrate that the transformed graph is the source of all potential coding opportunities. Furthermore, to identify every potential coding scenario for the node, an algorithm is generated as well. The coding capacity of every network node can be established in this way. Moreover, a range of topologies are used to assess the proposed approach. A valid suggestion is also made that coding gains at a node depend on the node degree and the edges amongst its neighbours.

Keywords: Wireless networks, network coding, ad hoc networks, Throughput. 

References:

1. Ahlswede, N. Cai, S.-Y. R. Li, and R. W. Yeung, " Network information ow," IEEE Transactions on Information Theory, Vol. 46, No. 4. (2000), pp. 1204-1216.
2. R. Li, R. W. Yeung, and N. Cai, " Linear network coding," In IEEE Transactions on Information Theory," 2003.
3. Koetter and M. Medard, " An algebraic approach to network coding," IEEE/ACM Transactions on Networking, 2003.
4. Katti, H. Rahul, W. Hu, D. Katabi, M. Medard, and J. Crowcroft, "XORs in the air: Practical wireless network coding," In SIGCOMM. Pisa, Italy: ACM, September 2006.
5. Wu, S.M. Das, and R. Chandra, "Routing with a Markovian Metric to Promote Local Mixing,"' MSR Technical Report, Nov 2006.
6. Sengupta, S. Rayanchu1, and S. Banerjee, "An Analysis of Wireless Network Coding for Unicast Sessions: The Case for Coding-Aware Routing," In proc. of IEEE INFOCOM, 26th IEEE International Conference on Computer Communications, May 2007.
7. Nishanth Gaddam, Anil Gathala, David Lastine and Arun Somani, "Energy Minimization through Network Coding for Lifetime Constrained Wireless Networks," In 17th Interna-tional Conference on Computer Communication and Networks (ICCCN), Aug 2008.
8. Chaporkar and A. Proutiere, "Adaptive network coding and scheduling for maximiz-ing throughput in wireless networks,", In proc. 13th Annual ACM conference on mobile computing and networking, 2007.
9. Goseling and J.H. Weber,"Energy Bene t of Network Coding for Multiple Unicast in Wireless Networks," In proc. 29th Symposium on Information Theory in the Benelux, 2008.
10. Cui, L. Chen, and T. Ho, "Energy E cient Opportunistic Network Coding for Wireless Networks,"' In proc. of IEEE INFOCOM, May 2008.
11. Srinivas Kanakala, Venugopal Reddy.A, Prashanthi.V, “Energy-Efficient Cluster Based Routing Protocol in Mobile Ad Hoc Networks Using Network Coding,” Journal of Computer Networks and Communications, vol. 2014, Article ID 351020, 12 pages, 2014. ISSN: 2090-715X .
12. Srinivas, A.Nagaraju, S.Ramachandram, G.Narsimha, "Performance Evaluation of Routing Protocols in Static and Dynamic Adhoc Networks Based on Energy Consumption”, proceedings of 2010 Second IEEE Vaagdevi International Conference on Information Technology for Real World Problems(VCON’2010), organized by Vaagdevi College of Engineering,Warangal,India,pp.47-51, Dec. 2010.
13. Srinivas, A.Venugopal Reddy, A.Nagaraju, “Network Coding-Aware Neighbor Based Topology Control in MANETs”, proceedings of 2013 IEEE 2nd International Conference on Advanced Computing, Networking and Security(ADCONS’2013),organized by NIT Karnataka, India, pp.175-180, Dec.2013.
14. Srinivas, A.Venugopal Reddy, A.Nagaraju, “Wireless Network Coding: An Innovative Technology”, proceedings of 2013 IEEE International conference on Green Computing, Communication and Conservation of Energy (ICGCE-2013),organized by RMD Engineering College,Chennai,India,pp.45-48,Dec.2013.
15. Srinivas, A.Venugopal Reddy, A.Nagaraju, “Performance Evaluation of Network Coding-Aware Neighbor based Topology control Protocols in MANETs”, proceedings of 2013 IEEE International conference on Green Computing, Communication and Conservation of Energy (ICGCE-2013) ,organized by RMD Engineering College, Chennai, India, pp. 753-756,Dec. 2013.
16. Srinivas, A.Venugopal Reddy, G.Narsimha, ”Network Coding-Aware Energy Efficient Routing Scheme in Mobile Ad-Hoc Networks ”, proceedings of IEEE International Conference on Communication and Signal Processing (ICCSP' 14), organized by Adhiparasakthi Engineering College, Melmaruvathur,India,pp.1030-1034,April.2014.
17. Kalani Geet, K.Srinivas, A.Nagaraju, ”Adaptive ant colony network coding to neighbor topology based broadcasting techniques in MANETs”, proceedings of IEEE International Conference on Advances in Computing ,Communications and Informatics(ICACCI2014),pp.21632167,september.2014.
18. Prashanthi, D.Suresh Babu, C.V.Guru Rao, Network Coding aware Routing for Efficient Communication in Mobile Ad-hoc Networks, International Journal of Engineering & Technology(UAE), ISSN: 2227-524X. 7 (3) (2018) 1474-1481.
19. Prashanthi, D.Suresh Babu, C.V.Guru Rao, Network coding based communication in wireless ad hoc networks, IEEE International Conference on Communication and Signal Processing, (ICCSP' 14), April 3-5, India.
20. Prashanthi, D.Suresh Babu, C.V.Guru Rao, Enhancing Performance of wireless ad-hoc Networks with Network Coding, “IEEE International Conference on Advanced Computing and Communication Systems (ICACCS -2015)”, Jan. 06 – 07, 2017, Coimbatore, INDIA.

140-144

26.

Authors:

Paper Title:

Abstract: The development in the field of wireless technology leads to the increase in application over wireless networks. This increased utilisation of wireless network promoted the research overthe energy efficient networks to generate maximum efficiency with limited battery resource. The mobile ad hoc network (MANET) is a type of wireless network in which the nodes are of mobile type. The nodes in MANET can move freely without any bound of limitations. The MANETS generate communication paths individually without the help of centralized infrastructure or centralised nodes like routers or switches. The communication is established using routing protocols which generates the path between sender and destination nodes, transfers data and control packets through the path and maintain the path information to renew the path in case of path failure. Some applications like military surveillance of the nodes in the MANET move randomly and update its location and information to the receiver node frequently. This leads to maximum consumption of energy in the nodes. The load balancing and energy efficient routing play a vital role in the MANET. A directional flooding approach is introduced to reduce the power consumption and extend the network life time. The performance of the directional flooding protocol was compared with on demand routing protocols like AODV and DSR routing protocol to measure the efficiency of the system in hardware environment.

Keywords: MANET, directional flooding, AODV, DSR, Energy efficiency.

References: 

1. Wu, S. Member, and F. Dai, “Virtual Backbone Construction in MANETs Using Adjustable Transmission Ranges,” 1188 IEEE Trans. Mob. Comput. VOL. 5, NO. 9, Sept. 2006 Virtual, vol. 5, no. 9, pp. 1188–1200, 2006.
2. K. S. Ali and U. V. Kulkarni, “Comparing and analyzing reactive routing protocols (aodv, dsr and tora) in qos of manet,” Proc. - 7th IEEE Int. Adv. Comput. Conf. IACC 2017, vol. 4, no. 5, pp. 345–348, 2017.
3. K. Araghi, M. Zamani, and A. B. T. Abdul Mnaf, “Performance analysis in reactive routing protocols in wireless mobile Ad Hoc networks using DSR, AODV and AOMDV,” Proc. - 2013 Int. Conf. Informatics Creat. Multimedia, ICICM 2013, vol. 4, no. 5, pp. 81–84, 2013.
4. Bai, Y. Sun, C. Phillips, and Y. Cao, “Toward Constructive Relay-Based Cooperative Routing in MANETs,” IEEE Syst. J., vol. 12, no. 2, pp. 1743–1754, 2018.
5. Brill and T. Nash, “A comparative analysis of MANET routing protocols through simulation,” 2017 12th Int. Conf. Internet Technol. Secur. Trans. ICITST 2017, vol. 5, no. 4, pp. 244–247, 2018.
6. El-hajj and A. Al-fuqaha, “On Efficient Network Planning and Routing in Large-Scale MANET,” 3796 IEEE Trans. Veh. Technol. VOL. 58, NO. 7, Sept. 2009, vol. 58, no. 7, pp. 3796–3801, 2009.
7. Gupta, R. Jain, and G. Vaszkun, “Survey of Important Issues in UAV Communication Networks,” IEEE Commun. Surv. Tutorials, vol. 18, no. 2, pp. 1123–1152, 2016.
8. A. K. Jeng and R. H. Jan, “Adaptive topology control for mobile ad hoc networks,” IEEE Trans. Parallel Distrib. Syst., vol. 22, no. 12, pp. 1953–1960, 2011.
9. Kanchan and S. C. Gupta, “Effects of traffic density on performance of reactive routing protocols,” CARE 2013 - 2013 IEEE Int. Conf. Control. Autom. Robot. Embed. Syst. Proc., vol. 4, no. 5, pp. 1–6, 2013.
10. Kumar, A. Srivastava, and S. C. Gupta, “Routing Protocols for MANET,” Perform. Comp. Pro-active React. Routing Protoc. MANET Deep., vol. 4, no. 6, pp. 1–4, 2011.
11. Liu, C. Zhang, G. Yao, and Y. Fang, “DELAR: A device-energy-load aware relaying framework for heterogeneous mobile ad hoc networks,” IEEE J. Sel. Areas Commun., vol. 29, no. 8, pp. 1572–1584, 2011.
12. H. Saeed, M. F. Abbod, and H. S. Al-Raweshidy, “MANET routing protocols taxonomy,” 2012 Int. Conf. Futur. Commun. Networks, ICFCN 2012, vol. 4, no. 5, pp. 123–128, 2012.
13. Sanchez-Iborra and M. D. Cano, “JOKER: A Novel Opportunistic Routing Protocol,” IEEE J. Sel. Areas Commun., vol. 34, no. 5, pp. 1690–1703, 2016.
14. K. Sharma and M. C. Trivedi, “Performance comparison of AODV, ZRP and AODVDR routing protocols in MANET,” Proc. - 2016 2nd Int. Conf. Comput. Intell. Commun. Technol. CICT 2016, vol. 4, no. 5, pp. 231–236, 2016.
15. Suryakant and N. Kushwaha, “To evaluate the impact of vector mobility modelover routing protocols in MANET,” 2014 Conf. IT Business, Ind. Gov. An Int. Conf. by CSI Big Data, CSIBIG 2014, vol. 4, no. 5, pp. 1–6, 2014.
16. Omkumar and S.Rajalakshmi, ”Analysis of Quality of Service using Distribution Coordination Function in Aodv” European Journal of Scientific Research, Vol.58 No.1 , 2011, pp 6-10.
17. Omkumar and S.Rajalakshmi, “Improving QoS of Ad-hoc Networks by using SNR and T-AODV Routing Protocol”, Asian Journal of Scientific Research, pp 1-8, Oct 2013.

145-149

27.

Authors:

Paper Title:

Abstract:  Improving the Quality of Service (QOS) in the network is a major subject to be discussed in development of MANET. Current researches are performed to generate congestion control measures to schedule the data packets from different network paths to process at the specific node. The performance of that specific node should be increased based on the capacity of routing the packets into multiple paths of the network. The performance of these mobile intermediate nodes are been improved by combining the network layer and transport layer approaches. Due to the MAC layer problems like congestion increases the packet drop, delay and error rate, decreases the delivery rate and throughput of the network. Frequent disconnection of nodes is also possible due to the mobile nature of the nodes in MANET. In order to overcome these limitations, a maximum dominant set based semi graph model is developed to frame transport layer mechanism and AODV. DSR protocols are involved to generate a routing path between the nodes of the network. The performance of the semi graph model along with the AODV and DSR routing protocols were evaluated by implementing in the hardware environment.

Keywords: Quality of Service, MANET, AODV, DSR, Semi Graph model, Maximum Dominant set, Edges and Vectors.

References:

1. Venkata Krishna, V. Saritha, G. Vedha, A. Bhiwal, and A. S. Chawla, “Quality-of-service-enabled ant colony-based multipath routing for mobile ad hoc networks,” IET Commun., vol. 6, no. 1, p. 76, 2012.
2. Li and H. Shen, “A QoS-oriented distributed routing protocol for hybrid wireless networks,” IEEE Trans. Mob. Comput., vol. 13, no. 3, pp. 693–708, 2014.
3. De Rango, P. Fazio, F. Scarcello, and F. Conte, “A new distributed application and network layer protocol for voip in mobile ad hoc networks,” IEEE Trans. Mob. Comput., vol. 13, no. 10, pp. 2185–2198, 2014.
4. De Rango, F. Guerriero, and P. Fazio, “Link-stability and energy aware routing protocol in distributed wireless networks,” IEEE Trans. Parallel Distrib. Syst., vol. 23, no. 4, pp. 713–726, 2012.
5. Awwad, A. Al-Fuqaha, B. Khan, and G. Ben Brahim, “Topology control schema for better QoS in hybrid RF/FSO mesh networks,” IEEE Trans. Commun., vol. 60, no. 5, pp. 1398–1406, 2012.
6. Silvius, A. Betances, and K. M. Hopkinson, “Context aware routing management architecture for airborne networks,” IET Networks, vol. 5, no. 4, pp. 85–92, 2016.
7. H. Bouk, N. Javaid, I. Sasase, and S. H. Ahmed, “Gateway Discovery Algorithm Based on Multiple QoS Path Parameters Between Mobile Node and Gateway Node,” J. Commun. Networks, vol. 14, no. 4, pp. 434–442, 2012.
8. Castellanos, J. C. Guerri, and P. Arce, “Performance Evaluation of Scalable Video Streaming in Mobile Ad hoc Networks,” IEEE Lat. Am. Trans., vol. 14, no. 1, pp. 122–129, 2016.
9. M. Chintalapalli and V. R. Ananthula, “M-LionWhale: multi-objective optimisation model for secure routing in mobile ad-hoc network,” IET Commun., vol. 12, no. 12, pp. 1406–1415, 2018.
10. H. Chen, E. H. K. Wu, and G. H. Chen, “Bandwidth-Satisfied Multicast by Multiple Trees and Network Coding in Lossy MANETs,” IEEE Syst. J., vol. 11, no. 2, pp. 1116–1127, 2017.
11. Haiyan, Y. Hong, and G. Jingming, “An Evolving Graph-Based Reliable Routing Scheme for VANETs,” Chinese J. Clin. Oncol., vol. 43, no. 11, p. 498, 2016.
12. Y. Jung, H. H. Choi, and J. R. Lee, “Survey of Bio-Inspired Resource Allocation Algorithms and MAC Protocol Design Based on a Bio-Inspired Algorithm for Mobile Ad Hoc Networks,” IEEE Commun. Mag., vol. 56, no. 1, pp. 119–127, 2018.
13. Lu and J. Zhu, “Genetic algorithm for energy-efficient QoS multicast routing,” IEEE Commun. Lett., vol. 17, no. 1, pp. 31–34, 2013.
14. G. Pease, I. W. Phillips, and L. Guan, “Adaptive Intelligent Middleware Architecture for Mobile Real-Time Communications,” IEEE Trans. Mob. Comput., vol. 15, no. 3, pp. 572–585, 2016.
15. M. Zhang, Y. Zhang, F. Yan, and A. V. Vasilakos, “Interference-based topology control algorithm for delay-constrained mobile Ad hoc networks,” IEEE Trans. Mob. Comput., vol. 14, no. 4, pp. 742–754, 2015.
16. Omkumar and S.Rajalakshmi, “Improving QoS of Ad-hoc Networks by using SNR and T-AODV Routing Protocol”, Asian Journal of Scientific Research, pp 1-8, Oct 2013.
17. Omkumar and S.Rajalakshmi, ”Analysis of Quality of Service using Distribution Coordination Function in Aodv” European Journal of Scientific Research, Vol.58 No.1 , 2011, pp 6-10.

150-155

28.

Authors:

Paper Title:

Abstract:  The neighbourliness business is developing quickly in its training and execution of green activities so as to protect the regular habitat and effectively address the issues and wants of green-disapproved of customers. The motivation behind this investigation was to evaluate the natural supportability rehearses in the lodging and extraordinary occasions with reference to four star inns in Chennai. Information was gathered from members speaking to seven unique inns and occasion settings in Chennai. The instrument utilized comprised of semi-organized survey, with some constrained decision inquiries questions relating to the manageability practices of every property. The outcomes showed that the seven properties are effectively setting up and rehearsing natural maintainability rehearses and are propelled to ceaselessly look for further enhancement. This is a basic report to be used in contrasting the region neighbourliness industry's present and future manageability advance with different regions all through the world.

Keywords: Supportability, Inn, Uncommon Occasions, Accommodation, Condition. 

References:

1. Bader, E.E. (2005). Sustainable hotel business practices. Journal of Retail & Leisure Property, 5(1), 70-77. doi: 10.1057/palgrave.rlp.5090008.
2. Bohdanowicz, P. (2005). European hoteliers’ environmental attitudes: Greening the business. Cornell Hotel and Restaurant Administration Quarterly, 46, 188-204. doi:10.1177/0010880404273891.
3. Butler, J. (2008). The compelling hard case for green hotel development.Cornell Hospitality Quarterly, 49(3), 234-244. doi:10.1177/1938965508322174.
4. Cognition/perception. (2013). Retrieved from McMaster University, Department of Psychology, Neuroscience &Behavior website: http://www.science.mcmaster.ca/ pnb/research/cognition-perception.html.
5. Dickson, C. (2010). Promoting sustainable event practice: The role of professional associations.International Journal of Hospitality Management, 29(2), 236-244. doi:10.1016/j.ijhm.2009.10.0Wu.
6. H.J., Dunn, S.C. (1995). Environmentally responsible logistics systems, International Journal of Physical Distribution & Logistics Management, 25(2), 20-38. doi:10.1108/09600039510083925.

156-160

29.

Authors:

Paper Title:

Abstract: With the increasing use of digital platforms for government as well as private services deployment and in the race of financial firms to make banking simple and easier, there comes in picture the need of a similar strong and leak proof authentication technology with negligible chances of failure. The market is already in a transition from traditional password authentication process to password less authentication technique. Because of its unique feature to identify every individual with different traits, voice recognition is gaining speed as authentication technique; however, it comes with some inherent limitations. In this paper, voice recognition is hybridized with behavioral authentication technique and a framework is proposed in expectation of overcoming some of the issues of voice recognition.

Keywords: Speech Recognition, Passwordless authentication, voice samples, verification and identification, secure banking, behavioral authentication, impersonation attack.  

References:

1. Nick Gaubitch, retrieved from url: “www.infosecurity-magazine.com/opinions/voice-verification-authentication/”.
2. Biometric and Traditional Mobile Authentication Techniques: Overviews and Open Issues - Scientific Figure on ResearchGate, retrieved from url: “www.researchgate.net/Voice-recognition-authentication-system_fig9_268388162”.
3. Voice and Speech Recognition, available at url: “www.findbiometrics.com/ solutions/ voice-speech-recognition/”.
4. Danny Thakkar,” Evolution of Voice Recognition: A Tech Boon”, retrieved from url: “www.bayometric.com/ evolution-voice-recognition-tech-boon/”.
5. How Does Voice Biometrics Work?, retrieved from url: “www.uniphore.com/blog/2018/03/how-does-voice-biometrics-work”.
6. Poddar, et.al. "Speaker Verification with Short Utterances: A Review of Challenges, Trends and Opportunities", IET Biometrics, Vol.7, issue-2,pp: 91–101, March 2018.
7. Judith A. Markowitz,” Voice Biometrics”, COMMN OF THE ACM, vol.-43,issue-9 Sep. 2000.
8. More than Meets the Eye,An Overview of Facial Recognition and its Applications in Retail, retrieved from url:”www.fungglobalretailtech.com/ research/meets-eye-overview- facial-recognition-applications-retail/”
9. Speech Recognition to Achieve 82 Percent Penetration in Mobile Devices by 2020, available at url: www.tractica.com/ newsroom/press-releases/speech-recognition - to- achieve-82-percent-penetration-in-mobile-devices-by-2020/
10. Voice Identification, Retrieved from url:”www.di-srv. unisa.it/~ads/corso-security/www./CORSO-9900/ biometria/Voice.htm.”
11. Voice recognition, available at url: “www. tutorialspoint.com/biometrics/voice_recognition.htm”.
12. ED Grabianowski“ How Speech Recognition Works”, retrieved from https://electronics.howstuffworks.com/ gadgets/high-tech-gadgets/speech-recognition3.htm, 8 October 2018.

161-165

30.

Authors:

Paper Title:

Abstract: This paper depicts a collective structure for carrying system tomography on topologies with different sources & numerous destinations, exclusive of expecting topology to be well-known. We present a novel different source dynamic estimation technique utilizing a semi-randomized probe testing plan and packet entry arrange estimations which don't require exact synchronization between the hosts. In past tomography work, the connection between wrong inference of topology set & % loss probability on every link has been utilized to derive the hidden topology. Conversely, our principle thought behind utilizing Network coding is to present relationships among probe packets in a topology subordinate way and furthermore create calculations that exploit these connections to surmise the system topology from end host perceptions. Primer reenactments outline the execution advantages of this methodology. Specifically, without packet loss, we can deterministically surmise the topology, with not very many tests; within the sight of packet loss, we can quickly derive topology, even at little loss rates.

Keywords: Broadcasting, Broadcast Storm Problem, Flooding, MANET, Network Coding, Redundancy, Topology.

References:

1. Ahlswede, Rudolf, et al. "Network information flow." IEEE Transactions on information theory4 (2000): 1204-1216
2. Fragouli, Christina, Jean-Yves Le Boudec, and Jörg Widmer. "Network coding: an instant primer." ACM SIGCOMM Computer Communication Review1 (2006): 63-68.
3. Gkantsidis, Christos, and Mitch Goldberg. "Avalanche: File swarming with network coding." Microsoft Research(2005).
4. Chen, Yan, et al. "An algebraic approach to practical and scalable overlay network monitoring." ACM SIGCOMM Computer Communication Review. Vol. 34. No. 4. ACM, 2004.
5. Duffield, Nick G., et al. "Multicast topology inference from measured end-to-end loss." IEEE Transactions on Information Theory1 (2002): 26-45.
6. Coates, Mark, et al. "Maximum likelihood network topology identification from edge-based unicast measurements." ACM SIGMETRICS Performance Evaluation Review. Vol. 30. No. 1. ACM, 2002.
7. Ho, Tracey, et al. "Network monitoring in multicast networks using network coding." Information Theory, 2005. ISIT 2005. Proceedings. International Symposium on. IEEE, 2005.
8. Adler, Micah, Tian Bu, Ramesh K. Sitaraman, and Don Towsley. "Tree layout for internal network characterizations in multicast networks." In International Workshop on Networked Group Communication, pp. 189-204. Springer, Berlin, Heidelberg, 2001.
9. Ng, T. E., & Zhang, H. (2002). Predicting Internet network distance with coordinates-based approaches. In INFOCOM 2002. Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE(Vol. 1, pp. 170-179).
10. Li, S-YR, Raymond W. Yeung, and Ning Cai. "Linear network coding." IEEE transactions on information theory2 (2003): 371-381.
11. Gkantsidis, Christos, and Pablo Rodriguez Rodriguez. "Network coding for large scale content distribution." In INFOCOM 2005. 24th Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings IEEE, vol. 4, pp. 2235-2245.
12. Andersen, D., Balakrishnan, H., Kaashoek, F., & Morris, R. (2001). Resilient overlay networks(Vol. 35, No. 5, pp. 131-145). ACM.
13. Ratnasamy, S., & McCanne, S. (1999, March). Inference of multicast routing trees and bottleneck bandwidths using end-to-end measurements. In INFOCOM'99. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings (Vol. 1, pp. 353-360).
14. Harfoush, K., Bestavros, A., & Byers, J. (2000). Robust identification of shared losses using end-to-end unicast probes. In Network Protocols, 2000. Proceedings. 2000 International Conference on(pp. 22-33).
15. Cáceres, Ramón, et al. "Multicast-based inference of network-internal loss characteristics." IEEE Transactions on Information theory7 (1999): 2462-2480.
16. Zhu, Ying, Baochun Li, and Jiang Guo. "Multicast with network coding in application-layer overlay networks." IEEE Journal on Selected Areas in Communications1 (2004): 107-120

166-171

31.

Authors:

Paper Title:

Abstract: In today’s digital world, privacy concerns for data over the internet have increased. In many communications we transmit digital images and these images contain confidential information. Making is vulnerable towards unauthorized personals attacking the image and leaking our information which demands higher privacy. So, there are a number of methods available for achieving this privacy, one of them being Random key Steganography. This make use of Mosaic image creation which has two distinct techniques, DWT (Discrete wavelet Transform) and DCT (Discrete Cosine Transform). The target image is randomly selected into blocks to uses of this image for hiding secret image. Target image and Secret image is divided into 2x2 blocks called image tile respectively. A secret image hiding scheme is proposed with new security features. This scheme utilizes the mosaic images, which is created from the secret and target images. A Combined (mosaic) image or watermark image is similar to source image. The secret image blocks are hidden in the target image by performing appropriate random pattern blocks.

Keywords: Steganography, Secure Transmission, Random Pattern Key, Block DWT-DCT, Mosaic Image. 

References:

1. Indrajeet Phutane, Dr.Sanjay Nalbalwar,” A New Method for Secret Image Transmission via Secret Fragment Visible Mosaic Image” IEEE International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) – 2016.
2. Ya-Lin Lee, and Wen-Hsiang Tsai,” A New Secure Image Transmission Technique via Secret-Fragment-Visible Mosaic Images by Nearly Reversible Color Transformations”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 24, No. 4, April 2014.
3. Asawari Chavan and Amrita Manjrekar,” A Novel Approach for Data Transmission Technique Through Secret Fragment Visible Mosaic Image”, Springer India 2016
4. Shahanaz N and Greeshma R,” Secret Image Transmission through Mosaic Image”, CCNET, CSIP, SCOM, DBDM – 2017.
5. Anitha Devi M.D, K B ShivaKumar,” Secured Covert Color Image Transmission Using Secret Fragment Visible Mosaic Image And Reversible Color Transformation Technique”, IEEE 2016 International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT).
6. Arthe Henriette Pascaline, Li Chun Fong Christopher, Maleika Heenaye-Mamode Khan, Sameerchand Pudaruth,” Using Photo mosaic And Steganography Techniques For Hiding Information Inside Image Mosaics”, 2015 IEEE.
7. Vidyasagar M. Potdar, Song Han, Elizabeth Chang,” A Survey of Digital Image Watermarking Techniques”, 2005 IEEE.
8. I-Jen Lai and Wen-Hsiang Tsai,” Secret-Fragment-Visible Mosaic Image–A New Computer Art and Its Application to Information Hiding”, IEEE Transactions on Information Forensics and Security, Vol. 6, No. 3, September 2011.
9. Deepali G. Singhavi, Dr. P. N. Chatur,” A New Method for Creation of Secret-Fragment Visible- Mosaic Image for Secure Communication”, IEEE Sponsored 2nd International Conference on Innovations in Information Embedded and Communication Systems ICIIECS'15.
10. Sheshang D. Degadwala & Dr. Sanjay Gaur “Privacy Preserving System Using Pseudo Zernike Moment with SURF & Affine Transformation on RST Attacks”,Vol. 15 No. 4 April International Journal of Computer Science and Information Security, 2017.
11. Sheshang D. Degadwala & Dr. Sanjay Gaur “An Efficient Image Watermarking for Combination of RST Attacks”, International Journal of Computer Applications (0975 – 8887) Volume 170 – No.5, July 2017
12. Sheshang D. Degadwala & Dr. Sanjay Gaur “An Efficient Watermarking Scheme Based on NonSymmetric Rotation Angles Attacks”, International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 21 (2017) pp. 10611-10616.
13. Sheshang D. Degadwala & Dr. Sanjay Gaur “An Efficient Privacy Preserving System Based on RST Attacks on Color Image”, Springer, ICFITT, 2017.
14. Sheshang D. Degadwala & Dr. Sanjay Gaur “A study of privacy preserving system based on progressive VCS and RST attacks”, International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC),IEEE, 2016.
15. Sheshang D. Degadwala & Dr. Sanjay Gaur “An Efficient Privacy Preserving System Using VCS, Block DWT-SVD and Modified Zernike Moment on RST Attacks”, ICAAMMA, IEEE, 2017.

172-176

32.

Authors:

Paper Title:

Abstract: For smart building applications, identifying and tracking the objects and people in and around a building is an inevitable problem. There exist many approaches for solving this problem. Nowadays, the RFID network based approaches have become most popular for its speed and accuracy. However, placing the RFID readers at optimal places in a building to cover all the areas in order to identify and track the objects and people is a cumbersome task. This paper proposes a model in which the RFID reader placement problem is formulated as a multi-objective optimization problem and also proposes an algorithmic framework to solve the same. The proposed algorithmic frame work consists of a multi-objective Differential Evolution algorithm which adds weights to each of the objective and also follows the opposition-based learning approach for initializing the populations. The results obtained in solving the RFID reader placement problem with proposed algorithmic framework is studied and reported in details for individual objectives, combined objectives with different schemes and for two different population initialization techniques.

Keywords: Multi-Objective optimization, Evolutionary Algorithms, RFID Reader Placement, Opposition-Based Learning, Weight-Vector approaches.

References:

1. Gu, Fangqing, Hai-Lin Liu, and Kay Chen Tan. "A multi-objective evolutionary algorithm using dynamic weight design method," International Journal of innovative Computing, Information and Control5, 2012, pp. 3677-3688.
2. Meneghini, Ivan Reinaldo, and Frederico Gadelha Guimarães. "Evolutionary method for weight vector generation in Multi-Objective Evolutionary Algorithms based on decomposition and aggregation," Evolutionary Computation (CEC), 2017 IEEE Congress on. IEEE, 2017.
3. Qi, Yutao, et al. "MOEA/D with adaptive weight adjustment," Evolutionary computation 22, No. 2, 2014, pp. 231-264.
4. Li, Miqing, and Xin Yao. "What Weights Work for You? Adapting Weights for Any Pareto Front Shape in Decomposition-based Evolutionary Multi-Objective Optimisation," arXiv preprint arXiv:1709.02679,
5. Segredo, Eduardo, et al. "On the comparison of initialisation strategies in differential evolution for large scale optimisation," Optimization Letters, 2017, pp. 1-14.
6. KazimipourBorhan, Xiaodong Li, and A. Kai Qin. "A review of population initialization techniques for evolutionary algorithms," 2014 IEEE Congress on Evolutionary Computation (CEC), 2014.
7. Rahnamayan, Shahryar, Hamid R. Tizhoosh, and Magdy MA Salama. "A novel population initialization method for accelerating evolutionary algorithms," Computers & Mathematics with Applications, Vol. 53, No. 10, 2007, pp. 1605-1614.
8. WaleedAlsalih,Riyadh and Saudi Arabia, “Load- aware Reader Placement Algorithms for RFID Networks,”Journal of Emerging Trends in Computing and Information Sciences, 2014, Vol. 5, No. 10.
9. NazishIrfan, Mustapha C.E. Yagoub, and KhelifaHettak, “Genetic-Based Approach for Efficient RFID Reader Antenna Positioning,” Int Journal of Information and Electronics Engineering, 2012, Vol. 2, No. 5.
10. Rakhmangulov, A, Muravev D andMishkurov, p “Optimal Placement Method of RFID Readers in Industrial Rail Transport for Uneven Rail Trafic Volume Management,” Open Engineering, 2016, Vol. 6, No. 1.
11. JianMiandYasutake Takahashi, “Design of an HF-Band RFID System with Multiple Readers and Passive Tags for Indoor Mobile Robot Self-Localization,” Sensors (Basel),  2016, Vol. 16, No. 8.
12. Ahmed Jedda and Hussein T.Mouftah,“Decentralized RFID Coverage Algorithms with Applications for the Reader Collisions Avoidance Problem,”IEEE Transactions on Emerging Topics in Computing, Vol., 4, No., 4, 2016, pp., 502-515.
13. Truijens,X.Wang, H. de Graaf and J.J.Liu,“Evaluating the Performance of Absolute RSSI Positioning Algorithm-Based Microzoning and RFID in Construction Materials Tracking,”Hindawi Publishing Corporation , mathematical problems in engineering, 2014.
14. Rubini, ChambuliVenkata Prashanthi, S. Subanidha, G. Jeyakumar, “An Optimization framework for Solving RFID Reader Placement Problem Using Differential Evolution Algorithm,”  In Proceedings of ICCSP-2017 – International Conference on Communication and Signal Proceedings, 2017.
15. Rubini, C. Prasanthi, S. Subanidha, T. N. S, Vamsi and G. Jeyakumar, “An optimization framework for solving RFID reader placement problem using greedy approach,” In Proceedings of International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017, pp. 900-905.
16. Storn, Rainer. "Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces," Technical Report, International Computer Science Institute, 1995.
17. Jeyakumar and C. ShunmugaVelayutham, “Distributed Mixed Variant Differential Evolution Algorithms for Unconstrained Global Optimization,” Memetic Computing, Vol. 5., No. 4., 2013, pp. 275-293.
18. Jeyakumar and C. ShunmugaVelayutham, “Distributed Heterogeneous Mixing of Differential and Dynamic Differential Evolution Variants for Unconstrained Global Optimization,” Soft Computing – Springer, Vol. 18, No. 10, 2014, pp. 1949-1965.
19. Dhanya M Dhanalakshmy, P. Pranav and G. Jeyakumar, “A Survey on Adaptation Strategies for Mutation and Crossover Rates of Differential Evolution Algorithm,” International Journal on Advanced Science, Engineering and Information Technology, Vol. 6., No. 5., 2016, pp. 613-623.
20. Aswani, V. Praveen and S. Thangavelu, “Performance Analysis of Variants of Differential Evolution on Multi-Objective Optimization Problems,” Indian Journal of Science and Technology, 2015, Vol 8, No. 17.

177-184

33.

Authors:

Paper Title:

Abstract: Variables plays significant role in programming and they ease programmers to write their programs flexibly. Variables are used to represent the data in a program. On executing a program, the variables are replaced with the real time values and hence it creates a possibility for a program to process different set of data. They are most predominant ones that involve in computation and bear the intermediate or final values in a program. These values are good enough in deciding the flow of the program and hence there is a necessity for analyzing flows. From the software tester’s perspective, there grows a need to analyze and test those variables structurally. Data-flow testing is a white-box based testing technique that utilizes control flow and data flow through the program for testing. There are two main forms of data flow testing called as define/use testing and slice-based testing. This paper gives a focus on define/use based testing where it uses simple rules that aids in ensuring whether all the variables are defined and used at the appropriate points within the program. It helps the tester in chalking out the values of a variable, recording them and can trace the changes in values within the program. It is done by using a concept of a program graph, which is closely related to the path testing, selected on a specific variable. 

Keywords: White-box Testing, Dataflow Testing, Define/Use Testing, du-path, dc-path.

References:

1. Beizer, Boris "Software Testing Techniques," Van Nostrand Reinhold, 1990.
2. Harrold, Mary J. and Soffa, Mary L. "Inter Procedural Data Flow Testing," 3rd Testing, Analysis and Verification SYMP (SIGSUFT89), 1989, 158-167.
3. Siegel, Shel 1996, "Object Oriented Software Testing-A Hierarchical Approach," John Wiley & Sons, Inc. 1996.
4. Rapps, S. and Weyuker, E. J. "Selecting Software Test Data Using Data Flow Information," IEEE Transactions on Software Engineering, SE-11 (4), April 1985, 367-375.
5. Harrold, Mary J. and Rothermel, G. "Performing Data Flow Testing on Classes," 2nd ACM SIGSOFT Symposium on the Foundations of Software Engineering, Dec. 1994, 154-163.
6. Laski, J. and Korel, B. "A Data Flow Oriented Program Testing Strategy," IEEE Transactions on Software Engineering, SE-9(3), May 1983, 347-354.
7. M. Herman, “A Data Flow Analysis Approach to Program Testing”, Australian Computer Journal, vol. 8, issue 3, pp. 92–96, 1976.
8. Srinivas Nidhra and Jagruthi Dondeti, "Black-box and White-box Testing Techniques-A Literature Review", International Journal of Embedded Systems and Applications (IJESA), Vol. 2, No. 2, June 2012.
9. Yogeshsingh, “Software Testing”, Cambridge University Press, pp 173-175, 2012.
10. Frankl, Phyllis G. and Weyuker, Elaine J. "An Applicable Family of Data Flow Testing Criteria," IEEE Transactions on Software Engineering, 14(10), 1988, 1483-1498.
11. Paul C. Jorgensen: Software Testing, A Craftsman’s Approach, 4th Edition, Auerbach Publications, 2013.

185-194

34.

Authors:

Paper Title:

Abstract: Elevator Group Control System (EGCS) has a significant role in the transportation system of buildings. This study presents an elevator group dynamic even and odd floor preference approach based on the real-time passenger traffic to overcome the difficulty of using elevators in the up peak and down-peak traffic mode. The proposed system framework divides building floors as even and odd based on the floor number, some lifts allocated for even numbered floors and some for odd floors. Even numbered lifts are responsible for serving calls of even floors, and odd number lifts are responsible for serving calls of odd floors. Proposed study uses time-based floor preference logic for dynamic allocation of elevators. The primary motive of the proposed research study is to reduce the passengers traveling time, average-waiting time and improve overall system performance in up-peak traffic and down-peak traffic conditions. The time-based floor preference logic used for dynamic allocation of elevators significantly minimizes the waiting time and traveling time of passengers.

Keywords: Down-peak traffic, Dynamic dispatching, Elevator, EGCS, time-based floor preference, Up-peak traffic.

References:

1. Yaowu Liu and Zhangyong Hu, Qiang SU, and Jiazhen Huo, “Energy Saving of Elevator Group Control based on Optimal Zoning Strategy with Inter floor Traffic,” International Conference on Information Management, Innovation Management and Industrial Engineering (ICIII), 26-28 Nov 2010.
2. Donghua Wang Baofeng Li, et al. “An Optimization Model of Elevators Group Zoning Dispatching and its Application,” International Symposium on Cryptography, Network Security, Data Mining, Knowledge Discovery, E-Commerce, and Its Applications, and Embedded Systems pp.18-21, 2010.
3. Qiu, Jiangdong, and zhaoyuan Jiang, "The research and simulation on the elevator group control system (EGCS) scheduling algorithm," Electrical and Control Engineering (ICECE), 2011 International Conference on. IEEE,
4. Jinglong Zhang, Qun Zong, et al. "Energy-saving scheduling optimization under up-peak traffic for the group elevator system in the building,” Energy and Buildings 66, 495–504, 2013.
5. Li, Zhonghua, Zongyuan Mao, and Jianping Wu, "Research on the dynamic zoning of elevator traffic based on an artificial immune algorithm," Control, Automation, Robotics, and Vision Conference, 2004. ICARCV 2004 8th. Vol. 3. IEEE, 2004.
6. Yang, Suying, Jianzhe Tai, and Cheng Shao, "Dynamic partition of an elevator group control system with destination floor guidance in up-peak traffic," journal of computers 4.1, 45-52, 2009.
7. Li, Zhonghua, Jianping Wu, and Zongyuan Mao, "Application of artificial immune algorithm in the dynamic zoning of elevator traffic," Fifth World Congress on Intelligent Control and Automation, WCICA, Vol. 3. IEEE, 2004.
8. So, Albert TP, J. K. L. Yu, and W. L. Chao, "Dynamic zoning based supervisory control for elevators," Proceedings of the 1999 IEEE International Conference on Control Applications, Vol. 2. IEEE, 1999.
9. Deying, GU, and Yan Dongmei, "Study on Fuzzy Algorithm of Elevator Group Control System," International Conference on Challenges in Environmental Science and Computer Engineering-Volume 01. IEEE Computer Society, 2010.
10. Fujino, Atsuya, et al., "An elevator group control system with floor-attribute control method and system optimization using genetic algorithms," IEEE Transactions on Industrial Electronics, 44.4, 546-552, 1997.
11. Covington, Michael A., "Logical control of an elevator with defeasible logic," IEEE Transactions on Automatic Control, 45.7, 1347-1349, 2000.
12. Utgoff, Paul E.and Margaret E. Connell, "Real-time combinatorial optimization for elevator group dispatching," IEEE (2012) Transactions on man, system and cybernetics, part A: System and Humans, 42.1, 130-146, 2012.
13. Srikumar Ramalingam, Arvind U. Raghunathan, and Daniel Nikovski," Submodular Function Maximization for Group Elevator Scheduling,” (ICAPS 2017) Automated Planning and Scheduling International conference6.2017
14. Wang Chuansheng, Chen Chunping, “Design of Elevator Group Control System Simulation Platform Based on Shortest Distance Algorithm,” International Conference on Electrical and Control Engineering, 2010.
15. Yine Zhang, Yun Yi, Jian Zhong, "The Application of the Fuzzy Neural Network Control in Elevator Intelligent Scheduling Simulation," Third International Symposium on Information Science and Engineering 2010.
16. Jun Wang, Airong Yu, et al., “Research of Dispatching Method in Elevator Group Control System, Based On Traffic Mode Identify,” International Conference on Business Intelligence and Financial Engineering, 2009.
17. Jafferi Jamaluddin, Nasrudin Abd. Rahim, and Wooi Ping Hew, “An Elevator Group Control System with a Self-Tuning Fuzzy Logic Group Controller,” IEEE Transactions on Industrial Electronics, Vol. 57, 12, December 2010.
18. Fernandez, P. Cortés, J. Munuzuri, and J. Guadix, “Dynamic Fuzzy Logic Elevator Group Control System with Relative Waiting Time Consideration,” IEEE Transactions on Industrial Electronics 2013.
19. [M. D. Sale, V. Chandra Prakash, “scheduling of elevators in Elevator Group Control System EGCS using even and odd elevators approach,” Journal of Advanced research in Dynamical and Control Systems, issue 18, 3231-3242, 2017.
20. M. D. Sale, V. Chandra Prakash,” Dynamic Scheduling of Elevators with Reduced Waiting Time of Passengers in Elevator Group Control System: Fuzzy System Approach,”  Innovations in Computer Science and Engineering: Proceedings of the Fourth ICICSE 2016 Volume 8 of Lecture Notes in Networks and Systems, Springer, 339-346 2017.H. Poor, An Introduction to Signal Detection and Estimation.   New York: Springer-Verlag, 1985, ch. 4.

195-200

35.

Authors:

Paper Title:

Abstract: Unmanned Aerial Vehicles (UAVs) or Drones has been developing brilliantly in the last few decades and they show a remarkable progress in application where human reach is either difficult or hazardous especially in the grimy, dull, and risky activities with advance technology. The technology advancement is making the UAV much better in its specification with advances in computing innovation, programming improvement, light-weight materials, worldwide route, propelled information joins, refined sensors and so on, are reinforcing the abilities and fueling the interest for UAVs worldwide. UAV also colloquially known as Drone is a robot that has the ability to operate autonomously without direct control from a human operator. For nations, it is a surveillance tool, meant to watch over nations as Drones have been used in the past for warfare as such, and activities alike. Drones have been a handy gadget to get “Ariel photography” for many decades and used for intelligence and anti-terrorist outcomes. Today the drones are much more versatile and are moving the industry ahead in its reach for activities as monitoring, investigation, product deliveries, aerial photography, healthcare, disaster relief management, agriculture and even drone racing. There are other types of drones too such as boats, submarines, and ground-based robots. The primary purpose is to explore the usage of drones in Humanitarian causes such as disaster relief and social causes as health care.

Keywords: Drones, Unmanned Ariel Vehicles, Geospatial mapping, Community and Social services, flying bots, Ariel view. 

References:

1. Altawy, R., & Youssef, A. M. (2017). Security, privacy, and safety aspects of civilian drones: A survey. ACM Transactions on Cyber-Physical Systems, 1(2), 7.
2. Bäckman, A., Hollenberg, J., Svensson, L., Ringh, M., Nordberg, P., Djärv, T., ... & Claesson, A. (2018). Drones for provision of flotation support in simulated drowning. Air medical journal, 37(3), 170-173.
3. Boucher, P. (2016). ‘You Wouldn’t have Your Granny Using Them’: Drawing Boundaries Between Acceptable and Unacceptable Applications of Civil Drones. Science and engineering ethics, 22(5), 391-1418.
4. Ferranti, L., Cuomo, F., Colonnese, S., & Melodia, T. (2018). Drone Cellular Networks: Enhancing the Quality of Experience of Video Streaming Applications. Ad Hoc Networks.
5. Goodchild, A., & Toy, J. (2017). Delivery by drone: An evaluation of unmanned aerial vehicle technology in reducing CO2 emissions in the delivery service industry. Transportation Research Part D: Transport and Environment. https://doi.org/10.1016/j.trd.2017.02.017
6. Hassanalian, M., Rice, D., & Abdelkefi, A. (2018). Evolution of space drones for planetary exploration: A review. Progress in Aerospace Sciences.
7. Khan, M. A., Ectors, W., Bellemans, T., Ruichek, Y., Janssens, D., & Wets, G. (2018). Unmanned Aerial Vehicle-based Traffic Analysis: A Case Study to Analyze Traffic Streams at Urban Roundabouts. Procedia computer science, 130, 636-643.
8. Klosterman, S., Melaas, E., Wang, J., Martinez, A., Frederick, S., O’Keefe, J., ... & Friedl, M. (2018). Fine-scale perspectives on landscape phenology from unmanned aerial vehicle (UAV) photography. Agricultural and Forest Meteorology, 248, 397-407.
9. Li, Y., Lu, H., Nakayama, Y., Kim, H., & Serikawa, S. (2018). Automatic road detection system for an air–land amphibious car drone. Future Generation Computer Systems, 85, 51-59.
10. Matolak, D. W. (2015, February). Unmanned aerial vehicles: Communications challenges and future aerial networking. In Computing, Networking and Communications (ICNC), 2015 International Conference on (pp. 567-572). IEEE.
11. Moskowitz, E. E., Siegel-Richman, Y. M., Hertner, G., & Schroeppel, T. (2018). Aerial drone misadventure: A novel case of trauma resulting in ocular globe rupture. American journal of ophthalmology case reports, 10, 35-37.
12. N Aswini, E Krishna Kumar, S V Uma, "UAV and obstacle sensing techniques - a perspective", International Journal of Intelligent Unmanned Systems, https://doi.org/10.1108/IJIUS-11-2017-0013
13. Nakamura, H., & Kajikawa, Y. (2017). Regulation and innovation: How should small unmanned aerial vehicles be regulated? Technological Forecasting and Social Change.
14. Peter Tatham, Frank Stadler, Abigail Murray, Ramon Z. Shaban, "Flying maggots: a smart logistic solution to anenduring medical challenge", Journal of Humanitarian Logistics and Supply Chain Management, https://doi.org/10.1108/JHLSCM-02-2017-0003
15. Rabta, B., Wankmüller, C., & Reiner, G. (2018). A drone fleet model for last-mile distribution in disaster relief operations. International Journal of Disaster Risk Reduction, 28, 107-112.
16. Rao, B., Gopi, A. G., & Maione, R. (2016). The societal impact of commercial drones. Technology in Society, 45, 83-90.
17. Seguin, C., Blaquière, G., Loundou, A., Michelet, P., & Markarian, T. (2018). Unmanned aerial vehicles (drones) to prevent drowning. Resuscitation, 127, 63-67.
18. Vacca, A., & Onishi, H. (2017). Drones: military weapons, surveillance or mapping tools for environmental monitoring? The need for legal framework is required. Transportation Research Procedia, 25, 51-62.
19. Woo, T. H. (2018). Anti-nuclear terrorism modeling using a flying robot as drone’s behaviors by global positioning system (GPS), detector, and camera. Annals of Nuclear Energy, 118, 392-399. https://doi.org/10.1016/j.anucene.2018.04.035
20. Yanmaz, E., Yahyanejad, S., Rinner, B., Hellwagner, H., & Bettstetter, C. (2018). Drone networks: Communications, coordination, and sensing. Ad Hoc Networks, 68, 1-15. https://doi.org/10.1016/j.adhoc.2017.09.001
21. Jeremy Tucker, 2017, A Role for Drones in Healthcare, Blog https://www.dronesinhealthcare.com/ (Visited on 25th May 2018)
22. Business Insider, Article, August 11, 2017, Drones Will Change Our World In The Next 5 Years. Here’s How., https://explorist.futurism.com/drones-will-change-our-world-in-the-next-5-years-heres-how/ (Visited on 25th May 2018).

201-205

36.

Authors:

Paper Title:

Abstract: The signal processing is widely used tool in biomedical field for extracting the information of physiological activities for diagnosis purpose. Aim of this paper is to give an overview on various transforms used for biomedical signal analysis, Fast Fourier Transform (FFT), Laplace Transform (LT), Hilbert Transform, Wavelet Transform (WT) and Hadamard Transform are discussed for ECG and EEG. The finally some advanced algorithms and methods for automatic detection of abnormalities in cardiovascular system and neuroscience have been considered in this study. Wavelet transform gives highest accuracy in feature identification of both ECG and EEG. The variety of transform techniques are explored in this study and found that wavelet transform is very good tool for both stationary (ST) and non-stationary(non-ST) biomedical signal analysis. The CWT and DWT are suitable for ECG and EEG signal analysis respectively

Keywords: Biomedical signals, ECG, EEG, Wavelet Transform, Hilbert Transform, Hadamard Transform.

References:

1. Rangayyan RM. Biomedical signal analysis: a case study approach. New York: Wiley-IEEE Press; (2001).
2. Kacar S, Sakoglu Ü. Design of a novel biomedical signal processing and analysis tool or functional neuroimaging. Comput Methods Programs Biomed (2016):46–57.
3. Jasjeet Kaur, Amanpreet Kaur. A Review on Analysis of EEG Signals, International Conference on Advances in Computer Engineering and Applications (ICACEA). (2015), pp 957-960.
4. Raez MBI, Hussain MS, Mohd-Yasin F. Techniques of EMG signal analysis: detection, processing, classification and applications. Biological Procedures Online. (2006);8:11-35. doi:10.1251/bpo115.
5. K. Islam, A. M. Haque, G. Tangim, et al. Study and Analysis of ECG Signal Using MATLAB & LABVIEW as Effective Tools. International Journal of Computer and Electrical Engineering, Vol. 4, No. 3, June 2012, pp 404-408.
6. Gacek A. An Introduction to ECG Signal Processing and Analysis. In: Gacek A., Pedrycz W. (eds) ECG Signal Processing, Classification and Interpretation. Springer, London, (2012). https://doi.org/10.1007/978-0-85729-868-3_2.
7. Goutham, Swapna & Ghista, Dhanjoo & Martis, Roshan & Peng Chuan Alvin, et. al. ECG signal generation and heart rate variability signal extraction: Signal processing, features detection, and their correlation with cardiac diseases. Journal of Mechanics in Medicine and Biology. 12(4), (2012): 1240012. DOI: 10.1142/S021951941240012X.
8. Lopes da Silva (2012). EEG: Origin and Measurement. DOI: 10.1007/978-3-540-87919-0_2.
9. R. Acharya et al. Automated EEG analysis of epilepsy: A review. Knowledge-Based Systems 45 (2013) pp. 147–165. DOI: http://dx.doi.org/10.1016/j.knosys.2013.02.014.
10. Himanshu G., Silky K., Rajesh K. (2011) Cardiac arrhythmias detection in an ECG beat signal using fast fourier transform and artificial neural network. J. Biomedical Science and Engineering, 4, (2011) pp. 289-296.
11. Inderbir K., Rajni R., Anupma M., (2016) ECG Signal Analysis and Arrhythmia Detection using Wavelet Transform. Journal of the Institute of Engineering (India), 97(4), (2016) pp. 499–507: DOI 10.1007/s40031-016-0247-3.
12. Llamedo, J.P. Martinez, Heartbeat classification using feature selection driven by database generalization criteria. IEEE Trans. Biomed. Eng. 58(3), (2011) pp. 616–625.
13. Sung-Nien, C. Ying-Hsiang, Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network, Elsevier. Pattern Recognit. Lett. 28(10), (2007) pp. 1142–1150.
14. Rajni, I. Kaur. Electrocardiogram signal analysis-an overview. Int. J. Comput. Appl. 84(7), (2013) pp. 22–25.
15. Ana M., Leif S., Pablo L. Detection of body position changes from the ECG using a Laplacian noise model. Biomedical Signal Processing and Control 14 (2014) 189–196.
16. Jager, R.G. Mark, G.B. Moody, S. Divjak. Analysis of transient ST segment changes during ambulatory monitoring using the Karhunen–Loève transform,in: Proceeding of Computers in Cardiology, IEEE Computer Society Press, (1992),pp. 691–694.
17. J. Bolton, L.C. Westphal. Hilbert transform processing of ECG’s, 1981 IREECON International Convention Digest, IREE, Melbourne, (1981), pp. 281–283.
18. S. Manikandan, Soman, . A novel method for detecting R-peaks in electrocardiogram (ECG) signal, Biomedical Signal Processing and Control 7 (2012) pp.118– 128.
19. Kora et al., ECG based Atrial Fibrillation detection using Sequency Ordered Complex Hadamard Transform and Hybrid Firefly Algorithm, Eng. Sci. Tech., Int. J. (2017), DOI: http://dx.doi.org/10.1016/j.jestch.2017.02.002.
20. M . akin,. Comparison of Wavelet Transform and FFT Methods in the Analysis of EEG Signals, Journal of Medical Systems, Vol. 26, No. 3, ( 2002) pp.241-247.
21. K. Kiymik, I.Guler, et al. Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application, Computers in Biology and Medicine 35 (2005) 603–616.
22. Qin Qin, Jianqing Li, Yinggao Yue, and Chengyu Liu. An Adaptive and Time-Efficient ECG R-Peak Detection Algorithm, Journal of Healthcare Engineering, Volume 2017, Article ID 5980541, 14 pages. DOI: https://doi.org/10.1155/2017/5980541.
23. Rajendra Acharya, Hamido Fujita, Oh Shu Lih, Yuki Hagiwara, Jen Hong Tan, Muhammad Adam. Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network. Information Sciences 405 (2017) 81–90.
24. Ali Yener Mutlu. Detection of epileptic dysfunctions in EEG signals using Hilbert vibration decomposition. Biomedical Signal Processing and Control 40 (2018) 33–40.
25. Nagai, D. Anzai, et al. Motion artefact removals for wearable ECG using stationary wavelet transform. Healthcare Technology Letters, Vol. 4, Iss. 4, (2017) pp. 138–141. DOI: 10.1049/htl.2016.0100.
26. Jihong Yan, LeiLu. Improved Hilbert–Huang transform based weak signal detection methodology and its application on incipient fault diagnosis and ECG signal analysis. Signal Processing 98(2014) pp. 74–87.
27. Nang Anija Manlong, Jagdeep Rahul, Marpe Sora "ST Segment Analysis for Early Detection of Myocardial Infarction." International Journal of Computer Sciences and Engineering 6.6 (2018): 1500-1504.
28. CM Khamhoo, J Rahul, M Sora.” Algorithm for QRS Complex Detection using Discrete Wavelet Transformed” International Journal of Electronics Engineering. Volume 10, Issue 2 (2018).pp. 352-357.

206-209

37.

Authors:

Paper Title:

Abstract: Data mining is the progression of finding the hidden information from the dataset available. Data mining started stepping and playing a major role in the medical field and it helps the medical practitioner to take a decision. Classification is considered as the major research issues in data mining. Classification is done based on the characteristics or features available. Currently, coronary artery disease (CAD) is getting evolve in South Asia countries, which is becoming a major cause for death. Data mining algorithms are being used to diagnosis of diseases, mostly in CAD. Currently available algorithms gets lack in classification towards accuracy. In this paper, a novel classification algorithm is proposed to effectively classify towards the prediction of CAD, namely ant colony optimization based support vector machine. It is designed to classify and predict CAD more accurately. The proposed algorithm classify the records in a dataset in a random manner instead of sequence manner. A threshold value is used for classification for more accurate results. The proposed algorithm is tested on Z-AlizadehSani dataset for the classification of heart disease among the patients. This research work uses the benchmark performance metrics namely sensitivity, specificity, and classification accuracy. The result shows that ACO-SVM is giving better results than SMO, BSMO, Bagging and NN algorithms.

Keywords: Ant Colony, Classification, Heart Disease, Prediction, SVM.

References:

1. D. Dolatabadi, S. E. Z. Khadem, B. M Asl, "Automated Diagnosis of Coronary Artery Disease (Cad) Patients using Optimized SVM", Computer Methods and Programs in Biomedicine, Vol 138, PP. 117-126, 2017.
2. A. Ddehsani, M. H. Zangooei, M. J. Hosseini, J. Habibi, A. Khosravi, M. Roshanzamir, F. Khozeimeh, N. Sarrafzadegan, S. Nahavandi, "Coronary Artery Disease Detection using Computational Intelligence Methods", Knowledge-Based Systems, Volume 109, pp. 187-197, 2016.
3. E. Shao, C. D. Hou, C. C. Chiu, "Hybrid Intelligent Modeling Schemes for Heart Disease Classification", Applied Soft Computing, Volume 14, Part A, pp. 47-52, 2014.
4. Zhang, J. Dong, X. Luo, K. S. Choi, X. Wu, "Heartbeat Classification using Disease-Specific Feature Selection", Computers in Biology and Medicine, Volume 46, pp. 79-89, 2014.
5. A. Jabbar, B. L. Deekshatulu, P. Chandra, "Classification of Heart Disease Using K- Nearest Neighbor and Genetic Algorithm", Procedia Technology, Volume 10, pp. 85-94, 2013.
6. Dewan and M. Sharma, "Prediction of Heart Disease Using a Hybrid Technique in Data Mining Classification", 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, pp. 704-706, 2015.
7. A. Jabbar and S. Samreen, "Heart Disease Prediction System Based on Hidden Naïve Bayes Classifier", 2016 International Conference on Circuits, Controls, Communications and Computing (I4C), Bangalore, pp. 1-5, 2016.
8. Purushottam, K. Saxena and R. Sharma, "Efficient Heart Disease Prediction System using Decision Tree", International Conference On Computing, Communication & Automation, Noida, 72-77, 2015.
9. H. Chen, S. Y. Huang, P. S. Hong, C. H. Cheng and E. J. Lin, "HDPS: Heart Disease Prediction System", 2011 Computing in Cardiology, Hangzhou, pp. 557-560, 2011.
10. Pecchia, P. Melillo, M. Sansone and M. Bracale, "Discrimination Power of Short-Term Heart Rate Variability Measures for Chf Assessment", in IEEE Transactions on Information Technology in Biomedicine, vol. 15, no. 1, pp. 40-46, Jan. 2011.
11. Melillo, N. De Luca, M. Bracale and L. Pecchia, "Classification Tree for Risk Assessment in Patients Suffering from Congestive Heart Failure Via Long-Term Heart Rate Variability", in IEEE Journal of Biomedical and Health Informatics, vol. 17, no. 3, pp. 727-733,  May 2013.
12. A. Rahman, L. G. Tereshchenko, M. Kongkatong, T. Abraham, M. R. Abraham and H. Shatkay, "Utilizing Ecg-Based Heartbeat Classification for Hypertrophic Cardiomyopathy Identification", in IEEE Transactions on NanoBioscience, vol. 14, no. 5, pp. 505-512, July 2015.
13. Santhanam T., Ephzibah E.P. "Heart Disease Classification using PCA and Feed Forward Neural Networks". In: Prasath R., Kathirvalavakumar T. (eds) Mining Intelligence and Knowledge Exploration. Lecture Notes in Computer Science, vol 8284, pp 90-99, Springer, 2013.
14. Piao M., Piao Y., Shon H.S., Bae JW., Ryu K.H. (2012) "Evolutional Diagnostic Rules Mining for Heart Disease Classification Using ECG Signal Data". In: Zeng D. (eds) Advances in Control and Communication. Lecture Notes in Electrical Engineering, Springer, Berlin, Heidelberg, vol 137. pp 673-680. 2012.
15. Bashir, S., Qamar, U. & Khan, BagMOOV: "A Novel Ensemble for Heart Disease Prediction Bootstrap Aggregation with Multi-Objective Optimized Voting", Australasian Physical & Engineering Sciences in Medicine, Springer, Volume 38, Issue 2, pp 305–323, June 2015.
16. Jabbar M.A., Deekshatulu B.L., Chandra P, "Prediction of Heart Disease Using Random Forest and Feature Subset Selection". In: Snášel V., Abraham A., Krömer P., Pant M., Muda A. (eds) Innovations in Bio-Inspired Computing and Applications. Advances in Intelligent Systems and Computing, Springer, Cham, vol 424, pp. 187-196, 2016.
17. Alizadehsani, J. Habibi, M. J. Hosseini, H. Mashayekhi, R. Boghrati, A. Ghandeharioun, B. Bahadorian, Z. A. Sani, "A Data Mining Approach for Diagnosis of Coronary Artery Disease", Computer Methods and Programs in Biomedicine, Volume 111, Issue 1, pp. 52-61, 2013.
18. Ahmed M. A. Elmoniem, H. Ibrahim, M. H. Mohamed, and Abdel-RahmanHedar, "Ant Colony and Load Balancing Optimizations for AODV Routing Protocol", International Journal of Sensor Networks and Data Communications, Vol. 1, Article ID X110203, pp.14, 2012.
19. Caruana, A. Niculescu-Mizil, "An Empirical Comparison of Supervised Learning Algorithms", in: Proceedings of the 23rd International Conference on Machine Learning, pp. 161–168, 2006.
20. Kong, J. Gao, Y. Xu, Y. Pan, J. Wang, J. Liu, "Classification of Autism Spectrum Disorder by Combining Brain Connectivity and Deep Neural Network Classifier, Neurocomputing, Volume 324, pp. 63-68, 2019.
21. Breiman, "Bagging Predictors", Machine Learning 24, pp. 123–140, 1996.
22. C. Platt, "Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines". Technical Report MSR-TR-98-14, Microsoft Research, 1998.

210-215

38.

Authors:

Paper Title:

Abstract: Video inpainting is considered as a complex problem in the current literature. This paper proposes a fast, efficient and automatic method of video inpainting to inpaint moving objects in the video. The presented algorithm employs the spatiotemporal coherency present in the video frames for inpainting while considering the fact that the background either has periodic motion or it remains stationary. The algorithm does not require any manual generation of the mask. Batch frame based inpainting is proposed to maintain motion information in case of background having periodic motion. A new dissimilarity measure; 3D N-SSD is introduced to find similar frames for frame-based video inpainting algorithms. The proposed algorithm is tested for different background and illumination conditions. We have done speed and quality test analysis by inpainting videos of different backgrounds. Quick execution times and high PSNR values for inpainted videos show effectiveness of our algorithm.

Keywords: 3D N-SSD, inpainting mask, periodic background, reference frame, temporal data, video inpainting. 

References:

1. Bertalmio, A.L. Bertozzi, and G. Sapiro, “Navier-stokes, fluid dynamics, and image and video inpainting,” CVPR 2001: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Kauai, Hawaii, USA, vol.1, pp.355-362, December 2001.
2. Jia, W. Tai-Pang, Y.W. Tai, and C.K. Tang, “Video repairing: inference of foreground and background under severe occlusion,” CVPR 2004: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, DC, USA, vol.1, pp.364-371, June-July 2004.
3. A. Patwardhan, G. Sapiro, and M. Bertalmio, “Video inpainting of occluding and occluded objects,” ICIP 2005: IEEE International Conference on Image Processing, Genoa, Italy, pp.69-72, September 2005.
4. C.S. Cheung, J. Zhao, M.V. Venkatesh, “Efficient Object-Based Video Inpainting,” ICIP 2006: IEEE International Conference on Image Processing, Atlanta, Georgia, USA, pp.705-708, October 2006.
5. Ghanbari, and M. Soryani, “Contour-Based Video Inpainting,” MVIP 2011: 7th Iranian Conference on Machine Vision and Image Processing, Tehran, Iran, pp.1-5, November 2011.
6. Criminisi, P. Pérez, K. Toyama, “Region filling and object removal by exemplar-based image inpainting”, IEEE Transactions on Image Processing, vol. 13, no. 9, pp.1200-1212, 2004.
7. Wexler, E. Shechtman, and M. Irani, “Space-time completion of video,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, pp.463-476, 2007.
8. Ghorai, P. Purkait, and B. Chanda, “A fast video inpainting technique,” International Conference on Pattern Recognition and Machine Intelligence, Berlin, Heidelberg, pp.430-436, December 2013.
9. Y.T. Jia, S.M. Hu, and R.R. Martin, “Video completion using tracking and fragment merging” The Visual Computer, vol. 21, no. 8, pp.601-610, 2005.
10. J. Jia, Y.W. Tai, T.P. Wu, and C.K. Tang, “Video repairing under variable illumination using cyclic motions,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 5, pp.832-839, 2006.
11. C.H. Ling, C.W. Lin, C.W. Su, Y.S. Chen, and H.Y.M. Liao, “Virtual contour guided video object inpainting using posture mapping and retrieval,” IEEE Trans. Multimedia, vol. 13, no. 2, pp.292-302, 2011.
12. S. Grover, A. Mittal, S. Gupta, A.K. Sarje, and N. Kaushik, “A robust framework for automated digital video inpainting,” International Journal of Signal and Imaging Systems Engineering, vol.1, no. 3/4, pp.185-196, 2008.
13. Wu, and Q. Ruan, “Object removal by cross isophotes exemplar-based inpainting,” ICPR 2006: 18th IEEE International Conference on Pattern Recognition,  Hong Kong, China, vol. 3, pp.810-813, August 2006.
14. Goswami, and C. Paunwala, C. “Exemplar-based image inpainting using ISEF for priority computation,” CSCITA 2014: IEEE International Conference on Circuits, Systems, Communication and Information Technology Applications, Mumbai, India, pp.75-80, April 2014.
15. Bombaywala, P. Goswami, and C. Paunwala, “Comparative analysis of exemplar based image inpainting techniques,” ET2ECN 2014: 2nd IEEE International Conference on  Emerging Technology Trends in Electronics, Communication and Networking, Surat, India, pp.1-6, December 2014.
16. B. Desai, S.R. Bombaywala, and C.N. Paunwala, “Sparsity based image inpainting using optimisation techniques,” TENCON 2015 IEEE Region 10 Conference, Macau, SAR, pp.1-6, November 2015.
17. Newson, A. Almansa, M. Fradet, Y. Gousseau, and P. Pérez, “Video inpainting of complex scenes,” SIAM Journal on Imaging Sciences, vol.7, no.4, pp.1993-2019, 2014.

216-223

39.

Authors:

Paper Title:

Abstract: Data security is a very important factor in any form of digital communication. Steganography can be used to enhance the security of digital communication. There are various methods to perform steganography on digital images, but very few deal with increasing imperceptibility and data embedding capacity together. In this paper, a high data embedding capacity spatial domain image steganography scheme is proposed which is highly imperceptible. In the proposed technique steganography is modeled as a search and optimization problem and Genetic algorithm (GA) is used to solve this problem to find the near-optimal solution. Optimal pixel adjustment procedure (OPAP) is further used to improve the quality of stego-image. Experimental results exhibited that the proposed technique provides an improvement in imperceptibility of stego-image at high data embedding rate when compared to several other popular steganography techniques. The average PSNR value of various stego-images at two bit per pixel data embedding rate was 46.39.

Keywords: Genetic algorithm (GA); Image steganography; Information hiding; Spatial domain; Steganalysis

References:

1. Cheddad A, Condell J, Curran K &McKevitt P, “Digital image steganography: Survey and analysis of current methods”, Signal processing, Vol. 90, No. 3, (2010), pp. 727-752.
2. Subhedar MS &Mankar VH, “Current status and key issues in image steganography: A survey”, Computer science review, 13, (2014), pp. 95-113.
3. Al-Dmour H & Al-Ani A, “A steganography embedding method based on edge identification and XOR coding”, Expert systems with Applications, Vol. 46, (2016), pp. 293-306.
4. Ker AD, “Steganalysis of LSB matching in grayscale images”, IEEE signal processing letters, Vol. 12, No. 6, (2005), pp. 441-444.
5. Bedi P, Bansal P &Sehgal P, “Using PSO in a spatial domain based image hiding scheme with distortion tolerance”, Computers & Electrical Engineering, Vol. 39, No. 2, (2013), pp. 640-654.
6. Li B, He J, Huang J & Shi, YQ, “A survey on image steganography and steganalysis”, Journal of Information Hiding and Multimedia Signal Processing, 2, No. 2, (2011), pp. 142-172.
7. Kanan HR &Bahram N, “A novel image steganography scheme with high embedding capacity and tunable visual image quality based on a genetic algorithm”, Expert Systems with Applications, Vol. 41, No. 14, (2014), pp. 6123-6130.
8. Wang S, Yang B &Niu X., “A secure steganography method based on genetic algorithm” Journal of Information Hiding and Multimedia Signal Processing, Vol. 1, No. 1, (2010), pp. 28-35.
9. Maheswari SU &Hemanth DJ, “Performance enhanced image steganography systems using transforms and optimization techniques”, Multimedia Tools and Applications, Vol. 76, No. 1, (2017), pp. 415-436.
10. Fontaine C, “Linear congruential generator”, Encyclopedia of Cryptography and Security, Springer, Boston, MA, (2011), pp. 721-721.
11. Shah PD &Bichkar RS, “A Secure Spatial Domain Image Steganography Using Genetic Algorithm and Linear Congruential Generator”, International Conference on Intelligent Computing and Applications, Advances in Intelligent Systems and Computing, Springer, Singapore, (2018), pp. 119-129.
12. Chan C & Cheng LM, “Hiding data in images by simple LSB substitution”, Pattern recognition, Vol. 37, No. 3, (2004), pp. 469-474.
13. Lin C & Tsai W, “Secret image sharing with steganography and authentication”, Journal of Systems and software, Vol. 73, No. 3, (2004), pp. 405-414.
14. Yang C, Chen T, Yu KH & Wang C, “Improvements of image sharing with steganography and authentication”, Journal of Systems and software, Vol. 80, No. 7, (2007), pp.1070-1076.
15. Chang C, Hsieh Y & Lin C, “Sharing secrets in stego-images with authentication”, Pattern Recognition, Vol. 41, No. 10, (2008), pp. 3130-3137.
16. Wu C, Kao S & Hwang S, “A high quality image sharing with steganography and adaptive authentication scheme”, Journal of Systems and Software, Vol. 84, No. 12, (2011), pp. 2196-2207.

224-229

40.

Authors:

Paper Title:

Abstract: In this paper, an integrated buck-boost buck (IB3) AC-DC converter is proposed with an extended voltage conversion ratio for the class-C&D applications. The proposed converter process the power from input to output in a single stage. This converter is an integration of traditional buck-boost converter and a buck converter shared by a common switch. To get unity input power factor the input buck-boost converter is designed to operate always in discontinuous conduction mode (DCM). The output buck converter is operated in continuous conduction mode (CCM). The necessary design equations have derived using theoretical analysis under steady-state conditions. The features of IB3 converter are fast and tightly regulated dc voltage with extensive voltage gain, unity input power factor and well suit for class-C&D applications with universal voltage range. The proposed IB3 converter is simulated using MATLAB/SIMULINK software to support the theoretical analysis.

Keywords: ac-dc converter, buck-boost buck, integrated converter, power factor correction (PFC), extended voltage gain. 

References:

1. CK Tse, MHL Chow, MKH Cheung. (2001). A family of PFC voltage regulator configurations with reduced redundant power processing. IEEE Trans. Power Electron. 16(6): 794–802. https://doi.org/1109/63.974377.
2. F Zhang, J Ni, Y Yu. (2013). High power factor AC-DC LED driver with film capacitors. IEEE Trans. Power Electron. 28(10): 4831–4840. https://doi.org/1109/TPEL.2012.2233498.
3. M Arias, MF Diaz, DG Lamar, D Balocco, AA Diallo, J Sebast´ıan. (2013). High-efficiency asymmetrical half-bridge converter without electrolytic capacitor for low-output-voltage AC-DC LED drivers. IEEE Trans. Power Electron. 28(5): 2539–2550. https://doi.org/1109/TPEL.2012.2213613.
4. CH Chang, CA Cheng, EC Chang, HL Cheng, BE Yang. (2016). An integrated high-power-factor converter with ZVS transition. IEEE Trans. Power Electron. 31(3): 2362-2371. https://doi.org/1109/TPEL.2015.2439963.
5. DDC Lu, DK.W Cheng, YS Lee. (2003). Single-stage AC-DC power factor corrected voltage regulator with reduced intermediate bus voltage stress. Inst. Electr. Eng. Electr. Power Appl., 150(5): 506–514. https://doi.org/10.1049/ip-epa:20030487.
6. EH Ismail, AJ Sabzali, MA Al-Saffar. (2008). Buck–boost-type unity power factor rectifier with extended voltage conversion ratio. IEEE Trans. Ind. Electron., 55(3): 1123-1132. https://doi.org/1109/TIE.2007.909763.
7. Sri Sivani, L., Nagi Reddy, B., Subba Rao, K., Pandian, A. A new single switch AC/DC converter with extended voltage conversion ratio for SMPS applications (2019) International Journal of Innovative Technology and Exploring Engineering, 8 (3), pp. 68-72.
8. J Qian FC Lee. (1998). A high efficiency single-stage single-switch high power factor AC/DC converter with universal line input. IEEE Trans. Power Electron. 13(4): 699–705. https://doi.org/1109/63.704141.
9. W Qiu, W Wu, S Luo, W Gu, I Batarseh. (2002). A bi-flyback PFC converter with low intermediate bus voltage and tight output voltage regulation for universal input applications. IEEE Appl. Power Electron.:256–262. https://doi.org/10.1109/APEC.2002.989256.
10. L Petersen RW Erickson. (2003). Reduction of voltage stresses in buck boost- type power factor correctors operating in boundary conduction mode. IEEE Appl. Power Electron. Conf: 664–670. https://doi.org/10.1109/APEC.2003.1179285.
11. A Lázaro, A Barrado, M Sanz, V Salas, E. Olías. (2007). New power factor correction AC-DC converter with reduced storage capacitor voltage. IEEE Trans. Ind. Electron., 54(1): 384–397. https://doi.org/1109/TIE.2006.888795.
12. JY Lee. (2007). Single-stage AC/DC converter with input current dead-zone control for wide input voltage ranges. IEEE Trans. Ind. Electron. 54(2): 724–735. https://doi.org/1109/TIE.2007.891765.
13. RW Erickson D Maksimovic. (2001). Fundamentals of Power Electronics. 2nd ed. New York: Kluwer.
14. Nagi Reddy. B, O. Chandra Sekhar, M. Ramamoorty. (2019). Implementation of zero-current switch turn-ON based buck-boost buck type rectifier for low power applications. International Journal of electronics (Accepted for publication).

230-236

41.

Authors:

Paper Title:

Abstract: Indian education system is governed by Ministry of Human Resource Development (MHRD) at center and by various departments at the states. A significant amount of fund is allocated for usage of technology in education under key Government schemes. Schemes such as ICT @ Schools have potential for fostering academic growth and upgrading skills of students, which help immensely in their future employability. Several such schemes exist which pertain to technology in education and executed either directly by the state or through private entities. A policy for implementation of ICT was thought out and designed at national level. One of the states, Odisha has significant ICT-related interventions in education system through e Content delivery and ICT based teachers’ training and monitoring. The current research study evaluates the ICT policy and assesses its implementation at School level in terms of effectiveness towards mass education of the state of Odisha. It recommends suitable measures for improvement in monitoring, implementation based on outcomes of the survey.

Keywords: ICT, teachers’ training, e Content, policy, implementation. 

References:

1. National Policy on Information and Communication Technology (ICT) in School Education. (2012), pp. 1-2. India: MHRD, Government of India.
2. Government of Odisha, School & Mass Education Department, ICT Team, OKCL & OMSM. (2016). e Vidyalaya News Letter. I(I), pp. 2-11. Retrieved from evidyalaya.org.
3. (2003). Ministerial round table on “Towards Knowledge Societies". Paris.
4. Sharma Ravi S., G. Z. (2014). Does ICT Effectively Contribute to the Delivery of Mass Education in Developing Countries? IEEE, 375-380.
5. (2002). Information and Communication Technology in Education–A Curriculum for Schools and Program for Teacher Development. Paris.
6. National Policy on Education 1986, as modified in 1992. (1992). India: Government of India. Retrieved May 15, 2017.
7. Bhattacharya, I., & Sharma, K. (2007). India in the knowledge economy – an electronic paradigm. International Journal of Educational Management, 21(6), 543-568.
8. (Released on 2018). Annual Status Education Report (ASER)-2017. PRATHAM. New Delhi: ASER Centre. Retrieved from www.asercentre.org
9. (2015). Recent statistics on ICT in education. Institute for Statistics, Moscow, Russian Federation. Retrieved June 4, 2017, from www.uis.unesco.org.
10. School & Mass Education Department, Government of Odisha. (2016). Odisha Primary Education Program Authority. Retrieved 2018, from http://opepa.odisha.gov.in:http://opepa.odisha.gov.in/website/DISE.aspx.

237-242

42.

Authors:

Paper Title:

Abstract: Conventional Local Binary Pattern (LBP) methods follow the patterns whose rotations are lesser than two or certain limited numbers are called rotation invariant binary patterns. In the conventional rotational-invariant encoding method has disadvantage due to neglecting information of the some patterns by its process of encoding. It ignores the patterns when their spatial transition is greater than two for maintaining the rotation-invariant nature. But these disregarded patterns will plays crucial role and have very much more discriminative power. Here, the present study proposing a novel model called OLBP by changing (sorting) the order of consecutive binary patterns without disturbing the property of rotational invariance. The result observed by experiments indicates the proposed work shows better classification rate which is worked on the standard databases when compared to previous existing methods.

Keywords: Texture; Neighborhood pixel; Local Binary Pattern (LBP); Histogram; Rotational Invariance; Classification;

References:

1. Ahonen, et al “Face recognition with Local Binary Patterns: application to face recognition,” IEEE Transactions On Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 2037-2041,2006.
2. M. Haralik, K. Shanmugam, and Dinstein, “Texture features for image classification,” IEEE Trans. on Systems, Man, and Cybertics, vol. 3,no. 6, pp. 610-621, 1973.
3. Tuceryan, et al , Texture analysis. C.H. Chen, et al “The handbook of pattern recognition and computer vision”, pp. 207–248:World Scientific Publishing Company., Singapore (1998).
4. Pietikainen et al, Computer Vision Using LBP, Computational Imaging and Vision 40,Ch 2, L B P for Still Images,PP13-47.
5. Nanni, et al : LBPatterns variants as texture descriptors for medical image analysis. AI Med. 49, 117–125 (2010).
6. Petpon, A., Srisuk, S.: Face recognition with LBP. In: Proceedings of International Conference on Image and Graphics, pp. 533–539 (2009).
7. Wolf, L.,et al.: Descriptor based methods in the wild. In: Proc. ECCV Workshop on Faces in Real-Life Images, pp. 1–14 (2008).
8. Hafiane, A., et al.: Median binary pattern for texture classification. In: Proc. International Conference on Image Analysis and Recognition, pp. 387–398 (2007).
9. Jin, H., et al: Face detection using improved LBP under Bayesian framework. In: Proceedings of International Conf on Image and Graphics, pp. 306–309 (2004).
10. Tan, X.and Triggs, B.: Enhanced local texture feature sets for face recog under difficult lighting conditions. In: Analysis and Modeling of Faces and Gestures. Notes in Computer Science, vol. 4778, pp. 168–182. Springer, Berlin (2007).
11. Nanni et al, A.: A local approach based on a local binary patterns variant texture descriptor. Expert Systems Applications. 37, 7888–7894 (2010).
12. Liao, S.,et al.: Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes. In: Proceedings of. IEEE Conf., on CV and PR, p. 8 (2010).
13. Trefny, J.,and Matas, J.: Extended set of local binary patterns for rapid object detection. In: Proc.Computer Vision Winter Workshop, pp. 1–7 (2010).
14. Liao, S., et al.: Learning multi-scale BLBP for face recognition. In: Proceedings of . Int., Conf on Biometrics, pp. 828–837 (2007)
15. Guo, Z.H.,et al : Rotation invariant texture classification using adaptive LBP with directional statistical features. In: Proc. Inter., Conf., on Image Processing, pp. 285–288 (2010).
16. Guo,ZH et al.: Rotation invariant texture classification using LBP variance (LBPV) with global matching. Pattern Recognition. 43(3), 706–719 (2010)
17. Anil kumar, Dr.U.Ravi Babu Leaf Classification Using Completed Local Binary Pattern Of Textures; 2017 IEEE 7th International Advance Computing Conference, 6th, 7th January 2017,PP 871-874.
18. Anil kumar, Dr.U.Ravi Babu, ”An efficient Leaf(Texture) Classification using Local Binary Pattern with Noise Correction” Journal of Engineering and Applied ciences12(21),2017,pp 5478-5484,Medwell Journals,2017.
19. Jongbin Ryu,et al Member, IEEE”SCLBP for Texture Classification” IEEE trans., on image processing, vol. 24, no. 7, july 2015, pp2254-2265.

243-247

43.

Authors:

Paper Title:

Abstract: Conventional reliability of a system consider the binary state assumption of the probability theory, i.e., either success or failed. But this assumption is unrealistic for large complex systems like harvesting systems due to lack of sufficient probabilistic information. The uncertainty of each individual component enhances the uncertainty of the whole system. In this paper, the concept of fuzzy reliability has been used for the analysis of fuzzy availability of a harvesting system. The effect of coverage factor, failure and repair rate of subsystems on system fuzzy availability has been analysed. Mathematical formulation of problem with help of mnemonic rule and Chapman-Kolmogorov differential equations has been done. The governing differential equations are solved by Runge–Kutta method of order four using MATLAB (Ode 45 function).

Keywords: Harvesting System, Markov Process, Fuzzy Availability, Runge-Kutta Method.

References:

1. Aggarwal, A. K., Kumar, S., & Singh, V. (2016). Mathematical modeling and fuzzy availability analysis for serial processes in the crystallization system of a harvesting system.Journal of Industrial Engineering International, 1-12.
2. Aggarwal, A. K., Singh, V., & Kumar, S. (2014). Availability analysis and performance optimization of a butter oil production system: a case study. International Journal of System Assurance Engineering and Management, 8(1), 538-554.
3. Cai, K. Y., Wen, C. Y., & Zhang, M. L. (1993). Fuzzy states as a basis for a theory of fuzzy reliability. Microelectronics Reliability, 33(15), 2253-2263.
4. Chongshan, G. (2009, November). Fuzzy availability analysis of a repairable consecutive-2-out-of-3: F System. In Grey Systems and Intelligent Services, 2009. GSIS 2009. IEEE International Conference on(pp. 434-437). IEEE.
5. Cai, K. Y. (1996). Introduction to Fuzzy Reliability. Kluwer Academic Publishers.Norwell, MA, USA.
6. Kumar, D., Singh, J., & Pandey, P. C. (1992). Availability of the crystallization system in the sugar industry under common-cause failure. IEEE Transactions on Reliability, 41(1), 85-91.
7. Kumar, K., & Kumar, P. (2011). Fuzzy availability modeling and analysis of biscuit manufacturing plant: a case study.International Journal of System Assurance Engineering and Management, 2(3), 193-204.
8. Cai-Yuan, C., &Chuan-Yuan, W. (1990). Street-lighting lamps replacement: a fuzzy viewpoint. Fuzzy Sets and Systems, 37(2), 161-172.
9. Cai-Yuan, C., Chuan-Yuan, W., & Ming-Lian, Z. (1991). Fuzzy variables as a basis for a theory of fuzzy reliability in the possibility context. Fuzzy sets and systems, 42(2), 145-172.
10. Kumar, K., Singh, J., & Kumar, P. (2009). Fuzzy reliability and fuzzy availability of the serial process in butter-oil processing plant. Journal of Mathematics and Statistics, 5(1), 65-71.
11. Singer, D. (1990). A fuzzy set approach to fault tree and reliability analysis.Fuzzy sets and systems, 34(2), 145-155.
12. Zadeh, L. A. (1965). Fuzzy sets.Information and control, 8(3), 338-353.
13. Kumar, A. and Saini. M,(2017), “Mathematical modeling of sugar plant: a fuzzy approach”Life Cycle Reliability and Safety Engineering.https://doi.org/10.1007/s41872-017-0038-0,

248-254

44.

Authors:

Paper Title:

Abstract: This paper reports delay cell for Voltage Controlled Oscillator. The new circuit is designed and simulated in UMC_18_CMOS, 180nm process with 1.8V supply using Cadence tool. Main focus of this design is to achieve low phase noise and less power consumption. Proposed design is 4 stages differential ring VCO. The simulation results are presented with frequency range 2.3 to 4.7 GHz and Power consumption is 7.704 mW at maximum oscillation frequency with phase noise of -91dBc/Hz at offset of 1MHz and -120 dBc/Hz at offset of 10MHz. These results are back annotated to the model and accurate model in verilog-A has been presented. 

Keywords: Cadence, Delay cell, Differential ring, Phase noise, Voltage controlled oscillator.

References:

1. C. Weltin, E. Familier, and I. Galton “A Liberalized Model for the Design of Fractional Digital PLLs Based on Dual-Mode Ring Oscillator FDCs” IEEE Transaction on Circuits and Systems-I, Vol. 62, No. 8, pp 2013-2023,August15
2. Miletic and R. Mason “Bandwidth Expansion in Sigma-Delta PLLs Using Multiphase VCOs” Canadian Conference on Electrical and Computer Engineering, 2006.
3. B. De Muer and M. S. J. Steyaert, "A CMOS monolithic sigma-delta-controlled fractional-N frequency synthesizer for DCS-1800," IEEE J. Solid State Circuits, vol. 37, no. 7, pp. 835-844, Jul. 2002
4. C Y Chen, J J Ho, W R Liou, R. Y. Hsiao “A 5.2GHz CMOS fractional-n frequency synthesizer with a MASH delta-sigma modulator” Symposium on Circuits and Systems, 2008. .
5. Miletic and R. Mason “Quantization noise reduction using multiphase PLLs” IEEE International Symposium on Circuits and Systems ISCAS, May 2006
6. Verma, Virendra; Misra, D. K. ; Gamad, R. S. “Comparative analysis of frequency sysnthesizers” International J. of Electronics and Communication Engineering .Vol. 2, Issue 4, Sep 2013, pp95-104
7. Hao Wei ; Wang Pinglian ; Zhao Hui “The design of an efficient satellite IF PLL PM demodulator with low complexity” International Conference on Computational Electromagnetic and Its Applications, Proceedings, pp: 316 – 319, 2004
8. C. Chung and C. Lee, “An all-digital phase-locked loop for high-speed clock generation,” IEEE Journal of Solid-State Circuits, vol. 38, pp. 347–351, Feb. 2003.
9. K. H. Cheng,S. Kuo, and C.M. Tu, “A Low Noise 2.0 GHz CMOS VCO Design” in Proc. IEEE Midwest Symposium on Circuit and Systems, pp. 205-208, Dec03
10. D. B. Leeson, “A simple model of feedback oscillator noise spectrum,” proc. IEEE, Vo. 54, pp.329-330, Feb 1966.
11. A.Tsitouras, F. Plessas, “Ultrawideband, low-power, 3–5.6GHz,CMOS voltage controlled oscillator” Microelectronics Journal vol. 40, 2009, pp897–904
12. J. Jalil, M. B. I. Reaz, M. A. S. Bhuiyan, L. F. Rahman and T. G. Chang; “Designing a Ring-VCO for RFID Transponders in 0.18 µm CMOS Process” Hindawi Publishing Corporation, the Scientific World Journal, Volume 14, Article ID 580385, 2014.

255-259

45.

Authors:

Paper Title:

Abstract: Cloud computing proposes on-request arrange admittance to the calculating resources over virtualization. This changes in perspective the PC resources to the cloud giving price adequacy and it additionally gives versatile users openness to working resources. This proposal is execution prototypes of these frameworks with acceptance of entry of jobs to the framework and a work may comprise of numerous no.of jobs with every job needs a virtual machine for its implementation. This Paper consider both steady and variable task sizes in no.of jobs amid their administration times. On account of steady job estimate, this paperpermit distinctive classes of jobs, which are resolved over their entry and administration rates and no.of works in a job. In the multiple kind a job creates arbitrarily novel tasks amid its administration time. The last requires dynamic task of virtual machines to a work, which will be required in the versatile cloud. In the two cases, framework is displayed utilizing birth-demise forms. On account of consistent job measure, here decided joint likelihood dispersion of the quantity of works from every class in the framework, work delaying likelihoods and appropriation of the usage of resources for together heterogeneous and homogeneous kinds of virtual machines. Paper displayed mathematical results and any estimates are confirmed by usage result.

Keywords: Cloud Computing, CRM, Multimedia, Virtual Machine

References:

1. Vinothina, Dr.R.Sridaran, Dr.PadmavathiGanapathi, A Survey on Resource Allocation Strategies in Cloud Computing, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 3, No.6, 2012.
2. Jiayin Li, MeikangQiu, Jian-Wei Niu, Yu Chen, Zhong Ming.(2011), Adaptive Resource Allocation for Preemp table Jobs in Cloud Systems,in 10th International Conference on Intelligent System Design and Application, Jan. , pp. 31-36.
3. Mohiuddin Ahmed, Abu Sina Md. Raju Chowdhury, Mustaq Ahmed, Md. Mahmudul Hasan Rafee, An Advanced Survey on Cloud Computing and Stateof-the-art Research Issues, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 1, No 1, January 2012 ISSN .
4. Ronak Patel, Sanjay Patel, Survey on Resource Allocation Strategies in Cloud Computing, International Journal of Engineering Research & Technology (IJERT) Vol. 2 Issue 2, February- 2013 ISSN.
5. Dorian Minarolli and Bernd Freisleben, Uitlity based Resource Allocations for virtual machines in cloud computing(IEEE,2011).
6. Xindong YOU, Xianghua XU, Jian Wan, DongjinYU:RAS-M ,Resource Allocation Strategy    based       on            Market     Mechanism              in         Cloud Computing(IEEE,2009).
7. Zhen Kong et.al : Mechanism Design for Stochastic Virtual Resource Allocation in Non-Cooperative Cloud Systems: 2011 IEEE 4th International Conference on Cloud Computing :pp,614-621.
8. Goudarzi H., PedramM.(2011),Multi-dimensional SLA-based Resource Allocation for Multi-tier Cloud Computing Systems,in IEEE International Conference on Cloud Computing, Sep. ,pp. 324-331.
9. Qi Zhang,Lu Cheng, Raouf Boutaba."Cloud Computing: State-ofthe-art and research challenges". The Brazilian Computer Society 2010.
10. Atsuo Inomata, TaikiMorikawa, Minoru Ikebe, Sk.Md. MizanurRahman: Proposal and Evaluation of Dynamin Resource Allocation Method Based on the Load Of VMs on IaaS(IEEE,2010),978-1-4244-8704-2/11.
11. AndrzejKochutet al. : Desktop Workload Study with Implications for Desktop Cloud Resource Optimization,978-1-4244-6534-7/10 2010 IEEE.
12. Tram Truong Huu& John Montagnat: Virtual Resource Allocations distribution on a cloud infrastructure (IEEE, 2010), pp.612-617.
13. Mell, T. Grance.(2011), The NIST Definition of Cloud Computing, NIST Special Publication 800-145, Department of Commerce, USA, pages: 2-3 .
14. A. Vouk.(2008), Cloud Computing - Issues, Research and Implementations, Journal of Computing and Information Technology - CIT 16(4),)pages: 235-236 .
15. Tram Truong Huu& John Montagnat.(2010), Virtual Resource Allocations distribution on a cloud infrastructure ,IEEE,pp.612-617.

260-262

46.

Authors:

Paper Title:

Abstract: The changes that the Quantum Cellular Automata devices have faced have been evolutionary but still the most advanced chips continue using the same computing paradigm as their old predecessors. There are a lot of expectations that new paradigms will be developed for the processing of information. The CMOS technology uses current switching but the QCA deals with encoding the binary information as it is the array of cells and every cell has quantum dots and its fast-dimensional scaling will in the end be expected to reach the fundamental limit. The interaction of the quantum mechanics with the coulomb in every cell, is the one that determines the cell state. Additionally, the molecular QCA has the ability of operating at a speed of Terahertz (THz) together with a very less power and extremely high gadget density.

Keywords: Quantum-Dot Cellular Automata, Clocking, Terahertz.

References:

1. Singhal, R. (2000). Logic Realization Using Regular Structures in Quantum-Dot Cellular Automata (QCA). doi:10.15760/etd.196.
2. VilelaNeto, O. P., Pacheco, M. A., Barbosa, C. R., &Masiero, L. P. (2016). Simulador de Quantum-Dot Cellular Automata (QCA) UtilizandoRedes de Hopfield. Anais do 7. CongressoBrasileiro de RedesNeurais. doi:10.21528/cbrn2005-090.
3. Tokunaga, K. (2011). Quantum-Chemical Design of Molecular Quantum-Dot Cellular Automata (QCA): A New Approach from Frontier Molecular Orbitals. Cellular Automata - Innovative Modelling for Science and Engineering. doi:10.5772/15697.
4. Mehta, U., & Dhare, V. (2017). Quantum-dot Cellular Automata (QCA): A Survey. arXiv preprint arXiv:1711.08153.
5. A Vyas, K., & Jamnani, J. G. (2012). Development of IEEE Complaint Software ‘Economical Substation Grounding System Designer’ Using MATLAB GUI Development Environment. International Journal on Electrical Engineering and Informatics, 4(2), 335-346. doi:10.15676/ijeei.2012.4.2.11.
6. Mustafa, M., &Beigh, M. R. (2013). Design and implementation of quantum cellular automata based novel parity generator and checker circuits with minimum complexity and cell count.
7. Pulimeno, A., Graziano, M., Abrardi, C., Demarchi, D., & Piccinini, G. (2011). Molecular QCA: A write-in system based on electric fields. The 4th IEEE International NanoElectronics Conference. doi:10.1109/inec.2011.5991702.
8. Laajimi, R. (2018). Nanoarchitecture of Quantum-Dot Cellular Automata (QCA) Using Small Area for Digital Circuits. Advanced Electronic Circuits - Principles, Architectures and Applications on Emerging Technologies. doi:10.5772/intechopen.72691.
9. Shin, S., & Jeon, J. (2014). Design of Programmable Quantum-Dot Cell Structure Using QCA Clocking Based D Flip-Flop. Journal of the Korea Industrial Information Systems Research, 19(6), 33-41. doi:10.9723/jksiis.2014.19.6.033.
10. Minsu, C., &Nohpill, P. (2005, July). Locally synchronous, globally asynchronous design for quantum-dot cellular automata (LSGA QCA). In Nanotechnology, 2005. 5th IEEE Conference on (pp. 121-124). IEEE.
11. Panyakeow, S. (2011). Quadra-Quantum Dots and Related Patterns of Quantum Dot Molecules: Basic Nanostructures for Quantum Dot Cellular Automata Application. Cellular Automata - Innovative Modelling for Science and Engineering. doi:10.5772/15990.
12. Vicky S. Kalogeiton, Dim P. Papadopoulos, Orestis Liolis, Vassilios A. Mardiris, Georgios Ch. Sirakoulis, and Ioannis G. Karafyllidis, “Programmable Crossbar Quantum-dot CellularAutomata Circuits”, arXiv:1604.07803v1[CS.ET], 26 Apr 2016.

263-265

47.

Authors:

Paper Title:

Abstract: This paper presents the design and implementation of vehicle’s over speed warning system. Road accidents are very common in the present world with prime cause being the careless driving. With the advancement in the technology, different governing bodies are demanding some sort of computerized technology to control the over speed driving. At this scenario, we are proposing a system to detect the vehicle which is being driven above the maximum speed limit. After the detection the system captures the image of the vehicle and gives the indication to the higher authorities.

Keywords: Speeding, Arduino Uno, Vehicle over speed .

References:

1. Vasanth B, Sreenevasan S, Mathanesh V.R (2017) , IJETSR Report on Over Speed Vehicle Marking System.
2. Mustafa Omer Nawari  (2016), IEEE Report On Vehicle Over Speed Detection System.
3. SaymumAhmmedSany (2017), Report on Speed Checker On
4. Monika Jain, Praveen Kumar (2015), Research Article on Detection Of Over Speeding Vehicles.
5. electronicshub.com[online]
6. allaboutcircuits.com[online]
7. https://www.arduino.cc[online]

266-268

48.

Authors:

Paper Title:

Abstract: Even, numerous polymers and composites of materials developed so far, the adopting them in MEMS components is still challenging .The mechanical properties, electrical properties of those materials varies independently. In addition to, the components used in MEMS have micro level dimensions and different specifications. Tedious work and concentration is need for Choosing of suitable metals and materials for specific application while fabricating MEMS Components. In this survey paper ,advancement in research for finding the metals and materials used for fabrication MEMS parts and components, metal micro films fabrication and metal conductors, structural parts of micro sensors and micro actuators magnetic actuators for micro-devices, are try to explore to researchers . Various research papers related to MEMS material were collected from the literature. The name of metal and materials are mentioned and described briefly in an order. The MEMS fabrication methods are not discussed. 

Keywords: MEMS, Matels, materials, alloys, fabrication.

References:

1. Peter Jardine John S.Madsen Peter G.Mercado published their article in  ,”characterization of the deposition and materials parameters of thin-film TiNi for micro actuators and smart materials,” Materials Characterization Volume 32, Issue 3, 1994, Pages, 169-178.
2. N Rajana A Zormana M Mehreganya, RDeAnnaa R.JHarvey in their research on “Erosion resistance of silicon micromachined atomizers by compatible  very thin film hard coatings”1998.
3. A Knappa D.M Follstaedta S.M Myersa J. C Barboura T .A Friedmanna . W Ager IIIbO. R Monteirob I.G Brown,” Finite-element modeling of nano indentation for evaluating mechanical properties of MEMS materials,” Surface and Coatings Technology Volumes 103–104, May 1998, Pages 268-275.
4. F Bahra J.S Robachb J.S Wright L.F Francis  W.W Gerberich  in their research papers viz “Mechanical deformation of PZT thin films for MEMS applications,” Materials Science and Engineering: A, Volume 259, Issue 1, 15 January 1999, Pages 126-131.
5. Singh R. Houriet R. Giovannini  H. Hofmann V. Craciun  R. K. Singh  “Challenges in making of thin films for Li_xMn_yO₄ rechargeable lithium batteries for MEMS”, Journal of Power Sources, Volumes 97–98 , Pages 826-831, July 2001.
6. Henry Baltes, Oliver Brand, Andreas Hierlemann, Dirk Lange, and Christoph Hagleitner, “CMOS MEMS – Present And Future”, DOI: 10.1109/MEMSYS.2002.984302 · Source: IEEE Xplore.
7. Jeffrey snyder, james r. Lim, chen-kuo huang and jean-pierre fleurial , “Thermoelectric micro device fabricated by a MEMS-like electrochemical process” nature materials. Vol 2, 2003 | www.nature.com/naturematerials.
8. Orlando Auciello, James Birrell, John A Carlisle, Jennifer E Gerbi, Xingcheng Xiao, Bei Peng and Horacio D Espinosa, “Materials science and fabrication processes for a new MEMS technology based on ultrananocrystalline diamond thin films,” Journal Of Physics: Condensed Matter, 16 ,2004,R539–R552.
9. Mohsen Shahinpoor, and Kwang J Kim, “Ionic polymer–metal composites: IV.Industrial and medical applications”. Smart Mater. Struct. 14 , 2005,197–214.
10. Shi-Chune Yao, Xudong Tang, Cheng-Chieh Hsieh , Yousef Alyousef ,Michael Vladimer , Gary K. Fedder , Cristina H. Amon ,“Micro-electro-mechanical systems (MEMS)-based micro-scale,” Energy 31, 2006, 636–649..
11. Michele Pozzi, Musaab Hassan, Alun J Harris, Jim S Burdess, Liudi Jiang, Kin K Lee, Rebecca Cheung, Gordon J Phelps, Nick G Wright, Christian A Zorman ,Mehran Mehregany,“Mechanical properties of a 3C-SiC film between room temperature and 600 ◦C,”Journal Of Physics D: Applied Physics, Vol.40, 2007, 3335–3342.
12. Ben Pecholt & Monica Vendan & Yuanyuan Dong & Pal Molian,“Ultrafast laser micromachining of 3C-SiC thin films for MEMS device fabrication,”Int J Adv Manuf Technol VOL.39, 2008,239–250.
13. Hyouk Kwon, Seong-Soo Jang, Yong-Hee Park, Tae-SikKim,Yong-Dae Kim, Hyo-Jin Nam and Young-Chang Joo, “ Investigation of the electrical contact behaviors in Au-to-Au thin-film contacts for RF MEMS switches,” Journal Of Micromechanics And Microengineering , Vol. 18,2008 105010 (9pp).
14. Elfrink, T. M. Kamel, M. Goedbloed, S. Matova, D. Hohlfeld, R.van Schaijk, R. Vullers,“Vibration EnergyHarvesting With Aluminum Nitride-Based Piezoelectric Devices,” IN Proceedings of Power MEMS 2008+ microEMS, Sendai, Japan, 2008.
15. Giorgio De Pasquale, Timo Veijolaand Aurelio Somà, De Pasquale, Giorgio Somà, Aurelio Veijola, Timo, “Modelling and validation of air damping in perforated gold and silicon MEMS plates,” IOP Publishing Ltd,Journal of Micromechanics and Microengineering,  20, 2010, 1,1158. 
16. Xiao Guang Qiao, Nong Gao, Zakaria Moktadir, Michael Kraft and Marco J Starink,“Fabrication of MEMS components using ultra fine grained aluminium alloys”,Journal OF Microengineering,VOL.20, 2010, 045029, 9pp.
17. Dzung Viet Dao, Koichi Nakamura, Tung Thanh Bui and Susumu Sugiyama,“ Micro/nano-mechanical sensors and actuators based on SOI-MEMS Technology”, Advances In Natural Sciences: Nano science And Nanotechnology, VOL.1 , 2010,013001 (10pp).
18. Adrian P. Pop, Gheorghe Bejinaru Mihoc, Leonard Mitu, “The systemic analysis of metals manufacturing used in MEMS fabrication”, Fascicle of Management and Technological Engineering", Volume X (XX), 2011 , NR2.
19. A G P Kottapalli, M Asadnia, J M Miao, G Barbastathis and M S Triantafyllou “A flexible liquid crystal polymer MEMS pressure sensor array for fish-like underwater sensing”, Smart Materials and Structures. VOL.,11, 2012 ,115030 (13pp)..
20. Fabio Alves, Dragoslav Grbovic, Brian Kearney,Nickolay V. Lavrik,2 and Gamani arunasiri1  “Bi-material terahertz sensors using metamaterial Structures”, Optical Society of America Vol. 21, 2013,No. 11 | DOI:10.1364/OE.21.013256 | Optics Express 13256.
21. Maflin Shabya, ,M.S.Godwin Premib,Betty Martinc , “ Enhancing the Performance of MEMS Piezoresistive Pressure Sensor using Germanium Nanowire,” Procedia Materials Science 10, 2015, 254 – 262.
22. Yoshihiko Uematsu1, Toshifumi Kakiuchi1, Hiromi Miura and Taishi Nozaki,“Effect of Grain Size on Fatigue Behavior in AZ61 Mg Alloys Fabricated by MDFing”, Materials Transactions, Vol. 57, No. 9, 2016, pp. 1454 to 1461.
23. Linares Aranda, W. Calleja Arriaga, A. Torres Jacome, C.R. Báez Álvarez, “ A modular and generic monolithic integrated MEMS fabrication process,” Superficies y Vacío,VOL. 30(3), 2017,30-39.
24. Gi-Dong Sim , Jessica A. Krogstad , Kelvin Y. Xie , Suman Dasgupta ,Gianna M. Valentino , Timothy P. Weihs , Kevin J. Hemker ,“Tailoring the mechanical properties of sputter deposited nanotwinned nickel-molybdenum-tungsten films,” Acta Materialia VOL.144, 2018 216-225,.

269-272

49.

Authors:

Paper Title:

Abstract: This paper exhibits the properties of Fuzzy Magic Graph on Intuitionistic Space.

Keywords: Fuzzy Magic Graph, Membership and Non membership values, Intuitionistic Space AMS Classification :05C72

References: 

1. Atanassov, K. T. Intuitionistic fuzzy sets, Fuzzy Sets and Systems, Vol. 20, (1986), 87–96.
2. J.A.,A Dynamic survey of graph labeling, Electron.J.Combin 16,(2009) #DS6
3. Kauffman, A. Introduction a la Theorie des Sous-emsemblesFlous, Masson etCie, Vol. 1, (1973).
4. NagoorGani,A.,Rajalaxmi.D “Properties of fuzzy labeling graph” Applied Mathematical Sciences, Vol: 6,(2012),no.70,3461-3466
5. Rosenfeld, A fuzzy graphs, In: Fuzzy Sets and their applications (Zadeh, L.A.,K.S.Fu,M.Shimura, Eds), Academic Press, New York, (1975),77-95
6. J, Theory of Graphs and its Applications. Proc. Symposium (1963) pp 163-167
7. Shannon,A.,Atanassov,K.T.,A First step to a theory of the intuitionistic fuzzy graphs, Proceeding of Fubest(D.Lakov,Ed.),(1994) , 59-61 
8. Zadeh, L. A. Fuzzy sets, Information and Control, Vol. 8, (1965), 338–353.
9. Zadeh, L. A. Similarity relations and fuzzy orderings, Information Sciences, Vol. 3, (1971),No. 2, 177–200.
10. Nagoor Gani. A. Novel properties of fuzzy labeling graphs, Journal of Mathematics, Vol 2014
11. Kishore kumar. K Magic labeling on interval valued intuitionistic fuzzy graphs, Journal of intelligent and fuzzy systems33(3999-4006)

273-278

50.

Authors:

Paper Title:

Abstract: Quality of an Image has a significant impact on the overall performance of an image processing system. In this technical era, when everything is being digitized, many of us prefer to transfer data in a digital form; to be more secure while using digital devices through biometric sensors. We encounter many more such instances in our day to day life where these devices and sensors are deployed. The biometric devices or systems require a quality input to achieve quality performance. There is always a need to enhance the quality of the sample after its acquisition. In this paper, we are going to discuss image quality enhancement for biometric recognition system where iris samples have been used; by proposing an image quality analysis approach of segmented irises based on quality filters. It means input images are not directly passed to the filters, they are firstly segmented and then segmented parts will be enhanced using different filters. Segmentation is one of the initial key steps of recognition systems and has a significant role in the biometric systems as the performance results of the system’s subsequent stages depend upon the segmentation results. We have segmented iris images into three major parts namely ROI, pupil and sclera. ROI represents the region of interest i.e. iris. Moreover, iris images from four directions viz. up, straight, left and right are taken into consideration to achieve more promising results. Unlike general case, filters are applied for enhancement after segmenting the input images. To measure the quality of input samples, various image quality metrics have been calculated and analyzed comprehensively to reach to the conclusion that Gaussian filter performs better as compared to Average and Circular Average Filter.

Keywords: Biometric, Iris, Image Quality Analysis, Segmentation, Filters.

References:

1. N. Uddin, S. Sharmin, E. Hasan, S. Hossain, and Muniruzzaman, “A Survey of Biometrics Security System,” IJCSNS International Journal of Computer Science and Network Security, vol. 11, no. 10, pp. 16–23, Oct. 2011.
2. Bhattasali, K. Saeed, N. Chaki, and R. Chaki, “A Survey of Security and Privacy Issues for Biometrics Based Remote Authentication in Cloud,” Computer Information Systems and Industrial Management Lecture Notes in Computer Science, pp. 112–121, 2014.
3. Gupta and N. Sharma, “A model for biometric security using visual cryptography,” 2016 5th International Conference on Reliability, Infocom Technologies andOptimization (Trends and Future Directions) (ICRITO), pp. 328–332, 2016.
4. Jain and K. Nandakumar, “Biometric Authentication: System Security and User Privacy,” Computer, vol. 45, no. 11, pp. 87–92, 2012.
5. Sabarigiri and T. Karthikeyan, “Acquisition of Iris Images, Iris Localization, Normalization, and Quality Enhancement for Personal Identification ,” International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) , vol. 1, no. 2, pp. 271–275, Aug. 2012.
6. Sanpachai and S. Malisuwan, “A Study of Image Enhancement for Iris Recognition,” Journal of Industrial and Intelligent Information, vol. 3, no. 1, pp. 61–64, 2014.
7. Feddaoui, H. Mahersia, and K. Hamrouni, “Improving Iris Recognition Performance Using Quality Measures,” Advanced Biometric Technologies, pp. 241–264, Sep. 2011.
8. Vatsa, R. Singh, and A. Noore, “Improving Iris Recognition Performance Using Segmentation, Quality Enhancement, Match Score Fusion, and Indexing,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 38, no. 4, pp. 1021–1035, 2008.
9. M. Monro, S. Rakshit, and D. Zhang, “DCT-Based Iris Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 4, pp. 586–595, 2007.
10. Poursaberi and B. Araabi, “Iris Recognition for Partially Occluded Images: Methodology and SensitivityAnalysis,” EURASIP Journal on Advances in Signal Processing, vol. 2007, no. 1, pp. 1–12, 2006.
11. Matyas and Z. Riha, “Security of biometric authentication systems,” 2010 International Conference on Computer Information Systems and Industrial Management Applications (CISIM), vol. 6, no. 2, pp. 19–28, 2010.
12. Ma, T. Tan, Y. Wang, and D. Zhang, “Personal identification based on iris texture analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 12, pp. 1519–1533, 2003.
13. Ma, T. Tan, Y. Wang, and D. Zhang, “Efficient Iris Recognition by Characterizing Key Local Variations,” IEEE Transactions on Image Processing, vol. 13, no. 6, pp. 739–750, 2004.
14. B. More , A. B. Ubale , and K. C. Jondhale , “Biometric Security ,” First International Conference on Emerging Trends in Engineering and Technology, pp. 701–704, 2008.
15. Schuch, S. Schulz, and C. Busch, “Survey on the impact of fingerprint image enhancement,” IET Biometrics, vol. 7, no. 2, pp. 102–115, Jan. 2018.
16. A. Shabeer and P. Suganthi, “Mobile Phones Security Using Biometrics,” International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007), pp. 270–272, 2007.
17. Malik, S. Belongie, J. Shi, and T. Leung, “Textons, contours and regions: cue integration in image segmentation,” Proceedings of the Seventh IEEE International Conference on Computer Vision, pp. 1–8, 1999.
18. Chen, X. Lin, and T. Ding, “Liveness detection for iris recognition using multispectral images,” Pattern Recognition Letters, vol. 33, no. 12, pp. 1513–1519, 2012.
19. Czajka, “Pupil Dynamics for Iris Liveness Detection,” IEEE Transactions on Information Forensics and Security, vol. 10, no. 4, pp. 726–735, 2015.
20. Das, U. Pal, M. A. F. Ballester, and M. Blumenstein, “Multi-angle based lively sclera biometrics at a distance,” 2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM), pp. 1–8, 2014.
21. Fierrez, J. Ortega-Garcia, D. T. Toledano, and J. Gonzalez-Rodriguez, “Biosec baseline corpus: A multimodal biometric database,” Pattern Recognition, vol. 40, no. 4, pp. 1389–1392, 2007.
22. Galbally, J. Ortiz-Lopez, J. Fierrez, and J. Ortega-Garcia, “Iris liveness detection based on quality related features,” 2012 5th IAPR International Conference on Biometrics (ICB), pp. 271–276, 2012.
23. Kumar and N. B. Puhan, “Iris liveness detection using texture segmentation,” 2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), pp. 1–4, 2015.
24. Thavalengal, T. Nedelcu, P. Bigioi, and P. Corcoran, “Iris liveness detection for next generation smartphones,” IEEE Transactions on Consumer Electronics, vol. 62, no. 2, pp. 95–102, 2016.
25. Hajari, U. Gawande, and Y. Golhar, “Neural Network Approach to Iris Recognition in Noisy Environment,” Procedia Computer Science, vol. 78, pp. 675–682, 2016.
26. Kaur and S. Pathania , “Image Enhancement and Iris Recognition using SIFT Feature Extraction ,” International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE) , vol. 5, no. 5, pp. 1254–1256, 2016.
27. Garg, B. Mittal, and S. Garg , “Histogram Equalization Techniques For Image Enhancement ,” International Journal of electronics & communication technology , vol. 2, no. 1, pp. 107–111, 2011.
28. Chen, Y. Liu, X. Zhu, F. He, H. Wang, and N. Deng, “Efficient Iris Recognition Based on Optimal Subfeature Selection and Weighted Subregion Fusion,” The Scientific World Journal, vol. 2014, pp. 1-19, 2014. 
29. Chen, Y. Liu, X. Zhu, H. Chen, F. He, and Y. Pang, “Novel Approaches to Improve Iris Recognition System Performance Based on Local Quality Evaluation and Feature Fusion,” The Scientific World Journal, vol. 2014, pp. 1–21, 2014.
30. D. Khan, S. A. Khan, F. Ahmad, and S. Islam, “Iris Recognition Using Image Moments and k-Means Algorithm,” The Scientific World Journal, vol. 2014, pp. 1–9, 2014.
31. Sun, L. Zhao, and S. Yang, “Retracted: Gabor Weber Local Descriptor for Bovine Iris Recognition,” Mathematical Problems in Engineering, vol. 2013, pp. 1–7, 2013.
32. Sazonova and S. Schuckers, “Fast and efficient iris image enhancement using logarithmic image processing,” Biometric Technology for Human Identification VII, vol. 5, no. 2, pp. 1–8, May 2010.
33. Yan, Y. Tian, C. Zhou, L. Cao, Y. Zhou, and H. Wu, “Non-ideal iris image enhancement algorithm based on local standard deviation,” The 27th Chinese Control and Decision Conference (2015 CCDC), vol. 3, no. 1, pp. 4755–4759, 2015.
34. -R. Tammam, A. H. Khalil, and N. S. A. Kader, “Image enhancement and iris localization based on 2D complex matched filter for noisy images,” 2016 28th International Conference on Microelectronics (ICM), pp. 161–164, 2016.
35. Ismail, H. S. Ali, and F. A. Farag, “Efficient enhancement and matching for iris recognition using SURF,” 2015 5th National Symposium on Information Technology: Towards New Smart World (NSITNSW), pp. 1–5, 2015.
36. Xu, Z. Zhang, and Y. Ma, “Improving the Performance of Iris Recogniton System Using Eyelids and Eyelashes Detection and Iris Image Enhancement,” 2006 5th IEEE International Conference on Cognitive Informatics, vol. 2, no. 1, pp. 871–876, 2006.

279-296

51.

Authors:

Paper Title:

Abstract: The objective of research is to propose energy efficient model for cloud computing [1]. As energy is divided symmetrically then load of traffic will be disseminated in network. Thus there is need to put minimum load over network. The same occurs during packet transmission. Reducing the size of packet using advanced logic resolves the issue of space and time consumption. These advanced techniques help in minimizing the energy consumption [3]. Appropriate load balancing is maintained by well managed cloudlets and virtual machine. This research states the impact of number of cloudlets and size over virtual machines.

Keywords: Energy Efficient cloud; packet size; load balancing, cloudlet; virtual machine.

References:

1. Rajkumar Buyya, Anton Beloglazov, and Jemal Abawajy (2010) Energy-Efficient Management of Data Center Resources for Cloud Computing: A Vision, Architectural Elements, and Open Challenges
2. Anton Beloglazov and Rajkumar Buyya(2011) Energy Efficient Resource Management in Virtualized Cloud Data Centers,
3. Antti P. Miettinen, Jukka K. Nurminen(2011) Energy efficiency of mobile clients in cloud computing,
4. Toni Masteli, and Ivona Brandi (2012) Recent Trends in Energy Efficient Cloud Computing, Journal of Latex Class Files, Vol. 11, NO. 4, DECEMBER 2012
5. Jayshri Damodar Pagare, Dr.Nitin A Koli (2013) Energy-Efficient Cloud Computing: A Vision, Introduction, andOpen Challenges, International Journal of Computer Science and Network, Vol 2, Issue 2, April 2013
6. Harmanpreet Kaur, Jasmeet Singh Gurm(2015) A Survey on the Power and Energy Consumption of Cloud Computing,   International Journal of Computer Science Trends and Technology (IJCST) – Volume 3 Issue 3, May-June 2015
7. Banashankari, Chandan Raj.(2016), “A Survey on Power Efficiency in Cloud Computing to Optimize the Cost”, National Conference on Advances in Computing, Communication and Networking (ACCNet – 2016)
8. Abusfian Elgelany, Nader Nada(2013) Energy Efficiency for Data Center and Cloud Computing: A Literature Review, International Journal of Engineering and Innovative Technology (IJEIT) Volume 3, Issue 4, October 2013
9. Arindam Banerjee, Prateek Agrawal and N. Ch. S. N. Iyengar (2013), “Energy Efficiency Model for Cloud Computing”, International Journal of Energy, Information and Communications Vol.4, Issue 6 (2013)
10. Karim Djemame, Django Armstrong, Richard Kavanagh(2013), “Energy Efficienc Embedded Service Lifecycle: Towards an Energy Efficient Cloud Computing Architecture
11. Mehiar Dabbagh, Bechir Hamdaoui, Mohsen Guizaniy and Ammar(2014) Towards Energy-Efficient Cloud Computing: Prediction, Consolidation, and Over commitment,
12. Nader Nada, Abusfian Elgelany(2014) Green Technology, Cloud Computing and Data Centers: the Need for Integrated Energy Efficiency Framework and Effective Metric, International Journal of Advanced Computer Science and Applications, Vol. 5, No. 5, 2014
13. Renuka M. Dhanwate, Vaishali B. Bhagat(2014) A Literature Review on Improving energy efficiency on Android using Cloud based services, International Journal of Advance Research Computer Science and Management Studies, Volume 2, Issue 12, December 2014
14. Dejene Boru, Dzmitry Kliazovich, Fabrizio Granelli, Pascal Bouvry, Albert Y, Zomaya (2015), “Energy-efficient data replication in cloud computing datacenters”, Springer Science+Business Media New York 2015
15. Pragya, Mrs Manjeet Gupta(2015), “Analysis of energy efficient scheduling algorithms in green cloud computing”  International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 4 Issue 5, May 2015[4]
16. Fuqing Zhao , Shuo Qin , Guoqiang Yang , Weimin Ma , Chuck Zhang , Houbin Song " A Differential-based Harmony Search Algorithm with Variable Neighborhood Search for Job Shop Scheduling Problem and Its Runtime Analysis" IEEE Access, Year: 2018 , ( Early Access ),Page s: 1 - 1
17. Sumedha Garg , Deepak Kumar" A K-Factor CPU Scheduling Algorithm" 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Year: 2018, Page s: 1 - 6
18. Manoj Hans , Pallavi Phad , Vivekkant Jogi , P. Udayakumar "Energy Management of Smart Grid using Cloud Computing" 2018 International Conference on Information , Communication, Engineering and Technology (ICICET). Year: 2018, Page s: 1 - 4.
19. Amit R. Gadekar , M V. Sarode , V M. Thakare "Cloud Security and Storage Space Management using DCACrypt" 2018 International Conference on Information , Communication, Engineering and Technology (ICICET), Year: 2018, Page s: 1 - 4.
20. Ubale Swapnaja , B. Ghadge Mayuri ,S. Apte Sulabha" Block Level Design for Secure Data Sharing in Cloud Computing" 2018 International Conference on Information , Communication, Engineering and Technology (ICICET), Year: 2018,Page s: 1 - 5
21. Prasad S. Halgaonkar , Atul B. Kathole , Jubber S. Nadaf , K P. Tambe "Providing Security in Vehicular Adhoc Network using Cloud Computing by secure key Method" 2018 International Conference on Information , Communication, Engineering and Technology (ICICET), Year: 2018, Page s: 1 - 3
22. Seethalakshmi , V. Govindasamy , V. Akila "Job Scheduling in Big Data - A Survey" 2018 Internat2018 International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC)ional conference on computation of power, energy, Information and Communication (ICCPEIC), Year: 2018, Page s: 023 - 031
23. Wojciech Bożejko , Mieczysław Wodecki"On Cyclic Job Shop Scheduling Problem" 2018 IEEE 22nd International Conference on Intelligent Engineering Systems (INES), Year: 2018,Page s: 000265 - 000270
24. Fuqing Zhao , Shuo Qin , Guoqiang Yang , Weimin Ma , Chuck Zhang , Houbin Song" A Differential-based Harmony Search Algorithm with Variable Neighborhood Search for Job Shop Scheduling Problem and Its Runtime Analysis" IEEE Access, Year: 2018 , ( Early Access ), Page s: 1 - 1
25. Sumedha Garg , Deepak Kumar"A K-Factor CPU Scheduling Algorithm" 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Year: 2018, Page s: 1 - 6
26. Shuhei Kawaguchi ,Yoshikazu Fukuyama" Improved Parallel Reactive Tabu Search Based Job-Shop Scheduling Considering Minimization of Secondary Energy Costs in Factories" 2018 57th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), Year: 2018, Page s: 765 - 770.
27. Shota Suginouchi , Toshiya Kaihara , Nobutada Fujii , Daisuke Kokuryo "Utilization of Pheromone in Production Scheduling by Negotiation and Cooperation Among Customers" 2018 57th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), Year: 2018, Page s: 773 - 778

297-301

52.

Authors:

Paper Title:

Abstract: The objective of this study was to examine pastes in order to check the possibility of utilizing unprocessed rice straw ash with or without micro silica in Pavement Quality Concrete. Physical (SEM, XRD, Specific Gravity etc.) & chemical properties (XRF) of materials used were analyzed. Different proportions of OPC, RSA & Micro silica were researched for normal consistency, initial and final setting time of paste etc. Different proportion of rice straw ash and microsilica used for part replacement of OPC in the cement paste were 5, 10, 15, 20, 25, 30% and 2.5, 5, 7.5, 10% by weight of OPC respectively. Rice straw ash was also mixed with micro silica to form a composite for replacing OPC in the mix. Mix R1M3 (5% Rice Straw Ash & 7.5% Micro Silica) could achieve highest pozzolanic reaction while mix R2M3 (10% Rice Straw Ash & 7.5% Micro Silica) could achieve maximum economy. In India, Rice Straw is produced in abundance and has extensive geographical coverage. Rice Straw Ash is technically of no use to farmers, and they treat it as a waste product. They tend to burn rice straw in order to clear their field for the next batch of crop. In this paper, rice straw admixed with micro silica is treated as a potential material for pavement construction, and various preliminary tests were done on cement-rice straw ash-micro silica paste to check the possibility of using rice straw ash in pavement quality concrete.

Keywords: Rice Straw Ash; Pastes; Micro silica; Consistency; Initial and Final Setting times. 

References:

5. Said Kenai, Wolé Soboyejo & Alfred Soboyejo,“Some Engineering Properties of Limestone Concrete”, Materials and Manufacturing Processes,19 (5), pp. 949-961, 2007, DOI: 10.1081/AMP-200030668.
6. Mahsa Madani Hosseini, Yixin Shao, Joann K. Whalen, “Biocement production from silicon-rich plant residues: Perspectives and future potential in Canada”, Biosystems Engineering, vol. 110(4), pp. 351-362, ISSN 1537-5110, 2011, DOI: 1016/j.biosystemseng.2011.09.010.
7. El-Sayed, M. A., El-Samni, T. M., “Physical and chemical properties of rice straw ash and its effect on the cement paste produced from different cement types.” Journal of King Saud University - Science, vol. 21(3), pp. 21-30, 2006, ISSN 1018-3647.
8. Pathak, A. Jain and N. Bhatia, “Crop residues management with conservation agriculture: Potential, Constraints and Policy Needs”, Indian Agricultural Research Institute, New Delhi, 2012.
9. H. Ravindranath, H.I. Somashekar, M.S. Nagaraja, P. Sudha, G. Sangeetha, S.C. Bhattacharya, P. Abdul Salam, “Assessment of sustainable non-plantation biomass resources potential for energy in India”, Biomass and Bioenergy, vol. 29(3), pp. 178-190, 2005, ISSN 0961-9534, DOI: 10.1016/j.biombioe.2005.03.005.
10. D. Ransinchung RN and Brind Kumar, “Investigations on pastes and mortars of ordinary portland cement admixed with wollastonite and microsilica”, Journal of materials in civil engineering, vol. 22(4), pp. 305-313, 2009, DOI: 10.1061/(ASCE)MT.1943-5533.0000019.
11. IS 8112 : 2013 “Ordinary Portland Cement, 43 Grade - Specification”, Bureau of Indian Standards, New Delhi, 2013.
12. IS 456 : 2000 “Plain and Reinforced Concrete – Code of Practice”, Bureau of Indian Standards, New Delhi, 2005.
13. Arunabh Pandey, Brind Kumar, “Analysis of Rice Straw Ash for Part Replacement of OPC in Pavement Quality Concrete”, International Journal of Advances in Mechanical and Civil Engineering, vol. 3(3), pp. 1-4, 2016, ISSN: 2394-2827 IRAJ DOI Number - IJAMCE-IRAJ-DOI-4861.
14. IS 2720 (III) “Method of Tests for Soils – Determination of Specific Gravity”, Bureau of Indian Standards, New Delhi, 1980.
15. Is 4031 (V) “Methods of Physical Tests for Hydraulic Cement – Determination of Initial and Final Setting times”, Bureau of Indian Standards, New Delhi, 1988.
16. Standard, A. S. T. M. "C618-08a: Standard Specification for Coal Fly Ash and Raw or Calcined Natural Pozzolan for Use in Concrete." Annual Book of ASTM Standards, 2008.

302-307

53.

Authors:

Paper Title:

Abstract: Topic modelling is started from text-mining technique for discovering the latent semantic structure in a collection of documents. In the concept of text mining each document is generated from collection of topics. Topic modelling is based on probabilistic modeling, it has a huge range of applications such as linguistic understanding, image detection, automatic music improvisation identification etc.. topic modeling is applied in various fields such as software engineering, political science, medical etc.In this paper we propose topic modelling using LDA (Latent Dirichelt Allocation).LDA is one kind of probabilistic model that work backwards to learn the topic representation in each document and the word distribution of each topic. this paper I will focusing on LDA algorithms and the results shown based on the 20 news group data set. I will also show how topic modelling works on news groups data set on R Tool. Topic Models to analysis news groups data set with tm and topic modelling package in R, to see what are those documents from different topics.

Keywords: Topic Modeling,Text,Corpus,LDA,LSA,Gibbs sampling. 

References:

1. Latent Drichlent Allocation David M.Blei,Andrew Y 2003
2. Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey Hamed Jelodar · Yongli Wang · Chi Yuan · Xia Feng · Xiahui Jiang · Yanchao Li · Liang Zhao
3. Blei, D.M., A.Y. Ng, and M.I. Jordan, Latent dirichlet allocation. Journal of machine Learning research, 2003. 3(Jan): p. 993-1022.
4. Surveying a suite of algorithms that offer a solution to managing large document archives. by David M. Blei 2012
5. Fuzzy Approach Topic Discovery in Health and Medical Corpora, Amir Karami Aryya Gangopadhyay   Bin Zhou   Hadi Kharrazi 2013
6. A TEXT MINING RESEARCH BASED ON LDA TOPIC MODELLING Zhou Tong,Haiyi Zhang 2016
7. Latent Dirichlet Allocation (LDA) for Topic Modeling of the CFPB Consumer Complaints Kaveh Bastani1 ,*, Hamed Namavari1,2, Jeffry Shaffer 2016
8. Comparative Text Analytics via Topic Modeling in Banking, Rubayyi Alghamdi, Khalid Alfalqi
9. Yu chen, Rhaad M.Rabbani,aparna gupta,mohammad j zaki ,comparative text analytics via topic modeling in banking .
10. Fuzzy Clustering for Topic Analysis and Summarization of Document Collections.
11. Extraction of Unigram and Bigram Topic List by using Latent Dirichlet Markov Allocation and sentiment Classification. PreetChandan Kaur, TusharGhorpade, Vanita Mane Department of Computer Engineering RamraoAdik Institute of Technologhy.
12. Application of text mining to biomedical knowledge extraction: analyzing clinical narratives and medical literature,A Neustein, SS Imambi, M Rodrigues, A Teixeira, L Ferreira, DeGyter publication, 2014
13. Extraction Of Biomedical Information From Medline Documents –A Text Mining  Approach”, International Journal of Science, Environment and Technology, Vol. 2, No 2, pp  267 – 274, ISSN: 2278-3687, 2013

308-317

54.

Authors:

Paper Title:

Abstract: The work considers a series of problems on bending of shallow and flat plates under a normal load, as well as contact problems for the given plates and a solid base. The equations of a theory refined by considering transverse shifts and reduction are taken as the input equations describing the stress-strain state in plates. A systematic effect of the transverse shifts on the stress state is observed in all problems due to the fact that the graphs of bending moments from changes in curvature and from transverse shifts are in antiphase.

Keywords: shallow plate, transverse shift, transverse contraction, bending moment, antiphase effect. 

References:

1. V. Mironov, “Ob otsenke vliyaniya ucheta poperechnykh deformatsiy v odnoy kontaktnoy zadache so svobodnoy granitsey” [On the evaluation of the effect of taking into account transverse strains in a contact problem with a free boundary]. Izv. RAN. MTT, 5, 2008, p. 52-67.
2. I. Mikhaylovskiy, “Matematicheskie modeli mekhaniki uprugikh tel” [Mathematical models of the mechanics of elastic bodies], Syktyvkar: Izd-vo Syktyvkarskogo un-ta, 2004, p. 322.
3. I. Mikhaylovskiy, A.V. Ermolenko, V.V. Mironov, E.V. Tulubenskaya, “Utochnennye nelineynye uravneniya v neklassicheskikh zadachakh mekhaniki obolochek” [Refined nonlinear equations in nonclassical problems of shell mechanics], Syktyvkar: Izd-vo Syktyvkarskogo un-ta, 2009, p. 141.
4. V. Ermolenko, A.N. Gintner, “Vliyanie poperechnykh sdvigov na ponizhenie napryazhennogo sostoyaniya plastiny” [The effect of transverse shears on the reduction of the stress state of the plate], Bulletin of the Syktyvkar University. Series 1: Mathematics. Mechanics. Computer science, 20, 2015, p. 91-96.
5. V. Ermolenko, “Teoriya ploskikh plastin tipa Karmana-Timoshenko-Nagdi otnositelno proizvolnoy bazovoy ploskosti” [The theory of flat plates of the Karman-Tymoshenko-Naghdi type with respect to an arbitrary base plane]. Krasnoyarsk: NITS, V mire nauchnykh otkrytiy, 8.1(20), 2011, p. 336-347.
6. I. Mikhaylovskiy, V.N. Tarasov, “O skhodimosti metoda obobshchennoy reaktsii v kontaktnykh zadachakh so svobodnoy granitsey” [On the convergence of the generalized reaction method in contact problems with a free boundary]. RAS. PMM, 57(1), 1993, p. 128-136.

318-321

55.

Authors:

Paper Title:

Abstract:  With globalization, blends and tremendous amount of electronic data usage, the consumer demands for the innovative techniques of data processing concerns. These issues require enterprises to rethink the strategies for utilizing the resources in an efficient manner. In order to gain and increase a competitive advantage, these enterprises are enforced to move accelerative to be more capable, flexible, efficient, and innovative. To meet the present generation demands for utilizing the resources of Information Technology, Cloud computing emerges the most important paradigm that provides on demand business services globally. In Cloud Computing, resources and applications are available on-demand on the internet. In this context the concept of third party is evolved which provides the services to the end users. By adopting the cloud computing, some social issues like trust, privacy, compliance and legal matters appears. On the adoption of cloud computing in the organizations, the outsourcing of data and trade as well as business applications to a third party causes the issues related to security and privacy critically. As a large amount of depository of the heterogeneous companies are placed in cloud, there is need to have the safety of the cloud environment. With the constant increase in the utility of cloud computing day by day, there must be a genuine effort to review and evaluate the current latest trends and evolutions in security. In this paper, a survey of different cloud computing models, different security risks, counter measures of security in cloud computing that affect the cloud environment in the area of confidentiality, integrity and computing on data are thoroughly reviewed.

Keywords: Cloud computing, CSP, DDoS, DoS, IaaS, PaaS, SaaS. 

References:

1. Mell P., Grance T., “The nist definition of cloud computing National Institute of Standards and Technology”, 2011 Special Publication 800-145
2. Xiao Z., Xiao Y., “Security and privacy in cloud computing IEEE Communications Surveys & Tutorials”, 2013 152 843 859 10.1109/SURV.2012.060912.00182 2-s2.0-84877272118
3. Latif, H. Abbas, S. Assar, and Q. Ali, “Cloud computing risk assessment: a systematic literature review in Future Information Technology”, 2014, pp. 285–295, Springer, Berlin, Germany.
4. Hussain Aljafer et al., “A brief overview and an experimental evaluation of data confidentiality measures on the cloud”, Journal of innovation in digital ecosystems, 2014, pp. 1– 11.
5. Sweta Agrawal and Aakanksha Choubey, “Survey of Fully Homomorphic Encryption and Its Potential to Cloud Computing Security”, In International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 4, Issue 7, 2014, pp.679 – 686.
6. The Cloud Services Measurement Initiative Consortium (CSMIC), “Service Measurement Index Framework Version 2.1”, July 2014, Carnegie Mellon University Silicon Valley Moffett Field, CA USA.
7. R.Khan, “Access Control in Cloud Computing Environment”, ARPN Journal of Engineering and Applied Sciences, Vol. 7, no 5, MAY 2012.
8. Sosinsky, Cloud Computing Bible, Ed.2011, United States of America: Wiley.
9. G.Min, Y.H.Bang, “Cloud Computing Security Issues and Access Control Solutions”, Journal of Security Engineering, vol.2, 2012.
10. Yong Yu et.al, “Assured Data Deletion with Fine-grained Access Control for Fog-based Industrial Applications”, IEEE Transactions on Industrial Informatics, Volume: 14, Issue: 10 , Oct. 2018.
11. AB. Fernandes, L. FB. Soares, J.V. Gomes, M.M. Freire, P. RM Inácio, “Security issues in cloud environments: a survey”, International Journal of Information Security, 13 (2) (2014) 113–170.
12. Hashizume, D.G. Rosado, E. Fernndez-Medina, E.B. Fernandez, “An analysis of security issues for cloud computing”, J. Internet Services Appl., 4 (1) (2013) 1–13.
13. Gonzalez, C. Miers, F. Redgolo, M. Simplcio, T. Carvalho, M. Nslund, M. Pourzandi, “A quantitative analysis of current security concerns and solutions for cloud computing”, Journal of Cloud Computing, 1 (1) (2012) 1–18.
14. Morsy MA, Grundy J, Müller I., “An analysis of the Cloud Computing Security problem”, In Proceedings of APSEC 2010 Cloud Workshop APSEC, Sydney, Australia.
15. Kevin Jackson, “Secure Cloud Computing: An Architecture Ontology Approach”,DataLine,2009, http://sunset.usc.edu/gsaw/gsaw2009/s12b/jackson.pdf
16. Singh N. et al., “SQL Injection Attack Detection & Prevention over Cloud Services”, International Journal of Computer Science and Information Security, Vol. 14, No. 4, April 2016.
17. Gupta S., Sharma L., “Exploitation of Cross-Site Scripting (XSS) Vulnerability on Real World Web Applications and its Defense”, International Journal of Computer Applications (0975 – 8887) Volume 60– No.14, December 2012.
18. Yadav S., Jaysawal A., “Prevention of MITM Attacks in Cloud Computing by Lock Box Approach Using Digital Signature”, International Journal of Advanced Research in Computer Science and Software Engineering Volume 7, Issue 5, May 2017.
19. Ruiping Lua and Kin Choong Yow, “Mitigating DDoS Attacks with Transparent and Intelligent Fast-Flux Swarm Network”, IEEE Network, vol. 25, no. 4, pp. 28-33, 2011.

322-326

56.

Authors:

Paper Title:

Abstract: The important information is collected from multiple input images and formed fused one which has extra quantitative content. Image fusion is executed either in spatial or transform platforms. In spatial domain spectral information is distributed in the entire image. In this work image fusion is implemented in transform domain. Slant Transform effectively represents linear brightness changes. Statistical measure discriminates the important blocks of the image efficiently. Smoothness measure identifies less noisy blocks efficiently. Hence, in this work image fusion in Slant Transform domain using smoothness is proposed. Smoothness of slant transformed blocks are compared to select the important block from multiple images. The eminence of the fused image can be judged using various performance metrics such as Feature Similarity (FSIM) index, Mutual Information (MI), Normalized Cross Correlation (NCC), and Edge Strength Orientation preservation (ESOP).Proposed method is suitable for multi-focus image fusion. 

Keywords: Image fusion; mean; variance; smoothness; Slant Transform;

References:

1. Radhika V, VeeraSwamy K., Srininvas Kumar S.: “Performance evaluation of statistical measures for image fusion in spatial domain,” IEEE International Conference on Networks & Soft Computing (ICNSC), 2014, pp. 348-354.
2. Mohammad Bagher Akbari Haghighat, Ali Aghagolzadeh, and Hadi Seyedarabi.: ‘Multi-focus image fusion for visual sensor networks in DCT domain’, Computers & Electrical Engineering, 2011, vol. 37, no. 5, pp 789-797.
3. William K. Pratt, Wn Hsiung Chen, and Lloyd R. Welch, “Slant Transform Image Coding,” IEEE Transactions on Communications, Vol. 22, no. 8, August 1974, pp. 1075-1093.
4. Amina Saleem, Azeddine Beghdadi, and Boualem Boashash.: ‘Image fusion-based contrast enhancement’. EURASIP Journal on Image and Video Processing 2012, pp. 1-17.
5. Andrew J. Asman, and Bennett A. Landman.: ‘Formulating Spatially Varying Performance in the Statistical Fusion Framework’, IEEE Transactions on Medical Imaging, 2012, Vol. 31, No. 6, pp. 1326 – 1337.
6. : ‘ Multivariate statistical analysis of measures for assessing the quality of image fusion’, International Journal of Image and Data Fusion, 2010, Vol. 1, No. 1, pp. 47 – 66.
7. Rafael C. Gonzalez, Richard E. Woods and Steven L. Eddins.: ‘Digital Image Processing , Using MATLAB’, 2006, Low price edition.
8. H. Qu, and D.L. Zhang.: ‘Information measure for performance of image’, Electronic Letters, 2002, Vol. 38. No.7, pp. 313-315.
9. CS Xydeas, and V.Petrovic.: ‘Objective image fusion performance measure’, Electronic Letters , 2000,Vol. 36, No.4, pp. 308-309.
10. Lin Zhang, Xuanqin Mou.: ‘FSIM: A Feature Similarity Index for Image Quality Assesment’, IEEE transactions on Image Processing, 2011, Vol.20. No.8, pp. 2378-2386.
11. RadhikaVadhi, VeeraSwamyKilari, and Srinivas Kumar. S.:“Smoothness Measure for Image Fusion in Discrete Cosine Transform,” International Journal of Advanced Computer Science and Applications, Vol. 7, no. 5, 2016, pp.103- 111.
12. Veera Swamy Kilari and Radhika Vadhi, “Performance Assessment of image Fusion Algorithms using Statistical measures in Hadamard Transform Domain”, Journal of Advanced Research in Dynamical and Control Systems , Scoupus indexed journal, Vol.9, SP-18, 2017, Pages: 2917-2927.
13. Radhika Vadhi , Veera Swamy Kilari and  Srinivas Kumar S, “digital Image Fusion using HVS in block based transforms”, Journal of Signal Processing Systems, Springer, DOI 10.1007/s11265-017-1252-8, July 2017.

327-331

57.

Authors:

Paper Title:

Abstract: The Smart City guidelines under “Smart Cities Mission” declared by Government of India insist 10% of the Smart City’s energy requirement come from solar energy by generating electricity through solar PV rooftop installation, street lightings etc. Considering the life span of a solar PV power plant more than 20years, the study on meteorological parameters such as the latitude, ambient temperature, humidity and pollution level is necessary for choosing the right PV technology and precisely forecasting the power generation. The negative impact on environment of the unrestrained population growth has also been discussed. This study assessed five different solar photovoltaic (PV) technologies - monocrystalline silicon (mcSi), polycrystalline silicon (pcSi), amorphous silicon (aSi), copper indium gallium diselenide (CIGS), and Cadmiun Telluride (CdTe) in various smart cities located in different zones of India using PVsyst simulation software and facilitated the PV installers to plan and identify the best PV technology for a particular smart city. Thin film technologies - CIGS and CdTe has better performance ratios (PR) and capacity utilisation factor (CUF) in all six zones due to their low temperature power coefficient and ability to perform in low light or diffuse radiation condition. 

Keywords: Temperature, Humidity, Linke Turbidity, Population, PV technologies

References:

1. Ministry of Housing and Urban Affairs, Government of India. moud.gov.in/cms/smart-cities.php.
2. Workshop on smart city mission. Solar Energy Applications in smart cities. 22^nd February, 2016. Ministry of New and Renewable Energy, Government of India.
3. Subhash Chander, A. Purohit , Anshu Sharma, S.P. Nehra, M.S. Dhaka. Impact of temperature on performance of series and parallel connected mono-crystalline silicon solar cells. Energy Reports (2015)1; 175–180.
4. Rajiv Dubey, Pranjal Batra, Shashwata Chattopadhyay, Anil Kottantharayil, Brij M. Arora, K. L. Narasimhan and Juzer Vasi. Measurement of temperature coefficient of photovoltaic modules in field and comparison with laboratory measurements. Photovoltaic Specialist Conference (PVSC), IEEE 42nd (2015) Page(s):1 – 5.
5. Priyanka Singh, N.M.Ravindra. Temperature dependence of solarcell performance-an analysis. Solar Energy Materials & Solar Cells (2012)101; 36–45.
6. Tuka Al Hanai, Rehab Bani Hashim, Lana El Chaar, Lisa Ann Lamont. Environmental effects on a grid connected 900 W photovoltaic thin-film amorphous silicon system. Renewable Energy (2011) 36, 2615-2622.
7. Furkan Dinçer, Mehmet Emin Meral. Critical factors that affecting efficiency of solar cells. Smart Grid and Renewable Energy (2010), 1, 47-50.
8. Radziemska. The effect of temperature on the power drop in crystalline silicon solar cells. Renewable Energy (2003)28; 1–12.
9. Olivier Dupr´e, Rodolphe Vaillon, and Martin A. Green. Experimental assessment of temperature coefficient theories for silicon solar cells. IEEE Journal of photovoltaics (2016), Vol. 6, No. 1.
10. Virtuani, D. Pavanello, and G. Friesen. Overview of temperature coefficients  of different thin film photovoltaic technologies. 25th European Photovoltaic Solar Energy Conference and Exhibition / 5th World Conference on Photovoltaic Energy Conversion, (2010)6-10, Valencia, Spain.
11. Stein, J. Sutterlüti, J. Ransome, S. Hansen, C. & King, B. Outdoor PV Performance evaluation of three different models: single-diode, SAPM and loss factor model. 28th EU PVSEC. Paris France 92013), 30 September-4 October.
12. Chetan Singh Solanki (2013). Solar photovoltaics – fundamentals, technologies and applications, second edition (PHI learning private limited) ppno.: 16-19.
13. Gwandu, B.A..L. and Creasey, D.J. Humidity: A factor in the appropriate positioning of a photovoltaic power station. Renewable Energy (1995), 6, No. 3, pp. 313-316.
14. Stultz, J.W., Wen, L.C. Thermal performance testing and analysis of photovoltaic modules in natural sunlight. LSA Task Report (1977)5101-31.
15. Zainuddin, H., Shaari, S., Omar, A. M., Zain, Z. M., Soumin, J., Surat, Z. Alam, S. Preliminary investigations on the effect of humidity on the reception of visible solar radiation and the effect of humidity and wind speed on PV module output. American Institute of Physics (2010) 978-0-7354-0797-8/10.55-58.
16. Abdullahi, S. Ayegba, Musa Abdul and Adejoh Joshua. Impacts of relative humidity and mean air temperature on global solar radiations of Ikeja, Lagos, Nigeria. International Journal of Scientific and Research Publications (2017) Volume 7, Issue 2, 315-319.
17. Mekhilef, S., Saidur, R., & Kamalisarvestani, M. Effect of dust, humidity and air velocity on efficiency of photovoltaic cells. Renewable and Sustainable Energy Reviews (2012) 16(5), 2920–2925.
18. Evans, D.L., Florschuetz, L.W. Cost studies on terrestrial photovoltaic power systems with sunlight concentration. Solar Energy (1977)19, 255–262.
19. Manoj Kumar Panjwani, Dr. Ghous Bukshsh Narejo. Effect of humidity on the efficiency of solar cell (photovoltaic). International Journal of Engineering Research and General Science (2014) Vol 2, Issue 4, 499-503.
20. Martin A. Green, Yoshihiro Hishikawa, Ewan D. Dunlop, Dean H. Levi, Jochen Hohl‐Ebinger, Anita W.Y. Ho Baillie. Solar cell efficiency tables (version 51). Prog Photovolt Res Appl. (2018) 26:3–12.
21. Ramanathan, V., Ramana, M. V., Roberts, G., Kim, D., Corrigan, C., Chung, C., & Winker, D. Warming trends in Asia amplified by brown cloud solar absorption. Nature (2007), 448(7153), 575–578.
22. Bohren, C.F., Huffman, D.R. Absorption and scattering of light by Small particles. John Wiley & Sons, New York (1983), 531 pp.; 65.
23. Ramanathan, Y. Feng. Air pollution, greenhouse gases and climate change: Global and regional perspectives. Atmospheric Environment (2009)43; 37–50.
24. Tapakis R, Charalambides AG. Enhanced values of global irradiance due to the presence of clouds in Eastern Mediterranean. Renew Energy (2014); 62:459–67.
25. Kaufman, Y. J. and Ilan Koren. Smoke and pollution aerosol effect on cloud cover. Science (2006), 313(5787), 655–658.
26. Eltbaakh, Y. A., Ruslan, M. H., Alghoul, M. A., Othman, M. Y., Sopian, K., & Razykov, T. M. Solar attenuation by aerosols: An overview. Renewable and Sustainable Energy Reviews (2012), 16(6), 4264–4276.
27. Sanjay Kumar Singh. CO₂ emissions from passenger transport in India: 1950-51 to 2020-21. http://www.ebtc.eu/pdf/CO2_Emissions_from_Passenger_Transport_in_India_1950-51_to_2020-21.pdf.
28. Gemechu Fanta Garuma, Jean-Pierre Blanchet, Éric Girard, Martin Leduc. Urban surface effects on current and future climate. Urban Climate (2018)24,121–138.
29. Landsberg H. E. (1981). In The Urban Climate, p. 275, International Geophysics Series (1981), Vol. 28. Academic Press, New York.
30. Zeki Ahmed Darwish, Hussein A. Kazem, K. Sopian, M. A. Al-Goul, Hussain Alawadhi. Effect of dust pollutant type on photovoltaic performance. Renewable and Sustainable Energy Reviews (2015) 41, 735–744.
31. Wei Tian, Yiping Wang, Jianbo Ren, Li Zhu. Effect of urban climate on building integrated photovoltaics performance. Energy Conversion and Management (2007)48, 1–8.
32. Amir Shahsavari, Morteza Akbari. Potential of solar energy in developing countries for reducing energy-related emissions. Renewable and Sustainable Energy Reviews (2018)90, 275–291.
33. Peter Huszár, Jan Karlický, Michal Belda, Tomáš Halenka, Petr Pišoft. The impact of urban canopy meteorological forcing on summer photochemistry. Atmospheric Environment (2018) 176, 209–228.
34. M. Robaa. Urban–rural solar radiation loss in the atmosphere of Greater Cairo region, Egypt. Energy Conversion and Management (2009) 50;194–202.
35. McKinsey Global Institute (2010). India’s urban awakening: building inclusive cities, sustaining economic growth. Pages: 1-229.
36. Climate Protection Partnership Division in the U.S. (2008). Reducing urban heat Islands: compendium of strategies. Pages: 1-19.
37. Hashem Akbari. Energy saving potentials and air quality benefits of urban heat island mitigation. Lawrence Berkeley National Laboratory. Publication Date: 2005-08-23.
38. PVsyst photovoltaic software. https://www.pvsyst.com/
39. Shiva Kumar, K. Sudhakar. Performance evaluation of 10 MW grid connected solar photovoltaic power plant in India. Energy Reports 1 (2015) 184–192.
40. Sreenath Sukumaran, K. Sudhakar. Fully solar powered airport: A case study of Cochin International airport. Journal of Air Transport Management 62 (2017) 176e188
41. https://weatherspark.com/about
42. Madhuchandrika Chattopadhyay and R Rajavel. A comparative study on performance of a grid connected solar PV system installed in the urban, rural and coastal region of India. 2nd International Conference on Inventive Systems and Control (ICISC). IEEE Xplore: 28 June 2018. pp. 131-135.
43. Indian Himalayan States, ENVIS Centre on Himalayan Ecology. G.B. Pant National Institute of Himalayan Environment and Sustainable Development (GBPNIHESD). Ministry of Environment, Forest & Climate Change, Govt of India. http://gbpihedenvis.nic.in/ihr.html
44. Swapnil Dubey, Jatin Narotam Sarvaiya, Bharath Seshadri. Temperature dependent photovoltaic (PV) efficiency and its effect on PV production in the World-a review. Energy Procedia (2013)33; 311 – 321.
45. Kotaro Kawajiri, Takashi Oozeki, Yutaka Genchi. Effect of temperature on PV potential in the World. Sci. Technol. (2011) 45, 9030–9035.
46. Laxmi Narain and S.N. Garg. Estimation of Linke turbidity factors for different regions of India. J. Environment and Waste Management (2013), Vol. 12, No. 1, 52-64.
47. E. Alnaser and N. S. Awadalla. The Linke turbidity factor and Ängstrom coefficient in humid climate of Bahrain. Earth, Moon and Planets (1995) 70: 61-74.
48. WHO Global Urban Ambient Air Pollution Database (update 2018).World Health Organisation. http://www.indiaenvironmentportal.org.in/content/454533/who-global-urban-ambient-air-pollution-database-update-2018/
49. National Renewable Energy Laboratory. U.S Department of Energy. India Solar Resource Maps and Data. https://www.nrel.gov/international/ra_india.html. 26.09.2018
50. Concern over poor air quality and traffic congestion in north east cities; action must gather momentum (2013). Centre for Science and Environment. https://www.cseindia.org/concern-over-poor-air-quality-and-traffic-congestion-in-north-east-cities-action-must-gather-momentum-4964.
51. Xuexi Tie, Junji Cao. Aerosol pollution in China: Present and future impact on environment. Particuology (2009) 7 (2009) 426–431.
52. Polash Mukerjee and Subhojit Goswami (2016). Delhi air pollution: Identifying perpetrators and fixing responsibility. Down to Earth. https://www.downtoearth.org.in/news/air/delhi-air-pollution-identifying-perpetrators-and-fixing-responsibility-56240.
53. Usman Nasim (2015). Paddy fields burning: Smog shrouds national capital. Down to Earth. https://www.downtoearth.org.in/news/air/burning-paddy-fields-deteriorate-air-quality-in-delhi-51687.
54. V. Viswanadham, K. R. Santosh. Air pollution potential over South India. Kluwer Academic Publishers (1989) Volume 48, Issue 3, pp 299–313.
55. Shekar Reddy, Chandra Venkataraman. Inventory of aerosol and sulphur dioxide emissions from India: I-Fossil fuel combustion. Atmospheric Environment (2002)36, 677–697.
56. Tanushree Ganguly (2018). Understanding North India’s pollution crisis. Down to Earth. https://www.downtoearth.org.in/blog/air/understanding-north-india-s-pollution-crisis-60426.
57. Vasanthy, R. Velmurugan and Babu Rajendran. Ambient air quality scenario at Port Blair, Andaman Island. Nature Environment and Pollution Technology (2007)Vol. 6 No. 3 pp. 437-441.
58. The German Energy Society (2008). Planning and Installing Photovoltaic Systems-A Guide for Installers, Architectures and Engineers. 2nd end. London: Earthscan.
59. Ransome, S. (2010). The present status of kWh/kWp measurements and modelling. 5th World PV Conference. Valencia Spain, 6-10 September, 2010.

332-340

58.

Authors:

Paper Title:

Abstract:  Due to the acceptance of community interacting and broadcasting allocation websites records of imageries uploaded and common on the internet have improved. It prompts the accessibility of greatly extensive amounts images that need aid labeled toward clients. Content Based Retrieval system contingent upon low level features. Content based Image retrieval utilization the machine learning approach on take care of the image Category features issues. So, there is need to utilized color texture based feature to extract image characteristic. Different Categories of images are presents in the datasets so it’s challenging task to find separation between them. Here comparison between Fuzzy Color Histogram (FCH) and Color Moment are done with Euclidean distance metric. For Texture Feature Gabor Wavelet Transform (GWT) is use with above two feature fusion and find batter among them. The time required for feature extraction and retrieval using Euclidean distance for our proposed system’s feature extraction technique and Existing feature extraction techniques GWT and Color Moment also done.

Keywords: Fuzzy Color Histogram, Color Moment, Gabor Wavelet, Retrieval, Euclidean

References: 

1. C-H. Lin, R.-T. Chen and Y.-K. Chan, “A smart content-based image retrieval system based on color and texture feature”, in Elsevier Image and Vision Computing, Vol. 27 , pp. 658–665,2009.
2. Zhi-chun huang, Patrick P. K. Chan, Wing W. Y. Ng, D aniel s. Yeung" Content-based image retrieval using color moment and Gabor texture feature", Proc. IEEE Ninth international Conference on Machine Learning and Cybernetics, 2010, pp 719-724.
3. Mohsen Sardari Zarchi, Amirhasan Monadjemi and Kamal Jamshidi, ” A concept-based model for image retrieval systems,” in Elsevier Computers & Electrical Engineering, vol. 46 , pp. 303-313, 2015
4. Singh, K. J. Singh and D. S. Kapoor, "Image Retrieval for Medical Imaging Using Combined Feature Fuzzy Approach," in proc. IEEE International Conference on Devices, Circuits and Communications (ICDCCom), 2014, pp. 1-5
5. Subrahmanyam Murala, Anil Balaji Gonde and R.P. Maheshwari, “Color and Texture Features for Image Indexing and Retrieval”, in proc. IEEE international advanced computing conference , 2009, pp. 1411-1416.
6. Elalami,“A New Matching Strategy for Content Based Image Retrieval System,” in ACM Appl. Soft Comput., vol. 14,pp. 407-418,2014
7. Goel and P. Sehgal,” Image Retrieval Using Fuzzy Color Histogram and Fuzzy String Matching: A Correlation-Based Scheme to Reduce the Semantic Gap", in proc. Springer Intelligent Computing, Networking, and Informatics,2014, pp. 327-341.
8. Nizampatnam Neelima and E. Sreenivasa Reddy, “An Efficient Multi Object Image Retrieval System Using Multiple Features and SVM”, in proc. Springer Advances in Intelligent Systems and Computing, Vol. 425,2015, pp 257-265.
9. Xianzhe Cao and Shimin Wang, “Research about Image Mining Technique,” in proc. Springer ICCIP,2012, pp.127-134.
10. Valentina Franzoni, Clement H.C. Leung, Yuanxi Li,Paolo Mengoni and Alfredo Milani,” Set Similarity Measures for Images Based on Collective Knowledge,” in proc. Springer ICCSA,2015,pp.408-417
11. S. Hiwale, D. Dhotre and G. R. Bamnote, "Quick interactive image search in huge databases using Content-Based image retrieval," in proc. IEEE International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), 2015, pp. 1-5.
12. Konstantinidis, A. Gasteratos, I. Andreadis, “Image Retrieval Based on Fuzzy Color Histogram Processing”, in Elsevier Optics Communications, Vol. 248, pp. 375-386, 2005.
13. Ahmad Alzu’bi, Abbes Amira and Naeem Ramzan, “Semantic content-based image retrieval: A comprehensive study,” in Elseveir Journal of Visual Communication and Image Representation, Vol. 32, pp. 20-54 ,2015
14. Franzoni, A. Milani, S. Pallottelli, C. H. C. Leung and Yuanxi Li, "Context-based image semantic similarity," in proc. IEEE twelfth international conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2015, pp. 1280-1284.
15. Mohsen Sardari Zarchi, Amirhasan Monadjemi and Kamal Jamshidi, ” A concept-based model for image retrieval systems,” in Elsevier Computers & Electrical Engineering; 2015. DOI: 10.1016/j.compeleceng.2015.06.018

341-344

59.

Authors:

Paper Title:

Abstract: A user application (Smartphone or personal computer’s) play’s an essential role in our daily life. As usage of smartphones and PC’s keeps on increases, every day in one life, each and everyone uses to do every task in their daily life using smartphone or PCs to access, develop, store data’s. By using this everyone access the data over the internet, the user’s sensitive data’s were shared during the payment process, private messages online, and using their personal data to access the resource to study etc. There is the possibility of occurring attacks to this user sensitive data’s. Weakness on the construction of user application will allow the hacker or attacker to steal user information. Spyware is one type of attack that steel user sensitive information without user knowledge. The proposal states that the method and technique used to detect and prevent the user application from malicious attack using deep learning AI (Artificial intelligence).

Keywords: AI (Artificial intelligence), internet, malicious code, Malware, Spyware, user application.

References:

1. Sugandhasharma [2018], ”Fighting Virus and Malware with Artificial Intelligence” Available at https://www.insightssuccess.com/fighting-virus-and-malware-with-artificial-intelligence/ [Accessed on 24 July 2018]
2. Jason Brownlee [2016], “What is Deep Learning?” Available at: https://machinelearningmastery.com/what-is-deep-learning/ Accessed on 24 July 2018.
3. Bisht V. Venkatakrishnan "Xss-guard: precise dynamic prevention of cross-site scripting attacks" in Detection of Intrusions and Malware and Vulnerability Assessment Springer pp. 23-43 2008.
4. Laskov N. Šrndić "Static detection of malicious javascript-bearing pdf documents" Proceedings of the 27th Annual Computer Security Applications Conference. pp. 373-382 2011.
5. KeterynaChumachenko [2017], ”Machine Learning Methods for Malware Detection and Classification” Processig of Kaakkois-Suomenammattikorkeakoulu, University of Applied Science in 2017.
6. Jinpei Yan, Yong Qi and Qifan Rao [2018],”Detecting malware with an ensemble method based on deep neural network” Proceeding on Security and Communication Networks Volume 2018, Article ID 7247095, 16 pages https://doi.org/10.1155/2018/7247095 .
7. Karishma Pandey, Madhura Naik, Junaid Qamar ,Mahendra Patil (2015), Spyware Detection using Data Mining, International Journal of Engineering and Techniques.
8. Milan Jain, Ms. Punam Bajaj (2014), Malicious Code Detection through Data Mining Techniques, International Journal of Computer Science & Engineering Technology (IJCSET)
9. Saba Arshad, Abid Khan, Munam Ali Shah, Mansoor Ahmed (2016), Android Malware Detection & Protection: A Survey, (IJACSA) International Journal of Advanced Computer Science and Applications.
10. Bakdash, Steve Hutchinson, Erin G. Zaroukian, Laura R. Marusich, Saravanan Thirumuruganathan , Charmaine Sample, Blaine Hoffman , and Gautam Das, Malware in the future? forecasting of analyst detection of cyber events Jonathan, University of Texas Dallas Dallas, TX, USA
11. Niklas Lavesson, Martin Boldt, Paul Davidsson, Andreas Jacobsson (2009), Learning to detect spyware using end user license agreements,
12. Androutsopoulos I, Paliouras G, Karkaletsis V, Sakkis G, Spyropoulos CD, Stamatopoulos P (2000), Learning to filter spam E-mail: a comparison of a naive bayesian and a memory-based approach.
13. Kirti Mathur (2013), A Survey on Techniques in Detection and Analyzing Malware Executables, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 4.
14. G. Schultz, E. Eskin, E. Zadok and S. J. Stolfo (2001), Data Mining Methods for Detection of New Malicious Executables, Proceedings of the 2001 IEEE Symposium on Security and Privacy, IEEE Computer Society.
15. Parisa Bahraminikoo (2012),Utilization Data Mining to Detect Spyware, IOSR Journal of Computer Engineering (IOSRJCE), Volume 4, Issue 3.
16. Datasets available at: https://zeltser.com/malware-sample-sources/

345-349

60.

Authors:

Paper Title:

Abstract: Due to rapid advancements in multimedia technology from consumer electronics to medical imaging, HDTV display systems image scale up/down process is necessary for efficient displaying entire scene without loss of its original quality. The edge oriented based image processing plays the major role in the Image processing technique. The current real time applications demands low complexity, low cost and high performance devices for portable applications and it is achieved through CMOS-VLSI technology. This paper presents an efficient approach for edge-oriented image scaling processor Technique with low power and low complexity VLSI architecture design for edge-oriented area of image pixel scaling technique. This paper approaches the horizontal scaling and vertical scaling processor technique for improving the size of the image with better image quality than the existing image scaling processor. The horizontal and vertical image scaling processor technique is implemented in the proposed technology in order to improve the input image size of 400 X 400 image into 800 X 800 with better image quality. The Proposed five stage VLSI architecture consists of three phases such as edge orientation, vertical scaling and horizontal pixels scaling blocks respectively. Then, this proposed edge oriented image scaling technique is implemented in the VHDL and synthesized in the XILINX ARTIX-7 FPGA and shown the comparison for power, area and delay reports.

Keywords: Bilinear, FPGA, HDTV display, Image scaling, VLSI architecture. 

References:

1. Jiang, Nan, and Luo Wang. "Quantum image scaling using nearest neighbor interpolation." Quantum Information Processing 14.5 (2015): 1559-1571.
2. Nuno-Maganda, Marco Aurelio, and Miguel O. Arias-Estrada. "Real-time FPGA-based architecture for bicubic interpolation: an application for digital image scaling." null. IEEE, 2005.
3. Zhi-Yong, P. A. N. G., Hong Zhou Tan, and C. H. E. N. Di-Hu. "An improved low-cost adaptive bicubic interpolation arithmetic and vlsi implementation." Acta Automatica Sinica39.4 (2013): 407-417.
4. Aho, Eero, et al. "Comments on" Winscale: an image-scaling algorithm using an area pixel Model"." IEEE Transactions on circuits and systems for video technology 15.3 (2005): 454-455.
5. Kum, Ki-Il, and Wonyong Sung. "Combined word-length optimization and high-level synthesis of digital signal processing systems." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 20.8 (2001): 921-930.
6. Li, Chao, et al. "High-level synthesis for FPGAs: code optimization strategies for real-time image processing." Journal of Real-Time Image Processing 14.3 (2018): 701-712.
7. Lin, Chung-Chi, et al. "An Efficient Architecture of Extended Linear Interpolation for Image Processing." J. Inf. Sci. Eng.26.2 (2010): 631-648.
8. Lin, Chung-chi, et al. "The efficient VLSI design of BI-CUBIC convolution interpolation for digital image processing." Circuits and Systems, 2008. ISCAS 2008. IEEE International Symposium on. IEEE, 2008.
9. Amanatiadis, Angelos, Ioannis Andreadis, and Konstantinos Konstantinidis. "Design and implementation of a fuzzy area-based image-scaling technique." IEEE Transactions on Instrumentation and Measurement 57.8 (2008): 1504-1513.
10. Chen, Shih-Lun, Hong-Yi Huang, and Ching-Hsing Luo. "A low-cost high-quality adaptive scalar for real-time multimedia applications." IEEE Transactions on circuits and systems for video technology 21.11 (2011): 1600-1611.
11. Hsia, Shih-Chang, Ming-Huei Chen, and Po-Shien Tsai. "VLSI implementation of low-power high-quality color interpolation processor for CCD camera." IEEE transactions on very large scale integration (VLSI) Systems 14.4 (2006): 361-369.
12. Chen, Shih-Lun. "VLSI implementation of an adaptive edge-enhanced image scalar for real-time multimedia applications." IEEE Transactions on circuits and systems for video technology 23.9 (2013): 1510-1522.
13. Chen, Shih-Lun. "VLSI implementation of a low-cost high-quality image scaling processor." IEEE Transactions on Circuits and Systems II: Express Briefs 60.1 (2013): 31-35.
14. Chen, Pei-Yin, Chih-Yuan Lien, and Chi-Pin Lu. "VLSI implementation of an edge-oriented image scaling processor." IEEE Transactions on very large scale integration (VLSI) systems 17.9 (2009): 1275-1284.
15. Martin, Grant, and Gary Smith. "High-level synthesis: Past, present, and future." IEEE Design & Test of Computers 26.4 (2009): 18-25.

350-355

61.

Authors:

Paper Title:

Abstract: As the advancement in technology ,banking industry is facing several changes. Customer is now at the driving seat of new financial industry scenario as the whole control is now in the hands of customer. Due to the prospering technology, traditional banking has totally changed. Banks need to establish a new customer driven environment with innovation in business models .Being a most trending technology many organizations wants to adopt clouds as a cost effective strategy ,to provide innovative client services and to increase and manage IT efficiency .But banking industry still have some issues such as security ,privacy ,compliance and authenticity which somewhere produces an obstacle to adopt this flexible and agile technology. So there is a need for some mechanism which can provide a secured cloud environment in banking industry. This paper presents a mechanism to secure the cloud in banking industry by combining some algorithms viz; Password Based Key Derivation Function (PBKDF2),Argon2, AES-256 and IDA algorithm. This paper also shows features of clouds and security challenges of clouds in banking industry.

Keywords: PBKDF2, Argon2,AES(Advanced Encryption Standard),IDA(Information Dispersal algorithm)

References:

1. Ranjana Singh, AS. Prof Kirti Patil, AS. Prof Ashish Tiwari, “A survey on online banking authentication and data security”, International Journal of Advanced Research in computer Engineering and Technology, Volume 5,Issue 2,2016.
2. Manisha R. shinde & Rahul D. Taur,”Encryption Algorithm for data security and privacy in cloud storage”, American Journal of Computer Science and Engineering Survey, Original article ,ISSN 2349-7238.
3. Sheel Ghule, Rupali Chikhale, kalpesh Kumar,” Cloud Computing in Banking Services”, International Journal of Scientific & Research Publications, Volume 4, Issue 6 ISSN 2250-3153, 2014.
4. S.V. Sainadh,U. Satish Kumar, S. Haritha Reddy, “ security issues in Cloud Computing”, International Journal of Modern Trends in Science and Technology”, Volume 3,special issue no.:01, ISSN: 2455-3778, 2017.
5. Dinesh Taneja, SS Tyagi, “Information Security in Cloud Computing: A systematic Literature review and Analysis”, International Journal of Scientific Engineering and Technology”, Volume 6,Issue 1,ISSN: 2277-1581,2017.
6. Chitrali Agre,” Implementation of Cloud in Banking sector”, International Journal of Computer science and Information Technology research, Volume 3, Issue 2 ,ISSN: 2348-1196, 2015.
7. Akshat Ajabrao Uike, Dr. M.A.Pund, “ An Overview of Cloud Computing: Platforms, security Issues and Applications”, International Journal of Science Technology Management and research”, Volume 2, Issue 5, ISSN : 2456-0006,2017.
8. Levent Ertaul, Manpreet Kaur,Venkata arun Kumar R Gudise, “ Implementation and performance analysis of PBKDF2, Bcrypt, Scrypt Algorithms”, International conference Wireless Networks, ISSBN : 1-60132-440-5.
9. Andrea Visconti, Simone Bossi, Hany Ragab, Alexandro Calo, “ On the weaknesses of PBKDF2”,International Conference on Cryptography and Network security”, Springer International Publishing, LNCS 9476.
10. George Hatzivasilis,” Password –Hashing Status”, Journal Cryptography 1020010.
11. Saini ,Garima, Naveen Sharma, “ Triple Security of Data in Cloud Computing”, International Journal of Computer Sience & Information Technologies, 5.4,2014
12. Alex Biryokov, Daniel Dinu, Dmitry Khovratovich,” Argon2: the memory hard function for password hashing and other applications,” version 1.3 of Argon2:PHC release,2017.
13. D. Vybornova,” Password –based key devivation function as one of Blum-Blum-Shub Pseudo-random generator applications”,3^rd International Conference,” Information Technology and nanotechnology, published by Elsevier, ITNT 2017,ISSN: 1877-7058
14. Jean Raphael Ngnie Sighom,Pin Zhang and Lin you, “Security Enhancement for Data Migration in the cloud”, Future internet,2017.
15. K. Subramanian, F.Leo.john,” Dynamic Data Slicing in Multi Cloud Storage using Cryptographic Technique”, World congress on computing and communication Technologies, 978-1-5090-5573/17/IEEE
16. Amanjot Kaur, Manisha Bhardwaj, “Hybrid Encryption For Cloud Database Security” ,International Journal of engineering Science and Advanced Technology”, Volume 2, Issue 3, ISSN : 2250-3676,2012.
17. VPN Tracker, Company Connect,” Connection Safe security Architecture,” equinox
18. Turan,MS,E.Barker,W.E.Burr and L.Chen ,” Recommendation for password based key Derivation”,http://csrc.nist.gov/publications/nistpubs/800-132/nist-sp800-132.pdf
19. Rabin, M.O. Efficient dispersal of Information for security, Load balancing and fault tolerance J. ACM,1989,36,335-348

356-361

62.

Authors:

Paper Title:

Abstract: Image retrieval based on the sketch-based descriptor is focused in this paper. The retrieval operation based on the sha pe details defined in a sketch input is used for image region descriptor and a distance based mapping approach is developed for image retrieval in a database system. The search overhead, decision accuracy and feature representation is constraint to such sketch based approach, hence in this paper, a context re-ranking model based on feedback modeling is proposed. The approach has an advantage of faster retrieval performance compared to the conventional retrieval system. The validation is made with the simulation result developed for the proposed approach over the conventional benchmark approach. 

Keywords: Sketch based image retrieval, context feature, re-ranking, feedback modeling.

References:

1. Hankyu Moon, Rama Chellappa, and Azriel Rosenfeld, “Optimal Edge-Based Shape Detection”, IEEE Transactions on Image Processing, Vol. 11, No.11, pp. 1209-1277, 2002.
2. Ken Chatfield, James Philbin and Andrew Zisserman, “Efficient retrieval of deformable shape classes using local self-similarities”, In: of 12^th International conference on computer vision workshops, Kyoto, Japan, pp. 264-271, 2009.
3. Ye Mei and Dimitrios Androutsos, “Robust affine invariant region-based shape descriptors: The ICA zernike moment shape descriptor and the whitening zernike moment shape descriptor”, IEEE Transaction on Signal processing, 16, No. 10, pp.877-880, 2009.
4. Jamie Shotton, Andrew Blake and Roberto Cipolla, “Contour-Based Learning for Object Detection”, In: Of 10^th International Conference on Computer Vision, Beijing, China, pp. 503-510, 2005.
5. Xiang Baia, Xingwei Yang and Longin Jan Latecki, “Detection and recognition of contour parts based on shape similarity”, Pattern recognition, 41, No.7, pp. 2189-2199, 2008.
6. Livari Kunttu, Leena Lepisto, Juhani Rauhamaa and Ari Visa, “Multiscale Fourier descriptor for shape-based image retrieval”, In: Of 17^thInternational conference on pattern recognition,Cambridge, UK, pp. 765-768, 2004.
7. Zhong and W. Liao, “Direct curvature scale space: theory and corner detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, No. 3, pp. 508–512, 2007.
8. Cui and B. Zhong, “Shape retrieval based on parabolically fitted curvature scale-space maps”, Intelligent Science and Intelligent Data Engineering, of Lecture Notes in Computer Science, Vol. 7751, pp.743–750, 2013.
9. Gao, G. Han, G. Li, Y.Wo, and D.Wang, “Development of current moment techniques in image analysis”, Journal of Image and Graphics, Vol. 14, No. 8, pp. 1495–1501, 2009.
10. Mehul P. Sampat, Zhou Wang, Shalini Gupta, Alan Conrad Bovik and Mia K. Markey, “Complex wavelet structural similarity: A new image similarity index”, IEEE transactions on image processing, 18, No. 11, pp. 2385-2401, 2009.
11. Suhas G. Salve and Kalpana C. Jondhale, “Shape matching and object recognition using shape contexts”,In: Of 3rd International Conference on Computer Science and Information Technology,Chengdu, China, pp. 471-474, 2010.
12. Sergie Belongie, Jitendra Malik and Jan Puzicha, “Shape matching and object recognition using shape contexts”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 24, pp. 509-522, 2002.
13. Dengsheng Zhang and Guojun Lu, “Review of shape representation and description techniques”, Pattern recognition, 37, No.1, pp. 1-19, 2004.
14. Gul-e-Saman, S. Asif and M. Gilani, “Object recognition by modified scale invariant feature transform”, In: Of 3^rdInternational Workshop on Semantic Media Adaptation and Personalization, Prague, Czech Republic, pp. 33-39, 2008.
15. Sadegh Abbasi, Farzin Mokhtarian and Josef Kittler, “Curvature scale space image in shape similarity retrieval”, Multimedia Systems, 7, No.6, pp. 467–476, 1999.
16. Donghoh Kim and Hee-Seok Oh, “EMD: A Package for Empirical Mode Decomposition and Hilbert Spectrum”, The R Journal, 1, No.1, pp 40-46s, May 2009.
17. Rui Hu and John Collomosse, “A Performance Evaluation of Gradient Field HOG Descriptor for Sketch Based Image Retrieval”, Computer Vision and Image Understanding, 117, No.7, pp. 790–806. 2013.
18. Konstantinos Ioannidis and Ioannis Andreadis, “A Digital Image Stabilization Method Based on the Hilbert–Huang Transform”, IEEE Transactions on Instrumentation and Measurement, 61, No. 9, pp. 2446-2457, 2012.
19. Jeffery C. Chan, Hui Ma, Tapan K. Saha and Chandima Ekanayake, “Self-adaptive Partial Discharge Signal De-noising Based on Ensemble Empirical Mode Decomposition and Automatic Morphological Thresholding”, IEEE Transactions On Dielectrics and Electrical Insulation,21, No. 1, pp. 294-303, 2014.
20. Piotr Dudek, David Lopez Vilarino, “A Cellular Active Contours Algorithm Based on Region Evolution”,In: OfInternational Workshop on Cellular Neural Networks and Their Applications, pp. 1-6, Istanbul, Turkey,2006.
21. http://zoi.utia.cas.cz/tree_leaves.
22. Tian Qiu, Yong Yan, and Gang Lu, “An Autoadaptive Edge-Detection Algorithm for Flame and Fire Image Processing”, IEEE Transactions on Instrumentation and Measurement, 61, No. 5, pp. 1486- 1495, 2012.
23. Rong Zhou, Liuli Cheng and Liqing Zhang, “Sketch-based image retrieval on a large scale database”, In Proc. of ACM international conference on Multimedia, Nara, Japan,  2012.

362-369

63.

Authors:

Paper Title:

Abstract: Next-generation attractive air interface solution for wireless local area networks is combination of MIMO-OFDM (Multiple-input multiple-output) with (Orthogonal frequency division multiplexing). In this research paper provides a review of the existing research of MIMO-OFDM technology by using machine learning and deep learning based on MIMO communications, channel estimation, signal detection and selection in OFDM systems, Opportunities and Challenges of Wireless Physical Layer, Physical layer channel authentication for 5G and MIMO data for machine learning application to beam selection. In this research work concludes with a discussion of relevant open areas for further research.

Keywords: Machine Learning, Wireless Physical Layer, MIMO, Deep Learning, WLANS and OFDM. 

References:

1. Jiang, H. Zhang, Y. Ren, Z. Han, K. C. Chen, and L. Hanzo, Machine Learning Paradigms for Next-Generation Wireless Networks,” IEEE  Wireless Commun., vol. 24, no. 2, pp. 98–105, Apr. 2017.
2. V. Klaine, M. A. Imran, O. Onireti, and R. D. Souza, “A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks,” IEEE Commun. Surveys Tuts., vol. 19, no. 4, pp. 2392–2431, 2017.
3. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, pp. 436–444, 2015.
4. Agiwal, A. Roy, andN. Saxena, “Next generation 5G wireless networks: A comprehensive survey,” IEEE Communications Surveys & Tutorials, vol. 18, no. 3, pp. 1617–1655, 2016.
5. Thompson, X. Ge, and H.-C.Wu, “5G wireless communication systems: prospects and challenges [Guest Editorial],” IEEE Communications Magazine, vol. 52, no. 2, pp. 62–64, 2014.
6. Perera, C. H. Liu, S. Jayawardena, and M. Chen, “A survey on internet of things from industrial market perspective,” IEEE Access, vol. 2, pp. 1660–1679, 2014.
7. Timothy J. O’Shea, Tugba Erpek,  and T. Charles Clancy, Deep Learning-Based MIMO Communications, (arXiv:1707.07980v1 [cs.IT] 25 Jul 2017).
8. Hao Ye, Geoffrey Ye Li, and Biing-Hwang Fred Juang, Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems(arXiv:1708.08514v1 [cs.IT] 28 Aug 2017)
9. Tianqi Wang, Chao-Kai Wen, Hanqing Wang, Feifei Gao, Tao Jiang and Shi Jin,Deep Learning for Wireless Physical Layer:Opportunities and Challenges,(arXiv:1710.05312v2 [cs.IT] 27 Oct 2017)
10. Songlin Chen , Hong Wen , JinsongWu,Jie Chen, Wenjie Liu, Lin Hu, and Yi Chen, Physical-Layer Channel Authentication for 5G via Machine Learning Algorithm, Volume 2018, Article ID 6039878, 10 pages.
11. Aldebaro Klautau, Pedro Batista et al. 5G MIMO Data for Machine Learning: Application to Beam-Selection using Deep Learning(https://www.researchgate.net/publication/328520448).

370-371

64.

Authors:

Paper Title:

Abstract:  The revelation of translucent solid a few years back opened another skyline in the field of structural building. Numerous scientists chipping away at it so as to pick up the same number of uses from this imaginative innovation so as to use as vitality sparing building material that grants transmission of light into indoor condition and this solid go about as an engineering reason for good aesthetical perspective of the building. Translucent concrete is, acquired by inserting optical strands in it. This solid has light-trans missive properties because of these inserted light optical components. Light is led through the concrete from one end to the next. Contingent upon the fiber structure, these outcomes into a specific light pattern on the other surface. These strands transmit light so viably that there is supplant traditional concrete by this new and testing material. This is a creative building envelope chiefly utilized for sunlight porousness for the misty parts of outside veneers and rooftops. This solid for all intents and purposes there is no loss of light led through the filaments. Not just there is the great quality and light entry by utilizing optical filaments yet since the optical strands have less density than that of concrete thus by utilizing optical strands in concrete we get light weight concrete with adequate different properties and valuable number of uses.

Keywords: Translucent concrete, optical strands, light weight concrete, aesthetical view, vitality sparing. 

References:

1. Computational Modelling of Translucent Concrete Panels by Aashish Ahuja; Khalid M. Mosalam and Tarek I. Zohdi in November 2014 journal of architectural engineering
2. Application of Translucent Concrete for Lighting Purposes in Civil Infrastructures and its optical characterization by A. Peña-García1, L.M. Gil-Martín and O. Rabaza in may 2015 Key engineering materials
3. Effect of Plastic Optical Fibre on Some Properties of Translucent Concrete by Dr. Shakir Ahmed Salih, Dr. Hasan Hamodi Joni , Safaa Adnan Mohamed in November 2014 Eng. &Tech. Journal, Vol. 32, Part (A), No.12, 2014
4. Compressive strength of translucent concrete by Salmabanu Luhar, Urvashi Khandelwal in Sept 2015 International Journal of Engineering Sciences & Emerging Technologies
5. Litracon by Shreyas.K in Sept 2018 International Journal of New Technologies in Science and Engineering.
6. Translucent concrete: Test of compressive strength and transmittance by A. Karandikar in 2015 International journal of engineering research and technology
7. Experimental Study of Light Transmitting Concrete Using Optical Fibre by Sachin Sahu, Amlan Kumar Sahoo, Aman Kumar Singhal, Kuramana Stephen, Tamo Talom, Subham Saroj Tripathy, Sidhant Das in 2018
8. Experimental Evaluation on Light Transmittance Performance of Translucent Concrete by Awetehagn Tuaum, Stanley Muse Shitote and Walter Odhiambo Oyawa in 2018 international journal of applied engineering research.
9. A novel translucent concrete panel with waste glass inclusions for architectural applications by Valerio R.M. Lo Verso, Simonetta L. Pagliolico and Laura Ligi in july 2015 the indian concrete journal.
10. Evaluation of The Mechanical Properties of Translucent Concrete by Dr. Shakir Ahmed Salih , Dr. Hasan Hamodi Jonj , Safaa Adnan Mohamad in april 2018 International Journal of Engineering Trends and Technology (IJETT).
11. Study of Translucent Glass Concrete by Sisira Sugunan , Nisha Babu, Sowparnika M. in 2016 IOSR Journal of Mechanical and Civil Engineering.

372-374

65.

Authors:

Paper Title:

Abstract: According to Government of India, around 1,46,000 people [20] lost their lives in five hundred thousand of accidents, where 7% of lives could have been saved if they would have got medical attention before-hand [21]. This can be achieved by intimating the accident to the nearby emergency unit in minimal time by using artificial intelligence based on the severity of accidents. In existing methods, the accidents are detected using On-based unit, and transmitted to the control unit using nearby antennas, where the severity of the accidents are classified using data-mining. Then the fetched data is compared with existing accident dataset which it is retrieved from previous accidents, the analysed results are then transmitted to the nearby emergency unit [1], [2]. This will lead to ambiguous prediction of data because if the data doesn’t exist in the database, intensity of accident must be analysed manually in which it leads to increase in time complexity for transmitting the data to the nearby emergency unit due to intermediate infrastructure. To overcome these drawbacks, in this proposed system the accidents are detected using sensors and the severity of accident will be calculated using machine learning algorithms like k-means clustering and Support vector machine (SVM) classification under reinforcement learning with help of force and impact obtain while vehicle crashes, then the values are transmitted to the nearby emergency unit using Breadth-first-search in the form of A* Search algorithm. 

Keywords: Vehicle Accident Detection, Smart Sensors, Support Vector Machine (SVM), K-means, Reinforcement Learning, Breadth-First-Search (BFS)

References:

1. Joseph Funke, Matthew Brown, Stephen M. Erlien, and J.ChristianGerdes, Collision Avoidance and Stabilization for Autonomous Vehicles in Emergency Scenarios,IEEE Transactions On Control Systems Technology, Vol. 25, No. 4, July 2017
2. AmitMeena, SrikrishnaIyer , Monika Nimje ,SaketJogJekar , SachinJagtap ,MujeebRahman,(2014),”Automatic Accident Detection and Reporting Framework for Two Wheelers”,IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT)
3. Donald Selmanaj, MatteoCorno Sergio, M.Savaresi, Hazard Detection for Motorcycles via Accelerators: A Self-Organizing Map Approach, IEEE Transactions on Cybernetics(Volume 47, Issue 11, Nov 2017)On Mobile Computing, Vol. 13, No. 5, May 2014.
4. Liang-Chien Liu, Chiung-Yao Fang, Sei-Wang Chen, A Novel Distance Estimation Method Leading a Forward Collision Avoidance Assist System for Vehicles on Highways, IEEE Transactions on Intelligent Transportation Systems (Volume: 18, Issue: 4, April 2017)
5. Luca Canzian, UgurDemiryurek, and Mihaela van der Schaar, Fellow, “collision detection by networked sensors”,IEEE Transactions On Signal And Information Processing Over Networks, Vol. 2, No. 1, March 2016.
6. Pin Wang, Ching-Yao Chan, Vehicle collision prediction at intersections based on comparison of minimal distance between vehicles and dynamic thresholds, IET, Intelligent Transport Systems (Volume: 11, Issue: 10, 12 2017).
7. Ajay DM, Umamaheswari.E., Why how cloud computing- How not and cloud security issues. Global Journal of Pure and Applied Mathematics (GJPAM) 2016;12(1):1-8
8. Ajay DM, Umamaheswari.E. An initiation for testing the security of a cloud service provider. Smart Innovation, Systems, Technologies. Switzerland: Springer Publications; 2016. p. 35-41.
9. SrdjanTadicRadeStancic Lazar V. SaranovacPredrag N. Ivanis,Vehicle Collision Reconstruction With 3-D Inertial Navigation and GNSS, IEEE Transactions on Instrumentation and Measurement (Volume: 66,Issue: 1, Jan. 2017)
10. Taewungkim and hyun-yongjeong,” a novel algorithm for crash detection under general road scenes using crash probabilities and an interactive multiple model particle filter”, IEEE Transcations on Intelligence Transportion System, vol.15, no.6, December 2014.
11. Yi Gao, Xue Liu, Wei Dong, A Multiple Vehicle Sensing Approach for Collision Avoidance in Progressively Deployed Vehicle Networks, IEEE Transactions Journal 2017-978-1-5090-6501- 1/17
12. Carlos T. Calafate, and PietroManzoni,”A system for automatic notification and severity estimation of automotive accidents”,IEEE Transactions On Mobile Computing, Vol. 13, No. 5, May 2014.
13. Zihao Wang SainaRamyar, Syed MoshfeqSalaken, AbdollahHomaifar Saied, Nahavandi Ali Karinoddini, A collision avoidance system with fuzzy danger level detection, Intellegent Vehicle Symposium (IV),2017 IEEE.
14. Scope of internet of things: a survey. Umamaheswari e, Ajay dm, umangsindal, april 2017, Asian journal of pharmaceutical and clinical research.
15. Computational Intelligence Principles, Techniques and Applications – AmitKonar, Springer.
16. Computational Intelligence Methods and techniques – Leszek Rutkowski, Springer.
17. Introduction to Machine Learning- 3rd edition – EthemAlpaydin- MIT Press.
18. Machine Learning in Python Essential Techniques for Predictive Analysis- Michael Bowles- Wiley.
19. Programming Python – 4th edition- Mark Lutz – O’REILLY
20. PRS Legislative Research | Road Accidents in India: https://www.prsindia.org/roadaccidents/index.html – last viewed 31/04/2018
21. Accident and their prevention- https://patient.info/in/doctor/accidents-and-their-prevention – last viewed 6/01/18.
22. IBM IoT Watson- https://console.bluemix.net/docs/services/IoT/index.html#getting started template-viewed 05/05/2018
23. Elsevier wordmark Ad Hoc Networks – https://www.journals.elsevier.com/ad-hoc-networks – last viewed 31/10/2017.
24. How to calculate Force Crash: https://sciencing.com/calculate-crashes-forces-6038611.html -last viewed 23/05/2018
25. How to calculate Force and impact: https://sciencing.com/calculate-force-impact-7617983.html- last viewed 21/05/2018
26. Simple Beginner’s guide to Reinforcement Learning & its implementation -https://www.analyticsvidhya.com/blog/2017/01/introduction-to-reinforcement-learning-implementation/ -viewed 2/02/2018
27. INTECH- Fuzzy www.interchopen.com -last viewed 6/03/2018.
28. Articles on artificial intelligence http://intelligence.worldofcomputing.net/ai-search/a-star-algorithm.html#.W0tQnNIzZPY-last viewed 15/07/2018
29. Design of an iterative auto-turning algorithm for fuzzy PID controller- https://www.iopscience.iop.org/article/10.1088/1748-6596/364/1/012052/meta -last viewed 5/03/18.
30. Intelligent data transfer- http://www.12dsynergy.com/blogpost/improving-data-transfer/-lastviewed 31/03/2018
31. Data Clustering -https://www.analyticsvidhya.com/blog/2016/11/an-introduction-to-clustering-and-different-methods-of-clustering/ --viewed 03/03/2018

375-379

66.

Authors:

Paper Title:

Abstract: In Wireless Sensor Network (WSN), energy consumption is a big challenge. The energy is mostly wasted by huge number of nodes even they are inactive. WSN is a collection of different technologies like embedded, processing, and communication technology. The use of WSN gets widened in the fields of health care, traffic management, monitoring of environment, and management during disaster. The main intention of research aims to analyze the WSN and propose a power aware routing protocol (PARP) to reduce consumption of energy by the wireless node in congestion. The proposed routing protocol works by constructing a multicast tree to send message to the destination with less effort and energy. In order to control multicast delivery system, this work selects the node nearest WSN node for the perfect position to the forwarding node for preserving the energy between two neighboring goals that is placed in multicast tree. This research work uses the Network Simulator version 2 (NS2) for evaluation purpose and the performance metrics as throughput, packet delivery ratio, energy consumption, and delay. The result indicate that PARP achieves its objectives in a efficient when compared with other approaches namely DACR (Distributed Adaptive Cooperative Routing) protocol and REER (Reliable Energy Efficient Routing) protocol.

Keywords: Energy, Load Balancing, Sensor, Routing, Wireless. 

References:

1. A., Bakar.K.A., Channa.M.I., Haseeb.K., Khan.A.W. "TERP: A Trust and Energy Aware Routing Protocol for Wireless Sensor Network," in IEEE Sensors Jour, Volume. 15, Issue. No. 12, 2015 pp. 6962-6972.
2. D., Bhondekar.AP. "Traffic and Energy Aware Routing for Heterogeneous Wireless Sensor Networks”, in IEEE Communications Letters, Volume. 22, Issue. No.8, 2018 pp. 1608-1611.
3. E., Xing.L., Vokkarane.V.M., Sun.Y.L. "Reliability-Oriented Single-Path Routing Protocols in Wireless Sensor Networks”, inIEEE Sensors Journal, Volume. 14, Issue. No.11, 2014, pp. 4059-4068.
4. F., Ahmed. M. H., "Optimal and Near-Optimal Cooperative Routing and Power Allocation for Collision Minimization in Wireless Sensor Networks”, IEEE Sensors Jour, Volume. 16, Issue. No.5, 2016, pp. 1398-1411.
5. H., Yin.H., Min.G., Zhang.J., Wu.Y., Zhang.X. "Energy-Aware Dual-Path Geographic Routing to Bypass Routing Holes in Wireless Sensor Networks”, IEEE Trans on Mobile Computing, Volume. 17, Issue. No.6, 2018, pp. 1339-1352.
6. Al-Jemeli.M., Hussin.F.A., "An Energy Efficient Cross-Layer Network Operation Model for IEEE 802.15.4-Based Mobile Wireless Sensor Networks”, IEEE Sensors Jour, Volume. 15, Issue. No.2, 2015, pp. 684-692.
7. M., Kwon.T, Mao. S, Yuan.Y, Leung.V, “Reliable and Energy Efficient Routing Protocol in Dense Wireless Sensor Networks”, International Jour of Sensor Networks, Volume. 4, Issue. No. 12, 2008, pp. 104–117.
8. M., Li.J., Yang.Y, "A Framework of Joint Mobile Energy Replenishment and Data Gathering in Wireless Rechargeable Sensor Networks”, IEEE Trans on Mobile Computing, Volume. 13, Issue. No.12, 2014, pp. 2689-2705.
9. Abdur Razzaque.Md., Ahmed.M.H.U., Hong.C.S., Lee.S. “Qos-Aware Distributed Adaptive Cooperative Routing in Wireless Sensor Networks”, Ad Hoc Networks, Volume 19, 2014, pp. 28-42.
10. N., Vidyarthi.D.P. "A Green Routing Algorithm for IoT-Enabled Software Defined Wireless Sensor Network”, IEEE Sensors Jour, Volume. 18, Issue. No.22, 2018, pp. 9449-9460.
11. Al Rubeaai.S.F., Abd.M.A., Singh.B. K., Tepe.K.E. "3D Real-Time Routing Protocol With Tunable Parameters for Wireless Sensor Networks”, IEEE Sensors Jour, Volume. 16, Issue. No.3, 2016. pp. 843-853.
12. T., Higgs.R., O’Hare.G.M.P. "A Stable Routing Framework for Tree-Based Routing Structures in WSNs”, IEEE Sensors Jour, Volume. 14, Issue. No.10, 2014, pp. 3533-3547.
13. Tunca Isik. S., Donmez. M. Y., Ersoy. C. "Ring Routing: An Energy-Efficient Routing Protocol for Wireless Sensor Networks with a Mobile Sink”, IEEE Trans on Mobile Computing, Volume. 14, Issue. No.9, 2015, pp. 1947-1960.
14. X., Wang.H. "RNOB: Receiver Negotiation Opportunity Broadcast Protocol for Trustworthy Data Dissemination in Wireless Sensor Networks”, IEEE Access, Volume. 6, 2018, pp. 53235-53242.
15. Y., Tang.Y., Tsai.M., "LF-GFG: Location-Free Greedy-Face-Greedy Routing With Guaranteed Delivery and Lightweight Maintenance Cost in a Wireless Sensor Network With Changing Topology”, IEEE Trans on Wireless Communications, Volume. 13, Issue. No.12, 2014, pp. 7025-7036.
16. Y., Dong. W., Chen. Y. "An Improved Routing Algorithm Based on Ant Colony Optimization in Wireless Sensor Networks”, IEEE Communications Letters, Volume. 21, Issue. No.6, 2017, pp. 1317-1320.
17. Y., Li.C., Duan.Y., Yang. J., Cheng.X. "An Energy-Efficient and Swarm Intelligence-Based Routing Protocol for Next-Generation Sensor Networks”, IEEE Intelligent Systems, Volume. 29, Issue. No.5, 2014, pp. 74-77.
18. E., Dong.E, "A Virtual Coordinate-Based Bypassing Void Routing for Wireless Sensor Networks", IEEE Sensors Jour, Volume. 15, Issue. No.7, 2015, pp. 3853-3862.

380-385

67.

Authors:

Paper Title:

Abstract: As of late, the quantity of vehicles on the road has expanded tremendously. Because of high thickness and portability of nodes, conceivable dangers and road accidents are expanding. Wireless communication permits sending safety and other basic data. Vehicular Ad-Hoc Network (VANET) is an innovation which accommodates the vehicle as node to interconnect with each other through a wireless network. The essential structure goal of these applications is to serve the clients and give security of human lives amid their journey. Security is a major issue in VANET as it can be life threatening. We propose ECEDS (Elliptic Curve Encryption and Digital Signature) gives system security by utilizing a digital signature for message communicated over the system. This framework likewise used to counteract Sybil attack by limiting timestamps given by RsU at a beginning stage itself. An attacker is one of sort of end client, yet their role in the system is negative and makes issues for different segments of system. A serious attack, known as Sybil attack, against ad-hoc networks includes an attacker misguidedly asserting numerous characters. A Sybil attack delivers different messages to different nodes. Every message contains distinctive source personality. In this paper, we discusses some of the techniques put forwarded by researchers to detect Sybil attack in VANET. In this paper, we propose a Preference Batch Authentication Algorithm (PRBAA) expecting to decrease the message loss rate of nodes and Road-side Units (RsU’s). PRBAA is utilized to characterize the requests acquired from various nodes so as to furnish prompt reaction to crisis nodes with less time delay.

Keywords: Elliptic Curve Encryption and Digital Signature (ECEDS), Preference Batch Authentication Algorithm (PRBAA), Sybil Attack, Vehicular Ad-Hoc Network (VANET). 

References:

1. Pathan, Al-Sakib Khan , “Security of Self- Organizing Networks: MANET, WSN, WMN, VANET “, CRC press, 2011.
2. Ram Shringar Raw, Manish Kumar, Nanhay Singh “security challenges, issues and their solutions for vanet”sept 2013.
3. priyanka sirola, amit joshi, kamlesh C. Purohit “An Analytical Study of Routing Attacks inVehicular Ad-hoc Networks (vanets)”July 2014.
4. ANSI X9.62-2005, 2005. “Public Key Cryptography for the Financial Services Industry: The Elliptic Curve Digital Signature Algorithm (ECDSA)”. American National Standards Institute, November 2005.
5. FIPS 186-3, 2009. “Digital Signature Standard (DSS)”. Federal Information Standards Processing Publication 186-3, National Institute of Standards and Technology, June 2009.
6. Raya and J. Hubaux, ‘‘The security of vehicular ad hoc networks’’, in Proc. 3rd ACM Workshop Secur. Ad Hoc Sensor Netw., 2005, pp. 11–21.
7. SEC1 Standards for Efficient Cryptography Group, SEC 1: Elliptic Curve Cryptography, Version 2.0, 2009.
8. FIPS 186-3, 2009. “Digital Signature Standard (DSS)”. Federal Information Standards Processing Publication 186-3, National Institute of Standards and Technology, June 2009.
9. F, “The Security of Vehicular Adhoc Networks”, In Computational Intelligence, Communication Systems and Networks (CICSyN), 2011 Third International Conference on 2011 Jul 26 (pp. 338-342). IEEE.
10. Manvi, S.S.; Kakkasageri, M.S.; Adiga, D.G, “Message Authentication in Vehicular Ad Hoc Networks: ECDSA Based Approach”, Future Computer and Communication, ICFCC 2009. International Conference on , vol., no., April 2009, pp.16-20, 3-5.
11. “Public Key Cryptography for the Financial Services Industry: The Elliptic Curve Digital Signature Algorithm (ECDSA)”, ANSI X9.62-2005, American National Standards Institute, November 2005.
12. Jaydip kamani,Dhaval parikh “A Review on Sybil Attack Detection Techniques”Mar 2015.
13. Chaitanya Kumar Karn and Chandra Prakash Gupta, “A Survey on VANETs Security Attacks and Sybil Attack Detection”, in International Journal of Sensors, Wireless Communications and Control, 2016, 6, 45-62.
14. Kafil P, Fathy M, Lighvan MZ. “Modeling Sybil attacker behavior in VANETs”, Information Security and Cryptology (ISCISC), 2012 9th International ISC Conference on 2012 Sep 13 (pp. 162-168). IEEE.
15. Vinh Hoa LA, Ana Cavalli “security attacks and solutions in vehicular ad hoc networks: a survey”april 2014.
16. “Prevention of Sybil attack and priority batch verification in VANETs”, by P. Vinoth kumar and M. Maheswari, ICICEs 2014.
17. Zhang Jianhong, Xu Min and Liu Liying, ”On the Security of a secure Batch verification with Group Testing for VANET”, International Journal of Networks, Vol.16, No.4, PP.313- 320,2014.
18. Jiun-Long Huang, Lo-Yao Yeh, and Hung-Yu Chien,” ABAKA: An Anonymous Batch Authenticated and Key Agreement Scheme for Value-Added Services in Vehicular Ad Hoc Networks” IEEE Transaction On Vehicular Technology, VOL. 60, NO. 1, Janauary 2011.
19. Chen Chen, Weili Han and Xin Wang, “Sybil attack detection based on signature vectors in VANETs”, Int. J. Critical Computer-Based Systems, Vol. 2, PP 455,2011.
20. Karamjeet Kaur , Sanjay Batish & Arvind Kakaria, “Survey of Various Approaches To Countermeasure Sybil Attack”, International Journal of Computer Science and Informatics ISSN (PRINT): 2231 –5292, Vol-1, Iss-4.,2012.
21. Mohamed Salah Bouassida, Gilles Guette, Mohamed Shawky and Bertrand Ducourthial, “Sybil Node Detection Based on Received Signal Strength Variation” International Journal of Network Security, Vol.9, No.1, 2009.
22. Soyoung Park,Baber Aslam,DamlaTurgut,Cliff C. Zou, ”Defense against Sybil attack in vehicular ad hoc network based on roadside unit”, IEEE conference Paper ID 900042,2009.  

386-391

68.

Authors:

Paper Title:

Abstract: Endless human evolution had led to certain level of change in mankind. Advancement in technology has minimized human effort and time. Eluding side effects due to technology growth has direct or indirect impact on human health. One such side effect caused by electrical devices is sensitiveness to Electromagnetic (EM) waves termed as Electro Magnetic Hypersensitivity (EHS). Cause, effects, symptoms, diagnosis, and treatment of EHS is discussed in this paper. The evidence of causality of exposures in effect is evaluated by means of criteria list. Other functional impairments with similar symptoms, causes and effects are also discussed. This paper discusses the e-survey conducted on different age group affected due to the effect of EHS. Hence from a limited quantity of research in this area concludes that diagnosis and awareness of EHS need to be improved to safeguard the mankind.

Keywords: Electromagnetic waves, hypersensitivity, medical symptom , radiowave sickness

References:

1. Rubin G.J. Das Munshi J. Wessely S,"A Systematic Review of Treatments for Electromagnetic Hypersensitivity", Psychother Psychosom. 2006;75(1):12-8.
2. Patrick Levallois."Prevalence of electrical hypersensitivity in populations of different countries"in Proc.International Workshop on EMF Hypersensitivity,Prague, Czech Republic, 2004, pp 57-62.
3. Berndt Stenberg "characterizing electrical hypersensitivity", in Proc.International Workshop on EMF Hypersensitivity,Prague, Czech Republic, 2004, pp. 29-38.
4. WHO ," Electromagnetic fields and public health - Electromagnetic hypersensitivityElectromagnetic fields and public health "Electromagnetic hypersensitivity Backgrounder,  Dec 2005.
5. Arthur Firstenberg, "Silent Wireless Spring",Center for diseases Control, Atlanta(2007).
6. Enrique A. Navarro, J. Segura, M. Portolés & Dr. Claudio Gómez‐Perretta de Mateo"The Microwave Syndrome": A Preliminary Study in Spain, 2009,pp 161-169.
7. Navarro EA, Segura J, Portoles M, Gomez-Perretta C ,"The Microwave Syndrome": A Preliminary Study in Spain. ,Electromagn Biol Med 2003; 22 (2-3): 161-169.
8. Arthur Firstenberg ,"No Place To Hide" , Special Issue on Russian and Ukranian Research, Russia, Ukraine, Cellular  Phone Task Force,Vol. 3, No. 1 Apr 2001.
9. Kjell Hansson Mild "Electromagnetic environment in electrical hypersensitivities homes and workplaces" in Proc.International Workshop on EMF Hypersensitivity, Prague, Czech Republic, 2004.pp. 63-78.
10. Hagström , J. Auranen, R. Ekman "Electromagnetic hypersensitive Finns: Symptoms, perceived sources and treatments, a questionnaire study "Turku University of Applied Sciences/Telecommunication and e-Business/Radio and EMC Laboratory, Joukahaisenkatu 3C, 20520 Turku, Finland, Vol. 20, Issue 2,  Apr.2013,pp 117–122.
11. David Ghezel-Ahmadi, Alice Engel, Jörg Weidemann, Lygia Therese Budnik, “Heavy metal  exposure in patients suffering from  electromagnetic hypersensitivity”, Vol 408, Issue 4, 15 Jan 2010, pp 774–778.
12. Dr Brian Clement "Electrosensitivity symptoms can be eliminated in 60% of people and significantly reduced in the rest ",la maison du 21e siecle magazine,quebec,Canada, Mar 2012.
13. Gem Newman, Catrina Duffy, Adrian Powell,Ryan Gray, Jason Locklin, "Electromagnetic hypersensitivity idiopathic environmental intolerance attributed to electromagnetic fields”,2012.
14. [Osman Erogul,Emin Oztas,Ibrahim Yildirim,Tayfun Kir."Effects of electromagnetic radiation from cell phone on human sperm motility" Vol 37, Issue 7, Oct 2006, pp 840–843.
15. Fejes I, Zavacki Z, Szollosi J, Koloszar S, Daru J, Kovacs L, Pal A." Isthere a relationship between cell phone use and semen quality?" Arch Androl . 2005 Sep-Oct;51(5):pp385-393.
16. Aitken RJ, Bennetts LE, Sawyer D, Wiklendt AM, King BV,"Impact of radio frequency electromagnetic radiation on DNA integrity in the male germline". Int J Androl 2005;28:171e179.
17. Hardell L, Hallquist A, Mild KH, Carlberg M, Påhlson A, Lilja A."Cellular and cordless telephones and the risk for brain tumours". Eur J Cancer Prev. 2002 Aug;11(4):377-86.
18. Khurana, Vini, "Mobile Phone-Brain Tumour Public Health Advisory" 3-4. self-pub, Retrieved on 2008.
19. Johansson A1, Nordin S, Heiden M, Sandström M "Symptoms, personality traits, and stress in people with mobile phone-related symptoms and electromagnetic hypersensitivity". Journal of Psychosomatic Research,| Vol 68, Iss 1, pp 1-104.
20. Muscat JE, “Handheld Cellular Telephone use and Risk of Brain Cancer,” JAMA,the journal of the American Medical Association 284.23:3001-7, 2000.
21. Nicola Zoppetti, Daniele Andreuccetti, Carlo Bellieni, AndreaBogi, IolePinto, "Evaluation and characterization of fetal exposures to low frequency magnetic fields generated by laptop computers,"Prog Biophys Mol Biol. 2011 Dec;107(3):456-63.
22. Ali Zamanian and Cy Hardiman, Fluor Corporation, Industrial and Infrastructure Group.,"Electromagnetic Radiation and Human Health" A Review of Sources and Effects, July2005, Summit Technical Media.G. O. Young, “Synthetic structure of industrial plastics (Book style with paper title and editor),” in Plastics, 2nd ed. vol. 3, J. Peters, Ed.  New York: McGraw-Hill, 1964, pp. 15–64.

392-398

69.

Authors:

Paper Title:

Abstract: The blockchain is an irrefutably a clever invention, where the digital information gets distributed across multiple nodes. The concept was hosted in bitcoin cryptocurrency systems for distribution of coins in a distributed ledger system. Introduction of smart contracts in Ethereum blockchain explored various distinct applications ranging from financial services, supply chain management, healthcare amid others. However, current set of literatures focus more on development and realization of blockchain and petite work is done on mathematical models, performance analysis and optimization of blockchain systems. In this paper, mathematical model is developed using M/M/1 queue model to evaluate transaction processing time. In M/M/1, M symbolizes Markovian arrival and departure of transactions in blocks. The arriving and departure of the blocks are denoted by symbols 𝜆, µ respectively and operates under exponential assumptions. Three different conditions of 𝜆, µ are taken into consideration of complete evaluation of blocks acceptance and the mathematical tactic will open a sequence of possibly favorable research in queueing theory of blockchain systems. The research founds its limitation in acceptance rate is always one unit larger than the arrivals of blocks (termed as an ergodic condition) for stable working of the complete system.  

Keywords: M/M/1 queue, blockchain, transaction processing time, Ergodicity. 

References:

1. Koteska, E. Karafiloski, and A. Mishev (2016), “Blockchain Implementation Quality Challenges: A Literature Review,” p. 8.
2. Tapscott and A. Tapscott, “Here’s Why Blockchains Will Change the World | Fortune,” 2016. [Online]. Available: http://fortune.com/2016/05/08/why-blockchains-will-change-the-world/. .
3. T. Insider, “10% of Global GDP to Be Stored on Blockchain,” Crypto Tech Insider, 11-Jan-2018. .
4. Carson, G. Romanelli, P. Walsh, and A. Zhumaev, “The strategic business value of the blockchain market | McKinsey.” [Online]. Available: https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/blockchain-beyond-the-hype-what-is-the-strategic-business-value. [Accessed: 26-Oct-2018].
5. Möser and R. Böhme, “Trends, Tips, Tolls: A Longitudinal Study of Bitcoin Transaction Fees,” in Financial Cryptography and Data Security, vol. 8976, M. Brenner, N. Christin, B. Johnson, and K. Rohloff, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015, pp. 19–33.
6. Altcoin, “Bitcoin and Ethereum vs Visa and PayPal – Transactions per second,” Altcoin Today, 2017.
7. Swan, Blockchain. 2015.
8. Changfu and L. Zhenyu, “Research of Transaction Request Handling Queueing System in the e-Business Environment Based on Queuing Theory,” in 2009 Asia-Pacific Conference on Information Processing, 2009, vol. 2, pp. 589–592.
9. Wu and N. Zhao, “Mean queue time approximation for a workstation with cascading,” in 2016 Winter Simulation Conference (WSC), Washington, DC, USA, 2016, pp. 2664–2675.
10. Farayibi, “Investigating the Application of Queue Theory in the Nigerian Banking System,” SSRN Electron. J., 2016.
11. P. Lehoczky, “Real-Time Queueing Theory,” in Proceedings of the 17th IEEE Real-Time Systems Symposium, Washington, DC, USA, 1996, pp. 186–192.
12. Kasahara and J. Kawahara, “Effect of Bitcoin fee on transaction-confirmation process,” Journal of Industrial & Management Optimization2018, Volume 13, Pages 1-22, 21-Apr-2018. [Online]. Available: http://www.aimsciences.org/article/doi/10.3934/jimo.2018047.
13. Kawase and S. Kasahara, “Transaction-Confirmation Time for Bitcoin: A Queueing Analytical Approach to Blockchain Mechanism,” in Queueing Theory and Network Applications, 2017, pp. 75–88.
14. -L. Li, J.-Y. Ma, and Y.-X. Chang, “Blockchain Queueing Theory,” ArXiv180801795 Cs Math, Aug. 2018.
15. Cocco and M. Marchesi, “Modeling and Simulation of the Economics of Mining in the Bitcoin Market,” PLOS ONE, vol. 11, no. 10, p. e0164603, Oct. 2016.
16. Zheng, S. Xie, H.-N. Dai, X. Chen, and H. Wang, “Blockchain challenges and opportunities: a survey,” p. 24.
17. Singh and R. Srivastava, “Design and Implementation of G/G/1 Queuing Model Algorithm for its Applicability in Internet Gateway Server,” p. 9.
18. Trivedi, Probability and Statistics with Reliability, Queuing, and
19. Computer Science Applications, 2nd Edition, 2nd ed., vol. 1, 1 vols.
20. California: Wiley, 2001.

399-401

70.

Authors:

Paper Title:

Abstract: Health-care is a field of the most needed service and an economically 2nd largest industry in 21st century. While we talk about the affordability and quality assurance in health-care industry, several statistical analysis is carried on to make health solutions more precise and flawless in this current era of increasing health problems and chronic diseases. Advancements on data driven intelligent technologies is disease diagnosis and detection, treatment and research are remarkable. Medical image analysis, symptom based disease prediction is the part where the most sought after brains are working. In this paper we aim to present our proposed model on the prediction on diagnosis of cardio vascular disease with ECG analysis and symptom based detection. The model aims to be researched and advance in further to become robust and end to end reliable research tool. We will discuss about the classical methods and algorithms implemented on CVD prediction, gradual advancements, draw comparison of performance among the existing systems and propose an enhanced multi-module system performing better in terms of accuracy and feasibility. Implementation , training and testing of the modules have been done on datasets obtained from UCI and Physionet data repositories. Data format have been modified in case of the ECG report data for betterment of action by the convolutional neural network used in our research and in the risk prediction module we have chosen attributes for training and implementing the multi-layered neural network developed by us. The further research and advancement possibilities are also mentioned in the paper.

Keywords: attribute, classification CVD(Cardio vascular disease), convolutional neural network, multi-layered neural network, Physionet, UCI (University of California,Irwin).

References:

1. Vijayarani, S. Sudha, “Comparative Analysis of         Classification Function Techniques for Heart Disease”,International Journal of Innovative Research in Computer and Communication Engineering, Vol. 1, Issue 3, May 2013.
2. Ade, Dhanashree, S. Medhekar, Mayur P. Bote, “Prediction using SVM and Naïve bayes”, International Journal of Engineering Sciences and Research Technology, May 2013.
3. “Clinical Electrocardiography- A simplified approach” by Ary L. Goldberger.
4. “Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks” by Pranav Rajpurkar, Awni Y. Hannun, Masoumeh Haghpanahi, Codie Bourn, Andrew Y. Ng.
5. Ashraf Osman Ibrahim, Siti Mariyam Shamsuddin, Nor Bahiah Ahmad, Sultan Noman Qasem “Three-Term Backpropagation Network Based On Elitist Multiobjective Genetic Algorithm For Medical Diseases Diagnosis Classification” Life Science Journal 2013. Pp 1815-1823.
6. T Hastie, R Tibshirani, and J H Friedman. The elements of statistical learning, volume 1. Springer New York, 2001.
7. Anbarasi, E. Anupriya and N.Iyengar, “Enhanced prediction of heart disease with feature subset selection using Genetic algorithm”, International Journal of Engineering Science
8. and Technology vol.2, pp.5370- 5376, 2010.
9. Nidhi Bhatla, Kiran Jyoti, ”An Analysis of Heart Disease Prediction using Different Data Mining Techniques”, International Journal of Engineering Research & Technology (IJERT) Vol. 1 Issue 8, October – 2012.
10. (https://www.physionet.org/physiobank/database/mitdb/) - Physiobank database source of MIT-BIH dataset.
11. Moody GB, Mark RG. The impact of the MIT-BIH Arrhythmia Database. IEEE Eng in Med and Biol 20(3):45-50 (May-June 2001). (PMID: 11446209).
12. (https://archive.ics.uci.edu/ml/datasets/heart+Disease) – UCI machine learning repository- heart disease dataset.
13. “Machine Learning with Convolutional Neural Networks in Medical Diagnosis” by Michael Smith- 200784771, MPhys Research Project.
14. “Predicting Cardiac Disease With Deep Learning” by Taylor Archibald, Corey Woodfield, Jesse Robinson, and Benjamin Bay- December 2017 CS 478–Machine Learning Brigham Young University.
15. “Principal component analysis. Chemometrics and intelligent laboratory systems” by Svante Wold, Kim Esbensen, and Paul Geladi.2(1-3):37–52, 1987

402-405

71.

Authors:

Paper Title:

Abstract: Steganography, technology of information hiding that allows people to communicate secretly, in which actual data will be kept hidden inside the cover object. This paper deals with multimedia hiding system in which secret multimedia file is kept hidden in dual cover images. The proposed model takes the secret multimedia file and divided it among two cover images of same dimensions and size. The multimedia files are considered as a continuous byte and then it is vertically split into two halves, in which one half is the most significant half bytes, and the other half is the least significant half bytes. Then the two halves are embedded inside the two cover images using a four bit least significant replacement technique. To avoid capture of stego images by an attacker, it is expected to sent stego images separately through different channels. The secret multimedia file can be extracted by combining least significant half bytes of two stego images. The recovered secret multimedia file is similar in structure and content with original hidden multimedia file.

Keywords: Steganography; Multimedia File; Dual Hiding; Cover Image; Vertical Splitting; Stego Image.

References:

1. Cheddad, J. Condell, K. Curran, & P. McKevitt, “Digital image steganography: survey and analysis of current methods”,  Signal Processing, 90(3), 727-752, 2010.
2. R. Koppola, “A high capacity data-hiding scheme in LSB-based image steganography, Master thesis, University of Akron, 2009.
3. G. Schaathun, “Machine learning in image steganalysis”, John Wiley & Sons, 2012.
4. Aljarf & S. Amin, “Filtering and Reconstruction System for Grey-Level Forensic Images”, 17th International Conference on Image Processing (ICIP 2015) Zurich, Switzerland, 2015.
5. Aljarf, S. Amin, J. Filippas, & J. Shuttelworth, “Develop a detection system for grey and colour stego images”, International Journal of Modeling and Optimization, 3(5), 2013.
6. D. Ker, “Batch steganography and the threshold Game”, in Security, Steganography, and Watermarking of Multimedia Contents IX, volume 6505 of Proc. SPIE, pages 04 1–13, 2007.
7. Joshi, K., & R. Yadav, “A new LSB-S image steganography method blend with Cryptography for secret communication”, 2015 Third International Conference on Image Information Processing (ICIIP), pp. 86-90, IEEE, 2015.
8. Morkel, “Image steganography applications for secure communication, Master thesis, University of Pretoria, 2012.
9. R. Manjula & Ajit Danti, “A novel hash based least significant bit (2-3-3) image steganography in spatial domain”, International Journal of Security, Privacy and Trust Management (IJSPTM) Vol 4, No 1, 2015.
10. Qasem, “Hiding secret image within RGB images using an enhanced LSB method”, Master thesis, Middle East University, Amman, Jordan, 2014.
11. T. Al-Bayati & M. M. Al-Jarrah, “DuoHide Steganography Dataset”, available online on: www.duohide.com, viewed on 30/6/2016.
12. T. Al-Bayati, “The hiding of multimedia secret files in dual RGB cover images using LSB steganography techniques, Master thesis, Middle East University, Amman, Jordan, 2016.
13. Marwa Tariq Al-Bayati, Mudhafar M. Al-Jarrah. "DuoHide: A Secure System for Hiding Multimedia Files in Dual Cover Images", 2016 9th International Conference on Developments in eSystems Engineering (DeSE), 2016
14. Johncy John, K.R. Joy. "Large Scale Image Search Using Data Compression Technique and Relevance Feedback in Raspberry Pi3", 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI), 2018
15. Sherin Sugathan. "An improved LSB embedding technique for image steganography", 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), 2016 Crossref
16. Raftari, Neda, and Amir Masoud Eftekhari Moghadam. "Digital Image Steganography Based on Integer Wavelet Transform and Assignment Algorithm", 2012 Sixth Asia Modelling Symposium, 2012. Crossref
17. Anita Babu, Vince Paul, Dimple Elizabeth Baby. "An investigation of biometric liveness detection using various techniques", 2017 International Conference on Inventive Systems and Control (ICISC), 2017

406-409