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Hill cipher, the symmetric encryption algorithm based on linear matrix transformation is no longer used due to the vulnerability in security aspects. This paper aims to present the applicability of Latin squares and magic squares of odd order n in the encryption and decryption of Hill cipher. The pair of orthogonal diagonal Latin square (ODLS) of odd order and the magic square so derived are used for double encryption and double decryption in the modified Hill cipher to make the cryptosystem more secure. Different cipher text can be produced from a single diagonal Latin square (DLS) and diagraph letters are introduced in addition to the existing 26 letters of English alphabet to make the encryption and decryption possible for the modified Hill cipher.

Hill cipher, diagonal Latin square, orthogonal diagonal Latin square, diagraph letters etc

[1] Abe G, “Unsolved Problems on Magic Squares”, Disc. Math. 127, 3-13, 1994,
[2] Ajaeb Elfadel, “Cryptography by Means of Linear Algebra and Number Theory” Master Thesis, Feb. 2014.
[3] B. D. McKay and I. M. Wanless. On the number of latin squares. Annals of combinatorics, 9(3):335–344, 2005.
[4] Edvard Vatutin, Stepan Kochemazov, Oleg Zaikin, Sergey Valyaev “Enumerating the transversal for diagonal Latin square of small order” Proceedings of the Third International Conference BOINC-based High Performance Computing: Fundamental Research and Development (BOINC:FAST 2017),Petrozavodsk, Russia, August 28 - September 01, 2017.
[5] Ervin Gergely “Note: A simple method for constructing doubly diagonalized Latin square, Journal of Combinatorial theory (A). 16, pp. 266 – 272, 1974.
[6] John Wesley brown et.al. “Completion of the Spectrum of orthogonal diagonal Latin square in graphs, Matrices & Design, Dekker, pp 43-49, 1992.
[7] J. Shao and W. Wei. A formula for the number of latin squares. Discrete mathematics, 110(1):293–296, 1992.
[8] K. Heinrich & A.J.W Hilton, “ Doubly diagonal Latin square” Discrete Math, pp. 173 – 182, 46 (1983).
[9] McCranie, Judson, “ Magic Squares of All Orders”, Mathematics Teacher, 674-678,1988
[10] Narendra B. Parmar, Dr. Kirit R. “Hill Cipher Modification: A Detailed Review” International Journal of Innovative Research in Computer and Communication Engineering, Vol. 3, Issue 3, March 2015.
[11] O. Zaikin and S. Kochemazov, “The search for Systems of Diagonal Latin Squares Using the SAT @home Project, International Journal of Open Information Technologies, Vol 5, No 11, 2015
[12] Padraic Bartlet “Lectures note on Latin squares and Magic” Mathcamp 2012.
[13] R. Bruce Mattingly “Even Order Regular Magic Square are Singular” The American Mathematical Monthly. Vol. 107, No. 9, pp. 777 – 782, Nov. 2000.
[14] Robert L. Solso, Paul F. Barbuto. Jr., Connie L. Juel, “ Methods & Designs, Bigram and trigram frequencies abd versatilities in the English language” Behavior Research Methods & Instrumentation, Vol. 11(5), 475-484, 1979.
[15] Romen T, Tomba I, Shibiraj N, “ Algorithm for a Modified Technique on Construction of Odd Magic Square using Basic Latin Squares”, IJSR(online), Volume - 6, Issue - 2, February 2017
[16] Shibiraj N., Romen T, Tomba I, “Constructing Doubly Even Magic Squares using Reversion Process: A Simple Technique” International Journal of Creative Research Thoughts, Vol. 6, Issue 1, March 2018
[17] Shibiraj N, Tomba I, “Fixed Points under Odd Magic Square Transformation” International Journal of Latest Trends in Engineering and Technology, Vol. (9) Issue (4), pp. 057 – 060, February-2018
[18] Tomba I: A Technique for constructing Odd-order Magic Squares using Basic Latin Squares, International Journal of Scientific and Research Publications, Vol. 2, Issue-5, May 2012
[19] Tomba I , Shibiraj N. “Successful Implementation of the Hill and Magic Square Ciphers: A New Direction” International Journal of Advanced Computer Technology (IJACT), Vol. 2, Issue-6, June 2013.
[20] V.K Pachghare, Cryptography and Information Security: PHI Learning Private Limited, New Delhi, 2009

Shibiraj N, Tomba I, "Modified Hill Cipher: Secure Technique using Latin Square and Magic Square," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.315-320, 2018.

Large amount of data produced through humans, machines and automated system can be monitored for improving the efficiency of systems. This data can be real time monitored through Big Data analysis. Big Data could be structured, unstructured or semi-structured and is characterized by Volume, Velocity, Variety, Veracity and Value. The data is novel, dynamic, and scalable. Data analysis performed on Big Data can be highly useful in E-governance. This paper explores the five ways in which big data is characterized, methods of its classification, Big data management and its role in effective implementation of e-governance like education, health care, revenue etc., where huge amount of data is generated. This data is useful in understanding the factors that can be monitored and analyzed for improvement and betterment of current policies initiated by government.

Big Data, Dynamic, Scslable, Data Silos, E-governance

[1] Big Data: Characteristics, Challenges and Data Mining, International Journal of Computer Applications (0975-8887); International Conference on Advances in Information Technology and Management ICAIM-2016s.
[2] Introduction to Big Data, Ilkay Altinas, www.coursera.com.
[3] Hiba Jasim Hadi, Ammar Hameed Shnain, Sarah Hadishaheed, Azizahbt Haji Ahmad,’ BIG DATA AND FIVE V’S CHARACTERISTICS’, International Journal of Advances in Electronics and Computer Science, ISSN: 2393-2835, Volume-2, Issue-1, Jan.-2015
[4] Walunj Swapnil K., Yadav Anil H., Sonu Gupta,’ Big Data: Characteristics, Challenges and Data Mining’, International Journal of Computer Applications (0975 – 8887)-2016.
[5] Motashim Rasool, Wasim Khan, Big Data: Study in Structured and Unstructured Data, International Journal of Technology Innovations and Research (IJTIR).
[6] K.Arun Dr.L.Jabasheela ‘Big Data: Review, Classification and Analysis Survey’, International Journal of Innovative Research in Information Security, ISSN: 2349-7017 Volume 1 Issue September 2014.
[7] Praful Koturwar,SheetalGirase, Debajyoti Mukhopadhyay ‘A Survey of Classification Techniques in the Area of Big Data’,
[8] YongjunPiao, Hyun Woo Park, Cheng Hao Jin, Keun Ho Ryu, “Ensemble Method for Classification of High-Dimensional Data,” 978-1-4799-3919-0/14, 2014, IEEE.
[9] Sindhujaa N , Vanitha C N , Subaira A S ,’ An Improved Version of Big Data Classification and Clustering using Graph Search Technique’, International Journal of Computer Science and Mobile Computing, Vol. 5, Issue.2, February 2016, pg.224 – 229.
[10] Managing Big Data. An interview with David Gorbet ODBMS Industry WatchJuly2012http://www.odbms.org/blog/2012/07/managin 2, g-big-data-an interview-with-david-gorbet
[11] Bakshi, K., 2012. Considerations for big data: Architecture and approach. In: 2012
IEEE Aerospace Conference, Big SkyMontana. pp. 1-7.
[12] Agneeswaran, V., 2012. Big-data - Theoretical, engineering and analytics perspective. In: Srinivasa, S., Bhatnagar, V. (Eds.), Big Data Analytics. Vol. 7678 of Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp. 8-15.
[13] Andrzej Chluski, Leszek Ziora ‘The role of Big Data Solutions in the management of organizations, review of selected practical examples’, International Conference on Communication, Management and Information Technology (ICCMIT, 2015).
[14] Schmarzo B.Big Data: Understanding how data powers businesses‘, Wiley 2013.
[15] Gerard George, Martine R.
Haas,Alex Pentland, ‘Big Data and Management’, Academy of Management Journal 2014, Vol. 57.
[16] Preet Navdeep, Dr. Manish Arora, Dr. Neeraj Sharma,’ Role of Big Data Analytics in Analyzing e-Governance Projects’ GIAN JYOTI E-JOURNAL, Volume 6, Issue 2 (Apr-Jun 2016) ISSN 250-348X
[17] Rajagopalan M.R, Solaimurugan vellaipandiyan,’ Big Data Framework for National e-Governance Plan’ , Eleventh International Conference on ICT and Knowledge Engineering-2013.
[18] Department of Electronics and informstion technologies India, http://deity.gov.in/content/national-e- governanceplan.
[19] Aapka Aaadhaar, Unique identity & address in India. http://uidai.gov.in/.

Harshita Saluja, Pallavi Asthana, Sumita Mishra, Sachin Kumar, Bramah Hazela, "Big Data In E-Governance Management," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.321-325, 2018.

Cloud Computing is a new paradigm that offers computing resources such as network, storage, servers, applications and services whenever there is a demand based on pay-as-you go standard. It is about sharing of resources among cloud customers. Cloud provides computing infrastructure with a development platform on which users can develop their own applications. Besides the advantages of Cloud, it has certain challenges to be resolved. In this article, the challenges of Cloud computing in various aspects are discussed.

cloud computing, resources, services, cloud service provider, internet, data, storage, applications, security

[1] Peter Mell, Timothy Grance, “The NIST Definition of Cloud Computing”, National Institute of Standards and Technology, U.S. Department of Commerce, Special Publication 800 – 145.
[2] Prof. M. R. Joshi, Bhagyashri V. Tikar, “Cloud-Computing its Services and Resent Trends”, International Journal of Computer Science and Mobile Computing, Vol.4 Issue.2, February- 2015, pg. 136-143.
[3] R.Balasubramanian, M.Aramudhan, “Security Issues: Public vs Private vs Hybrid Cloud Computing”, International Journal of Computer Applications (0975 – 8887), Volume 55– No.13, October 2012.
[4] Adnaan Arbaaz Ahmed, Dr.M.I.Thariq Hussan, “Cloud Computing: Study of Security Issues and Research Challenges”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), Volume 7, Issue 4, April 2018, ISSN: 2278 – 1323. [5] Y Z An, Z F Zaaba, N F Samsudin, “Reviews on Security Issues and Challenges in Cloud Computing”, International Engineering Research and Innovation Symposium (IRIS), IOP Conf. Series: Materials Science and Engineering 160 (2016) 012106.
[6] Chirag Modi, Dhiren Patel, Bhavesh Borisaniya, Avi Patel, Muttukrishnan Rajarajan, “A survey on security issues and solutions at different layers of Cloud computing”, Springer Science+Business Media, New York, 2012.
[7] Qusay Kanaan Kadhim, Robiah Yusof, Hamid Sadeq Mahdi, Sayed Samer Ali Al-shami, Siti Rahayu Selamat, “A Review Study on Cloud Computing Issues”, IOP Conf. Series: Journal of Physics: Conf. Series 1018 (2018) 012006.
[8] Gururaj Ramachandra, Mohsin Iftikhar, Farrukh Aslam Khan, “A Comprehensive Survey on Security in Cloud Computing”, Procedia Computer Science 110 (2017), pp. 465–472.
[9] V. Kiran Kumar, E. Hari Prasad, “Proposed Model for Ensuring more Security in Cloud by Data Fragmentation Method”, International Journal of Computer Sciences and Engineering, Vol. 6, Issue – 11, Nov. 2018.

S. Edel Josephine Rajakumari, T. Daisy Premila Bai, H. M. Leena, "Analysis of Cloud Computing and Its Challenges," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.326-329, 2018.

In this paper we have proposed a novel segmentation method for classification of diseased and healthy maize and paddy leaves using Opposite Color Local Binary Pattern (OCLBP). The proposed works have been done on the maize and paddy leaves, the dataset has the diseased and healthy leaves, diseased leaves have the yellowish brown patches. Disease in maize and paddy leaves may be due to biotic causes. Generally, leaves spotted with yellow at initial stage and appear bronzed brown color at end stage at its disease levels. The diseased spots are all having color transition from yellow to Bronzed brown color. This yellow to bronzed brown color transition is appeared in between red and green colors of RGB color cube. This color transition motivated us to use OCLBP as a segmentation tool. The OCLBP textured image is the image of segmented diseased part which helps in extract the features. So here considered red color channel against green color channels to get the OCLBP textured image. SVM is used for diseased and heathy leaves classification. We have attempted to introduce the best segmentation, feature selection and dimensionality approaches for image texture which support fast and accurate pattern recognition and object identification.

Feature Selection, Local Binary Pattern, Gabor features, OCLBP

[1] B. Lee, “ A new method for classification of structural textures”, International Journal of Control, Automation, and Systems, 2(1), 125-133, 2004.
[2] A. HALIMI, A. ROUKHE, & OUHMAD, “ Defect Detection and Identification in Textile Fabric by SVM Method”, IOSR Journal of Engineering (IOSRJEN) Vol. 04,), PP 69-77, 2014.
[3] K. Bahl, & S. Kainth, “ Evaluation of yarn quality in fabric using image processing techniques”, Int. J. Sci. Res, 3, 558-561, 2014.
[4] F.S. Nadaf, N. Kamble, & R. Gadekar, Fabric Fault Detection Using Digital Image Processing, 2017.
[5] X. Zhang, J. Cui, W. Wang, & C. Lin, “ A study for texture feature extraction of high-resolution satellite images based on a direction measure and gray level co-occurrence matrix fusion algorithm”, Sensors, 17(7), 1474, 2017.
[6] P. Mohanaiah, P. Sathyanarayana, & L. GuruKumar, “ Image texture feature extraction using GLCM approach”, International Journal of Scientific and Research Publications, 3(5), 1, 2013.
[7] A. Ross, & R. Govindarajan, “ Feature level fusion using hand and face biometrics”, In Proceedings of SPIE conference on biometric technology for human identification II (Vol. 5779, pp. 196-204), 2005.
[8] N. Varghese, V. Verghese, P. Gayathri, & N. Jaisankar, “A survey of dimensionality reduction and classification methods”, International Journal of Computer Science and Engineering Survey, 3(3), 45, 2012.
[9] V.B. Shereena, & J.M. David, “SIGNIFICANCE OF DIMENSIONALITY REDUCTION IN IMAGE PROCESSING”, Signal & Image Processing , An International Journal (SIPIJ) Vol.6, No.3, June 2015.
[10] Y. Nan, Q. Feng, & S. Zuolei, “ Image Classification by Feature Dimension Reduction and Graph based Ranking”, arXiv preprint arXiv:1304.2683, 2013.
[11] M. Ponti, T.S. Nazaré & G.S. Thumé, “ Image quantization as a dimensionality reduction procedure in color and texture feature extraction”, Neurocomputing, 173, 385-396, 2016.
[12] I. Guyon, & A. Elisseeff, “ An introduction to variable and feature selection”, Journal of machine learning research, 3(Mar), 1157-1182, 2003.
[13] L. Liu, P. Fieguth, X. Wang, M. Pietikäinen, & D. Hu, “ Evaluation of LBP and deep texture descriptors with a new robustness benchmark”, In European Conference on Computer Vision (pp. 69-86). Springer, Cham, 2016.
[14] P.E. Rauber, R.R.O.D. Silva, S. Feringa, M.E. Celebi, A.X. Falcão, & A.C. Telea, “Interactive image feature selection aided by dimensionality reduction”, Proc. EuroVA, 54-61, 2015.
[15] Z. Sun, X. Yuan, G. Bebis, & S.J. Louis, “ Neural-network-based gender classification using genetic search for eigen-feature selection”, In Neural Networks, Proceedings of the 2002 International Joint Conference on (Vol. 3, pp. 2433-2438). IEEE, 2002.
[16] P. Kushwaha, & R.R. Welekar, “ Feature Selection for Image Retrieval based on Genetic Algorithm”, IJIMAI, 4(2), 16-21, 2016.
[17] T. Ojala, M. Pietik¨ainen, and D. Harwood, “A comparative study of texture measures with classification based on featured distributions,” Pattern Recognition, 29(1), pp 51–59, 1996.
[18] T. Ahonen, A. Hadid, and M. Pietikainen. Face description with local binary patterns: Application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell., 28(12):2037–2041, 2006.
[19] A. Hadid, M. Pietik¨ainen, and T. Ahonen, “A discriminative feature space for detecting and recognizing faces,” In CVPR (2), 2004, pp 797–804.
[20] G. Zhao and M. Pietik¨ainen, “Dynamic texture recognition using local binary patterns with an application to facial expressions,” IEEE Transaction Pattern Analysis and Machine Intelligence, 29(6): pp 915–928, 2007.
[21] S.D. Newsam and C. Kamath “Retrieval using texture features in high resolution multi-spectral satellite imagery,” SPIE Conference on Data Mining and Knowledge Discovery, 2004.

T. Harisha Naik, M. Suresha, "A novel segmentation method for classification of Diseased and Healthy Maize and Paddy Leaves Using OCLBP," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.330-334, 2018.

Software testing process ordinarily expends no less than half of the aggregate cost required in programming advancement. Programming advancement associations spend significant part of their financial plan and time in testing related tasks. Software testing is an indispensable component in the Software Development Life Cycle (SDLC) and can outfit brilliant outcomes; if directed appropriately and successfully in an improved way. Lamentably, Software testing is frequently less formal and thorough than it ought to. Regression testing means to reveal all the undesired reactions of code corrections on rest of the code. Regression testing ensures that settling of programming deficiencies does not present whatever other issues, which were absent prior. Regression testing is iterative process, where size and many-sided quality of experiments continues expanding. Along these lines, Optimization of experiments is profoundly sought to finish the regression testing inside settled time and cost limitations. Streamlining of experiments amid regression testing is an open research problem as there is no single procedure which can supersede every other system on all parameters. Along these lines, researchers ought to evolve new experiment minimization systems for regression testing to improve its feasibility in view of different parameters. This paper reports a work on building up a novel minimization procedure for regression testing utilizing firefly based optimization.

Regression Testing,Test case Minimization, Soft computing ,Object Oriented Testing, Software Maintenance

[1] M. Utting and B. Legeard, Practical model-based testing: a tools approach. 2010.
[2] P. R. Srivastava, M. Ray, J. Dermoudy, B. Kang, and T. Kim, “Test Case Minimization and Prioritization Using CMIMX Technique *,” vol.333031, pp. 25–26.
[3] Z. Li, M. Harman, and R. M. Hierons, “Search algorithms for regression test case prioritization,” IEEE Trans. Softw. Eng., vol. 33, no. 4, pp.225–237, 2007.
[4] S. Elbaum, A. G. Malishevsky, and G. Rothermel, “Test case prioritization: A family of empirical studies,” IEEE Trans. Softw. Eng.,2002.
[5] P. Parashar, A. Kalia, and R. Bhatia, “How Time-Fault Ratio helps in Test Case Prioritization for Regression Testing,” no. 1, 2016.
[6] R. Singh and M. Santosh, “Test Case Minimization Techniques : A Review 1,2,” Int. J. Eng. Res. Technol., vol. 2, no. 12, pp. 1048–1056, 2013.
[7] B. S. Ahmed, “Test case minimization approach using fault detection and combinatorial optimization techniques for configuration-aware structural testing,” Eng. Sci. Technol. an Int. J., 2016.
[8] S. Biswas, M. S. Kaiser, and S. A. Mamun, “Applying Ant Colony Optimization in software testing to generate prioritized optimal path and test data,” in 2nd International Conference on Electrical Engineering and Information and Communication Technology, iCEEiCT 2015, 2015.
[9] E. Engström, P. Runeson, and M. Skoglund, “A systematic review on regression test selection techniques,” Information and Software Technology. 2010.
[10]S. Sharma and A. Singh, “Model-based test case prioritization using ACO: A review,” in 2016 4th International Conference on Parallel, Distributed and Grid Computing, PDGC 2016, 2016.
[11]Vandana and A. Singh, “Multi-objective test case minimization using evolutionary algorithms: A review,” in Proceedings of the International Conference on Electronics, Communication and Aerospace Technology, ICECA 2017, 2017, vol. 2017–Janua.
[12]M. Rani, “Review of Regression Test Case Selection Techniques,” vol. 3, no. 5, pp. 1029–1034, 2014.
[13]S. Yoo and M. Harman, “Regression Testing Minimisation, Selection and Prioritisation : A Survey,” Test. Verif. Reliab, vol. 00, pp. 1–7, 2007.
[14]T. L. Graves, M. J. Harrold, J. Kim, A. Porters, and G. Rothermel, “An empirical study of regression test selection techniques,” in Proceedings of the 20th International Conference on Software Engineering, 1998, pp. 188–197.
[15]H. Srikanth, L. Williams, and J. Osborne, “System test case prioritization of new and regression test cases,” in 2005 International Symposium on Empirical Software Engineering, ISESE 2005, 2005, vol. 00, no. c, pp. 64–73.
[16]P. McMinn and M. Holcombe, “The state problem for evolutionary testing,” … Evol. Comput. 2003, 2003.
[17]C. Catal and D. Mishra, “Test case prioritization: A systematic mapping study,” Softw. Qual. J., vol. 21, no. 3, pp. 445–478, 2013.
[18]B. Korel, L. H. Tahat, and M. Harman, “Test prioritization using system models,” in IEEE International Conference on Software Maintenance, ICSM, 2005, vol. 2005, pp. 559–568.
[19]P. Gaur and R. S. Singhal, “A critical review on test case prioritization and optimization using soft computing techniques,” International Journal of Control Theory and Applications. 2016.
[20]R. Kruse, C. Borgelt, C. Braune, S. Mostaghim, and M. Steinbrecher, Computational Intelligence: A Methodological Introduction. 2016.
[21]A. Alert and L. Grunske, “Test data generation with a Kalman filter-based adaptive genetic algorithm,” J. Syst. Softw., 2015.
[22]H. Duan, Q. Luo, G. Ma, and Y. Shi, “Hybrid Particle Swarm Optimization and Genetic Algorithm for Multi-UAV Formation Reconfiguration,” Ieee Comput. Intell. Mag., 2013.

Ajmer Singh, Vandana, Rajvir Singh, "Regression Test Case Minimization with Firefly based Algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.335-340, 2018.

As the technology is evolving day by day, new challenges are coming in a computing world. One such challenge is Cyber Security. Cyber Security is also termed as Computer security or Information Security. Cyber security experts are facing lot of problems with the outburst of technologies like IOT. In order to avoid these outbursts Cyber security experts should be aware of many security attacks and breaches and should be in a position to classify all the attacks and prevent the system from these types of attacks. But due to heavy traffic and many more security breaches, the areas under Cyber security threat can’t be handled by humans. It is quite difficult to create standard automation algorithms to prevent certain Cyber area threats. To implement these standard automation algorithms, an area of Intelligence called as Artificial Intelligence is adapted. Using these Intelligence techniques many security breaches and cyber-attacks can be avoided. This paper focuses on Artificial Intelligence applications and techniques for Cyber Security, in order to be responsive to many Cyber-attacks.

Cyber Security, Artificial Intelligence, CAPTCHA, Expert Systems, Neural nets, Intelligent Agents

[1] Arockia panimalar, Giri Pai,”AI Techniques for Cyber Security”, IJRET, Volume: 05, 122-124.
[2] Arpitha, kaustubh Dutta, “Impact of Machine Learning and Artificial Intelligence on Mankind”,I2C2,2017.
[3] Narendra kumar, Nidhi, Rashi,” Ethical aspects and Future of AI”,2016,ICICCS,111-114.
[4] Arlindo Oliveria,” Cyber Security and role of Artificial Intelligence”.
[5] Dr. Sunil Bhutada, Preethi Butada,” Applications of AI in CyberSecurity”,IJERCSE,Volume 5,Issue 4,214-219.
[6] P. Santra,”An Expert Forensic Investigation System for Detecting Malicious Attacks and Identifying Attackers in Cloud Environment” Research Paper | Journal (IJSRNSC) Vol.6, Issue.5, pp.1-26, Oct-2018

Rashmi B H, "Impact of Artificial Intelligence on Cyber Security," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.341-343, 2018.

Usability Testing is the extent to which a product can be used by a particular user to achieve a defined target with effectiveness, efficiency and achieve satisfaction of use in a particular context. Usability is one of the important criteria to measure website quality and reliability. In addition to visual esthetics, usability of a website is a strong determinant for user’s satisfaction and pleasure. Designing user’s expectations is quite challenging job for any websites designer. Users have different usability requirements based on their age group and experience level. Therefore, it is very challenging to gather and implement the diverse nature of expectations against different users. Usability Testing is the extent to which a product can be used by a particular user to achieve a defined target with effectiveness, efficiency and achieve satisfaction of use in a particular context. There are five common usability testing characteristics that may lead to the best practices in the usability testing. The five characteristics are specific goals for each test, participants representing real users, participants doing real tasks, usability researcher observes and records what participants do and say and usability researcher doing the data analysis, diagnoses the problems, and recommends changes. Websites are the key aspect for satisfying the user’s demands. This papers aims to test the usability and reliability of websites.

Usability Testing, Functional Testing, Websites

[1] Ruili Geng , Jeff Tian, “Improving Web Navigation Usability by Comparing Actual and Anticipated Usage”, IEEE Transactions on human machine systems, VOL. 45, NO. 1,February 2015.
[2] P. J. Meyers, “25 Point Website Usability Checklist,” 2009. [Online]. Available: http://drpete.co/blog/25-point-websiteusability-checklist. [Accessed: 20-Apr-2015].
[3] Y.-L. Theng and J. Sin, “Evaluating Usability and Efficaciousness of an E-learning System: A Quantitative, Model-Driven Approach,” July 2012 IEEE 12th Int. Conf. Adv. Learn. Technol., pp. 303–307.
[4] N. S. Aziz, A. Kamaludin, and N. Sulaiman. “Assessing Web Site Usability Measurement”, In IJRET, vol. 2(9), pp. 386–392, 2013.
[5] J. Rubin and D. Chisnell, “Handbook of Usability Testing: How to Plan, Design and Conduct Effective Tests”, John Wiley & Sons, 2011.
[6] M. H . TIlOwfeek and M. N. A. Salam, "Students` Assessment on the Usability of E-Ieaming Websites," in Procedia - Social and Behavioral Sciences, 2014, vol. 141, pp. 916-922.
[7] Usability Testing on Government Agencies Web Portal: “A Study on Ministry of Education Malaysia (MOE) Web Portal”, Dec 2015 9th Malaysian Software Engineering Conference, Malaysia.
[8] Ms. Jyoti Arora “Web Testing using UML Environment Models”, International Conference on Computing, Communication and Automation (ICCCA2016)
[9] Sharmistha Roy, Prasant Kumar Pattnaik, Rajib Mall “Quality assurance of academic websites using usability testing: an experimental study with AHP”, Int J Syst Assur Eng Management, 2016.
[10]Swapnil S. Patil1, Hridaynath P. Khandagale ”Enhancing Web Navigation Usability using web usage mining techniques”, International Research Journal of Engineering and Technology (IRJET), 2016.
[11] Marjorie Rush Hovde Indiana University-Purdue University Indianapolis.”Connecting in Online Technical Communication Courses: Addressing Usability Challenge for Students and Faculty Members”, 2015 IEEE.
[13] Dawam Dwi Jatmiko Suwawi1, Eko Darwiyanto2, Martiana Rochmani3 1, 2, 3, School of Computing Telkom University Bandung, Indonesia,”Evaluation of Academic Website Using ISO/IEC 9126”, 2015 3rd International Conference on Information and Communication Technology (ICoICT).
[14] Mohammed Naim Khan, Namita Arya and Amit Prakash Singh ,” MBT for Functional Testing of Embedded Systems” IJCSE ISSN:2347-2693(E) Vol.04 , Issue.05 , pp.10-16, Jul-2016

K. Jaganeshwari, S. Djodilatchoumy, "A Study on Quality and Reliability of websites using Functional testing and Usability testing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.344-348, 2018.

Software testing is a principal however complex piece of software development life cycle. It points towards recognizing the bugs and faults in the program of functional behavior. It always needs generation of test cases and suites for confirming their input ranges. The Optimization of Software testing is the foremost challenge. Subsequently to achieve greatest coverage the test must be created from overall dispersed regions of input areas, known Partition testing. Random testing serve better than partition testing nevertheless it also generating high computational overheads. Another technique is Adaptive Random technique having adaptive nature of recovering and finishing a portion of the test cases back to the input for correcting the next test cases lined to be passed. Adaptive Random Partition Testing (ARPT) was used to test software which utilized AT and RT in an alternative manner. The computational intricacy issue of random partitioning in ARPT strategies was resolved by utilizing clustering algorithms. It expends additional time and it prompts overhead procedure to estimate parameters of ARPT. In this paper, the parameters of ARPT 1 and ARPT 2 are optimized using Bacterial Foraging Algorithm (BFA) and Improved BAT algorithm which improves the accuracy of ARPT software testing strategies. However, the BFA has the most critical parameter step size that has strong influence in the convergence and stability of algorithm. In order to solve these problems, the improvised BAT optimization algorithm is proposed in this paper. It improves the accuracy and reduces time consumption of parameter setting of ARPT testing strategies.

Software testing, Adaptive Random Partition Testing, BFA, Improvised BA

Arcuri, A., & Briand, L.” Formal analysis of the probability of interaction fault detection using random testing”. IEEE Transactions on Software Engineering, 38(5), 1088-1099, 2012
[2] Bashir, M. B., & Nadeem, A. (2017). Improved Genetic Algorithm to Reduce Mutation Testing Cost. IEEE Access.
[3] Deak, A., Stålhane, T., & Sindre, G., “Challenges and strategies for motivating software testing personnel.”, Information and Software Technology, 73, 1-15. 2016.
[4] Devika Rani Dhivya K., Meenakshi V.S. “An Optimized Adaptive Random Partition Software Testing by Using Bacterial Foraging Algorithm”, Lecture Notes in Computational Vision and Biomechanics, vol 28. Springer, Cham, Print ISBN978-3-319-71766-1, Online ISBN978-3-319-71767-8. Pg.No. 542-555, 2018.
[5] Huang, R., Liu, H., Xie, X., & Chen, J. “Enhancing mirror adaptive random testing through dynamic partitioning”. Information and Software Technology, 67, 13-29, 2015.
[6] Iyad Alazzam1 , Izzat Alsmadi2 and Mohammed Akour ,”Test Cases Selection Based on Source Code Features Extraction,” International Journal of Software Engineering and Its Application, Vol.8, No.1, pp.203-214. 2014.
[7] Lv, J., Hu, H., Cai, K. Y., & Chen, T. Y. , “Adaptive and random partition software testing”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44(12), 1649-1664, 2014.
[8] Machado, B. N., Camilo-Junior, C. G., Rodrigues, C. L., & Quijano, E. H. “SBSTFrame: a framework to search-based software testing”, In Systems, Man, and Cybernetics (SMC), 2016 IEEE International Conference on (pp. 004106-004111), 2016.
[9] Mao, C. Adaptive Random Testing Based on Two-Point Partitioning. Informatica (Slovenia), 36(3), 297-303. 2012.
[10] M. Beskirli and I. Koc, "A Comparative Study of Improved Bat Algorithm and Bat Algorithm on Numerical Benchmarks," 2015 4th International Conference on Advanced Computer Science Applications and Technologies (ACSAT), Kuala Lumpur, pp. 68-73, 2015.
[11] Shahbazi, A., Tappenden, A. F., & Miller, J. ,“Centroidal voronoi tessellations-a new approach to random testing”, IEEE Transactions on Software Engineering, 39(2), 163-183, 2013.
[12] Schwartz, A., & Do, H. “Cost-effective regression testing through Adaptive Test Prioritization strategies”, Journal of Systems and Software, 115, 61-81, 2016.
[13] Singh, K., & Rani, S. “Anti-random test generation in software testing”. Journal of Global Research in Computer Science, 2(5), 17-24, 2011.
[14] X.-S. Yang, A New Metaheuristic Bat-Inspired Algorithm, in: Nature Inspired Cooperative Strategies for Optimization, (Eds. J. R. Gonzalez et al.), Studies in Computational Intelligence, Springer Berlin, 284, Springer, 65-74, 2010.

K. Devika Rani Dhivya, V.S. Meenakshi, "Comparative Analysis on Parameter Optimization for ARPT," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.349-354, 2018.

Software Defined Network is used to simplify network management by separating network control logic and forwarding mechanism which is gaining popularity as a Network in the recent years. Distributed Mobility Management is an efficient Mobility Management technique which ensures network based Mobility Management, eliminates Single Point of Failure. On the other hand, Software Defined Networks increase scalability by separating Control and Data plane unlike traditional network system. In this paper, we implement DMM scheme by distributing Control and Data plane for Mobility Management in IPv6 based networks. This scheme is expected to minimize tunneling overhead due to encryption/decryption of the data packet and minimize signaling cost. Here, we distribute both control and data plane into multiple entities and provide a suitable algorithm for selecting an appropriate agent for incoming digital Mobile Node. The performance of the selection algorithm is planning to analyze in term of handover delay, signaling costs, packet Delivery cost end to end delay and throughput.

Software Defined Network Centralized Mobility Management, Distributed Mobility Management, Single Point of Failure, Tunneling overhead

[1] ONF Market Education Committee. Software-defined networking: the new norm for networks. ONF White Paper, 2012.
[2] Yili Gong, Wei Huang, Wenjie Wang and Yingchun Lei, “A survey on software defined networking and its applications”, Frontiers of Computer Science, vol. 9(6), pp. 827-845, 2015.
[3] Zuo Q, Chen M, “Openflow-based SDN technologies”, Journal of Software, vol. 24(5), pp. 1078–1097, 2013.
[4] O.N. Foundation, “Software-Defined Networking (SDN) Definition”, [Online]. Available: http://goo.gl/O2eTti.
[5] N. McKeown, T. Anderson, H. Balakrishnan, G. Parulkar, L. Peterson, J. Rexford, S. Shenker, and J. Turner, “OpenFlow: enabling innovation in campus networks”, SIGCOMM Comput. Commun. Rev., vol. 38(2), pp. 69–74, March 2008.
[6] ONF, “Open networking foundation”, [Online]. Available: https://www.opennetworking.org/
[7] Morteza Karimzadeh, Anna Sperotto and Aiko Pras, “Software Defined Networking to Improve Mobility Management Performance”, 8th IFIP WG 6.6 International Conference on Autonomous Infrastructure, Management, and Security, Brno, Czech Republic, springer LNCS vol. 8508, pp. 118–122, 2014.
[8] Li Yi, Huachun Zhou, Daochao Huang and Hongke Zhang, “D-PMIPv6: A distributed mobility management scheme supported by data and control plane separation”, Mathematical and Computer Modelling, vol. 58, pp. 1415-1426, 2013.
[9] Yuhong Li, H. Wang, M. Liu, B. Zhang and H. Mao, “Software defined networking for distributed mobility management”, IEEE Globecom Workshops (GC Wkshps), Atlanta, GA, pp. 885-889, Dec. 2013.
[10] Morteza Karimzadeh, Luca Valtulina and Georgios Karagiannis, “Applying SDN/OpenFlow in Virtualized LTE to support Distributed Mobility Management (DMM)”, 4th International Conference on Cloud Computing and Services Science, Barcelona, Spain, pp. 86-91, Dec. 2014.
[11] L. Valtulina, M. Karimzadeh, G. Karagiannis, G. Heijenk and A. Pras, “Performance evaluation of a SDN/OpenFlow-based Distributed Mobility Management (DMM) approach in virtualized LTE systems”, IEEE Globecom Workshops (GC Wkshps), Austin, TX, pp. 18-23, Dec. 2014.
[12] I. Elgendi, K. S. Munasinghe and A. Jamalipour, “A Three-Tier SDN based distributed mobility management architecture for DenseNets”, IEEE International Conference on Communications (ICC), Kuala Lumpur, pp. 1-6, May 2016.
[13] H. Yang and Y. Kim, “SDN-based distributed mobility management”, International Conference on Information Networking (ICOIN), Kota Kinabalu, pp. 337-342, April 2016.
[14] Nitul Dutta and Iti Saha Misra, “Multilayer Hierarchical Model for Mobility Management in IPv6: A mathematical exploration”, Wireless Personal Communications (Springer), Vol. 78 (2), pp. 1413-1439, September 2014.
[15] E. Hyytia, P. Lassila and J. Virtamo, “Spatial node distribution of the random waypoint mobility model with applications” IEEE Transactions on Mobile Computing, vol. 5(6), pp. 680-694, 2006.
[16] Hassan Ali-Ahmad, Meryem Ouzzif, Philippe Bertin and Xavier Lagrange, “Performance Analysis on Network-Based Distributed Mobility Management", Wireless Personal Communications, vol. 74(4), pp. 1245-1263, 2014.

Nikita Bhalani, Mayur Chavan, "A Distributed Mobility Management Scheme based on Software Defined Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.355-360, 2018.

Cloud computing is a powerful technology used to perform massive scale computing. Cloud computing avoids high cost of hardware, software and other resources for computing in the local machine. Now a day’s massive amount of big data is generated through cloud system. Big data is complex in nature so analyzing it requires large computational infrastructure like cloud systems. In this paper, the use of big data in cloud system is reviewed. The comparison of big data and various cloud platforms are also discussed. Different issues of big data and how cloud platforms are addressed the above said issues are considered here .Furthermore, research challenges related to storage and securities are investigated. Finally open challenges that require high degree of research inputs and efforts are also summarized.

Bigdata,CloudComputing,Cloudsecurity

[1] X. Wu, X. Zhu, G. Q. Wu, and W. Ding, “Data mining with big data,” IEEE Trans. Knowl. Data Eng., vol. 26, no. 1, pp. 97–107, 2014.
[2] M. Chen, S. Mao, and Y. Liu, “Big Data : A Survey,” no. January, pp. 171–209, 2014.
[3] J. C. Jackson, V. Vijayakumar, A. Quadir, and C. Bharathi, “Survey on Programming Models and Environments for Cluster , Cloud , and Grid Computing that defends Big Data,” Procedia - Procedia Comput. Sci., vol. 50, pp. 517–523, 2015.
[4] G. Yang, “The Application of MapReduce in the Cloud Computing,” 2011.
[5] V. Qxudgkd, L. Ndoedqgl, J. Frp, M. Ylw, and D. F. Lq,”A brief introduction on Big Data 5 V’s characteristics and Hadoop Technology” vol. 48, no. Iccc, pp. 319–324, 2015.
[6] Y. Li, K. Gai, L. Qiu, M. Qiu, and H. Zhao, “Intelligent cryptography approach for secure distributed big data storage in cloud computing,” Inf. Sci. (Ny)., vol. 0, pp. 1–13, 2016.
[7] C. Yang, Q. Huang, Z. Li, K. Liu, and F. Hu, “Big Data and cloud computing : innovation opportunities and challenges,” Int. J. Digit. Earth, vol. 0, no. 0, pp. 1–41, 2016.
[8] A. Fernández et al., “Big Data with Cloud Computing : an insight on the computing environment , MapReduce , and programming frameworks,” vol. 4, no. October, 2014.
[9] M. Sookhak, A. Gani, M. K. Khan, and R. Buyya, “Dynamic remote data auditing for securing big data storage in cloud computing,” Inf. Sci. (Ny)., 2015Lin C., Lee B., “Exploration of Routing Protocols in Wireless Mesh Network”, In the Proceedings of the 2015 IEEE Symposium on Colossal Big Data Analysis and Networking Security, Canada, pp.111-117, 2015.
[10] V. N. Inukollu, S. Arsi, and S. R. Ravuri, “SECURITY I SSUES A SSOCIATED WITH B IG DATA IN CLOUD COMPUTING,” vol. 6, no. 3, pp. 45–56, 2014.
[11] M. Gu, X. Li, and Y. Cao, “Optical storage arrays : a perspective for future big data storage,” no. March, 2014.
[12] C. Liu, J. Chen, S. Member, L. T. Yang, and X. Zhang, “Authorized Public Auditing of Dynamic Big Data Storage on Cloud with Efficient Verifiable Fine-Grained Updates,” vol. 25, no. 9, pp. 2234–2244, 2014.
[13] J. P. D. Comput, M. D. Assunção, R. N. Calheiros, S. Bianchi, M. A. S. Netto, and R. Buyya, “Big Data computing and clouds : Trends and future directions,” J. Parallel Distrib. Comput., vol. 79–80, pp. 3–15, 2015.
[14] G. Lafuente, “The big data security,” Netw. Secur., vol. 2015, no. 1, pp. 12–14, 2015.
[15] Ertaul, L. and Singhal, S. 2009. Security Challenges in Cloud Computing. California State University, East Bay.
[16] H. K. Patil and R. Seshadri, “Big data security and privacy issues in healthcare,” pp. 775–778, 2014.
[17] V. Thayananthan and A. Albeshri, “Big data security issues based on quantum cryptography and privacy with authentication for mobile data center,” Procedia - Procedia Comput. Sci., vol. 50, pp. 149–156, 2015.
[18] L. E. I. Xu, C. Jiang, and J. Wang, “Information Security in Big Data : Privacy and Data Mining,” pp. 1149–1176, 2014.
[19] C. Ji, Y. Li, W. Qiu, U. Awada, and K. Li, “Big Data Processing in Cloud Computing Environments,” pp. 17–23, 2012.
[20] D. Singh and C. K. Reddy, “A survey on platforms for big data analytics,” pp. 1–20, 2014.
[21] L. E. I. Xu, C. Jiang, and J. Wang, “Information Security in Big Data : Privacy and Data Mining,” pp. 1149–1176, 2014.

Meble Varghese, Victor Jose, "Big Data and Cloud computing: Review and future trends," International Journal of Computer Sciences and Engineering, Vol.6, Issue.12, pp.361-365, 2018.