Volume-4 , Issue-8 , Aug - 2016 Go Back

Software testing requires functional and non functional test cases with the values of test data.Automated testing are a method to generate the test cases with test data automatically. Optimality of test case is required for fastest data generation. Test case optimization through search based techniques is used to optimize and generate optimal test cases from the set of data values. Firefly Algorithm (FA) is a bio-inspired, evolutionary, meta-heuristic algorithm based on mating or flashing behavior of fireflies. In this paper the role of Firefly meta-heuristic search technique which is analyzed to generate and optimize random test cases with test data by applying in a case study, i.e., a withdrawal method in Bank ATM and it is observed that this algorithm is able to generate suitable automated test cases as well as test data. In this case the test case generation is very efficient and effective. This paper further, gives the brief details about the Firefly method which is used for test case generation and optimization.

Software Testing, Test Data Generation, Firefly Algorithm, Test Case Optimization

[1] Ausiello, Giorgio; et al., Complexity and Approximation (Corrected ed.), Springer, ISBN 978-3-540-65431-5,2003.
[2] B. Korel , “Automated software test generation”, IEEE Trans. on Software Engineering,16(8): 870–879,1990.
[3]. Iqbal, Zafar, Zyad, “Multi-objective optimization of test sequence generation using multi-objective firefly algorithm (MOFA)”, Robotics and Emerging Allied Technologies in Engineering (iCREATE), 2014.
[4] MA Sasa, Xue Jia, Fang Xingqiao, Liu Dongqing, “Research on Continuous Function Optimization Algorithm Based on Swarm Intelligence”, 5th International Conference on Computation, pg no. 61-65,2009.
[5].Hitesh Tahbildar and Bichitra Kalita,”Automated software test data generation: Direction of Research”,International Journal of Computer Science and Engineering Survey(IJCSES),Vol.2,No.1,2011.
[6]. Ojha.D,Sahoo.R.K.,Dash.S,”Automatic Generation Of Timetable Using Firefly Algorithm”,International Journal of Advanced Research in Computer science and software engineering,Vol.6,Issue-4,pp.589-593,2016.
[7]. P. Srivatsava, B. Mallikarjun, X.Yang,“ Optimal test sequence generation using firefly algorithm”- Swarm and Evolutionary Computation, Volume 8, pp. 44-53, February 2013.
[8]. P. R. Srivastava, M. Chis, S.Deb, X.S. Yang, “An Efficient Optimization Algorithm for Structural Software Testing”, International journal of artificial intelligence, 2012.
[9].R.Malhotra and M.Garg.”An adequacy based test data generation technique using Genetic algorithm”,Journal of Information Processing Systems,7(2),2011.
[10] Pei-Wei TSai, Jeng-Shyang Pan, Bin-Yih Liao, Shu-Chuan Chu, Enhanced Artificial Bee Colony Optimization , International Journal of Innovative Computing, Information and Control, Volume 5, Number 12, December 2009.
[11] R. Poli, J. Kennedy, T. Blackwell, Particle swarm optimization: An overview (Springer Science and Business Media, LLC 2007).
[12]Sahoo.R.K,Ojha.D,Dash.S:Nature Inspired Metaheuristic Algorithms-A Comparative Review,International Journal of Development Research,Vol.06,Issue.07, pp.8427-8432, 2016,.
[13]. Sudhir, “Performance Evaluation of Regression Test Suite Prioritization Techniques”, International Journal of Advanced Engineering and Global Technology Vol-2, Issue-10, October 2014.
[14].Vikas Panthi,D.P.Mohapatra.”Test Scenarios Generation using Path Coverage”,International Journal Of Computer Science and Informatics,Vol.3,Issue-2, pp.2231-5292, 2013.
[15] Xin-She Yang and Amir H. Gandomi, Bat Algorithm: A Novel Approach for Global Engineering Optimization, Engineering Computations, Vol. 29, Issue 5, pp. 464-483, 2012.
[16] X. S. Yang, Firefly Algorithm: Stochastic Test Functions and Design Optimisation, Int. J. Bio- Inspired Computation, Vol. 2, No. 2, pp.78–84, 2010.
[17] Yang, X. S., Nature-Inspired Metaheuristic Algorithms ( Luniver Press),2008.
[18].Yeresime Suresh,Santanu Ku.Rath,”A genetic Algorithm based approach for test data generation in basis path Testing”,International Journal of Soft computing and Software Engineering(JSCSE),Vol.3,No.3,2013.
[19] Sh. M. Farahani, A. A. Abshouri, B. Nasiri, and M. R. Meybodi, “A Gaussian Firefly Algorithm”, International Journal of Machine Learning and Computing, Vol. 1, No. December 2011.
[20] Xin-She Yang, Chaos-Enhanced Firefly Algorithm with Automatic Parameter Tuning, International Journal of Swarm Intelligence Research, December 2011.

Rajesh Kumar Sahoo, Durga Prasad Mohapatra, Manas Ranjan Patra, "A Firefly Algorithm Based Approach for Automated Generation and Optimization of Test Cases," International Journal of Computer Sciences and Engineering, Vol.4, Issue.8, pp.54-58, 2016.

Machine learning is the self-driven technology. It is the science of getting computers to act without being explicitly programmed. Machine learning refers to self-improving algorithms, explores the study and construction of algorithms that can learn from and make predictions on data. These are predefined processes conforming to specific rules, performed by a computer can be applied to any learning task and it is flexible and it don’t need a programmer or human expert.Machine learning algorithms are common in web applications that we use every day and have a growing relevance to enterprise applications. Two of the most widely adopted machine learning methods are supervised learning and unsupervised learning. While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster is a recent development.

Data mining, Artificial Intelligence, Neural Networks and Machine learning

[1] Abdullahi Uwaisu Muhammad, Abdullahi Garba Musa, Kamaluddeen Ibrahim Yarima,” Survey on Training Neural Networks “, International Journal of Advanced Research in Computer Science and Software Engineering.
[2] Anish Talwar, Yogesh Kumar,” Machine Learning: An artificial intelligence methodology”, International Journal Of Engineering And Computer Science ISSN:23197242.
[3] Bora Gaze,Steven Minton,” Overview of AutoFeed: An Unsupervised Learning System for GeneratingWebfeeds”, Fetch Technologies 2041 Rosecrans Ave.El Segundo, California, USA.
[4] S.Balaji, Dr.S.K.Srivatsa,” Unsupervised Learning in Large Datasets for Intelligent Decision Making”, International Journal of Scientific and Research Publications, Volume 2, Issue 9, September 2012 1 ISSN 2250-3153.
[5] S.R.K. Branavan, Harr Chen, Luke S. Zettlemoyer, Regina Barzilay,” Reinforcement Learning for Mapping Instructions to Actions”, Computer Science and Artificial Intelligence Laboratory.
[6] Carlos Diuk, Andre Cohen, Michael L. Littman,” An Object-Oriented Representation for Efficient Reinforcement Learning”, RL3 Laboratory, Department of Computer Science, Rutgers University, Piscataway, NJ USA.
[7] Charles Mathy, Nate Derbinsky, Jose Bento, Jonathan Rosenthal, Jonathan Yedidia,” The Boundary Forest Algorithm for Online Supervised and Unsupervised Learning”, Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence.
[8] Dasika Ratna Deepthi, G.R.Aditya Krishna, K. Eswaran,”Automatic pattern classification by unsupervised learning using dimensionality reduction of data with mirroring neural networks”.
[9] R.Deepa Lakshmi, N.Radha,” Supervised Learning Approach for Spam Classification Analysis using Data Mining Tools”, (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 08, 2010, 2760-2766.
[10] Gao Huang, Shiji Song, Jatinder N. D. Gupta, and Cheng Wu,” Semi-supervised and unsupervised extreme learning machines”, IEEE transactions on cybernetics.
[11] Gideon S. Mann, Andrew McCallum,” Simple, Robust, Scalable Semi-supervised Learning via Expectation Regularization”, Proceedings of the 24 th International Conference on Machine Learning, Corvallis, OR, 2007. Copyright 2007 by the author(s)/owner(s).
[12] Gilles Blanchard, Gyemin Lee Clayton Scott,” Semi-Supervised Novelty Detection”, Journal of Machine Learning Research 11 (2010) 2973-3009.
[13] Hetal Bhavsar, Amit Ganatra,” A Comparative Study of Training Algorithms for Supervised Machine Learning”, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-4, September 2012.
[14] Iqbal Muhammad, Zhu Yan,” Supervised machine learning approaches: a survey”, ictact journal on soft computing, april 2015, volume: 05, issue: 03.
[15] Jennifer G. Dy, Carla E. Brodley,” Feature Selection for Unsupervised Learning”, Journal of Machine Learning Research 5 (2004) 845–889.
[16] Jens Kober, J. Andrew Bagnell, Jan Peters,” Reinforcement Learning in Robotics: A Survey”, Kober IJRR 2013.
[17] Junhui Wang, Xiaotong Shen, Wei Pan,” On Efficient Large Margin Semisupervised Learning: Method and Theory”, Journal of Machine Learning Research 10 (2009) 719-742.
[18] Koushal Kumar, Gour Sundar Mitra Thakur,” Advanced Applications of Neural Networks and Artificial Intelligence: A Review”, I.J. Information Technology and Computer Science, 2012, 6, 57-68.
[19] Krishnakumar Balasubramanian, Pinar Donmez, Guy Lebanon,” Unsupervised Supervised Learning II: Margin-Based Classification without Labels”, Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS) 2011, Fort Lauderdale, FL, USA. Volume 15 of JMLR: W&CP 15. Copyright 2011 by the authors.
[20] Lei Jimmy Ba, Brendan Frey,” Adaptive dropout for training deep neural networks”, Advances in Neural Information Processing Systems.
[21] Leslie Pack Kaelbling, Michael L. Littman, Andrew W. Moore,” Reinforcement Learning: A Survey”, Journal of Artificial Intelligence Research 4 (1996) 237-285.
[22] Manju.S, M.Punithavalli,”Neural network-based ideation learning for intelligent agents: e-brainstorming with privacy preferences”,International Journal of Computational Vision and Robotics,Vol. 5, No. 3, 2015.
[23] Manju.S, M. Punithavalli, “An Analysis of Q-Learning Algorithms with Strategies of Reward Function, International Journal on Computer Science and Engineering”,ISSN : 0975-3397 Vol. 3 No. 2 Feb 2011.
[24] Marc’Aurelio Ranzato, Fu-Jie Huang, Y-Lan Boureau, Yann LeCun,”Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition”.
[25] Michał Kozielski, Malte Nuhn, Patrick Doetsch, Hermann Ney,” Towards Unsupervised Learning for Handwriting Recognition”, Human Language Technology and Pattern Recognition Group.
[26] Mykola Pechenizkiy, Alexey Tsymbal, and Seppo Puuronen,” Local Dimensionality Reduction and Supervised Learning Within Natural Clusters for Biomedical Data Analysis”,IEEE transactions on information technology in biomedicine, vol. 10, no. 3, july 2006.
[27] G. Nguyen, A. Bouzerdoum & S. Lam. Phung, "A supervised learning approach for imbalanced data sets," in International Conference on Pattern Recognition, 2008, pp. 1-4.
[28] Oliver Brdiczka, Patrick Reignier & James L. Crowley,” Supervised Learning of an Abstract Context Model for an Intelligent Environment”, Grenoble, october 2005 Joint sOc-EUSAI conference.
[29] Olivier Chapelle, Vikas Sindhwani, Sathiya S. Keerthi,” Optimization Techniques for Semi-Supervised Support Vector Machines”, Journal of Machine Learning Research 9 (2008) 203-233.
[30] Quoc V,Marc’Aurelio Ranzato,Rajat Monga,Matthieu Devin,Kai Chen,Greg S. Corrado,Jeff Dean,Andrew Y. Ng,” Building High-level Features Using Large Scale Unsupervised Learning”, Proceedings of the 29 th International Conference on Machine Learning, Edinburgh, Scotland, UK, 2012.
[31] Rich Caruana,Alexandru Niculescu-Mizil,” An Empirical Comparison of Supervised Learning Algorithms”, Proceedings of the 23 rd International Con-ference on Machine Learning, Pittsburgh, PA, 2006.
[32] Rie Kubota Ando, Tong Zhang,” A High-Performance Semi-Supervised Learning Method for Text Chunking”, IBM T.J. Watson Research Center Yorktown Heights, NY 10598, U.S.A.
[33] Rohit J. Kate and Raymond J. Mooney,” Semi-Supervised Learning for Semantic Parsing using Support Vector Machines”, In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, Short Papers (NAACL/HLT-2007), pp. 81-84, Rochester, NY, April 2007.
[34] Saneem Ahmed C.G,Harikrishna Narasimhan,Shivani Agarwal,"Bayes Optimal Feature Selection for Supervised Learning with General Performance Measures”.
[35] R. Sathya, Annamma Abraham,” Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification”, (IJARAI) International Journal of Advanced Research in Artificial Intelligence, Vol. 2, No. 2, 2013.
[36] Satinder Singh, Andrew G. Barto, Nuttapong Chentanez,” Intrinsically Motivated Reinforcement Learning”, NSF grant CCF 0432027 and by a grant from DARPA’s IPTO program.
[37] Shoushan Li, Zhongqing Wang, Guodong Zhou, Sophia Yat Mei Lee,” Semi-Supervised Learning for Imbalanced Sentiment Classification”, Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence.
[38] Ms. Sonali. B. Maind, Ms. Priyanka Wankar,” Research Paper on Basic of Artificial Neural Network”, International Journal on Recent and Innovation Trends in Computing and Communication, ISSN: 2321-8169.
[39] B. H. Sreenivasa Sarma, B. Ravindran,” Intelligent Tutoring Systems using Reinforcement Learning to teach Autistic Students”.
[40] Steve Dini ,Mark Serrano,” Combining Q-Learning with Artificial Neural Networks in an Adaptive Light Seeking Robot”, International Joint Conference on Neural Networks.
[41] Stuart Russell, Andrew L. Zimdars,”Q-Decomposition for Reinforcement Learning Agents”, Computer Science Division, University of California, Berkeley, Berkeley CA 94720-1776 USA.
[42] Taylor Berg-Kirkpatrick, Alexandre Bouchard-Cot, John DeNero,Dan Klein,” Painless Unsupervised Learning with Features”, Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the ACL.
[43] Tim Paek,” Reinforcement Learning for Spoken Dialogue Systems: Comparing Strengths and Weaknesses for Practical Deployment”, Microsoft Research One Microsoft Way, Redmond, WA 98052.
[44] Timothy P. Jurka, Loren Collingwood, Amber E. Boydstun, Emiliano Grossman, and Wouter van Atteveldt,” RTextTools: A Supervised Learning Package for Text Classification” ,The R Journal Vol.5/1 June ISSN 2073-4859.
[45] Vibha Soni, Meenakshi R Patel,” Unsupervised Opinion Mining From Text Reviews Using SentiWordNet”, International Journal of Computer Trends and Technology (IJCTT) – volume 11 number 5 – May 2014.
[46] Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra ,Martin Riedmiller,” Playing Atari with Deep Reinforcement Learning”, DeepMind Technologies.
[47] Xiang Wang, David Sontag, Fei Wang,”Unsupervised Learning of Disease Progression Models”, KDD’14, August 24–27, 2014, New York, NY, USA.
[48] Xiaojin Zhu, John Lafferty, Zoubin Ghahramani,” Combining Active Learning and Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions”, Proceedings of the ICML-2003 Workshop on The Continuum from Labeled to Unlabeled Data, Washington DC, 2003.
[49] Xinghao Pan, Joseph Gonzalez,Stefanie Jegelka,Tamara Broderick, Michael I. Jordan, “Optimistic Concurrency Control for Distributed Unsupervised Learning”.
[50] Yong Cao, Petros Faloutsos, Frédéric Pighin,” Unsupervised Learning for Speech Motion Editing”, Eurographics/SIGGRAPH Symposium on Computer Animation (2003).
[51] Yu-Feng Li, James T. Kwok, Zhi-Hua Zhou,” Semi-Supervised Learning Using Label Mean,” Proceedings of the 26 th International Conference on Machine Learning”, Montreal, Canada, 2009. Copyright 2009 by the author(s)/owner(s).
[52] Yuriy Nevmyvaka, Yi Feng, Michael Kearns,” Reinforcement Learning for Optimized Trade Execution”, Proceedings of the 23 rd International Conference on Machine Learning, Pittsburgh, PA, 2006.
[53] M.Subba Rao, Dr.B.Eswara Reddy,” Comparative Analysis of Pattern Recognition Methods: An Overview”, Indian Journal of Computer Science and Engineering, Vol. 2 No. 3 Jun-Jul 2011.

S . Parvathavardhini and S . Manju, "Analysis on Machine Learning Techniques," International Journal of Computer Sciences and Engineering, Vol.4, Issue.8, pp.59-77, 2016.

In the current computer world, preserving the information is very difficult. Some interrupts can occur on the local organization or network based system. Without security measures and controls in place our data might be subjected to an attack .Now a day’s numerous attacks are evolve. The Dos attack is the most popular attack in network and internet.This kind of attack ingests a large amount of network bandwidth and occupies network equipment resources by flooding them with packet from the machinesdispersed all over the world. Dos attacks are usually doing by following methods: 1) Send unlimited quantity of packets to the server.2) Implementing malwares.3) Teardrop attack.4) Application level flood. A DDos attack is propelled by a mechanism called Botnet through a network of controlled computers. Distributed denial of service (DDos) attack has been regularly in the works attacks that badly intimidate the stability of the internet. In accordance to CERT coordination center, there are mainly three categories of DDoS attacks: flood attacks, protocol attack and logical attack.

Ddos Attack, Wireless Sensor Network, CERT Coordination Centre, Protocol Attack And Logical Attack

[1] Thapngam, Theerasak, et al. "Discriminating DDoS attack traffic from flash crowd through packet arrival patterns." Computer Communications Workshops (INFOCOM WKSHPS), 2011 IEEE Conference on. IEEE, 2011.
[2] Jun, Jae-Hyun, Hyunju Oh, and Sung-Ho Kim. "DDoS flooding attack detection through a step-by-step investigation." Networked Embedded Systems for Enterprise Applications (NESEA), 2011 IEEE 2nd International Conference on. IEEE, 2011.
[3] Han, Young-Tae, et al. "Vulnerability of small networks for the TTL expiry DDoS attack." Computing, Communications and Applications Conference (ComComAp), 2012. IEEE, 2012.
[4] SoundarRajam, V. K., et al. "Autonomous system based traceback mechanism for DDoS attack." Advanced Computing (ICoAC), 2013 Fifth International Conference on. IEEE, 2013.
[5] Sanmorino, Ahmad, and SetiadiYazid. "Ddos attack detection method and mitigation using pattern of the flow." Information and Communication Technology (ICoICT), 2013 International Conference of. IEEE, 2013.
[6] Bhuyan, Monowar H., Dhruba Kumar Bhattacharyya, and Jugal Kumar Kalita. "Information metrics for low-rate DDoS attack detection: A comparative evaluation." Contemporary Computing (IC3), 2014 Seventh International Conference on. IEEE, 2014.
[7] Anantvalee, Tiranuch, and Jie Wu. "A survey on intrusion detection in mobile ad hoc networks." Wireless Network Security. Springer US, 2007. 159-180.
[8] Chhabra, Meghna, and B. B. Gupta. "An Efficient Scheme to Prevent DDoS Flooding Attacks in Mobile Ad-Hoc Network (MANET)." Research Journal of Applied Sciences, Engineering and Technology 7.10 (2014): 2033-2039.
[9] Alqahtani, Sarra, and Rose Gamble. "DDoS Attacks in Service Clouds."System Sciences (HICSS), 2015 48th Hawaii International Conference on. IEEE, 2015.
[10] Jae-Hyun Jun, Hyunju Oh, andSung Kim. "Real time detection and classification of DDoS attacks using Enhanced SVM with string kernels." Recent Trends in Information Technology (ICRTIT), 2015 International journals on. IEEE, 2015.

Harpinder Kaur and Bikrampal Kaur, "Selective DDoS Attacks in Application server and Wireless Network – Survey," International Journal of Computer Sciences and Engineering, Vol.4, Issue.8, pp.78-80, 2016.

Code clones means duplicate fragments of source code, have been identified as “a major source of faults, which means that duplicating can be a considerable problem during development and maintenance”. As a consequence, a large body of planned has been industrialized on how to prevent, or spot and remove code clones. The problem with code clones is that they are related only by their resemblance, i.e., implicitly rather than explicitly which makes it difficult to notice them. Therefore, changes like promotions or patches that are often meant to affect all clones in a similar way are frequently not applied to all of them uniformly. Code clone helps the developers from probable mistakes, to save time and exertion in planning the logic, to help in decoupling of classes or components and more important it reduces development cost. But identical code is generally considered as unwanted for number of reasons. Introduction of bad design and lack of good legacy structure or concept may be caused due to code clones. Probably the biggest problem in model clone detection is defining exactly what a model clone is just as for code clones a small part of a domain model of the “Library Management System”.

Code Clone, Source Code, Duplicate Fragments, Problems And Domain Model

[1] Störrle, Harald. "Effective and Efficient Model Clone Detection." Software, Services, and Systems. Springer International Publishing, 2015. 440-457.
[2] Chen, Jian, et al. "Detecting Android Malware Using Clone Detection."Journal of Computer Science and Technology 30.5 (2015): 942-956.
[3] Wyss-Coray, Anton, et al. "Biomarkers of aging for detection and treatment of disorders." U.S. Patent Application No. 13/575,437.
[4] Ritu garg, et al. "Code Clone v/s Model Clones: Pros and Cons." International Journal of Computer Applications (0975 – 8887) Volume 89 – No 15, March 2014.
[5] Patil, Ritesh V., et al. "Software code cloning detection and future scope development-Latest short review." Recent Advances and Innovations in Engineering (ICRAIE), 2014. IEEE, 2014.
[6] B. Baker. “Finding Clones with Dup: Analysis of an Experiment." IEEE Transactions on Software Engineering. vol. 33. no. 9. pp. 608-621. 2007.
[7] B. Baker. "On Finding Duplication and Near-Duplication in Large Software Systems", in Proceedings of the Second U’orking Conference on Reverse Engineering (WCRE 195). pp. 86-95. Toronto. Ontario. Canada. July 1995.
[8] C .K. Roy. J.R. Cordy and R. Koschke, “Comparison and Evaluation of Code Clone Detection Techniques and Tools: A Qualitative Approach." Science of Computer Programming, vol.74. no. 7. pp. 470-495. May 2009.
[9] Chao Liu. Chen ChenJiawei Han and Philip S. Yu.,"GPLAG: Detection of Software Plagiarism by Program Dependence Graph Analysis", In the Proceedings of the 13”’ ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 872-881. Philadelphia. USA. August 2006.
[10] EttoreMerlol. "Detection of Plagiarism in University Projects Using Metrics- based Spectral Similarity." In the Dagsmhl Seminar: Duplication, Redundancy, and Similaritv in Software. 2007.

Gagandeep Kaur and Bikrampal Kaur, "Various Methods for Measuring Similarity and Code Clone Detection," International Journal of Computer Sciences and Engineering, Vol.4, Issue.8, pp.81-84, 2016.

Energy consumption is found to be critical task in today’s high performance computing. Consuming the energy in efficient way is still an open area of research. This paper has proved that the use of heuristics like Greedy method, Random method etc doesn’t provide consistent result every time when scheduling process is being run. Greedy can’t guarantee global optimal solution however Random method based upon certain global ability sometime may get best solution sometime may not. The use of variable neighbourhood search algorithm ignore in resistant task allocation for multicore processor. So in order to overcome these issues a variable neighbourhood search based approach is proposed to decrease the energy consumption rate.

Green Computing, Energy Consumption, Variable Neighborhood Search

[1] Sheikh.H.F. And Ahmad.I, "Efficient Heuristics for Joint Optimization of Performance, Energy, and Temperature in Allocating Tasks to Multi-core Processors," International Green Computing Conference (IGCC), pp.1-8, Nov 2014.
[2] Xing Yi, Pasricha sudeep, “Soft and Hard Reliability aware scheduling for multicore embedded System with energy harvesting” IEEE Transaction on multiscale computing System, vol. 1, pp no. 220-235, Dec 2015.
[3] Sarma Santanu, Dutt Nikil, "Cross Layer Exploration of heterogeneous Multicore Processor Configuration" 28th International Conference on VLSI Design , pp. 147-152, 2015.
[4] Cuesta David, Acquaviva Andrea, Ayala Jose’L, Hidalgo jose’I, Atienza David, Macii Enrico, "Adaptive Task Migration Policies for Thermal Control in MPSOC’s" ,VLSI 2010 Annual Symposium, pp-110-115, 2010.
[5] Betting.B, Brinkschulte.U, Pacher.M, "Evaluation and Superiority Analysis of a decentralized task control mechanism for dependable real time SOC Architectures," 16th IEEE International symposium on object/component/service oriented Real time distributed computing (ISORC), pp. 1-8, 2013.
[6] Imes Connor, Kim David H.K, Maggio Martina, Hoffmann Henry, "POET: a portable Approach to minimizing energy under soft real time Constraints," 21st IEEE Real time and embedded technology and application Symposium, pp. 75-86, 2015.
[7] Sheikh.H.F. and Ahmad.I, "Simultaneous Optimization of Performance, Energy and Temperature for DAG Scheduling in Multicore Processors," International Green Computing Conference, pp.1-6, June 2012.
[8] Sheikh.H.F and Ahmad.I, "Fast Algorithms for Thermal Constrained Performance Optimization in DAG Scheduling on Multi-Core Processors," 2011 International Green Computing Conference and Workshops (IGCC), pp.1-8, 25-28 July 2011.
[9] Mohammed.R.K, Sahan.R.A, Prabhugoud.M, ”Design Challenges of thermal margining tools for Silicon Validation” 12th Intersociety conference on,pp.1-8,2010
[10] Ajami.A.H, Banerjee.K, and Pedram.M, "Modelling and Analysis of Nonuniform Substrate Temperature Effects on Global Ulsi Interconnects," IEEE Trans. Computer-Aided Design of Integrated Circuits and Systems, vol. 24, no. 6, pp. 849-861, June 2005.
[11] Brest.J, Zumer.V, "A Performance Evaluation of list scheduling heuristics for task graphs without communication costs" Parallel processing, proceeding International workshop on , pp.421-428, 2000.
[12] Zhou.J, Wei.T, Chen.M, Yan.J,” Thermal Aware task scheduling for energy minimization in heterogeneous real time MPSOC system” IEEE Transaction on Computer Aided Design of Integrated circuits and system, pp-1, Nov 2015
[13] Viswananth.R, Wakharkar.V, Watwe.A, and Lebonheur.V, “Thermal Performance Challenges from Silicon to Systems,” Intel Technol. J., Q3, vol. 23, p. 16, 2000.
[14] Kwok Yu Kwong, Ahmad.I” Dynamic Critical Path scheduling: An effective techniques for allocating task graph to multiprocessor” IEEE Transaction on Parallel and Distributed System, vol-7,pp 506-521, Aug 2002
[15]Gui.J, Maskell D.L “A Fast High level event Driven Thermal Estimator for Dynamic Thermal Aware Scheduling” IEEE Transaction on Computer Aided Design of Integrated circuits and systems, vol 31, pp 904-917, June 2012.
[16] C. Hsu and W. Feng. A power-aware run-time system for high- performance computing. In Proceedings of the 2005 ACM/IEEE conference on Supercomputing. IEEE Computer Society washing ton, DC, USA, 2005.
[17] Qinghui Tang, Sandeep K. S. Gupta, and Georgios Varsamopoulos. Energy-Efficient Thermal-Aware Task Scheduling for Homogeneous High-Performance Computing Data Centers: A Cyber-Physical Approach.IEEE Trans. Parallel Distrib. Syst., 19(11):1458{1472, 2008.
[18] Power Consumption curve of an Intel Core i7 950 CPU diagram,http://www.xbitlabs.com/articles/cpu/display/power-consumption-overclocking_11.html,june 3, 2016

Prabhjot Kaur, Manoj Agnihotri, "Minimizing Energy Consumption in the Multicore Processors Using Energy Aware Variable Neighborhood Search," International Journal of Computer Sciences and Engineering, Vol.4, Issue.8, pp.85-90, 2016.

Cloud computing provide facilitates computing assets on demand by the use of service provider. In IT communications cloud computing is out sourcing by the use of the Internet and maintaining own hardware and software environment. It is there whenever you need it, as much as you need, and you pay as you go and only for what you use. Security is a prim concern in the use of cloud computing. In this paper, we have presented encryption based security algorithms for cloud computing. Data Sharing is an important functionality in cloud storage. We show how to securely share data. We describe new public key cryptosystems which produce constant size cipher text but the other encrypted files outside the set remain confidential.

Computing, Advance Encryption Standard, Dara Encryption Stancared, Key aggregate cryptography.

[1] Ronald L. Krutz, Russell Dean vines, “Cloud Security: A Comprehensive Guide to Secure Cloud Computing” Indianapolis, Ind. Wiley Publication, First Edition -2010,ISBN: 978-470-58987-8.
[2] Nicholas J. Daras, Michael Th. Rassias, “Computation Cryptography and network security, Springer Publication, First Edition – 2015,ISBN:978-3319182742
[3] Oded Goldreich, “Modern Cryptography Probabilistic Proofs and Pseudo-randomness” Springer Publication, 1999 Editio-1998, ISBN: 978-3540647669
[4] Peter Gutmann,“ Cryptographic Security Architecture”, Springer-verlag Publication, First Edition-2003, ISBN:978-0387953878.
[5] Maram Mohammed Falatah, Omar Abdullah Batarfi, “Cloud Scalability Considerations”, International Journal of Computer Science & Engineering Survey,Volume-05, Issue-04,Page No(1-19) August 2014.
[6] Zuzana Priscakova and Ivana Rabova, “Model of solution for data security in cloud computing”, International Journal of Computer Science, Engineering and Information Technology, Volume-03,Issue-03,Page no(1-18),June 2013.
[7] Anisaara Nadaph And Vikas Maral “Cloud Computing – Partitioning Algorithm and Load Balancing Algorithm” International Journal of Computer Science, Engineering and Information Technology,Volume-04, Issue-05,Page no(1-4) October 2014.
[8] Satish s Hottin and Mr. S. Pradeep,” "Efficient Secure Date Sharing In Cloud Storage Using Key-Aggregate Cryptosystems", International Journal of Engineering Development and Research, Volume-03, Issue-02, Page no (38-44), May 2015.
[9] Mithun V Mhatre1, Dr. M. Z. Shaikh, “ Key-Aggregate Cryptosystem for Scalable Data Sharing in Cloud Storage- A Review”, International Journal of Advanced Research in Computer Science and Software Engineering,Volume 5, Issue 7,Page no( 1-4)July 2015.
[10] G.Tzeng, “A Time:-Bound Cryptography key assignment scheme for access control in a hierarchy”, IEEE Trans. Knowledge and data engineering,Volume-14,Issue-01,Page no(182-188)Feb-2002.
[11] Cheng-Kang Chu, Sherman S. M. Chow, Wen-Guey Tzeng, Jianying Zhou, and Robert H. Deng, “Key-Aggregate Cryptography for scalable data sharing in cloud storage”, IEEE Transactions on parallel and distributed system,Volume-25,Issue-02,Page no(468-477),Feb-2014.
[12]Overview of Cryptography, www.garykessler.net/library/crypto.html,Aug 7, 2016.
[13] Encryption Algorithm www.storagecraft.com/blog/5-common-encryption-algorithms,31 July 2014
[14] Y. Sun and K. J. R. Liu, “Scalable Hierarchical Access Control in Secure Group Communications, ”org by-at The convention and exhibition center ,Hong Kong in Proceedings of the 23th IEEE International Conference on Computer Communications (INFOCOM’04). March 7-11, 2004.
[15] R. Curtmola, J. Garay, S. Kamara, R. Ostrovsky. “Searchable symmetric encryption: improved definitions and efficient constructions”,Proceedings of the 13th AC conference on Computer and Communications Security, ACM Press, pp. 79-88, 2006.
[16] R. Curtmola, J. Garay, S. Kamara, R. Ostrovsky. “Searchable symmetric encryption: improved definitions and efficient constructions”, In: Proceedings of the 13th AC conference on Computer and Communications Security, ACM Press, pp. 79-88, 2006.

Sonali A. Karale, Sachin D. Choudhari, "Key Aggregate Cryptography in Cloud for Scalable Data Sharing," International Journal of Computer Sciences and Engineering, Vol.4, Issue.8, pp.91-95, 2016.

Transcended C editor is a software that edits plain texts and comprises of Java Swings and AWT. This project has all the frames prepared in Swing. The Transcended C editor enables us to code C programs more efficiently. This editor is provided with features such as auto completions, signature help, syntax highlighting, string searching, descriptions, different font styles, sizes etc, along with an added option for compilation and run which makes it different from other editors.

IntelliSense, AWT, GUI, GCC

[1] Cay S Horstmann and Gary Cornell, “Core Java-Advanced Feature,” Prentice Hall, Vol.2, 9th Edition.
[2] Herbert Schildt, “The Complete Reference Java,” Tata McGraw-Hill Education , 7th Edition.
[3] E Balagurusamy, “Programming in ANSI C” Tata McGraw-Hill Education, 6th Edition, Jan-2012.
[4] Manuel D. Rossetti, “Java Simulation Library (JSL): An open-source object-oriented library for discrete-event simulation in Java”, Int. J. of Simulation and Process Modelling, Vol. 4, 2008, No.1 pp. 69 – 87.
[5] Yu Yan, Nakano Hiroto, Hara Kohei , Suga Shota , AiguoHe,
“A C Programming Learning Support System and Its Subjective Assessment”, IEEE International Conference on Computer and Information Technology, ISBN: 978-1-4799-6239-6, Sept 11-13, 2014.
[6] Vamsi Krishna Myalapalli, Sunitha Geloth, “High performance JAVA programming”, International Conference on Pervasive Computing, ISBN: 978-1-4799-6272-3, April 16, 2015.

Rinu Rani Jose, Niya Charles, Bessy Anna Varghese and Alakananda, "Transcended C Editor," International Journal of Computer Sciences and Engineering, Vol.4, Issue.8, pp.96-99, 2016.

Cloud computing is becoming popular day by day, due to its wide range of applications. As demand of cloud computing is increasing, it increases the number of request too. Thus providing high availability to its user is a challenging task. So load balancing techniques become good alternative of these techniques. In optimization issue, Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO) have already been referred to as excellent option method. GA is created by adopting the organic progress process, while ACO is encouraged by the foraging behavior of ant species. That paper has offered a hybrid GAACO based scheduling technique to improve the load balancing further. In this technique, GA can view and maintain the fittest ant in each period in most era and just unvisited spots will be evaluated by ACO. The overall objective of this paper is proposes hybrid GA-ACO based analytical model to enhance the results further.

Cloud Computing, Load Balancing, Ant colony optimization, and Genetic algorithm

[1] Singh Aarti et.al. ” Autonomous Agent Based Load Balancing Algorithm in Cloud Computing “International Conference on Advanced Computing Technologies and Applications (ICACTA- 2015, pp. 832 – 841, 2015.
[2] J. Geethu Gopinath P et.al.” An in-depth analysis and study of Load balancing techniques in the cloud computing environment” 2nd International Symposium on Big Data and Cloud Computing (ISBCC’15), pp.427 – 432, 2015.
[3] Santanu Dam et.al. “Genetic Algorithm and Gravitational Emulation Based Hybrid Load Balancing Strategy in Cloud Computing” 2015 IEEE
[4] Muhammad H. Raze et.al. ”Application of Network Tomography in Load Balancing” 3rd International Workshop on Survivable and Robust Optical Networks (IWSRON), pp. 1120 – 1125, 2015.
[5] Mala Kalra et.al. “A review of met heuristic scheduling techniques in cloud computing” Egyptian Informatics Journal (2015) 16, 275–295
[6] Tingting Wang, et.al. “Load Balancing Task Scheduling based on Genetic Algorithm in Cloud Computing”, IEEE 12th International Conference on Dependable, Autonomic and Secure Computing 2014
[7] C. Y. Liu; Dept. of Inf. Eng. and et.al” A Task Scheduling Algorithm Based on Genetic Algorithms and Ant Colony Optimization in Cloud Computing” Distributed Computing and Applications to Business, Engineering and Science (DCABES), 2014 13th International Symposium on Page(s):68 - 72 ,2014
[8] Shilpa V Pius and Shilpa T S, “Survey on Load Balancing in Cloud Computing”, International Conference on Computing, Communication and Energy Systems (ICCCES), 2014.
[9] Monir Abdullah and Mohamed Othman” Cost-Based Multi-QoS Job Scheduling using Divisible Load Theory in Cloud Computing”, International Conference on Computational Science, ICCS , pages no. 928 – 935, 2013.
[10] Kousik Dasgupta et.al. “A Genetic Algorithm (GA) based Load Balancing Strategy for Cloud Computing” International Conference on Computational Intelligence: ModelinTechniques and Applications (CIMTA) 340 – 347, 2013.
[11] J. Gu, J. Hu, T. Zhao, and G. Sun, “A new resource scheduling strategy based on genetic algorithm in cloud computing environment.” Journal of Computers, vol. 7, no. 1, 2012.
[12] Weiwei Lin et.al.” Bandwidth-aware divisible task scheduling for cloud computing “Weiwei Lin, School of Computer Engineering and Science, South China University of Technology, Guangzhou, China. Pract. Exper. Pp.163–174, 2012.
[13] K. Zhu ; Sch. of Eng. & Compute. Sci. et.al. “Hybrid Genetic Algorithm for Cloud Computing” Applications Services Computing Conference (APSCC), 2011 IEEE Asia-Pacific Page(s): 182 - 187, 2011.

Mandeep Kaur, Manoj Agnihotri, "A Hybrid Technique Using Genetic Algorithm and ANT Colony Optimization for Improving in Cloud Datacenter," International Journal of Computer Sciences and Engineering, Vol.4, Issue.8, pp.100-105, 2016.

Multimodal biometrics is the combination of two or more modalities such as face and speech modalities. In two modalities have been presented with genetic algorithm to show the effect of Genetic algorithm in the accuracy level of a biometric system. Face recognition is the most popular physiological characteristic used to identify a person in biometric systems, because of feasibility, permanence, distinctiveness, reliability, accuracy, and acceptability. On the other hand speech recognition is the most popular behavioral characteristic used in biometric systems. Thus, we believe that the combination of these two methods will have reliable and accurate results. So, this paper has provided the review using an evolutionary method.

Multimodal Biometrics, Genetic Algorithm, Security

[1] A. K. Jain, K. Nandakumar, X. Lu, and U. Park. “Integrating Faces, Fingerprints and Soft Biometric Traits for User Recognition. In Proceedings of ECCV International Workshop on Biometric Authentication (BioAW)”, volume LNCS 3087, pages 259–269, Prague, Czech Republic, May 2004, Springer.
[2] A. Ross and R. Govindarajan. “Feature Level Fusion Using Hand and Face Biometrics”. In Proceedings of SPIE Conference on Biometric Technology for Human Identification II, volume 5779, pages 196–204, Orlando, USA, March 2005.
[3] A. Ross, K. Nandakumar, and A. K. Jain. “Handbook of Multibiometrics”. Springer, 2006.
[4] Inthavisas, K., and D. Lopresti. "Secure speech biometric templates for user authentication." IET biometrics, vol. 1, pp. 345-349, 2012.
[5] Jun Qi et.al “Auditory Features Based on Gamma tone Filters for Robust Speech Recognition” May 2013
[6] K.D.Mitnick,W.L.Simon.“The Art of Deception:Controlling the Human Element of Security” Wiley, 2002.
[7] S.T.Pan,“A canonic-signed-digit coded genetic algorithm for designing finite impulse response digital filter”, Digital Signal Process. 20 (2) (2010) 314– 327.
[8] Seyed Hassan Sadeghzadeh, Morteza Amirsheibani and Anseh Danesh Arasteh “Fingerprint and Speech Fusion: A Multimodal Biometric System”, International Journal of Electronics Communication and Computer Technology (IJECCT), Volume 4 Issue 2, pp. 456-459, March 2014
[9] Seyed Hassan Sadeghzadeh, Morteza Amirsheibani and Anseh Danesh Arasteh “Fingerprint and Speech Fusion:A Multimodal Biometric System”,International Journal of Electronics Communication and Computer Technology (IJECCT), Volume 4 Issue 2, pp. 456-459, March 2014
[10] Shikha Gupta, Jafreezal Jaafar, Wan Fatimah wan Ahmad and Arpit Bansal, "Feature Extraction Using Mfcc" Signal & Image Processing : An International Journal (SIPIJ) Vol.4, No.4, August 2013.
[11] W. E. Burr, D. F. Dodson, and W.T.Polk. “Information Security: Electronic Authentication Guideline”. Technical Report Special Report 800-63, NIST, April 2006.
[12] X. Liu and T. Chen.“Geometry-assisted Statistical Modeling for Face Mosaicing”. In Proceedings of IEEE International Conference on Image Processing (ICIP),volume 2,pages 883–886,Barcelona,Spain,September 2003.
[13] Yohei Ishii, “Face and Head Detection for a Real-Time Surveillance System”, Proceedings of the 17th International Conference on Pattern Recognition (ICPR’04) 1051-4651/04 $ 20.00 IEEE .

Nancy Bansal and Samanpreet Singh, "A Review of Face and Speech multimodal Biometrics for security using Genetic Algorithm," International Journal of Computer Sciences and Engineering, Vol.4, Issue.8, pp.91-94, 2016.

The current service composition techniques and tools are mainly designed for use by Service-Oriented Architecture (SOA) professionals to solve business problems. Little attention has been paid to allowing end-users without sufficient service composition skills to compose services and integrate SOA In this paper provides a brief survey of the approaches to semantic integration developed by researchers in the ontology community. The system focus on the approaches that differentiate the ontology research from other related areas. The goal of the paper is to provide a reader who may not be very familiar with ontology research with introduction to major themes in this research and with pointers to different research projects. We discuss techniques for finding correspondences between ontologies, declarative ways of representing these correspondences, and use of these correspondences in various semantic-integration tasks.

Ontology, Service-oriented architecture, Service composition, Service discovery and Web services

[1]. K. Kritikos and D. Plexousakis, “Requirements for QoS-based Web service description and discovery,” IEEE Transactions on Services Computing, vol. 2, no. 4, pp. 320–337, 2009.
[2]. D. Lee, J. Kwon, S. Lee, S. Park, and B. Hong, “Scalable and efficient web services composition based on a relational database,” Journal of Systems and Soware, vol. 84, no. 12, pp. 2139–2155, 2011.
[3]. L. Sha, G. Shaozhong, C. Xin, and L. Mingjing, “A QoS based web service selection model,” in Proceedings of the International Forum on Information Technology and Applications (IFITA ‘09), pp. 353–356, May 2009.
[4]. C.-F. Lin, R.-K. Sheu, Y.-S. Chang, and S.-M. Yuan, “A relaxable service selection algorithm for QoS-based web service composition,” Information and Sofware Technology, vol. 53, no. 12, pp. 1370–1381, 2011.
[5]. W. Rong, K. Liu, and L. Liang, “Personalized web service ranking via user group combining association rule,” in Proceedings of the IEEE International Conference on Web Services (ICWS ‘09), pp. 445–452, July 2009.
[6]. N. Laga, E. Bertin, and N. Crespi, “User-centric services and service composition, a survey,” in Proceedings of the 32nd Annual IEEE So aware Engineering Workshop (SEW ‘08), pp. 3–9, November 2009.
[7]. Z. Gu, J. Li, and B. Xu, “Automatic service composition based on enhanced service dependency graph,” in Proceedings of the IEEEInternational Conference on Web Services (ICWS ‘08), pp. 246–253, IEEE, Beijing, China, September 2008.
[8]. D. Ardagna, M. Comuzzi, E. Mussi, B. Pernici, and P. Plebani, “PAWS: a framework for executing adaptive web-service processes,” IEEE So aware, vol. 24, no. 6, pp. 39–46, 2007.
[9]. R. Berbner, M. Spahn, N. Repp, O. Heckmann, and R. Steinmetz, “Heuristics for QoS-aware web service composition,” in Proceedings of theIEEE International Conference on Web Services (ICWS ‘06), pp. 72–79, September 2006.
[10]. D. Berardi, D. Calvanese, G. De Giacomo, M. Lenzerini, and M. Mecella, “Automatic service composition based on behavioral descriptions,” International Journal of Cooperative Information Systems, vol. 14, no. 4, pp. 333–376, 2005.
[11]. R. Hamadi and B. Benatallah, “A petri net-based model for web service composition,” in Proceedings of the 14th Australasian Database Conference (ADC ‘03), pp. 191–200, Adelaide, Australia, February 2003.
[12]. D. Skogan, R. Gronmo, and I. Solheim, “Web service composition in uml,” in Proceedings of the 8th IEEE International Enterprise Distributed Object Computing Conference (EDOC ‘04), pp. 47–57, IEEE, September 2004.
[13]. L.-Z. Zeng, B. Benatallah, M. Dumas, J. Kalagnanam, and Q. Z. Sheng, “Quality driven web services composition,” in Proceedings of the 12th International Conference on World Wide Web (WWW ‘03), pp. 411–421, ACM, May 2003.
[14]. M. Zhenhua, C. Hongming, and J. Lihong, “Service selection problem with multiple QoS constraints based on genetic algorithm,” Computer Applications and Soware, 2009.
[15]. B.-Y. Wu, C.-H. Chi, S.-J. Xu, M. Gu, and J.-G. Sun, “QoS requirement generation and algorithm selection for composite service based on reference vector,” Journal of Computer Science and Technology, vol. 24, no. 2, pp. 357–372, 2009.
[16]. J. M. Ko, C. O. Kim, and I.-H. Kwon, “Quality-of-service oriented web service composition algorithm and planning architecture,” Journal of Systems and Sofware, vol. 81, no. 11, pp. 2079–2090, 2008.
[17]. L. Zhao, Y. Ren, M. Li, and K. Sakurai, “Flexible service selection with user-speci_c QoS support in service-oriented architecture,” Journal of Network and Computer Applications, vol. 35, no. 3, pp. 962–973, 2012.
[18]. R. Chinnici, J. Moreau, A. Ryman, S. Weerawarana, “Web Service Description Language”, W3C Recommendation (2007) 8. M. P. Carlson, A H. H. Ngu, R. M. Podorozhny, L. Zeng, “Automatic Mash Up of Composite Applications,” International Conference on Service Oriented Computing (ICSOC) 2008, Sydney, Australia, December 1-5, 2008, pages: 317-330
[19]. C. Engelke and C. Fitzgerald, “Replacing Legacy Web Services with RESTful Services,” WS-REST 2010 First International Workshop on RESTful Design
[20]. R. Ennals and D. Gay. “Building Mashups by Example”, Proceedings of IUI (2008)
[21]. R. J. Ennals, M. N. Garofalakis, “MashMaker: mashups for the masses,” Proceedings of the 2007 ACM SIGMOD international conference on Management of data, ACM.

G. Narayanan and Pon Periasamy, "Survey on Web Compositioon Based on Semantics," International Journal of Computer Sciences and Engineering, Vol.4, Issue.8, pp.95-99, 2016.