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Big Data can bring big benefits for all sectors of our life via smarter moves, for examples, by analyzing huge dataset immediately and allowing for making decisions based on what they have learned, by gauging customer needs immediately and analyzing customer satisfaction in a timely manner, or by providing many diagnosis or treatment options quickly. These can driving business and economy growth. Until recently, it was hard to get benefits of Big Data without heavy infrastructure investments; for that, the enterprises suffered from many challenges which related to the lack of capacity to process and store the huge dataset adequately, and inability to manage and extract value from these huge dataset; but times have changed. The technology of cloud computing was evolved rapidly to bridge the storage and processing gap and opened up a lot of options for using Big Data by both individuals and organizations without having to invest in massive on-site storage and data processing facilities. This paper presents the concept, advantages, characteristics, processing and applications of Big Data. Then proposes a model to integrate Big Data and cloud computing technology based on three basic cloud service layers to present a new model of Big Data as a Service (BDaaS). The proposed BDaaS model allows enterprise to implement various Big Data functions using variety outsourcing (like Hadoop, Altiscale and Qubole) clearly, easily and moving them out of the expensive whirlpool of updating and maintaining their infrastructure.

Big Data; BDaaS; Cloud Computing; Hadoop; Altiscale; Qubole.

[1] Thomas Erl, Wajid Khattak, Paul Buhler, "Big Data Fundamentals: Concepts, Drivers & Techniques", Prentice Hall, 2016.
[2] Bernard Marr, "Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results", Wiley, 2016.
[3] Viktor Mayer-Sch�nberger and Kenneth Cukier, "Big Data: A Revolution That Will Transform How We Live, Work, and Think", Eamon Dolan/Mariner Books, 2014.
[4] Tom Davenport, Jill Dych�, "Big Data in Big Companies Report", SAS, 2013.
[5] Christian Prokopp, �The four types of Big Data as a Service (BDaaS)�, Big Data Science and Cloud Computing Blog, 2014-06-23. [Online] Available at http://www.semantikoz.com/blog/big-data-as-a-service-definition-classification
[6] Bernard Marr, "Big Data-As-A-Service Is Next Big Thing", Forbes, APR 2015. [Online] Available at http://www.forbes.com/sites/bernardmarr/2015/04/27/big-data-as-a-service-is-next-big-thing/#6efeb7c93f9a.
[7] Poornima Sharma, Varun Garg, Prof. Randeep Kaur, Prof. Satendra Sonare, �Big Data in Cloud Environment", IntInternational Journal of Computer Sciences and Engineering, Volume-01, Issue-03, pp (15-17), Nov 2013.
[8] Thomas H. Davenport and Jill Dych�, �Big Data in Big Companies�, SAS Institute, 2013. [Online] Available at http://www.sas.com/en_us/whitepapers/bigdata-bigcompanies-106461.html .
[9] Doug Laney, �3D Data Management: Controlling Data Volume, Velocity and Variety�, META Delta Group, 2001. [Online] Available at http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf .
[10] Vishal Kumar Gujare, Pravin Malviya, "A Novel Algorithm for Big Data Clustering", International Journal of Computer Sciences and Engineering, Volume-04, Issue-08, Page No (38-40), Aug -2016, E-ISSN: 2347-2693
[11] �Extracting business value from the 4 V�s of big data�, [Online] Available at http://www.ibmbigdatahub.com/infographic/extracting-business-value-4-vs-big-data .
[12] �History and evolution of big data analytics�, SAS Institute, [Online] Available at http://www.sas.com/en_us/insights/analytics/big-data-analytics.html#dmhistory .
[13] Tom White, "Hadoop: The Definitive Guide", 4th Edition, O�Reilly, 2015.
[14] Hadoop Official Website, http://hadoop.apache.org/ .
[15] Donald Miner, Adam Shook, "MapReduce Design Patterns: Building Effective Algorithms and Analytics for Hadoop and Other Systems", 2012.
[16] PIG Official Website, http://pig.apache.org , Last accessed 7th October 2016.
[17] Spark Official Website, http://spark.apache.org , Last accessed 10th October 2016.
[18] Splunk Official Website, http://www.splunk.com , Last accessed 7th October 2016.
[19] Qubole Official Website, http://www.qubole.com , Last accessed 23th October 2016.
[20] Tanuja A, Swetha Ramana D, "Processing and Analyzing Big data using Hadoop", International Journal of Computer Sciences and Engineering, Volume-04, Issue-04, Page No (91-94), Apr -2016, E-ISSN: 2347-2693
[21] HBase Official Website, http://hbase.apache.org , Last accessed 10th October 2016.
[22] P.Kodimalar, "A Study on Big Data and Big Data Analytical Research and Issues", International Journal of Computer Sciences and Engineering, Volume-03, Issue-11, Page No (171-179), Nov -2015, E-ISSN: 2347-2693
[23] Bernard Marr, "Big Data: Using SMART Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance", Wiley, 2015.
[24] Mehjabeen Sultana, "An Overview of Emerging Analytics in Big Data: In-Situ", International Journal of Computer Sciences and Engineering, Volume-04, Issue-05, Page No (166-169), May -2016, E-ISSN: 2347-2693.

M.S. Al-Hakeem, "A Proposed Big Data as a Service (BDaaS) Model," International Journal of Computer Sciences and Engineering, Vol.4, Issue.11, pp.1-6, 2016.

Real time object tracking is a perplexing task in computer vision. Many algorithms exist in literature like Mean shift, background-weighted histogram (BWH) and Corrected background-weighted histogram(CBWH) for tracking the moving objects in a video sequence.This paper attempts to do the comparative analysis of the three methods in terms of performance parameters like Normalised Centroid Distance , Overlap and number of iterations using two types of features i.e., color histogram and color texture histogram. Experimental results show that the performance of CBWH gives better performance when compared with basic Mean shift and BWH.

Object Tracking, Mean Shift Algorithm, Target Feature Modelling, Candidate Feature Modelling, Bhattacharya Coefficients

[1] Babenko B, Yang M H, Belongie S. "Robust object tracking with online multiple instance learning", IEEE Transactions on Pattern Analysis and Machine Intelligence" 2011,33 (8): 1619- 1632.
[2] Gandham. Sindhuja, Renuka Devi S.M.: "A Survey on detection and tracking of objects in a Video sequence", International Journal of Engineering Research and General Science Volume 3, Issue 2, Part 2, March-April, 2015, pp.418-426.
[3] K. Fukunaga, L.D. Hostetler, "The Estimation of the Gradient of a Density Function with Application in Pattern Recognition", IEEE Trans. Information Theory, vol. 21, no. 1, pp. 32-40, Jan. 1975.
[4] Saravanakumar, S. Vadivel and A. Saneem Ahmed, "Multiple human object tracking using background subtraction and shadow removal techniques", The International Conference on Signal and Image Processing ,2010, pp. 15-17.
[5] A. Yimaz, O. Javed and M. Shah, "Object tracking: A survey," ACM Computing Surveys, Vol. 38, No. 4, Article 13, December 2006, pp. 13-20.
[6] Gandham Sindhuja, Renuka Devi S.M. Comparative analysis of mean shift in object tracking. IEEE Conference on Power, Control, Communication and Computational Technologies for Sustainable Growth (PCCCTSG),2015, 283-287.
[7] Meng G, Jiang G, "Real-time illumination robust maneuvering target tracking based on color invariance", Proceedings of the 2nd International Congress on Image and Signal, 2009, pp. 15.
[8] Gammeter S, Bossard L, Gassmann A, et al. "Server-side object recognition and client-side object tracking for mobile augmented reality", CVPR, IEEE Computer Society Conference, 20 I 0: 1-8.
[9] N. A Gmez. "A Probabilistic Integrated Object Recognition and Tracking Framework for Video Sequences", University at Rovira I Virgili, PHD thesis, Espagne, 2009.
[10] Collins,R T, Yanxi Liu and Leordeanu, M, "Online selection of discriminative tracking features", IEEE Transactions on Pattern Analysis and Machine Intelligence , 2010, Vol. 10, pp. 1631-1643.
[11] Ying-JiaYeh, Chiou-Ting Hsu, "Online Selection of Tracking Features Using AdaBoost", IEEE Transactions on Circuits and Systems for Video Technology, 2009, VoU, pp. 442-446.
[12] Comaniciu D., Ramesh V. and Meer P.: �Kernel-Based Object Tracking�, IEEE Trans. On Pattern Anal. Machine Intell., 2003, 25, (2), pp. 564-577.
[13] Ning, Lei Zhang, David Zhang and C. Wu, "Robust Mean Shift Tracking with Corrected Background-Weighted Histogram," to appear in lET Computer Vision.(2011).
[14] D. Comaniciu and P. Meer. Mean shift: "A robust approach toward feature space analysis". PAMI,24(5):603-619, 2002.
[15] K. Nummiaro, E. Koller-Meier, and L. 1. Van Gool. "Object tracking with an adaptive color-based particle filter. In Proc. Of the 24th DAGM Symposium on Pattern Recognition, pages 353-360, London, UK, 2002. Springer-Verlag.
[16] Ess A, Schindler K, Leibe B, "Object detection and tracking for autonomous navigation in dynamic environments". IJRR, 2010, 29(14): 1707-1725.
[17] Shah M, Saleemi I, Hartung L, "Scene understanding by statistical modeling of motion patterns", IEEE Conference CVPR, 2010: 2069-2076.
[18] Patel, Sandeep Kumar, and Agya Mishra. "Moving object racking techniques: A critical review." Indian Journal of Computer Science and Engineering 4.2 (2013): 95-102.
[19] ]cmp.felk.cvut.czl-vojirtom/datasetl, www.iai.unibonn.de/-kleindltracking. clickdamage.coml..Jcv _ datasets.php
[20] Ning J, Zhang L, Zhang D, et al. "Robust object tracking using joint color-texture histogram." International Journal of Pattern Recognition and Artificial Intelligence, 2009,23(07): 1245-1263.
[21] Pietik�inen, Matti, et al. "Local binary patterns for still images." Computer vision using local binary patterns. Springer London, 2011. 13-47.

S.M.R. Devi, "Experimental analysis of Mean shift method of tracking objects," International Journal of Computer Sciences and Engineering, Vol.4, Issue.11, pp.7-12, 2016.

This paper aims at circular chess on the new mode of playing board games by having an automated physical platform. Hence it discusses the development of an automatic chess board called as Chess. Which enables the user to play the game of chess in different formats with the opponents moves completely based user. It uses various types in graph sets the domination game played on a graph G consists of two players, Dominator and Staller, who alternate taking turns choosing a vertex of G such that whenever a vertex is chosen by either player, at least one additional vertex is dominated. Dominator wishes to dominate the graph in as few steps as possible and Staller wishes to delay the process The game domination number Gamma (G) is the number of vertices chosen when Dominator starts the game and the Staller as much as possible. Sushisen algorithms using game domination number Gamma�(G) when Staller starts the game. An imagination strategy is developed as a general Matlab tool for proving results in the domination game.

Domination, Bipartite Graph, Game Domination number, Independence sets, Coverings and Coloring

[1] Yinkui Li,Cycle multiplicity of some total graphs,Applied Mathematics and Computation, Volume 292, 1 January 2017, Pages 107-113
[2] Sofiya Ostrovska, Mikhail I. Ostrovskii,Nonexistence of embeddings with uniformly bounded distortions of Laakso graphs into diamond graphs,Discrete Mathematics, Volume 340, Issue 2, 6 February 2017, Pages 9-17
[3] Francesco Belardo, Irene Sciriha, Slobodan K. Simić, On eigenspaces of some compound signed graphs,
[4] Linear Algebra and its Applications, Volume 509, 15 November 2016, Pages 19-39
[5] Isidoro Gitler, Enrique Reyes, Juan A. Vega, CIO and ring graphs: Deficiency and testing,
[6] Journal of Symbolic Computation, Volume 79, Part 2, March�April 2017, Pages 249-268
[7] Akbar Jahanbani, Some new lower bounds for energy of graphs, Applied Mathematics and Computation, Volume 296, 1 March 2017, Pages 233-238
[8] J. Mark Keil, Joseph S.B. Mitchell, Dinabandhu Pradhan, Martin Vatshelle, An algorithm for the maximum weight independent set problem in outerstring graphs, Computational Geometry, Volume 60, January 2017, Pages 19-25
[9] Iva Jestrović, James L. Coyle, Ervin Sejdić, A fast algorithm for vertex-frequency representations of signals on graphs, Signal Processing, Volume 131, February 2017, Pages 483-491
[10] Cemil Dibek, Tınaz Ekim, Didem G�z�pek, Mordechai Shalom, Equimatchable graphs are image-free for image,
[11] Discrete Mathematics, Volume 339, Issue 12, 6 December 2016, Pages 2964-2969.
[12] Nguyen Minh Hai, Tran Dang Phuc, Le Anh Vinh, Existentially closed graphs via permutation polynomials over finite fields, Discrete Applied Mathematics, Volume 214, 11 December 2016, Pages 116-125.
[13] Dong Ye, Yujun Yang, Bholanath Mandal, Douglas J. Klein, Graph invertibility and median eigenvalues, Linear Algebra and its Applications, Volume 513, 15 January 2017, Pages 304-323
[14] Bobo Hua, Yong Lin, Stochastic completeness for graphs with curvature dimension conditions, Advances in Mathematics, Volume 306, 14 January 2017, Pages 279-302
[15] Marta R. Hidalgo, Robert Joan-Arinyo, A Henneberg-based algorithm for generating a tree-decomposable minimally rigid graphs, Journal of Symbolic Computation, Volume 79, Part 2, March�April 2017, Pages 232-248
[16] Arman Boyacı, Tınaz Ekim, Mordechai Shalom, Shmuel Zaks, Graphs of edge-intersecting non-splitting paths in a tree: Representations of holes�Part I, Discrete Applied Mathematics, Volume 215, 31 December 2016, Pages 47-60
[17] Klavdija Kutnar, Paweł Petecki, On automorphisms and structural properties of double generalized Petersen graphs, Discrete Mathematics, Volume 339, Issue 12, 6 December 2016, Pages 2861-2870
[18] Yao Lu, Kaizhu Huang, Cheng-Lin Liu, A fast projected fixed-point algorithm for large graph matching, Pattern Recognition, Volume 60, December 2016, Pages 971-982
[19] Chun-Cheng Lin, Weidong Huang, Wan-Yu Liu, Shierly Tanizar, Shun-Yu Jhong, Evaluating esthetics for user-sketched layouts of clustered graphs with known clustering information, Journal of Visual Languages & Computing, Volume 37, December 2016, Pages 1-11
[20] Xueyi Huang, Qiongxiang Huang, On regular graphs with four distinct eigenvalues, Linear Algebra and its Applications, Volume 512, 1 January 2017, Pages 219-233
[21] Chunmei Luo, Lance Zuo, Metric properties of Sierpiimageski-like graphs, Applied Mathematics and Computation, Volume 296, 1 March 2017, Pages 124-136
[22] Primo� Potočnik, Rok Po�ar, Smallest tetravalent half-arc-transitive graphs with the vertex-stabilizer isomorphic to the dihedral group of order 8, Journal of Combinatorial Theory, Series A, Volume 145, January 2017, Pages 172-183
[23] Brijnesh J. Jain, On the geometry of graph spaces, Discrete Applied Mathematics, Volume 214, 11 December 2016, Pages 126-144
[24] Yidong Li, Hong Shen, Congyan Lang, Hairong Dong, Practical anonymity models for protecting private weighted graphs, Neurocomputing, Volume 218, 19 December 2016, Pages 359-370
[25] Domingos M. Cardoso, Oscar Rojo, Edge perturbation on graphs with clusters: Adjacency, Laplacian and signless Laplacian eigenvalues, Linear Algebra and its Applications, Volume 512, 1 January 2017, Pages 113-128

I, Baidari, S P Sajjan, V.K. Gurani, "Domination Easymove Game on A Square Beehive," International Journal of Computer Sciences and Engineering, Vol.4, Issue.11, pp.13-19, 2016.

The concept of co-operative communication is one of the fastest growing areas of research in wireless sensor networks. Energy efficiency is the main issue in the Wireless Sensor Networks (WSN). The energy consumption is minimized using cooperative communication technique. Number of techniques has been proposed in minimizing the energy consumption in wireless sensor networks. In this paper, the two techniques proposed for minimizing the energy consumption have been discussed. During the route discovery process AODV (Ad hoc On demand distance vector) floods the entire network with large number of control packets, and hence it finds many unused routes between the source and destination. This becomes a major drawback to AODV since this causes routing overhead, consuming bandwidth and node power. The proposed enhancement to AODV optimizes CAODV (Cluster Based AODV) by reducing the number of control messages generated during the route discovery process. The optimization method uses the idea of clustering the nodes of the network and managing routing by cluster heads and gateway nodes. Routing using clusters effectively reduces the control messaged flooded during the route discovery process by replacing broadcasting of RREQ packets with forwarding of RREQ packets to Cluster Heads. The performance evaluation of CAODV is carried out through simulation tests, which evince the effectiveness of this protocol in terms of network energy efficiency when compared against other well-known protocols.

Clustered wireless sensor networks, co-operative communication, energy efficiency. AODV, Routing, Gateway Node

Ghaidaa Muttasher, Abdul sahib, Norrozila Sulaiman, Osamah Ibrahem Khalaf, �Improving Ad Hoc Network Behaviour Using Clustering T technique with NS2� of IJIRCCE, Vol. 2, Issue 10, October 2014
[2] C.C. Chiang, H.K. Wu, W. Liu and M. Gerla, �Routing in Clustered Multihop, Mobile Wireless Networks With Fading Channel�, Proceedings of IEEE Singapore International Conference on Networks SICON�97, pages 197-211, Singapore, Apr. 14-17, 1997.
[3] C.R. Lin and M. Gerla, �Adaptive Clustering for Mobile Wireless Networks�, IEEE Journal on Selected Areas in Communications, Vol. 15, No. 7, pp. 1265-1275, September 1997.
[4] M.K. Denko, �Analysis of Clustering Algorithms in Mobile Ad Hoc Networks, Proceedings of International Conf. on Wireless Networks�, pp. 98-105, 2003.
[5] M. Chatterjee, S. K. Das and D. Turgut, �An On-Demand Weighted Clustering Algorithm (WCA) for Ad-Hoc Networks�, Proceedings of IEEE GLOBECOM 2000.
[6] Dr. Mohammad Bokhari and Hatem S.A.Hamatta, �A Review of Clustering Algorithms as Applied in MANETs� IJARCSSE 2012.
[7] S Reno Robert .R. �Enhanced AODV for directional flooding using Coordinate System�, 201O International Conference on Networking and Information Technology, 2010 IEEE.
[8] Aswathy M and Tripti �A Cluster Based Enhancement to Aodv for Inter-Vehicular Communication in Vanet�, IJGCA, Vol.3, No.3, September 2012
[9] Ketan Vora, Pragnesh Patel, Vikram Agrawal, �Enhance Performance Of Aodv Using Clustering Approach In Mobile Ad-Hoc Networks�, IJARIIE, Vol-1 Issue-4 2015.
[10] Villas L.A, Boukerche A, Ramos H.S, De and Loureiro A.A.F (2013) �DRINA: A Lightweight Aggregation in Wireless Sensor Networks, IEEE Trans� on computers, vol.62 No.4, pp 676-689., 2013.

B.V. Patil, P.V. Baviskar, "Co-operative Communication with Energy-Efficient in A Clustered Wireless Sensor Network," International Journal of Computer Sciences and Engineering, Vol.4, Issue.11, pp.20-25, 2016.

This paper presents an approach to automatic detection of liver tumor in CT images by using region-growing and Support Vector Machine (SVM) which is successfully classifies the liver cancer types such as hepatoma, hemangioma and carcinoma.The method rectifies the problem of manual segmentation and classification which is time consuming due to the variance in the characteristics of CT images.Our proposed method has been tested on a group of CT images obtained from hospitals in Kerala with a promising results both in liver and tumor segmentation. The average error rate and accuracy rate obtained from our proposed method is 0.02 and 0.9.

Region-growing,preprocessing,feature extraction,Segmentation, SVM Classifier.

[1] Daniel, D. Arul Pon, K. Thangavel, and K. T. Rajakeerthana. "Empirical study on early detection of lung cancer using breath analysis." Innovations in Information, Embedded and Communication Systems (ICIIECS), 2015 International Conference on. IEEE, 2015.
[2] Gao, Fei, and Yuanjin Zheng. "A correlated microwave-acoustic imaging method for early-stage cancer detection." 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2012.
[3] Zhang, Xing, et al. "Interactive liver tumor segmentation from ct scans using support vector classification with watershed." 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2011.
[4] Chen, Bin, et al. "Segmentation of liver tumor via nonlocal active contours."Image Processing (ICIP), 2015 IEEE International Conference on. IEEE, 2015.
[5] Meng, Lei, Changyun Wen, and Guoqi Li. "Support vector machine based liver cancer early detection using magnetic resonance images." Control Automation Robotics & Vision (ICARCV), 2014 13th International Conference on. IEEE, 2014.
[6] Zhou, Jiayin, et al. "Segmentation of hepatic tumor from abdominal CT data using an improved support vector machine framework." 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2013.
[7] Selvathi, D., C. Malini, and P. Shanmugavalli. "Automatic segmentation and classification of liver tumor in CT images using adaptive hybrid technique and Contourlet based ELM classifier." Recent Trends in Information Technology (ICRTIT), 2013 International Conference on. IEEE, 2013.
[8] Huang, Weimin, et al. "Liver tumor detection and segmentation using kernel-based extreme learning machine." 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2013.
[9] Huang, Weimin, et al. "Random feature subspace ensemble based Extreme Learning Machine for liver tumor detection and segmentation." 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2014.
[10] Lei Meng; Changyun Wen, Guoqi Li proposed in their journal �Support Vector Machine based Liver Cancer Early Detection using Magnetic Resonance Images� published in 2014.
[11] Abd-Elaziz, O. Fekry, M. Sharaf Sayed, and M. Ibrahim Abdullah. "Liver tumors segmentation from abdominal CT images using region growing and morphological processing." Engineering and Technology (ICET), 2014 International Conference on. IEEE, 2014.
[12]Javed, U., et al. "Detection of lung tumor in CE CT images by using weighted support vector machines." Applied Sciences and Technology (IBCAST), 2013 10th International Bhurban Conference on. IEEE, 2013.
[13] Lee, Seungchan. "Weighted Support Vector Machine for Data Classification.", term project

R. Sreeraj, G. Raju, "Automatic Detection of Liver Tumor in CT Image Using Region Growing and SVM Classifier," International Journal of Computer Sciences and Engineering, Vol.4, Issue.11, pp.26-29, 2016.

Cyber Security is one of the key elements of any system. Breaching of cyber security can lead to loss of confidential and private data. To prevent the attacks on network an Intrusion Detection System Using Hybrid Classification Technique is proposed. This IDS uses a decision tree algorithm to classify the known attack types in the dataset and SVM is used to classify the normal data from the dataset, there by detecting the unknown attacks. Dataset used is the NSL-KDD Dataset.

AdTree, SVM, NSL-KDD, IDS

[1] Rajesh Wankhede and Vikrant Chole (2016), Intrusion Detection System using Classification technique, International Journal of Computer Applications (0975 � 8887) Volume 139 � No.11, pp. 25-28.
[2] Gisung Kim and Seungmin Lee (2014), A Novel Hybrid Intrusion Detection Method Integrating Anomaly Detection With Misuse Detection, ELSEVIER, Expert Systems with Applications vol. 41 pp. 1690 � 1700.
[3] Zhi-Song Pan, Song-Can Chen, Gen-Bao Hu, DaoQiang Zhang, (2010), ―Hybrid Neural Network and C4.5 for Misuse Detection ‖, Proceedings of the second International conference on Machine Learning and Cybernetics, November, pp. 2463 � 2467.
[4] H.F. Eid, A. Darwish A. H. Ella and A. Abraham, ―Principle components analysis and Support Vector Machine based Intrusion Detection System,‖ 2010, 10th International Conference on Intelligent Systems Design and Applications (ISDA), 2010.
[5] Tsang, C. H., Kwong, S., & Wang, H.,‖ Genetic-fuzzy rule reordering in mining approach and evaluation of feature selection techniques for anomaly intrusion detection‖, Pattern Recognition,40 (9), pp. 2373�2391, 2007. .
[6] Juan Wang, Qiren Yang, Dasen Ren, ―An intrusion detection algorithm based on decision tree technology‖, In the Proc. of IEEE Asia-Pacific Conference on Information Processing, 2009.
[7] M. Revathi, T.Ramesh - Network Intrusion Detection Sysytem using reduced dimentioality Indian Journal of Computer Science and Engineering (IJCSE), Vol. 2 No. 1, pp . 61-67.
[8] Yonav Freund et.al, ―The Alternating Decision Tree Algorithm‖, ICML �99 Proceedings of the Sixteenth International Conference on Machine Learning, pp 124-133.
[9] Tavallaee M, Bagheri E, Lu W, Ghorbani A. ―A detailed analysis of the KDD CUP 99 data set‖, IEEE Symposium on Computational intelligence for security and defense applications, 2009,pp 1-6.
[10] Hong Kuan Sok et.al, ―Using the ADTree for Feature Reduction through Knowledge Discovery‖ Instrumentation and Measurement Technology Conference (I2MTC), 2013 IEEE International ,pp1040 � 1044.
[11] Mrutyunjaya Panda and Manas Ranjan Patra, ―A Comparative Study Of Data Mining Algorithms For Network Intrusion Detection‖, First International Conference on Emerging Trends in Engineering and Technology, pp 504-507, IEEE, 2008.
[12] Shi-Jinn Horng and Ming-Yang Su (2011), ―Novel Intrusion Detection System Based On Hierarchical Clustering and Support Vector Machines‖, ELSEVIER, Expert Systems with Applications. pp. 38 306-313.

R. Wankhede, V. Chole, "Intrusion Detection System Using Hybrid Classification Technique," International Journal of Computer Sciences and Engineering, Vol.4, Issue.11, pp.30-33, 2016.

Image denoising has become a very essential in the case of noisy images for better information extraction. On the other hand, processed image must reserve the relevant details of the primary image. This noise suppression is very useful in many applications. Speckle noise is one of the major noises causing digital hologram. So we need some mechanism for denoising the noisy content by preserving the valuable information. This paper presents a comparative study on BEMD (bi-dimensional empirical mode decomposition) and MBEMD (multilevel bi-dimensional empirical mode decomposition) along with the frost filter.

Image denoising, Speckle reduction, Bi-dimensional empirical mode decomposition, Frost filter

[1] A Cheremkhin, I. A.Shevkunov, N. V. Petrov, �Multiple-wavelength color digital holography for monochromatic image reconstruction,� 4th International Conference on Photonics and Information Optics, PhIo 2015, pp.28�30 January 2015.
[2] P. Memmolo, V. Bianco, F. Merola, L. Miccio, M.Paturzo, P. Ferraro, �Breakthroughs in photonics 2013: Holographic imaging�, vol. 6, no. 2, April 2014
[3] J. W. Goodman and R. W. Lawrence, �Digital image formation from electronically detected holograms,� Appl. Phys. Lett. 11(3), pp.77�79, 1967.
[4] A. Uzan, Y. Rivenson, and A. Stern, �Speckle denoising in digital holography by nonlocal means filtering,� Appl. Opt., vol. 52, pp. A195�A200, 2013.
[5] G. Rilling, P. Flandrin, and P. Gonalvs, �On empirical mode decomposition and its algorithms,� in Proc. 6th IEEE/EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP �03), Grado, Italy, 2003.
[6] J.C. Nunes, O. Niang, Y. Bouaoune, E. Delechelle, Ph. Bunel, �Image analysis by bidimensional empirical mode decomposition�, Image and Vision Computing, pp. 1019�1026, April 2003
[7] R. Piccolo, M. Leo, C. Distante, P. Memmolo, M. Paturzo, and P. Ferraro, �Multilevel bidimensional empirical mode decomposition: A new speckle reduction method in digital holography,� Opt. Eng., vol. 53, no.11, p. 112314, 2014.
[8] Pasquale Memmolo, Marco Leo, Cosimo Distante, Melania Paturzo and Pietro Ferraro,�Coding Color Three-Dimensional Scenes and Joining Different Objects by Adaptive Transformations in Digital Holography�, journal of display technology, vol. 11, No. 10, October 2015
[9] V. S. Frost, J. A. Stiles, K. S. Shanmugan, and J. C. Holtzman, �A model for radar images and its application to adaptive digital filtering of multiplicative noise,� IEEE Trans. Pattern Anal. Machine Intell.,vol. 4, no. 2, pp.157�166, Feb.1982.
[10] Y. Sheng and Z.G. Xia, �A comprehensive evaluation of filters for radar speckle suppression,� in Proc. Int. Geoscience and Remote Sensing Symposium, IGARSS �96. �Remote Sensing for a Sustainable Future, pp. 1559�1561, 1996.
[11] M. Mansourpour, M. A. Rajabi, and J. A. R. Blais, �Effect and performance of speckle noise reduction filter on active RADAR and SAR images,� in Proc. ISPRS, Ankara, Turkey, 2006.
[12] Sumit Kuswaha and Dr. Rabindra Kumar Singh, �Study of various image noises and teir behaviour,� in International Journal of Computer Sciences and Engineering, E-ISSN:2347-2693, vol. 3, issue 3, March 2015.

C. Amrutha , L.C. Manikandan, V.A Akhila , "A Study And Analysis Of Speckle Reduction Method In Digital Holography," International Journal of Computer Sciences and Engineering, Vol.4, Issue.11, pp.34-37, 2016.

The wireless sensor networks are the type of network in which sensor nodes sense the environmental conditions and pass the sensed information to base station. The sensor network is deployed on the far places like forests, deserts etc. The size of the sensor node is very small due to which it is very difficult to recharge or replace battery of these sensor nodes. The various techniques has been proposed in the previous times, to reduce energy consumption of the network. Among various proposed techniques, clustering is the efficient technique to reduce energy consumption of the sensor networks. The clustering is of two types dynamic and static and in this article techniques of both type of clustering is reviewed and compared in terms of various parameters.

WSN, Clustering, Static and Dynamic, energy efficient

[1] Michael Stein, Dominic Lerbs, Mohamed Hassa, Mathias Schnee, �Evaluation Study for Clustering in Wireless Sensor Networks�, 2016, IEEE, 978-1-5090-2526-8
[2] Sushanta Mohan Rakshit, Michael Hempel, Hamid Sharif,� Wireless Sensor Networks in Surface Transportation�, 2016, IEEE, 978-1-5090-2526-8
[3] Prof Chayapathi A.R, Ravitheja .S,� Energy Efficient Routing Techniques In Wireless Sensor Network�, 2014, (IJSRET), ISSN 2278 � 0882, Volume 3, Issue 8
[4] Zain ul Abidin Jaffri, Sundas Rauf,� A Survey on �Energy Efficient Routing Techniques in Wireless Sensor Networks Focusing on Hierarchical Network Routing Protocols�, 2014, ISSN 2250-3153, Volume 4, Issue 2
[5] Himangi Pande, M. U. Kharat,� Adaptive Energy Efficient MAC Protocol for Increasing Life of Sensor Nodes in Wireless Body Area Network�, 2016, IOTA, 978-1-5090-0044-9
[6] Jamal N. Al-Karaki, Ahmed E. Kamal,� Routing Techniques in Wireless Sensor Networks: A Survey�, 2010, ICUBE, volume8, issue 1
[7] Saman Siavoshi, Yousef S. Kavian, Hamid Sharif,� Load-balanced energy efficient clustering protocol for wireless sensor networks�, 2014, IET Wireless Sensor Systems, Vol. 6, Iss. 3
[8] Jetendra Joshi, Prakhar Awasthi, Sibeli Mukherjee, Rishabh Kumar, Divya Sara Kurian and Manash Jyoti Deka,� SEED: Secure and Energy Efficient Data Transmission in Wireless Sensor Networks�, 2016, IEEE, ISBN: 978-1-4673-9879-4
[9] Bhaskar Prince, Prabhat Kumar, M. P. Singh and Jyoti Prakash Singh,� An Energy Efficient Uneven Grid Clustering based Routing Protocol for Wireless Sensor Networks�, 2016, IEEE, 978-1-4673-9338-6
[10] Awatef Balobaid,� A Survey and Comparative Study on Different Energy Efficient MAC-Protocols for Wireless Sensor Networks�, 2016 International Conference on Internet of Things and Applications (IOTA) 978-1-5090-0044
[11] Jing Jing Yan, Meng Chu Zhou, and Zhi Jun Ding,� Recent Advances in Energy-efficient Routing Protocols for Wireless Sensor Networks: A Review�, 2016, IEEE, 2169-3536
[12] Somasekhar Kandukuri, Jean Lebreton, Nour Murad, Richard Lorion, and Jean-Daniel Lan-Sun-Luk,� Energy-Efficient Cluster-Based Protocol using An Adaptive Data Aggregative Window Function (A-DAWF) for Wireless Sensor Networks�, 2016, IEEE, 978-1-5090-2185-7
[13] Abdul Razaque, Musbah Abdulgader, Chaitrali Joshi,� P-LEACH: Energy Efficient Routing Protocol for Wireless Sensor Networks�, 2016, LISAT, 7494137-3456-546-54

Neelam, D. Khosla, "The Energy Efficient Techniques for Wireless Sensor Networks: A review," International Journal of Computer Sciences and Engineering, Vol.4, Issue.11, pp.38-41, 2016.

Techniques including minimal path can efficiently extract curve-like structures by optimally finding the integral minimal-cost path between two seed points. In the first method, a novel minimal path-based algorithm which works on more general curve structures with fewer demands on the user for initial input compared to prior algorithms based on minimal paths. The main novelties and benefits of this new approach are that it may be used to find both closed and open curves, including complex topologies containing both multiple branch points and multiple closed cycles without demanding pre-knowledge about which of these types is to be extracted, and it requires only one input point which, in contrast to older methods, is no longer constrained to be an endpoint of the desired curve but truly may be any point along the desired curve. The second method MPP-BT (Minimal Path Propagation with Backtracking) first applies a minimal path propagation from one single starting point and then, at each reached point,backtracks few steps back to the starting point. Researchers in different areas like geometric optics, computer vision, robotics, and wire routing have previously solved related minimum-cost path problems using graph search and dynamic programming principles.

Curve Detection; Minimal Path Propagation with Unknown end points; key points; accumulation problem; backtracking; stop propagation

[1]L. D. Cohen and R. Kimmel, �Global minimum for active contour models: A minimal path approach,� Int. J. Comput. Vis., vol. 24, no. 1, pp. 57�78, Aug. 1997.
[2]Y. Rouchdy and L. D. Cohen, �Geodesic voting methods: Overview,extensions and application to blood vessel segmentation,� Comput. Methods Biomech. Biomed. Eng., Imag. Visualizat., vol. 1, no. 2,pp. 79�88, Mar. 2013.
[3]F. Benmansour and L. D. Cohen, �Fast object segmentation by growing minimal paths from a single point on 2D or 3D images,� J. Math. Imag.Vis., vol. 33, no. 2, pp. 209�221, Dec. 2009.
[4]R. Ardon, L. D. Cohen, and A. Yezzi, �Fast surface segmentation guided by user input using implicit extension of minimal paths,� J. Math. Imag.Vis., vol. 25, no. 3, pp. 289�305, Oct. 2006.
[5]V. Kaul, A. Yezzi, and Y. C. Tsai, �Detecting curves with unknown endpoints and arbitrary topology using minimal paths,� IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 10, pp. 1952�1965, Oct. 2012.
[6]Yang Chen, Yudong Zhang, Jian Yang, Qing Cao, Guanyu Yang, Jian Chen, Huazhong Shu, Limin Luo, Jean-Louis Coatrieux and Qianjing Feng, �Curve-Like Structure Extraction Using Minimal Path Propagation With Backtracking,� IEEE Trans.Image Processing., vol. 25, no. 2,pp.988-1003, Feb. 2016.
[7] Azzeddine Riahi, "Image Segmentation Techniques Based on Fuzzy C-Means and Otsu, Applied to the Brain MRI in Tumor Detection", International Journal of Computer Sciences and Engineering, Volume-03, Issue-12, Page No (89-101), Dec -2015.

S.M. Nisha , S. Nair ,T. Mohanan, K.K. Faisal , "Survey on Curve Detection Algorithms," International Journal of Computer Sciences and Engineering, Vol.4, Issue.11, pp.42-45, 2016.

Clones are the piece of Software, which is creating from the copy of the original software. To be more specific, the idea behind software cloning is to create a new software that replicates the aspect and usefulness of the original software in possible. It is important to understand that cloning does not have to involve any source code in the original software. Software Cloning typically occurs in the source code for the original software is not available. In a result, software cloning does not imply source code copying. Since software cloning goes way beyond simply executing a similar user interface. The goal in cloning is to create a new software program that mimics everything the original software does and the way in which it does .

Code clone, Syntatic method, Clone detect, Clone removal, Abstract Syntax Trees(AST)

[1] T. Kamiya, S. Kusumoto, and K. Inoue �CCFinder: a multilinguistic token-based code clone detection system for large scale source code�, IEEE Transactions on Software Engineering, vol. 28, no. 7, pp. 654 - 670, July 2002
[2] M.Kim, and D. Notkin �Mining Software Repositories (MSR): Using a clone genealogy extractor for understanding and supporting evolution of code clones�, Proceedings of the 2005 international workshop on Mining software repositories MSR �05, pp. 1-5, May 2005.
[3] M.Kim, V. Sazawal, D. Notkin, and G. Murphy �An empirical study of code clone genealogies�, Proceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering ESEC/FSE-13, pp. 187-196, September 2005
[4] R.Koschke, R. Falke, and P. Frenzel �Clone Detection Using Abstract Syntax Suffix Trees�, Proceedings of the 13th Working Conference on Reverse Engineering (WCRE �06), pp. 253- 262, October 2006
[5] C.K.Roy and J.R. Cordy, NICAD, �Accurate Detection of Near-Miss Intentional Clones Using Flexible Pretty- Printing and Code Normalization� in Proceedings of the 16th IEEE International Conference on Program Comprehension, ICPC 2008.
[6] Mohammed Abdul Bari. �Code Cloning: The Analysis, Detection and Removal� in proceedings of International Journal of Computer Applications (0975 � 8887)
[7] R.Koschke, R.Falke and P. Frenzel,� Clone Detection Using Abstract Syntax Suffix Trees� in Proceedings of the 13th Working Conference on Reverse Engg. WCRE 2006.
[8] J.Krinke�Advanced slicing of sequential and concurrent Programs Proceedings of the 20th IEEE International Conference on Software Maintenance, pp. 464-468, September 2004.
[9] C.K.Roy and J. Cordy. NICAD: Accurate detection of near miss intentional clones using flexible pretty-printing and code normalization. In Proc. 16th IEEE International Conference on Program Comprehension, pages 172�181, 2008.
[10] C.K.Roy and J. R. Cordy. A survey on software clone detection research. Technical report, Queen�s University at Kingston, Ontario, Canada, 2007.

L. Sridevi, R. Kannan, "Clone Detection Using Abstract Syntax Trees," International Journal of Computer Sciences and Engineering, Vol.4, Issue.11, pp.46-48, 2016.