Best Paper Award

In recent years, load balancing is the challenging task which affects the performance in allotting the resources on homogeneous and heterogeneous cluster computing environment. This research proposes an enhancement in ACCS (Adaptively Circulates job among all servers by taking account of both Client activity and System load) policies by incorporating Map Reduce to overcome the problem in balancing the workload for resources. This technique provides simplicity and flexibility for data partitioning, localization and processing jobs as indicated by their present sizes and ranks the servers based on their loads by giving high priority to the smaller jobs. Map Reduce emphasizes more on high throughput of data on low-latency of job execution in a cluster to accomplish huge execution advantages. Trace driven simulations demonstrate the viability and robustness of Map Reduce under numerous different situations.

Load Balancing, Map Reduce, Web Server Clusters, AdaptLoad, ACCS

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[9]Bachmat, E., &Sarfati, H. (2010). Analysis of SITA policies. Performance Evaluation, 67(2), 102-120.
[10]Riska, A., Sun, W., Smirni, E., &Ciardo, G. (2002). ADAPTLOAD: effective balancing in clustered web servers under transient load conditions. InDistributed Computing Systems, 2002.Proceedings. 22nd International Conference on (pp. 104-111). IEEE.
[11]Luis, A., &Azer, B. (2000). Load balancing a cluster of web servers. InProceedings of IEEE International Performance, Computing, and Communications Conference (IPCCC‟ 00), ISBN: 0-7803-5979-
[12]Crescenzi, P., Gambosi, G., Nicosia, G., Penna, P., & Unger, W. (2007). On-line load balancing made simple: Greedy strikes back. Journal of Discrete Algorithms, 5(1), 162-175.
[13]Niu, Y., Chen, H., Hsu, F., Wang, Y. M., & Ma, M. (2007, February). A Quantitative Study of Forum Spamming Using Context-based Analysis.InNDSS.
[14]Garg, A. (2015). A Framework to Optimize Load Balancing to Improve the Performance of Distributed Systems. International Journal of Computer Applications, 122(15).
[15]Psaras, I., &Mamatas, L. (2011). On demand connectivity sharing: Queuing management and load balancing for user-provided networks. Computer Networks, 55(2), 399-414.
[16] Gupta, R. K., & Ahmad, J. (2014). Dynamic Load Balancing By Scheduling In Computational Grid System. Computer Engineering and Intelligent Systems, 5(6), 39-45.
[17] Ungureanu, V., Melamed, B., &Katehakis, M. (2008). Effective load balancing for cluster-based servers employing job preemption. Performance Evaluation, 65(8), 606-622.

Deepti Sharma and Vijay B. Aggarwal, "Mapreduce- A Fabric Clustered Approach to Equilibrate the Load", International Journal of Computer Sciences and Engineering, Vol.4, Issue.3, pp.116-123, 2016.

The power allocation has for long been considered a major problem for communication between many users who share common resources. With the emergence of new paradigms such as ad hoc networks, unregulated frequency bands and cognitive radio, the study of power allocation distributed protocols becomes particularly relevant. In fact in such networks, terminals can freely choose their power allocation strategy without following the rules imposed by a central node. The terminals are considered to be independent actors and it is reasonable to consider that they are rational, that is to say, by regulating their transmission power levels, terminals wish to maximize their communication quality. In this context, it is natural to study the problem of power allocation of each terminal as part of game theory, considering the terminal as each players looking to maximize their own utility function by controlling their power emission. Game theory allows particularly to study the existence and multiplicity of balancing power allocation strategies that terminal has no interest to deviate unilaterally .In a multiple access channel, the signal from a terminal received by the other terminals as interference to their own signals. Each terminal of the transmission quality depends directly of the transmission power level of other terminals.

Game Theory, Fairness Optimization, Access Methods, Resource Allocation, Power.

[1] Mohanakrishnan M.Azath ,’’Survey on Network and Device Aware QoS Approach for mobile Streaming ‘’,International Journal of Computer Sciences and Engineering,IJCSE ,Volume-3,Issue -1,Page No(76-79),2014.
[2] Apoorva Nayak and Rahul Sharma,’’Performance Evaluation of image transmission over Physical Layer of IEEE 802.16d with Antenna Diversity Scheme ‘’, International Journal of Computer Sciences and Engineering, IJCSE, Volume -2, Issue-12, Page No (132-136), 2014.
[3] Jaeok Park and Mihaela van der Schaar, ‘’ The Theory of Intervention Games for Resource Sharing in Wireless Communications’’, arXiv: 1101.3052v2 [cs.GT], Page No (1-29), 16 Jul 2011.
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[7] Ashok M. Kanthe,’’ Power Control through Noncooperative Game Theory on Wireless Sensor Network’’, Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia, Page No (1-5), 2012.
[8] Walid Saad, Zhu Han, Merouane Debbah, Are Hjørungnes , Tamer Basar,’’Coalitional Game Theory for Communication Networks: A Tutorial’’, IEEE Signal Processing Magazine, Special Issue on Game Theory, to appear, Page No(1-28) ,2009.
[9] Mark Felegyhazi, Jean-Pierre Hubaux,’’ Game Theory in Wireless Networks: A Tutorial’’, EPFL Technical report: LCA-REPORT, Page No (1-15), 2006.
[10] Roberta DELPIANO , Vito FRAGNELLI , Federica GARIN , Roberto TADEI , Isabella VARIO,’’ Game Theory and Wireless Communication Networks’’, Game Theory and Wireless Communication Networks, AIRO 2004,Lecce - 7/10 ,Page No(1-10),September 2004.

[11] Bo Liang,’’ POWER CONTROL AND SECURITY GAMES FOR WIRELESS COMMUNICATION NETWORKS’’, Lincoln, Nebraska, Page No (11-62), December 2011.
[12] BADR BENMAMMAR, FRANCINE KRIEF,’’ Game theory applications in wireless networks: A survey’’, Applications of Information Systems in Engineering and Bioscience, ISBN: 978-960-474-381-0,Page No(208-215) ,2013.
[13] Wei Huang,’’ Application of Game Theory in Wireless Communication Networks’’, the faculty of graduate studies,Electrical and Computer Engineering, The University of British Columbia (Vancouver), Page No(1-160),February 2012.
[14] Giacomo Bacci, Marco Luise,’’ Game Theory in Wireless Communications with an Application to Signal Synchronization’’, ADVANCES IN ELECTRONICS AND TELECOMMUNICATIONS, VOL. 1, NO. 1, Page No (86-97), APRIL 2010.
[15]Sara Riahi, Ali El Hore, Jamal El Kafi,” analysis and simulation of ofdm”, IJSR, ISSN Online: 2319-7064, volume 3, Issue 3, Page No (405-409), March 2014. .
[16]Sara Riahi, Ali El Hore, Jamal El Kafi,” Study and Analysis of a Noisy Signal by Viterbi
Decoding’’, IJSR, ISSN Online: 2319-7064, Volume 3 Issue 10, Page No (392-398), October 2014.
[17] Sara Riahi, Ali El Hore, Jamal El Kafi,’’ Performance study of the OFDM modulation for the use in Wireless communication Systems of the 4G ‘’, e-ISSN: 2395-0056, www.irjet.net p-ISSN: 2395-0072, Volume: 02 Issue: 06 | ,Page No(1219-1227),Sep-2015.
[18] Farshad Naghibi,’’ Uplink Resource Scheduling in Dynamic OFDMA Systems’’, Communication Systems,Department of Signals and Systems,CHALMERS UNIVERSITY OF TECHNOLOGY Göteborg, Sweden, Page No(1-64) ,2008.
[19] Chapter 9, ‘’Multiple Access Techniques for Wireless Communications’’,School of information science and Engineering, SDU, Page No(1-79)
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Sara Riahi, Ali El Hore, Jamal El Kafi, "Optimization of Resource Allocation in Wireless Systems Based on Game Theory", International Journal of Computer Sciences and Engineering, Vol.4, Issue.1, pp.1-13, 2016.

Data mining is the process of selecting, exploring and modeling a large database in order to discover model and pattern that are unknown [1]. Enormous gathered data in Health care Information society are scattered with different archive systems which are not connected with one another. This unorganized data leads to delay in monitoring, improper planning, defocus the analysis which leads to inaccuracy in decision making. The purpose of this study is to explore Supervised and Non Supervised WEKA filters on the data mining algorithm NavieBayes which is used for classification the data sets of Arrhythmia and Parkinson�s diseases. This in turn helps in increasing the performance accuracy of the classifier used for knowledge discovery [2] . Both the Datasets were taken from UCI Repository [3].

Filters, Parkinson�s Data, Arrhythmia Data, NavieBayes, Performance Matrices

[1] I.H. Witten, E. Frank. Data Mining: Practical machine learning tools and techniques, 2nd Edition. Morgan Kaufmann, San Francisco, 2005.
[2] Basilis Boutsinas Nikolaos Mastrogiannis and Ioannis Giannikos. A method for improving the accuracy of data mining classification algorithms. Computers & Operations Research, vol. 36, no. 10, pp. 2829-2839, 2009.
[3] A. Asuncion, D.J. Newman. UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science,2007,http://www.ics.uci.edu/~mlearn/MLRepository.html.
[4] Arrhythmia�s in adults with congenital heart disease John K Triedman Heart 2002; 87: 383 389.
[5] Parkinson�s Disease , Challenges ,Progress And Promise ,November 2004 , National Institute Of Neurological Disorders and Stroke ,National Institutes Of Health.
[6] Diagnosis and Pharmacological Management Of Parkinson�s Disease, A National Clinical Guideline By Scottish Intercollegiate Guidelines Network.
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[8] D. Pedro and M. Pazzani "On the optimality of the simple Bayesian classifier under zero-one loss". Machine Learning, 29:103�137, 1997.
[9] Witten, T.H and Frank, E. 2000 Data mining: Practical machine learning tools and techniques with Java implementations. Morgan Kaufmann, San Francisco.
[10] Hirdes J.P., Perez E., Curtin-Telegdi N., et al, 1999. RAI-Mental Health (RAI-MH) Training manual and resource Guide Version 1.0.
[11] P.T.Kavitha, Dr.T.Sasipraba , Knowledge Driven HealthCare Decision Support System using Distributed Data Mining, Indian Journal of Computer Science and Engineering (IJCSE) , Vol. 3 No.3 Jun-Jul 2012.
[12] Blaz Zupan Riccardo Bellazzi. Predictive data mining in clinical medicine. International Journal of Medical Informatics, vol. 77, no. 2, pp. 81-97, 2008.
[13] Nickolas Savarimuthu Sarojini BalaKrishnan, Ramaraj, NarayanaSwamy and Rita Samikannu. Feature Selection using FCBF in TYPE II Diabetes Databases. Proceedings of 7th Annual Conference on Information Science, Technology and Management New Delhi, 2009.

T.A. Shaikh, A. Chhabra, "Effect of WEKA Filters on the Performance of the NavieBayes Data Mining Algorithm on Arrhythmia and Parkinson�s Datasets", International Journal of Computer Sciences and Engineering, Vol.2, Issue.5, pp.45-51, 2014.

For accessing the information from web log, this is very important task and this task can be accomplished by web usage mining technique. Through web usage mining technique we can find out visitors behavior which can automatically and very fast access intrinsic information from huge amount of web log data, such as interesting access path, identify the user, accessing the web page group, web user clustering and web pre-fetching. Web usage mining is milestone for decision making process for an organization. Data preprocessing is very important concepts for the mining process. If our web log data is preprocessed then we can easily find out the desire information about visitor and also retrieve other hidden information from web log data. In this paper we focus on data preprocessing technique of web usage mining, after completion of data preprocessing, any king of irrelevant information can be sort out. We have also proposed an algorithm and its implementation for web log preprocessing in web usage mining. Every page has been assigned with an individual token. According to this token and frequency, data mining technique (Classification, Association Rules, and Clustering) can be applied. In this article we can easily find the highest and lowest value according to page access frequency.

Web Usage Mining, Preprocessing, Web Log Data, Frequency, Clustering

[1] Theint Theint Aye, "Web Log Cleaning of Web Usage Patterns," IEEE, 2011.
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[3] Arshi Shamsi, Rahul Nayak, Pankaj Pratap Singh and Mahesh Kumar Tiwari , "Web Usage Mining by Data Preprocessing," IJCST, vol. 3, 2012.
[4] Mahendra Pratap Yadav,Pankaj Kumar Keserwani and Shefalika Ghosh Samaddar, "An Efficient Web Mining Algorithm for Web Log Analysis: E-Web Miner," IEEE, 2012.
[5] Shaimaa Ezzat Salama, Mohamed I. Marie, "Web Server Logs preprocessing for Web Intrusion Detection," Computer and Information Science, vol. 4, 2011.
[6] Jaideep Srivastava, Robert Cooley, Mukund Deshpande and Pang-Ning Tan, "Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data," SIGKDD Explorations, vol. 1, 2000.
[7] Liu Kewen, "Analysis of Preprocessing Methods for Web Usage Data," International Conference on Measurement , Information and control(MIC),IEEE,2012.
[8] R. Cooley,B. Mobasher and J Shrivastava, "Web Mining:information and pattern discoveryon the World Wide web," Ninth International Conference, 2011.
[9] Web Log Data, "http://ita.ee.lbl.gov/html/contrib/NASA-HTTP.html,".
[10] Zhuang Like, Kou Zhongbao and Zhang Changshui, "Session identification based on time intervals in Web log mining," Journal of Tsinghua University (Science and Technology), 2005.
[11] N. Zhang and W. F. Lu, " An Efficient Data Preprocessing Method for Mining Customer Survey Data," IEEE, 2007.
[12] Tasawar Hussain, Dr. Sohail Asghar, Dr. Nayyer Masood, " Web Usage Mining: A Survey on Preprocessing of Web Log File," IEEE, 2010.
[13] T. Murata and K. Saito, "Extracting Users` Interests from Web Log Data," Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings, 2006.
[14] Ling Zheng , Hui Gui and Feng Li, "Optimized Data Preprocessing Technology for Web Log Mining," International Conference On Computer Design And Appliations ICCDA, 2010.
[15] R. Cooley, B. Mobasher and J. Srivastava, "Data preparation for mining world wide web browsing patterns," Knowledge and Information System, 1999.
[16] Brijesh Bakariya and G.S.Thakur, "Preprocessing on Web Log Data in Web Usage Mining," International Conference on Intelligent Computing and Information System ICICIS, 2012.
[17] Thi Thanh Sang Nguyen, Hai Yan Lu and Jie Lu, "Web-page Recommendation based on Web Usage and Domain Knowledge," IEEE, 2013.

B. Bakariya, G.S. Thakur, "Effectuation of Web Log Preprocessing and Page Access Frequency using Web Usage Mining", International Journal of Computer Sciences and Engineering, Vol.1, Issue.1, pp.1-5, 2013.