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An online navigation behavior grows each passing day, and thus extracting information intelligently from it is a difficult issue. Web Usage Mining (WUM) is the process of extracting knowledge from Web users access data by exploiting Data Mining technologies. It can be used for different purposes such as personalization, system improvement and site modification. In our propose work, user navigation patterns describe as the common browsing behaviors among a group of users. Since many users may have common interests up to a point during their navigation, navigation patterns should capture the overlapping interests or the information needs of these users. In addition, navigation patterns should also be capable to distinguish among web pages based on their different significance to each pattern. we would perfect the algorithm and apply some classification methods for classifying user request. This can be used in WUM based prediction systems. We proposed a complete generic framework that utilizes underlying domain ontology available at web applications. On which any sequential pattern mining algorithm can fit.

Ontology, Semantic Distance , Next Page Request Prediction ,Web Prefetching

[1] Sneha Y.S, G. Mahadevan,� Semantic Information and Web based Product Recommendation System � A Novel Approach� International Journal of Computer Applications (0975 � 8887) Volume- 55 Issue-9,Page no.(10-14) October 2012.
[2] J Vellingiri, S.Chenthur Pandian,� A Survey on Web Usage Mining� Global Journal of Computer Science and Technology Volume 11 Issue 4 Version 1.0, Page no.(67-72) March 2011.
[3] Li Xue Ming Chen Yun Xiong Yangyong Zhu,� User Navigation Behavior Mining using Multiple Data Domain Description� IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Volume 3 Page no.(132-135) September 2010.
[4] B. C. Grau, I. Horrocks, Y. Kazakov, and U. Sattler, "Extracting Modules from Ontologies: A Logic-Based Approach," Modular Ontologies, LNCS 5445, Springer-Verlag, Page no.(159-186) , 2009
[5] N. R. Mabroukeh and C. I. Ezeife. A taxonomy of sequential and web pattern mining algorithms. ACM Computing Surveys, Volume 43 Issue 1 Article 3 March 2011.
[6] Jawahir Che Mustapha Yusuf, Mazliham Mohd Su�ud, Patrice Boursier, Muhammad Alam, � Extensive Overview of an Ontology-based Architecture for Accessing Multi-format Information for Disaster Management� IEEE Page no (294-299), 2012
[7] J. Bao, G. Slutzki, and V. Honavar, "A Semantic Importing Approach to Knowledge Reuse from MUltiple Ontologies," Proc. The 2nd AAAI Conference on Artificial Intelligence, AAAI Press, Page no(1304-1309) ,2007
[8] M. Deshpande and G. Karypis. Selective markov models for predicting web page accesses. Transactions on Internet Technology,Volume 4 Issue 2 Page no(163� 184), 2004.

A. Mahajan, M. Singh, "Advance Approach of Integrate Semantic Information Usage Mining for next Page Prediction," International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.55-58, 2014.

In this paper, a real-time embedded multimodal biometric recognition system for authentication on Automated teller machine is being developed. The system is implemented on an embedded platform and equipped with novel multimodal recognition algorithms, so this system is an intelligent security system; it is developed based on a teller machine concept. Wherein, the transaction is successful, If and only if the biometric used is matched and GSM technology is used.

Bimodal Biometric, Decision Level Fusion, Finger vein, Iris Recognition, GSM

[1]. Houda Benaliouche andMohamed Touahria , �Comparative Study of Multimodal Biometric Recognition by Fusion of Iris and Fingerprint�, Computer Science Department, University of Ferhat Abbas S�etif 1, P�ole 2 - El Bez, 19000 S�etif, Algeria Received 28 August 2013;
[2]. Yanagawa,A.K. Aoki,Y and Ohyama,I,�An Europe`s first finger-vein biometric ATMs installed� ,Proc. IEEE International Symposium on Intelligent Control in Poland July 2011.
[3]. Sri Shimal Das and Smt. Jhunu Debbarma �Designing a Biometric Strategy (Fingerprint) Measure for Enhancing ATM Security in Indian e-banking System� Volume 1 No. 5, September 2011 ISSN-2223-4985 IJICT ,2011.
[4]. Sanchez-Reillo,R.Fernandez-Saavedra,B. & Liu-Jimenez,J,�Iris Biometrics for Embedded Systems� Very Large Scale Integration (VLSI) Systems, IEEE Transactions on (Volume:19, Issue:2 ) Biometrics Compendium, IEEE Trans, Feb. 2009
[5]. Vanathi G, Nigarihaa R, Uma Maheswari G & Sujitha R �Real Time Recognition System Using Finger-Vein � Electronics and Communication Engineering, Avinashilingam, University Coimbatore, India
[6]. �Optimized Daugman�s Algorithm for Iris Localization� Dr. Mohamed A. Hebaishy National Authority for Remote Sensing and Space Science Gozif Titp St., Elnozha Elgididah. Egypt (11769).
[7]. Anil K. Jain,Michigan State University and Karthik Nandakumar Institute for Infocomm Research, Singapore �Biometric Authentication: System Security and User Privacy�
[8]. Jammi Ashok, VAKA SHIVASHANKAR and P.V.G.S.MUDIRAJ, �An Overview of Biometrics� (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 07, 2010, 2402-2408.

Vithya T., Parthiban S., "A Bimodal Biometric Technique Based Enhanced Real-Time ATM System with Intelligence Security Measures," International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.59-63, 2014.

Energy consumption is a primary concern in the Wireless Sensor Network. Which leads to pursue the maximum energy saving at sensor nodes, where a relay is used to transfer the data packet. This leads to the increase in the data gathering latency due to low moving velocity of the mobile collector. In this paper we study the tradeoff between energy saving and data gathering latency in mobile data gathering by exploring a balance between the relay hop count of local data aggregation and the moving tour length of the mobile collector. In this we propose a polling based mobile gathering approach, which leads to optimization problem named bounded relay hop mobile data gathering (BHR - MDG). A subset of sensors are used for the polling points. Thus these are the two efficient algorithms for selecting polling points among sensors.

Wireless Sensor Networks, Mobile Data Gathering, Relay Hop Count, Polling Points, Moving Tour

[1] Bounded Relay Hop Mobile Data Gathering in Wireless Sensor Networks Miao Zhao, Member, IEEE, and Yuanyuan Yang, Fellow, IEEE 2012
[2] Data Pre-Forwarding for Opportunistic Data Collection in Wireless Sensor Networks Xiuchao Wu, Kenneth N. Brown, and Cormack J. Sreenan Department of Computer Science, University College Cork, Ireland, 2010
[3] Mobile Relay Configuration in Data-intensive Wireless Sensor Networks Fatme El-Moukaddem, Eric Torng, Guoliang Xing, 2010
[4] Analysis of Smartphone User Mobility Traces for Opportunistic Data Collection_Xiuchao Wu Kenneth N. Brown Cormac J. Sreenan Department of Computer Science, University College Cork, Ireland, 2012
[5] Ubiquitous Data Collection for Mobile Users in Wireless Sensor Networks Zhenjiang Li��, Mo Li�, Jiliang Wang� and Zhichao Cao� �Department of Computer Science and Engineering, Hong Kong University of Science and Technology �School of Computer Engineering, Nanyang Technological University, 2010
[6] TEEN: A Routing Protocol for Enhanced Efficiency in Wireless Sensor Networks Arati Manjeshwar and Dharma P. Agrawal Center for Distributed and Mobile Computing, ECECS Department, University of Cincinnati, Cincinnati, OH 45221-0030, 2001
[7] A Coordinated Data Collection Approach: Design, Evaluation, and Comparison William C. Cheng, Cheng-Fu Chou, Leana Golubchik, Member, IEEE, Samir Khuller, and Yung-Chun (Justin) Wan, journal 2004
[8] A Mobile Embedded Networked Sensor Platform Richard Pon1,2, Maxim A. Batalin1,3, Jason Gordon1,2, Aman Kansal1,2, Duo Liu1,2, Mohammad Rahimi1,3, Lisa Shirachi1,2, Yan Yu1,4, Mark Hansen1,5, William J. Kaiser1,2 , journal 2005
[9] Joint Mobility and Routing for Lifetime Elongation in Wireless Sensor Networks Jun Luo Jean-Pierre Hubaux School of Computer and Communication Sciences Ecole Polytechnique F&rale de Lausanne (EPFL) , CH-1015 Lausanne, Switzerland , journal 2005
[10] Mobile Element Scheduling with Dynamic Deadlines Arun A. Somasundara, Aditya Ramamoorthy, Member, IEEE, and Mani B. Srivastava, Senior Member, IEEE, journal 2007
[11] SenCar: An Energy-Efficient Data Gathering Mechanism for Large-Scale Multihop Sensor Networks Ming Ma, Student Member, IEEE, and Yuanyuan Yang, Senior Member, IEEE journal 2007
[12] ASAP: An Adaptive Sampling Approach to Data Collection in Sensor Networks Bugra Gedik, Member, IEEE, Ling Liu, Senior Member, IEEE, and Philip S. Yu, Fellow, IEEE journal 2007
[13] A Distributed Algorithm for Maximum Lifetime Routing in Sensor Networks with Mobile Sink Marios Gatzianas and Leonidas Georgiadis, Senior Member, IEEE journal 2008
[14] Adaptive Data Collection Strategies for Lifetime-Constrained Wireless Sensor Networks Xueyan Tang, Member, IEEE, and Jianliang Xu, Senior Member, IEEE journal 2008
[15] Data Gathering in Wireless Sensor Networks with Mobile Collectors Ming Ma and Yuanyuan Yang Department of Electrical and Computer Engineering, State University of New York, Stony Brook, NY 11794, USA journal 2008
[16] Maximizing the Lifetime of Wireless Sensor Networks with Mobile Sink in Delay-Tolerant Applications Young Sang Yun, Student Member, IEEE, and Ye Xia, Member, IEEE journal 2010
[17] Efficient Data Gathering with Mobile Collectors and Space-Division Multiple Access Technique in Wireless Sensor Networks Miao Zhao, Student Member, IEEE, Ming Ma, and Yuanyuan Yang, Fellow, IEEE journal 2011
[18] Analysis on Data Collection with Multiple Mobile Elements in Wireless Sensor Networks Liang He1,2, Jianping Pan1, and Jingdong Xu2 1University of Victoria, Victoria, BC, Canada 2Nankai University, Tianjin, China journal 2011
[19] Wireless Sensor Networks F. L. LEWIS Associate Director for Research Head, Advanced Controls, Sensors, and MEMS Group Automation and Robotics Research Institute The University of Texas at Arlington 7300 Jack Newell Blvd. S Ft. Worth, Texas 76118-7115 journal 2004.
[20] R.Nathiya and S.G.Santhi, "Energy Efficient Routing with Mobile Collector in Wireless Sensor Networks (WSNs)", International Journal of Computer Sciences and Engineering, Volume-02, Issue-02, Page No (36-43), Feb -2014.
[21] Rajesh Verma, "New Approach for Sampling Mobile Phone Accelerometer Sensor Data for Daily Mood Assessment", ISROSET-International Journal of Scientific Research in Network Security and Communication, Volume-01, Issue-03, Page No (16-20), Jul -Aug 2013

S.G. Santhi, R. Nathiya, "Power Consumption Based Efficient Routing With Mobile Collector in Wireless Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.64-70, 2014.

H.264/AVC is newest video coding standard of the ITU-T Video Coding Experts Group and the ISO/IEC Moving Picture Experts Group. The main goals of the H.264/AVC standardization effort have been enhanced compression performance and provision of a �network-friendly� video representation addressing �conversational� (video telephony) and �non conversational� (storage, broadcast, or streaming) applications. H.264/AVC has achieved a significant improvement in rate-distortion efficiency relative to existing standards. This article provides an overview of the technical features of H.264/AVC, describes profiles and applications for the standard, and outlines the history of the standardization process.

Video Compression Systems, ITUT H.264,Compression Standards

[1]. Wang, Shiqi, Abdul Rehman, Zhou Wang, Sitheyi Ma, and TheynGao. "Rate-SSIM optimization for video coding." In Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on, pp. 833-836. IEEE, 2011.
[2]. Dembla, Deepak, Biren Patel, Ashish Kumar, and YogeshBhomia. "Comparison of H. 264 and MPEG-4 Codec Based on PSNR-Peak Signal to Noise Ratio Algorithm." International Journal 3, no. 3 (2013).
[3]. Seo, Sangwon, Mark Woh, S. Mahlke, T. Mudge, Sunfaram Vijay, and ChaitaliChakrabarti. "Customizing wide-SIMD architectures for H. 264." In Systems, Architectures, Modeling, and Simulation, 2009. SAMOS`09. International Symposium on, pp. 172-179. IEEE, 2009.
[4]. Cheng, Xiaoyin. "Subjectively optimized HDTV video coding." (2009).
[5]. Shafique, Muhammad, Lars Bauer, and J�rg Henkel. "Optimizing the H. 264/AVC video encoder application structure for reconfigurable and application-specific platforms." Journal of Signal Processing Systems 60, no. 2 (2010): 183-210.
[6]. Kulikov, DrDmitriy, and Alexander Parshin. "Mpeg-4 avc/h. 264 video codecs comparison 2010-appendixes. MSU Graphics & Media Lab (Video Group), 2010."
[7]. Brand�o, Tom�s, Miguel Chin, and Maria Paula Queluz. "From PSNR to perceived quality in H. 264 encoded video sequences." proc. Of QoEMCS, Lisbon, Portugal (2011).
[8]. Prasantha, H. S., H. L. Shashidhara, K. N. B. Murthy, and M. Venkatesh. "Performance evaluation of H. 264 decoder on different processors." International Journal on Computer Science & Engineering 2, no. 5 (2010).

Urvashi, K. Jain, "Review On H.264/AVC Mpeg 4 Part-10 Compression Methods," International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.71-75, 2014.

In this era information security is a very important issue in every field such as Government Agencies (CBI, FBI), Research Organization, E-commerce and etc. where internet is being used. We want to secure our data from unauthorized user. Cryptography is a technique to secure data on the network from unauthorized user. There are different types of a cryptography algorithm (a) symmetric and (b) asymmetric has been designed. To secure data it is necessary to know which algorithm provides better security, efficiency, accuracy and effectiveness. This paper presents the complete analysis of various symmetric key encryption algorithms (DES, 3DES, CAST-128, MARS, IDEA, Blowfish, AES, and RC6) based on different parameters such as: Architecture, Scalability, Security, Flexibility, and limitations.

Symmetric Key, Information Security, Performance Matrices, Encryption, AES, DES, 3DES, CAST-128, MARS, IDEA, Blowfish, RC6

[1] A. K. Mandal, C. Parakash and M. A. Tiwari, �Performance Evaluation of Cryptographic Algorithms: DES and AES�, 2012 IEEE Student�s Conference on Electrical, Electronics and Computer Science.
[2] V. Singh and S. K. Dubey, �Analyzing Space Complexity Of Various Encryption Algorithms�, International Journal of Computer Engineering and Technology (IJCET), Volume 4, Issue 1, January- February (2013).
[3] Tingyuan Nie, Yansheng Li and Chuanwang Song, �Performance Evaluation for CAST and RC5 Encryption Algorithms�, International Conference on Computing, Control and Industrial Engineering, IEEE, 2010.
[4] E. Thambiraja, G. Ramesh and Dr. R. Umarani " A Survey on Various Most Common Encryption Techniques", IJARCSSE, Volume 2, Issue 7, July 2012.
[5] A.Ramesh and Dr.A.Suruliandi, �Performance Analysis of Encryption Algorithms for Information Security�, International Conference on Circuits, Power and Computing Technologies [ICCPCT-2013], IEEE, 2013.
[6] Md Asif Mushtaque, H. Dhiman, S. Hussain and Shivangi Maheshwari,�Evaluation of DES, TDES, AES, Blowfish and Twofish Encryption Algorithm Based on Space Complexity�, International Journal of Engineering Research & Technology (IJERT), Vol. 3 Issue 4, April � 2014.
[7] Tingyuan Nie, Yansheng Li and Chuanwang Song, �International Conference on Computing, Control and Industrial Engineering�, IEEE, 2010.
[8] Harmanpreet Singh, Amritpal Singh Danewalia, Deepak Chopra and Naveen Kumar N, "Randomly Generated Algorithms and Dynamic Connections", ISROSET-International Journal of Scientific Research in Network Security and Communication, Volume-02, Issue-01, Page No (1-4), Jan -Feb 2014.
[9] Kirti Aggarwal, Jaspal Kaur Saini, Harsh K. Verma, �Performance Evaluation of RC6, Blowfish, DES, IDEA, CAST-128 Block Ciphers�, International Journal of Computer Applications (0975 � 8887), April 2013, Volume 68� No.25, pp. 10-16.
[10] E. Thambiraja, G. Ramesh and Dr. R. Umarani� A Survey on Various Most Common Encryption Techniques�, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, July 2012.
[11] Z. Zahang and Shiliang sun, �Image encryption algorithm based on logistic chaotic system and s-boxes scrambling�, Image and Signal Processing (CISP), 4th International Congress on (Volume: 1),2011 .
[12] �Practical-Cryptology-and-Web-Security�, http://www.scribd.com/doc/126378371/Practical-Cryptology-and-Web-Security, accessed on 5th april 2014.
[13] Michal Halas, Ivan Bestak, Milos Orgon, and Adrian Kovac, �Performance Measurement of Encryption Algorithms and Their Effect on Real Running in PLC Networks�, IEEE, 2012.
[14] Fei Shao, Zinan Chang and Yi Zhang, �AES Encryption Algorithm Based on the High Performance Computing of GPU�, Second International Conference on Communication Software and Networks DOI 10.1109/ICCSN.2010.124, IEEE, 2010.
[15] Shashi Mehrotra Seth, Rajan Mishra, �Comparative Analysis of Encryption Algorithms for Data Communication�, IJCST , Vol. 2, Iss ue 2, June 2011.
[16] Hamdan.O.Alanazi, B.B.Zaidan, A.A.Zaidan, Hamid A.Jalab, M.Shabbir and Y. Al-Nabhani, �NewComparative Study between DES, 3DES and AES�, journal of computing, volume 2, issue 3, march 2010.
[17] Md Imran Alam and Mohammad Rafeek Khan,� Performance and Efficiency Analysis of Different Block Cipher Algorithms of Symmetric Key Cryptography�, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 10, October 2013.

Md.A. Mushtaque, "Comparative Analysis on Different parameters of Encryption Algorithms for Information Security," International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.76-82, 2014.

In this paper an analysis of the existing Social Media Marketing practices as a new investigating tool has been executed that can yield some indispensable data to keep up with the trends, competitor strategies, and developments in the market. The objective of this exercise is to expedite the process of making a novice entrepreneur into a seasoned pro with the help of social networking. The less known brands and small business have been scrutinized to devise an analysis based on the above mentioned peculiarity.

Social Media Marketing, Content Management System, Search Engine Optimization

[1]. Content Marketing, www.copyblogger.com/content-marketing/
[2]. Social Marketing Strategy, www.business2community.com/socialselling/social-marketing-strategy-big-picture-0645681#!NHHP
[3]. Social Media Marketing, socialmediatoday.com/socialbarrel/1617111/tips-master-social-media-marketing-small-businesses-infographic
[4]. Social Media Marketing Elements, www.copyblogger.com
[5]. Small Business Marketing, mashable.com/2009/10/28/small-business-marketing/
[6]. Social Media, blogs.technet.com/b/keep-your-business-moving/archive/2013/08/01/why-should-small-business-use-social-media-marketing.aspx
[7]. Small Business, www.smallbusiness.wa.gov.au/recent-polls/
[8]. Social Networking for Business, personalweb.about.com/od/social-marketing/a/Social-Networking-For-Business.htm
[9]. Digital Marketing, socialmediatoday.com/tompick/1647801/101-vital-social-media-and-digital-marketing-statistics-rest-2013
[10]. Google+, en.wikipedia.org/wiki/Google+
[11]. Small Business, graphs.net/infographics-on-small-business.html
[12]. Social Media Marketing, graphs.net/social-media-marketing-statistics.html
[13]. Impact of Social Media, graphs.net/social-media-impact.html
[14]. Business Strategies, mashable.com/2009/09/30/small-business-strategies/
[15]. Patnaik, S. "Going Social: Case studies of Successful Social Media Marketing", 2011
[16]. Vinerean, S., Cetina, I., Dumitrescu, L., & Tichindelean, M. "The Effects of Social Media Marketing on Online Consumer Behavior", International Journal of Business and Management, 2013.

N. Anand, R. Mahajan, "A Statistical Analysis of Social Media as a New Investigative Tool in Marketing," International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.83-86, 2014.

Clustering is a practice of splitting data into set of analogous objects; these sets are identified as clusters. Each cluster comprised of points that are alike among them and unalike compared to points of other cluster. This paper is being set to study and put side by side different data clustering algorithms. The algorithms under exploration are: k-means algorithm, hierarchical clustering algorithm, k-medoids algorithm, and density based algorithms. All these algorithms are analyzed on R-tool by taking same data-set under observation.

Clustering, K-Means Algorithm, Hierarchical Clustering Algorithm, K-Medoids Algorithm, Density Based Algorithm

[1]. Caiming Zhong ,Duoqian Miao,� Minimum spanning tree based split-and-merge: A hierarchical clustering method�, Journal of Information Sciences, Volume 181 Issue 16,August 2011, Elsevier ScienceInc.New York,USA,pages:3397-3410.
[2]. E. Mooi and M. Sarstedt, �A Concise Guide to Market Research�,DOI 10.1007/978-3-642-12541-6_9,Springer-Verlag Berlin Heidelberg 2011.
[3]. Xindong Wu � Vipin Kumar � J. Ross Quinlan,� Top 10 algorithms in data mining�, International Conference on Data Mining (ICDM) in December 2006.
[4]. A. K. Jain and R. C. Dubes. �Algorithms for Clustering Data.� Prentice-Hall, Englewood Cliffs, NJ, 1988.
[5]. L. Kaufman and P.J. Rousseeuw. �Finding Groups in Data: An Introduction to Cluster Analysis.� Wiley, New York, 1990.
[6]. S.Anitha Elavarasi and Dr. J. Akilandeswari and Dr. B. Sathiyabhama, January 2011, A Survey On Partition Clustering Algorithms.
[7]. F. Murtagh. A survey of recent advances in hierarchical clustering algorithms.Computer Journal, 26(4):354�359, 1983.
[8]. W. Day and H. Edelsbrunner., �Efficient algorithms for agglomerative hierarchical clustering methods�. Journal of Classification, 1(7):7�24, 1984.
[9]. S. Guha, R. Rastogi, and K. Shim, 1998. CURE: An Efficient Clustering Algorithm for Large Databases. Proc. ACM Int�l Conf. Management of Data : 73-84.
[10]. J. Hartigan and M. Wong. Algorithm as136: A k-means clustering algorithm. Applied Statistics, 28:100�108, 1979.
[11]. Kilian Stoffel and Abdelkader Belkoniene �Parallel k/h-Means Clustering for Large Data Sets�, P. Amestoy et al. (Eds.): Euro-Par`99, LNCS 1685, pp. 1451{1454, 1999.c Springer-Verlag.
[12]. Zha, H., Ding, C., Gu, M., He, X., & Simon, H. (2002)�Spectral relaxation for K-means clustering.� Advances in Neural Information Processing Systems 14 (NIPS�01),1057�1064.
[13]. Raymond T. Ng and Jiawei Han.,� CLARANS: A Method for Clustering Objects for Spatial Data Mining. �IEEE Transactions on Knowledge and Data Engineering, 14(5):1003{1016, 2002.
[14]. L. Kaufman and P. J. Rousseeuw. �Finding Groups in Data: an Introduction to Cluster Analysis�. John Wiley & Sons,1990
[15]. R. T. Ng and J. Han. ,�Efficient and Effective clustering methods for spatial Data Mining�, Proc. of the 20th Int�l Conf.on Very Large Databases, Santiago, Chile, pages 144�155,1994.
[16]. D.Napoleon , P.Ganga Lakshmi,� An Enhanced k-means algorithm to improve the Efficiency Using Normal Distribution Data Points �,(IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 07, 2010, 2409-2413.

S. Joshi, F.U. Khan, N. Thakur, "Contrasting and Evaluating Different Clustering Algorithms: A Literature Review," International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.87-91, 2014.

Data mining technology provides a user oriented approach to extract the hidden information from the large database. There are different algorithms used in data mining techniques like decision tree, Bayesian classifier, naive Bayes, neural network, , clustering etc. . Data mining in healthcare medicine deals with learning models to predict patients� disease. Data mining applications can greatly benefit all parties involved in the healthcare industry. For example, data mining can help healthcare insurers detect fraud and abuse, healthcare organizations make customer relationship management decisions, physicians identify effective treatments and best practices, and patients receive better and more affordable healthcare services. The main goal of this paper is to analyze and implement the data mining algorithms using WEKA tool and comparison between c4.5 and Bayesian classifier.

Bayesian Classification, Bayesian Networks, C4.5, Neural Network

[1]. Mariscal, Gonzalo, �scar Marb�n, and Covadonga Fern�ndez. "A survey of data mining and knowledge discovery process models and methodologies."Knowledge Engineering Review 25.2 (2010): 137.
[2]. Lokanatha C. Reddy, A Review on Data mining from the Past to the Future, International Journal of Computer Applications (0975 � 8887) Volume 15� No.7, February 2011
[3]. Varsha Kavi and Divyesh Joshi , "A Survey on Enhancing Data Processing of Positive and Negative Association Rule Mining", International Journal of Computer Sciences and Engineering, Volume-02, Issue-03, Page No (139-143), Mar -2014
[4]. Ozer, Patrick. "Data Mining Algorithms for Classification." (2009).
[5]. Drazin, Sam, and Matt Montag. "Decision Tree Analysis using Weka." Machine Learning-Project II, University of Miami (2012): 1-3.
[6]. Drazin, S., & Montag, M. (2012). Decision Tree Analysis using Weka. Machine Learning-Project II, University of Miami, 1-3.
[7]. Bouckaert, Remco R. "Bayesian network classifiers in weka for version 3-5-7." Artificial Intelligence Tools 11.3 (2008): 369-387.
[8]. Bouckaert, Remco R. Bayesian network classifiers in weka. Department of Computer Science, University of Waikato, 2004.
[9]. Singh, Yashpal, and Alok Singh Chauhan. "Neural networks in data mining." Journal of Theoretical and Applied Information Technology 5.6 (2009): 36-42.
[10]. Suyal, Neha. "Data Mining Using Neural Networks."
[11]. bin Othman, Mohd Fauzi, and Thomas Moh Shan Yau. "Comparison of different classification techniques using WEKA for breast cancer." 3rd Kuala Lumpur International Conference on Biomedical Engineering 2006
[12]. Sharma, Trilok Chand, and Manoj Jain. "WEKA Approach for Comparative Study of Classification Algorithm."
[13]. Arbelaitz,Olatz, etal. "J48Consolidated: An implementation of CTC algorithm for WEKA�." (2013).

M. Dey, S.S. Rautaray, "Disease Predication of Cardio- Vascular Diseases, Diabetes and Malignancy in Lungs Based on Data Mining Classification Techniques," International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.82-98, 2014.

The use of hands as biometric evidence is not very new, and commercially available. The proposed method is compared to other methods a method for extension. This will give better result as seen from the output. The personal attributes used in a biometric recognition system may be hand recognition This paper investigates a new approach to achieve performance improvement for hand area geometry systems by recognition of area of finger and hand and also the relative area between finger and hand which reduces the conventional hand geometry based personal verification systems.

Biological Models, Contour Detection, Human Computer Interaction, Image Analysis

[1]. Konstantinos G. Derpanis. �A Review of Vision-Based Hand Gestures� (2004).
[2]. Sushmita Mitra, and Tinku Acharya, �Gesture Recognition:A Survey�, IEEE Transactions on Systems, Man and Cybernetics�Part C: Applications and Reviews, 37(3)(2007).
[3]. T. S. Hunang and V. I. Pavloic, �Hand Gesture Modeling,Analysis, and Synthesis,� Proc. of Int. Workshop onAutomatic Face and Gesture Recognition, Zurich, (1995), 73-79.
[4]. Sharma, R., Huang, T. S., Pavovic, V. I., Zhao, Y., Lo, Z.,Chu, S., Schulten, K., Dalke, A., Phillips, J., Zeller, M. &Humphrey, W. �Speech/Gesture Interface to a VisualComputing Environment for Molecular Biologists�. In:Proc. of ICPR�96 II (1996), 964-968.
[5]. Gandy, M., Starner, T., Auxier, J. & Ashbrook, D. �The Gesture Pendant: A Self Illuminating, Wearable, InfraredComputer Vision System for Home Automation Control and Medical Monitoring�. Proc. of IEEE Int. Symposium onWearable Computers. (2000), 87-94.
[6]. Goza, S. M., Ambrose, R. O., Diftler, M. A. & Spain, I. M.�Telepresence Control of the NASA/DARPA Robonaut on a Mobility Platform�. In: Proceedings of the 2004 Conference on Human Factors in Computing Systems. ACM Press, (2004) 623�629.
[7]. Stotts, D., Smith, J. M. & Gyllstrom, K. �Facespace: Endoand Exo-Spatial Hypermedia in the Transparent Video Facetop�. In: Proc. of the Fifteenth ACM Conf. on Hypertext & Hypermedia. ACM Press, (2004) 48�57.
[8]. Smith, G. M. & Schraefel. M. C. �The Radial Scroll Tool:Scrolling Support for Stylus-or Touch-Based Document Navigation�. In Proc. 17th ACM Symposium on User Interface Software and Technology. ACM Press, (2004) 53�56.

M. Das, S.K. Bandyopadhyay, "Image Based Hand Recognitions," International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.99-102, 2014.

Future share market price prediction for any company mostly requires the help of AI and data mining techniques. The nature of any company almost depends upon fluctuation in share market. Some people think that buying or selling the stock of company is an act of gambling or any illegal activity but it is not the case instead it helps to us personally and for our countries growth also. However it is now possible to predict the future price by developing the effective pattern using neural network and data mining technique with the help of Back-propagation algorithm. By fetching historical data of any company from yahoo finance server and applying some data mining technique on it we are going to predict the future price.

Stock Market Forecasting; Back Propagation Algorithm; Data Mining; Technical Indicator; Neural Network

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