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The exponential explosion of various contents on the Web, made Recommendation Systems increasingly indispensable. Innumerable different kinds of recommendations are made on the Web every day, including movies, music, images, books recommendations, query suggestions, tags recommendations, etc. The proposed system uses the historical browsers data for search key words and provides users with most relevant web pages. All the users’ click-through activity such as number of times he visited, duration he spent, his mouse movements and several other variables are stored in database. The proposed system uses this database and process to rank them. We have proposed a Radial Basis Function Neural Network [RBFNN]. The results obtained using the standard measures like precision, coverage and F1 measure on the proposed technique, produces the most relevant results as compared to aggregation technique based method and iPACT method. The RBFNN algorithm shows better prediction precision, coverage and the F1 measure than the iPACT method. The proposed framework can be utilized in many recommendation tasks on the World Wide Web, including expert finding, image recommendations, image annotations etc.

Image Recommendation, Neural Network, Query Suggestion, Recommendation System, Webpage Recommendation

[1] Hao Ma, Irwin King and Michael R Lyu, “Mining Web Graphs for Recommendations”, In IEEE Transactions on Knowledge and Data Engineering. volume-24, No-6, June 2012.
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[5] G Linden, B Smith and J York, “Amazon.com Recommendations: Item-to-Item Collaborative Filtering”, In: IEEE Internet Computing, page no-76-80, Jan/Feb 2003.
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[10] H Cui, J R Wen, J Y Nie and W Y Ma, “Query Expansion by Mining User Logs”, IEEE Transactions on Knowledge and Data Engineering, volume-15, No-4, page no- 829-839, 2003.
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[16] W Gao, C Niu, J Y Nie, M Zhou, J Hu, K F Wong and H W Hon, “Cross-Lingual Query Suggestion using Query Logs of Different Languages”, In: Proceedings of SIGIR, page no-463-470, 2007, Amsterdam, The Netherlands.
[17] E Agichtein, E Brill and S. Dumais, “Improving Web Search Ranking by Incorporating User Behavior Information”, In: Proceedings of SIGIR Seattle, page no- 19-26, Washington, USA, 2006.
[18] X Wang and C Zhai, “Learn from Web Search Logs to Organize Search Results”, In: Proceedings of SIGIR, page no- 87-94, Amsterdam, The Netherlands, 2007.
[19] T Joachims and F Radlinski, “Search Engines that Learn from Implicit Feedback”, volume-40, No-8, page no-34-40, 2007.
[20] D Shen, M Qin, W Chen, Q Yang and Z Chen, “Mining Web Query Hierarchies from Click through Data”, In: Proceedings of AAAI, page no- 341-346, 2007.
[21] M Pasca and B V Durme, “What you Seek is What you Get: Extraction of Class Attributes from Query Logs”, In: Proceedings of IJCAI, page no-2832-2837, 2007.
[22] Y H Yang, P T Wu, C W Lee, K H Lin, W H Hsu and H. Chen, “Context Seer: Context Search and Recommendation at Query Time for Shared Consumer Photos”, In: Proceedings of Multimedia, page no- 199-208, Vancouver, Canada, 2008.
[23] Yahya AlMurtadha, Md. Nasir Bin Sulaiman, Norwati Mustapha and Nur Izura Udzir, “IPACT: Improved Web Page Recommendation System Using Profile Aggregation Based On Clustering of Transactions”, American Journal of Applied Sciences, volume- 8, No-3, page no-277-283, 2011.

Pushpa C N, Thriveni J, Venugopal K R and L M Patnaik, "Radial Basis Neural Network Technique based Web Page Recommendation System," International Journal of Computer Sciences and Engineering, Vol.2, Issue.9, pp.1-7, 2014.

Data mining is a well-known technique for automatically and intelligently extracting useful information or knowledge from a large amount of data, but it can also disclose sensitive information of an individual or a company. This promotes the need for privacy preserving data mining which is becoming an increasingly important field of research and many researchers have proposed techniques for handling this concept. However, most of the privacy preserving data mining approaches concentrate on fixed disclosure threshold strategy for all sensitive information. This article proposes an approach for group-based threshold strategy which may help facilitate to use varying sensitivity level for the information to be hidden.

Restricted patterns, Sanitization, Sensitive transactions, Group-based Threshold

Richard Yevich, "Data Mining," In: Joyce Bischoff, Ted Alexander and Sid Adelman (editors). DataWarehouse: Practical Advice from the Experts. Upper Saddle River, N.J.: Prentice Hall, (1997).
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[3] S. Rizvi, J. Haritsa, “Maintaining Data Privacy in Association Rule Mining”, In Proceedings the 28th International Conference on Very Large Data Bases, pp.682-693, 2002.
[4] L. Sweeney, “k-Anonymity: A Model for Protecting Privacy”, International Journal of Uncertainty, Fuzziness and Knowledge-based Systems, vol.10, no.5, pp.557-570,2002.
[5] X.K. Xiao, Y.F. Tao, “Personalized Privacy Preservation”, In Proceedings of the ACM Conference on Management of Data (SIGMOD), pp.229-240, 2006.
[6] M. Kantarcioglu, C. Clifton, “Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data”, IEEE Transactions on Knowledge and Data Engineering, vol.16, no.9, pp.1026-1037, 2004.
[7] Lindell, Yehuda, Pinkas, “Privacy preserving data mining”, In Proceedings of the Advances in Cryptology–CRYPTO, pp.36–54,2000.
[8] J. Vaidya, C. Clifton, “Privacy Preserving Association Rule Mining in Vertically Partitioned Data”, In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.639-644, 2002.
[9] Cynthia Selvi P., Mohamed Shanavas A.R., “Towards Information Privacy Using Transaction-Based Maxcover Algorithm”, World Applied Sciences Journal 29 (Data Mining and Soft Computing Techniques): 06-11, 2014, ISSN 1818-4952, © IDOSI Publications, 2014, DOI:10.5829/ idosi.wasj.2014.29. dmsct.2, Pages. 06-11
[10] The Dataset used in this work for experimental analysis was generated using the generator from IBM Almaden Quest research group and is publicly available from http://fimi.ua.ac.be/data/.
[11] Pavon J, Viana S, Gomez S, “Matrix Apriori: speeding up the search for frequent patterns,” Proc. 24th IASTED International Conference on Databases and Applications, 2006, pp. 75-82.

Cynthia Selvi P and Mohamed Shanavas A.R, "Trade-off between Utility and Security using Group Privacy Threshold Sanitization," International Journal of Computer Sciences and Engineering, Vol.2, Issue.9, pp.8-11, 2014.

Information Retrieval (IR) has become very tedious given the amount of data handled these days. Search engines are posed with an ever increasing responsibility of giving precise responses to user queries in minimal time. In this paper, we present a query clustering approach which identifies Frequently Asked Questions (FAQs) for answering future queries. The proposed approach is based on identification of distinct subjects from queries enquired& logged in the past. The queries falling under each of the subject category are then reduced to a group which represents the frequently asked queries. In the past, these queries have been asked frequently & thus have an inclination of being repeated in the future. This will give the interface (e.g. search engines) an ability to predict future queries and respond in a time efficient manner. We extend this approach on a Real Estate data warehouse which proves its viability and efficiency in Real Estate domain as well.

Data Warehouse, Information Retrieval, Query Clustering, Apriori, Subject Area Identification

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G. Dubey, R. Nayak, N. Wadhwa, A. Rana, "Giving Future Vision to IR: A Query Clustering Approach," International Journal of Computer Sciences and Engineering, Vol.2, Issue.9, pp.12-17, 2014.

The fastest services of path finding were provided by numerous web maps along with local search engines for a long instance. User behavior of driving typically differs in their progressing driving experiences hence a good quality routing service should believe three aspects such as routes, traffic, as well as drivers, which are far away from extent of shortest or fastest path computing. In our approach of finding driving directions, Variance Entropy Clustering method is used to find out allocation of travel time between two landmarks in dissimilar time period. Two stage routing algorithm used to find fastest and safest route to reach destination. Rough and refined are the two routing stage algorithm used. Label setting algorithm used to find shortest distance between source and destination. Interactive voting map matching algorithm used to construct Landmark graph. GPS equipped taxis are employed as mobile sensors searching traffic rhythm of a city in physical world. If instantaneous sensor data are obtainable for several road segments, our method is combined to make available improved routes for end users

Path finding, User behavior, GPS-equipped taxis, Qualified divers. GPS, Driving Directions, Data mining, Spatial databases and GIS, Time-dependent fast route, Taxitrajectories, Road Network

[1] J. Yuan, Y. Zheng, C. Zhang, and X. Xie, �An Interactive-Voting Based MapMatching Algorithm,� Proc. Int�l Conf. Mobile Data Management (MDM), Pages 43-52, ISBN:978-0-7695-4048-1 2010.
[2] Y. Lou, C. Zhang, Y. Zheng, X. Xie, W. Wang, and Y. Huang �Map-Matching for Low-Sampling-Rate GPS Trajectories,� Proc.Int�l Conf. Advances in Geographic Information Systems (GIS), ISBN:978-1-60558-649- 6/09/11, November 4-6, 2009.
[3] J. Yuan, Y. Zheng, C. Zhang, W. Xie, G. Sun, H. Yan, and X. Xie, �T-Drive: Driving Directions Based on Taxi Trajectories,� Proc.18th SIGSPATIAL Int�l Conf. Advances in Geographic Information Systems (GIS), Page No(99-108), ISBN: 978-1-4503-0428-3, 2010.
[4] E. Kanoulas, Y. Du, T. Xia, and D. Zhang, �Finding Fastest Paths on a Road Network with Speed Patterns,� Proc. Int�l Conf. Data Eng. (ICDE), ISBN: 0-7695-2570-9 2006.
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[9] U. Demiryurek, F. Banaei-Kashani, and C. Shahabi, �A Case for Time-Dependent Shortest Path Computation in Spatial Networks,� Proc. Int�l Conf. Advances in Geographic Information Systems (GIS), 2010.
[10] A. Thiagarajan, L. Ravindranath, K. LaCurts, S. Madden, H. Balakrishnan, S. Toledo, and J. Eriksson, �Vtrack: Accurate, Energy-Aware Road Traffic Delay Estimation Using Mobile Phones,� Proc. Seventh ACM Conf. Embedded Networked Sensor Systems, 2009.
[11] Jing Yuan, Yu Zheng, Xing Xie, Guangzhong Sun, "T-Drive: Enhancing Driving Directions with Taxi Drivers` Intelligence", IEEE Transactions on Knowledge & Data Engineering, vol.25, no. 1, pp. 220-232, Jan. 2013, doi:10.1109/TKDE.2011.200.
[12] G. Sivaiah1 and P Krishna Rao �A Comprehensive Survey on Providing Efficient Driving Directions Using GPS and Driver`s Ability� International Journal of Computer Sciences and Engineering, Vol.-2(7), PP (79-82) July 2014, E-ISSN: 2347-2693.

G. Sivaiah, PK. Rao, "Providing Efficient Driving Directions Using GPS and Driver`s Ability," International Journal of Computer Sciences and Engineering, Vol.2, Issue.9, pp.18-21, 2014.

Web services are loosely-coupled software systems considered hold up interoperable machine-to-machine communication over a system. The most undemanding approach personalized cloud service quality of service ranking is to assess the entire service candidates at user side and position services base on observed values of quality of service. The materialization of web services has produces unprecedented prospect for organizations to setup additional agile as well as versatile collaborations with other organizations. Comparable to established component-based systems, cloud applications normally entail numerous cloud components that communicate over application programming interface. To attack this crucial challenge, we put forward a personalized ranking prediction structure, named cloud Rank to forecast quality of service ranking concerning a set of cloud services devoid of requiring extra real-world service invocations from the projected users. The target users of cloud rank structure are cloud applications, which require personalized cloud service ranking in support of building selection of optimal service.

Cloud Service, Quality Of Service, Cloud Rank, Personalized Service

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[2] N. N. Liu and Q. Yang, �Eigenrank: a ranking-oriented approach to collaborative filtering,� in Proc. 31st Int�l ACM SIGIR Conf. On Research and Development in Information Retrieval (SIGIR�08), 2008, pp. 83�90.
[3] H. Ma, I. King, and M. R. Lyu, �Effective missing data prediction for collaborative filtering,� in Proc. 30th Int�l ACM SIGIR Conf. On Research and Development in Information Retrieval (SIGIR�07), 2007, pp. 39�46.
[4] J. Marden, Analyzing and Modeling Ranking Data. Chapman & Hall, New York, 1995.
[5] P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, �Grouplens: An open architecture for collaborative filtering of netnews,� in Proc. of ACM Conf. Computer Supported Cooperative Work, 1994, pp. 175�186.
[6] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, �Item-based collaborative filtering recommendation algorithms,� in Proc. 10th Int�l Conf. World Wide Web (WWW�01), 2001, pp. 285�295.
[7] J. Wu, L. Chen, Y. Feng, Z. Zheng, M. Zhou, and Z. Wu, �Predicting qos for service selection by neighborhood-based collaborative filtering,� IEEE Transactions on System, Man, and Cybernetics, Part A, to appear.
[8] C. Yang, B. Wei, J. Wu, Y. Zhang, and L. Zhang, �Cares: a rankingoriented cadal recommender system,� in Proc. 9th ACM/IEEE-CS joint conference on Digital libraries (JCDL�09), 2009, pp. 203�212.
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[11] Z. Zheng and M. R. Lyu, �WS-DREAM: A distributed reliability assessment mechanism for Web services,� in Proc. 38th Int�l Conf. Dependable Systems and Networks (DSN�08), 2008, pp. 392�397.
[12] Z. Zheng, H. Ma, M. R. Lyu, and I. King, �WSRec: A collaborative filtering based Web service recommender system,� in Proc. 7th Int�l Conf. Web Services (ICWS�09), 2009, pp. 437�444.

G.P. Kumar, K. Morarjee , "Ranking Prediction for Cloud Services from The Past Usages," International Journal of Computer Sciences and Engineering, Vol.2, Issue.9, pp.22-25, 2014.

Information security has become a major cause of concern because intruders are concerned with reading the information. It is because of electronic dropping security is under threat. This paper deals with the comparative analysis of steganography over coloured images. �Steganography� is a greek word which means �hidden writing�. It is the art of hiding the secret message within a image. The goal of steganography is to avoid drawing suspicious to transmission of hidden message. It serves a better way of securing message than cryptography which provide security to content of message and not the existence. Original message is being hidden within a carrier such that the changes occurred in carrier are not observed. The hidden message in carrier is difficult to detect without retrieval.Different techniques are described in this paper for steganography over coloured images. One of them is spatial steganography. In this technique some bits in the image pixel is used for hiding data. Second technique is Transform Domain Technique which is a more complex way of hiding information in an image. Using Distortion technique, a stego object is created by applying a sequence of modifications to the cover image. The message is encoded at pseudo-randomly chosen pixels. Masking and Filtering technique embed the information in the more significant areas than just hiding it into the noise level. The hidden message is more integral to the cover image. Steganography is efficient, simple and decreases the degree of attack on secret information and improve image quality.

Steganography, Cryptography, Secret Information, Distortion, Spatial, Tranform Domain, Masking and Filtering

[1] R.Amirtharajan and R.John Bosco Balaguru. ―Constructive Role of SFC& RGB Fusion versus Destructive Intrusion‖.Proc. International Journal of Computer Applications1(20):30�36
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[5] Shareza Shirali, M.H, �Anew Approach to persain/Arabic Text Steganography�, Computer and Information Science, 2006, ICISCOMSAR 2006,Proc. 5th IEEE/ACIS International Conference, 10- 12 July 2006 pp 310-315.
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S. Suri, H. Joshi, V. Mincoha, A. Tyagi, "Comparative analysis of Steganography for coloured images," International Journal of Computer Sciences and Engineering, Vol.2, Issue.9, pp.26-29, 2014.

Currently, several solutions are available for monitoring patient health using body sensors. In hospitals, healthcare wireless sensor networks (HWSNs) offer support to access these sensors to allow for continuous patient monitoring. In order to improve the quality-of-life of hospitalized patients, it is important to let them walk around the monitored area. This ability brings several challenges to HWSNs with mobility support. Due to the crucial importance of the sensed parameters the HWSNs must be in continuous communication with the body sensors. The connection between body sensors and a healthcare wireless sensor network is performed through an access point. Indoor communications are limited in terms of signal propagation and, therefore, several access points to cover large areas are deployed. In order to maintain the sensor�s accessibility these should frequently change their point of attachment by performing a mechanism known as a handover. Handover mechanisms are able to support the intra-mobility of sensors in networks within the same domain. This paper surveys the most recent intra-mobility solutions with special focus on handover approaches that can be used in healthcare wireless sensor networks. An in depth review of the related literature is performed in order to present the state of the art on this topic, to discuss the available solutions, and to point out open issues for further research work. In healthcare scenarios it is important that technology maybe focused on the patients� quality-of-life. The use of HWSNs improves patients� health monitoring. These technologies can be used for patient monitoring in both a real-time and continuous manner. When hospitalized, the patients should be autonomous and their mobility should be preserved whenever possible. That way the HWSNs should support this mobility and ensure continuous patient monitoring.

HWSNs; Healthcare Wireless Sensor Networks; Body Sensor; Quality-Of-Life; Intra-Mobility

[1] Jo�o M. L. P. Caldeira, Joel J. P. C. Rodrigues, and PascalLorenz, � Intra-Mobility Support Solutions for HealthcareWireless Sensor Networks � Handover Issues�, in IEEE Wireless Sensor Networks, Vol. 45, No. 3, January 2014.
[2] Sergio Gonz�alez-Valenzuela, Min Chen, and Victor C. M. Leung, �Mobility Support for Health Monitoring at HomeUsing Wearable Sensors�, in IEEE Information Technology in Biomedicine, Vol. 15, No. 4, July 2011.
[3] Sang-Joong Jung, Risto Myllyl�, and Wan-Young Chung,�Wireless Machine-to-Machine Healthcare SolutionUsing Android Mobile Devices in Global Networks�, in IEEE Sensors Journal, Vol. 13, No. 5, May 2013, pp. 1419-1424.
[4] Praveen Halapeti, Prof.Shantala Patil �Healthcare Monitoring System Using Wireless Sensor Networks� ,in International Journal of Advanced Research in Computer Science & Technology Vol. 2, Issue 2, Ver. 3 (April - June 2014), pp. 443-446.
[5] Moshaddique Al Ameen and Kyung-sup Kwak, �Social Issues in Wireless Sensor Networks with Healthcare Perspective�, in The International Arab Journal of Information Technology, Vol. 8, No. 1, January 2011, pp. 52-58.
[6] Shantala Devi Patil &Vijayakumar B. P �Secure Health Monitoring In Wireless Sensor Networks With Mobility-Supporting Adaptive Authentication Scheme�, in International Journal of Computer Networking, Wireless and Mobile Communications , Vol. 4, Issue 1, Feb 2014, 27-34.
[7] Manisha Mittal, Dr. D. K.Chauhan, �Secured Solutions for Mobility in Wireless Body Area Networks�, in International Journal of Emerging Technology and Advanced Engineering, Volume 4, Issue 2, February 2014,pp.157-161.
[8] Gayathri Devi Kotapati, Bhargavi Vepuri and Mannam Venkata Deepak, �High Level Anatomy For Energy Conservations Schems in WSN�,International Journal of Scientific and Research Publications, Volume 3, Issue 11, November 2013,pp. 1-7.
[9] V.Shrinithi and R.Rohini, �A Survey on Data Collection in Wireless Sensor Network with Mobile Elements�, in International Journal of Latest Trends in Engineering and TechnologyVol. 3, Issue 2, November 2013, pp. 152-156.
[10] Antonio J. Jara, Miguel A. Zamora and Antonio F. G. Skarmeta, �An Initial Approach to Support Mobility in Hospital WirelessSensor Networks based on 6LoWPAN (HWSN6)�, in Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, volume: 1, number: 2/3, pp. 107-122.

C.R. Suryawanshi, Y.C. Bhute, "A WSN based System for Enhancing Intra Mobility Solution for Healthcare � A Review," International Journal of Computer Sciences and Engineering, Vol.2, Issue.9, pp.30-34, 2014.

Cloud computing is a model for enabling convenient and on-demand network access to a shared pool of configurable computing resources like networks, servers, storage, application and services that can be rapidly provisioned and released with minimal management effort or service provider interaction. These resources are pooled for the usage of customers to cater the elastic need of resources of customers due to varying workload. Clients need to pay only for the amount of resources they use. The need of the hour is to reduce the cost of cloud and increase its efficiency. As the cost of cloud resources is very high, so the solution is to use Simulation tools for designing a cloud service and form experiments with the same. This paper compares various simulating tools namely CloudSim, Green Cloud, MDCSim and CloudAnalyst based on their features, advantages and disadvantages.

Cloud; Simulation; CloudSim; Green Cloud; MDCSim; CloudAnalyst

[1] Armburst, Michael, Fox, Armando, Griffith, Rean, Joseph, Anthony D., Katz, Randy, Konwinski, Andrew, Lee, Gunho, Patterson, David, Rabkin, Ariel, Stoica, Ion and Zaharia, Matei, �A view of cloud computing. Commun.�, ACM Volume-53 Issue- 4, Page No (50-58), Apr 2010.
[2] Shaheen Ayyub, Devshree Roy, �Cloud Computing Characteristics and Security Issues�, IJCSE Volume-1, Issue-4, Page (18-22), Dec 2013.
[3] Firas D. Ahmed and Amer Al Nejam, �Cloud computing: Technical challenges and CloudSim functionalities�, IJSR Volume -2 Issue-1, Page No (26-30), Jan 2013.
[4] CloudSim: A Framework For Modeling And Simulation Of Cloud Computing Infrastructures And Services, http://www.cloudbus.org/cloudsim/, Sept 15, 2014
[5] Simulation Software, http://en.wikipedia.org/wiki/Simulation_software, Sept 14, 2014
[6] Rodrigo N. Calheiros, Rajiv Ranjan, Cesar A. F. De Rose and Rajkumar Buyya, �CloudSim: A Novel Framework for Modeling,and simulation of Cloud Computing Infrastructures and Services�, Software: Practice and Experience Volume-41, Issue-1, Page No (23-50), Jan 2011.
[7] MacSim: Simulator for hetrogeneous architecture, https://code.google.com/p/macsim/, Sept 14, 2014
[8] Bhathiya Wickremasinghe, �CloudAnalyst: A CloudSim based tool for modeling and Analysis of Large Scale Cloud Computing Simulators,� MEDC project report, 2009.
[9] GreenCloud- The Green Cloud Simulator, http://greencloud.gforge.uni.lu/, Sept 14, 2014

H. Kaur, V. Gautam, "A Survey of various Cloud Simulators," International Journal of Computer Sciences and Engineering, Vol.2, Issue.9, pp.35-38, 2014.

Finding objects, such as keys, workplace equipment, people, or smooth an enemy vehicle, is an appeal that has conventional a lot of care over new year�s. With the appearance of wireless nets and moveable calculating devices, if location-aware skill and facilities to new presentations has grown important for developers. New developments in device skill consume permissible wireless device nets to deliver site services. Frequent presentations of wireless device nets shoulder that the plans are location-aware. In this paper, we deliberate the hunt localization system. Using only the radio topographies of the sensors, the hunt scheme delivers two methods for finding an object. The scheme delivers decent results, nonetheless numerous postponements are deliberated to brand it additional climbable and reliable.

WSN, Hunt Interface, RSSI, Control Consumption

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[2] Xuhui Chen ; Coll. of Comput. & Commun., Lanzhou Univ. of Technol., Lanzhou, China ; Peiqiang Yu, �Research on hierarchical mobile wireless sensor network architecture with mobile sensor nodes� Published in: Biomedical Engineering and Informatics (BMEI), 2010 3rd International Conference on (Volume:7 ) Date of Conference: 16-18 Oct. 2010 Page(s): 2863 � 2867
[3] Zhang, Xuyuan ; Sch. of Commun. & Inf. Eng., Dept. Commun. Eng., Shanghai Univ., Shanghai, China, �Model Design of Wireless Sensor Network Based on Scale-Free Network Theory� Published in: Wireless Communications, Networking and Mobile Computing, 2009. WiCom `09. 5th International Conference on Date of Conference: 24-26 Sept. 2009 Page(s): 1 � 4
[4] Yong-Sik Choi ; Dept. of Comput. Sci. & Eng., Univ. of Incheon, Incheon, South Korea ; Young-Jun Jeon ; Sang-Hyun Park, �A study on sensor nodes attestation protocol in a Wireless Sensor Network� Published in: Advanced Communication Technology (ICACT), 2010 The 12th International Conference on (Volume:1 ) Date of Conference: 7-10 Feb. 2010 Page(s): 574 � 579
[5] Yang Wenguo ; Coll. of Engneering, Grad. Univ. of the Chinese Acad. of Sci., Beijing, China ; Guo Tiande, �Notice of Retraction The Non-uniform Property of Energy Consumption and its Solution to the Wireless Sensor Network� Published in: Education Technology and Computer Science (ETCS), 2010 Second International Workshop on (Volume:2 ) Date of Conference: 6-7 March 2010 Page(s): 186 � 192
[6] Weiping Zhu ; Dept. of Comput., Hong Kong Polytech. Univ., Kowloon, China ; Jiannong Cao ; Yi Xu ; Lei Yang more authors, �Fault-Tolerant RFID Reader Localization Based on Passive RFID Tags� Published in: Parallel and Distributed Systems, IEEE Transactions on (Volume:25 , Issue: 8 ) Date of Publication: Aug. 2014 Page(s): 2065 � 2076
[7] Guangjie Han ; Nantong Ocean & Coastal Eng. Res. Inst., Hohai Univ., Nantong, China ; Aihua Qian ; Xun Li ; Jinfang Jiang more authors, �Performance evaluation of localization algorithms in large-scale Underwater Sensor Networks� Published in: Communications and Networking in China (CHINACOM), 2013 8th International ICST Conference on Date of Conference: 14-16 Aug. 2013 Page(s): 493 � 498
[8] Po Yang ; Fac. of Comput., Eng. & Technol., Staffordshire Univ., Stafford, UK ; Wenyan Wu ; Moniri, M. ; Chibelushi, C.C. �Efficient Object Localization Using Sparsely Distributed Passive RFID Tags� Published in: Industrial Electronics, IEEE Transactions on (Volume:60 , Issue: 12 ) Date of Publication: Dec. 2013 Page(s): 5914 � 5924
[9] Hui Suo ; Coll. of Inf. Eng., Guangdong Jidian Polytech., Guangzhou, China ; Jiafu Wan ; Lian Huang ; Caifeng Zou, �Issues and Challenges of Wireless Sensor Networks Localization in Emerging Applications� Published in: Computer Science and Electronics Engineering (ICCSEE), 2012 International Conference on (Volume:3 ) Date of Conference: 23-25 March 2012 Page(s): 447 � 451
[10] [10] Park, J.Y. ; Dept. of Comput. Eng., Hongik Univ., Seoul ; Ha Yoon Song, �Multilevel localization for Mobile Sensor Network platforms� Published in: Computer Science and Information Technology, 2008. IMCSIT 2008. International Multiconference on Date of Conference: 20-22 Oct. 2008 Page(s): 711 � 718
[11] Subhan, F. ; Dept. of Inf. Technol. & Eng., Nat. Univ. of Modern Languages-NUML, Islamabad, Pakistan ; Ahmed, S. ; Ashraf, K. �Extended Gradient Predictor and Filter for smoothing RSSI� Published in: Advanced Communication Technology (ICACT), 2014 16th International Conference on Date of Conference: 16-19 Feb. 2014 Page(s): 1198 � 1202
[12] Garrosi, M.T. ; Commun. Res. Lab. (CRL), Ilmenau Univ. of Technol., Ilmenau, Germany ; Zafar, B. ; Haardt, M. �Prolonged network life-time in self-organizing peer-to-peer networks with E-RSSI clustering� Published in: Communications (ICC), 2012 IEEE International Conference on Date of Conference: 10-15 June 2012 Page(s): 5558 � 5562
[13] Zhen Fang ; State Key Lab. of Transducer Technol., Beijing, China ; Zhan Zhao ; Geng, Daoqu ; Yundong Xuan more authors, �RSSI variability characterization and calibration method in wireless sensor network� Published in: Information and Automation (ICIA), 2010 IEEE International Conference on Date of Conference: 20-23 June 2010 Page(s): 1532 � 1537
[14] Sahu, P.K. ; Dept. d`Inf. et de Rech. Operationnelle, Univ. de Montreal, Montreal, QC, Canada ; Wu, E.H.-K. ; Sahoo, J. �DuRT: Dual RSSI Trend Based Localization for Wireless Sensor Networks� Published in: Sensors Journal, IEEE (Volume:13 , Issue: 8 ) Date of Publication: Aug. 2013 Page(s): 3115 � 3123
[15] Wang Xue ; Electron. & Inf. Eng. Inst., Henan Univ. of Sci. & Technol., Luoyang, China ; Song Shu Zhong ; Li Meng, �Design of personnel position system of mine based on the average of RSSI� Published in: Automation and Logistics (ICAL), 2012 IEEE International Conference on Date of Conference: 15-17 Aug. 2012 Page(s): 239 - 242

B. Nithya, L. VijayaKalyani, "A Localization Scheme Using Wireless Net Sensors: A Judgment of two Methods," International Journal of Computer Sciences and Engineering, Vol.2, Issue.9, pp.39-43, 2014.

Now-a-days, web applications have become prevalent around the world. This technology has made it possible to carry on business along the web. Many companies developing web applications. There is a need of locating faults in web applications. In this report, we present a technique to locate faults in web applications. Dynamic Fault Localization is a process to localize the faulty statements in web application programs. To locate faulty statement in web programs we implement Tarantula, Ochiai, and Jaccard Coefficients.

The word Coefficient, Suspiciousness and Prediction are one are the same, fault, faulty statement, malformed statement

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[2] Improving Browsing Environment Compliance Evaluations for Websites Cyntrica Eaton 4166 A. V. Williams Building Department of Computer Science University of Maryland,
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[14] M.Y. Chen, E. Kiciman, E. Fratkin, A. Fox, and E. Brewer, �Pinpoint: Problem Determination in Large, Dynamic Internet Services,� Proc. Int�l Conf. Dependable Systems and Networks, pp. 595-604, 2002

V.K. Akula, Chaitanya Kumar .N, "Dynamic Fault Localization in Web Application," International Journal of Computer Sciences and Engineering, Vol.2, Issue.9, pp.44-49, 2014.