International Journal of Inventive Engineering and Sciences(TM)
Exploring Innovation| ISSN:2319-9598(Online)| Reg. No.:68563/BPL/CE/12| Published by BEIESP| Impact Factor:3.47
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Volume-3 Issue-3: Published on February 20, 2015
Volume-3 Issue-3: Published on February 20, 2015
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Volume-3 Issue-3, February 2015, ISSN: 2319-9598 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.



Surve Pranjali, Ubale V. S

Paper Title:

Hand Gesture Recognition Systems: A Survey

Abstract: The first mode of communication in the early ages was gesture. Later on the verbal communication was developed very well. But still non-verbal communication has its importance.  Non – verbal communication is used for the physically challenged people, for different applications. For e.g. aviation, surveying, music direction etc. It is the method to interact with other peripheral devices, such as keyboard, mouse. The important steps involved in HGR systems are data acquisition, gesture modelling, feature extraction and hand gesture recognition. Different algorithm are followed by different researchers or sometimes used their own algorithm. This paper studies different methods of HGR and finally the desired characteristics of a robust and efficient hand gesture recognition system have been described.

Hand gesture recognition, Principal component analysis (PCA), Rotation invariant.


1.        RafiqulZaman Khan & Noor Adnan Ibraheem (2012), omparative study of hand gesture recognition system”  Department of Computer Science, A.M.U., Aligarh, India.
2.        SushmitaMitra, Senior Member, IEEE, and Tinku Acharya, Senior Member, IEEE (2007), “Gesture Recognition: A Survey”

3.        PrateemChakraborty, PrashantSarawgi, AnkitMehrotra, Gaurav Agarwal, Ratika Pradhan (2008), “Hand Gesture Recognition: A Comparative study”IMECS 2008, 19-21 March, 2008, Hong Kong.

4.        Sudheesh.P, Gireesh Kumar T. July 2012, “Vision based Robot Navigation for Disaster Scenarios”AmritaVishwa Vidyapeetham Coimbatore, India.

5.        Kwang-HoSeok, Chang-Mug Lee, Oh-Young Kwon, Yoon Sang Kim (2009),”A Robot Motion Authoring using Finger robot interaction” Korea University of Technology and Education Cheonan, Korea.




Ahmed Mudassar Ali, M. Ramakrishnan

Paper Title:

Web Personalized Index Based N-GRAM Extraction

Abstract: Web mining is the analysis step of the "Knowledge Discovery in Web Databases" process which is an intersection of computer science and statistics. In this process results are produced from pattern matching and clustering which may not be relevant to the actual search. For example result for tree may be apple tree, mango tree whereas the user is searching for binary tree. N-grams are applied in applications like searching in text documents, where one must work with phrases. Eg: plagiarism detection. Thus relevancy becomes major part in searching. We can achieve relevancy using n-gram algorithm. We show an index based method to discover patterns in large data sets. It utilizes methods at the conjunction of AI, machine learning and statistics. We also induce a method of personalization where the search engine is used for indexing purposes in addition to the current n-gram techniques. A collaborative web search method is used for user’s personalization in the web search engine to extract the required accurate data.

Web Mining, Knowledge Discovery, N-Gram, Stemming.


1.        “Language Identification of Web Pages on Improved N-gram Algorithm”- Yew Choong Chew, Yoshiki Mikami, Robin Lee Nagano  , IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 3, No. 1, May 2011, ISSN (Online): 1694-0814.   
2.        “Automatically building a stop word list for an information retrieval system”- Masoud Makrehchi  Mohamed S. Kamel, ECIR 2008, LNCS 4956, pp. 222-233, 2008.

3.        “Advanced database indexing”- Y.Manolopoulos  , Y.Theodoeidis and V.J.Tsotras, Springer 2000.

4.        “Probabilistic Latent Semantic Indexing”- Thomas Hofmann, SIGIR '99 Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, Pages 50-57,1999.   

5.        SRCluster: Web Clustering Engine based on Wikipedia : Yuvarani Meiyappan, N. Ch. S. Narayana Iyengar and A. Kannan ,  International Journal of Advanced Science and Technology Vol. 39, February, 2012.

6.        XML with Cluster Feature Extraction For Efficient Search : D. Divya, Dr. A. Muthukumaravel, Dr. P. Mayilvahanan , International Journal of Emerging Technology and Advanced Engineering Website: (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 8, August 2013).

7.        A Model for XML Schema Integration : Kalpdrum Passi, Louise Lane, Sanjay Madria, Bipin C. Sakamuri, Mukesh Mohania, and Sourav Bhowmick ,  EC-Web 2002, LNCS 2455, pp. 193–202, 2002.

8.        Learning to Cluster Web Search Results : Hua-Jun Zeng Qi-Cai He Zheng Chen Wei-Ying Ma Jinwen Ma, SIGIR’04, July 25–29, 2004, Sheffield, South Yorkshire, UK.

9.        WEB SEARCH RESULT CLUSTERING- A REVIEW : Kishwar Sadaf1 and Mansaf Alam2, International Journal of Computer Science & Engineering Survey (IJCSES) Vol.3, No.4, August 2012.

10.     Web Usage Mining Based on Complex Structure of XML for Web IDS : Marjan Eshaghi, S.Z. Gawali, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-2, Issue-5, April 2013.

11.     Beyond Single-Page Web Search Results : Ramakrishna Varadarajan, Vagelis Hristidis, and Tao Li , IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 20, NO. 3, MARCH 2008

12.     Extracting Knowledge from Web Search Engine Results : Andreas Kanavos, Evangelos Theodoridis, Athanasios Tsakalidis , IEEE 24th International Conference on Tools with Artificial Intelligence, 2012.

13.     A New Search Results Clustering Algorithm based on Formal Concept Analysis : Yun Zhang, Boqin Feng, Yewei Xue, School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an, China, Fifth International Conference on Fuzzy Systems and Knowledge Discovery., FSKD’08, IEEE, pp. 356-360, 2008.




R. Sathis Kumar, M. Krishna Raj, R. Vinston Raja

Paper Title:

Agent Based Trusted Routing and Attitude Based Intrusion Detection for Worm Hole Attack in MANET

Abstract: The Existence of misbehaving nodes may paralyze the routing operation in MANET. To overcome this behavior that trustworthiness of the network nodes should be consider in the route selection process combined with the next count. The trustworthiness is achieved by measuring the trust value for each node in the network. In this paper a new protocol based on agent based monitoring followed the dynamic source routing (DSR) algorithm is presented. This protocol is applied in agent based trusted Dynamic source routing protocol for MANET’s. The objective of this protocol is to mange trust information among self nodes with minimal overhead in terms of time delay and data loss. This objective is achieved through Collaborative Agent Monitoring System (CAMS) by installing in each participated node in the network .CAMS Consist of two types of agent: Self monitoring agent and routing agent. A proposed realistic objective model for measuring trust value is introduced. One of the significant attack in ad hoc network is wormhole attack is more hidden in character and tougher to detect. In this paper an Attitude Agent Intrusion Detection System (AAIDS).

Routing -Worm hole – Intrusion - Detection.


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4.        Gonzalez, F. A., & Dasgupta, D. (2003). Anomaly detection using real-valued negative selection. Journal Genetic Programming and Evolvable Machines, 4, 383 403.

5.        Dasgupta, D., & Gonzalez, F. (2002). An immunity based technique to characterize intrusions in computer networks. IEEE Transactions on Evolutionary Computation, 3, 281–291.

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7.        Azandaryani, A. H. M., & Meybodi, M. R. (2009). A learning automata based artificial immune system for data classification. In Proceedings of IEEE Computer Conference (pp. 530–535).

8.        Sompayrac, L. M. (2003). How the immune system works (2nd ed.). Oxford: Blackwell.

9.        Sarafijanovic, S., & Le Boudec, J.-Y. (2005). An artificialimmune system approach with secondary response for misbehavior detection in mobile ad hoc networks. IEEE Transactions on Neural Networks, 5, 1076–1087.

10.     Forrest, S., Perelson, A. S., Allen, L., & Cherukuri, R. (1994). Self-nonself discrimination in a computer. In Proceedings of the IEEE Symposium on Research in Security and Privacy (pp.202–212). IEEE Computer Society Press.

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