Articles

  1. AN EFFICIENT SEMANTIC DATA ALIGNMENT BASED FCM TO INFER USER SEARCH GOALS USING FEEDBACK SESSIONSDownload Article

    *1Dr.V.Venkatesa Kumar PhD, *2Ms.R.Saranya

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    Web search applications represent user information needs by submission of query to search engine. But still the entire query submitted to search engine doesn’t satisfy the user information needs, as users may want to obtain information on diverse aspects when they submit the same query. From this discovering the numeral of dissimilar user search goals for query and depicting each goal with several keywords automatically become complex. The inference of user search goals can be very valuable in improving search engine importance and user knowledge. To efficiently reflect user information needs to generate a pseudo-document to map the different user feedback sessions. Clustering pseudo-documents by K-means clustering is computationally difficult and semantic similarity between the pseudo terms is also important while clustering. To conquer this problem proposed a FCM clustering algorithm to group the pseudo documents and it also measures the semantic data alignment between the pseudo terms in the documents. The FCM algorithm divides pseudo document data in dissimilar size cluster by using fuzzy systems. FCM choosing cluster size and central point depend on fuzzy model. The FCM clustering algorithm assembles quickly to a local optimum or grouping of the pseudo documents in well-organized way. Semantic data alignment between the pseudo terms is used for comparing the similarity and diversity of pseudo terms. Finally, experimental results measures the clustering results with parameters like classified average precision (CAP), Voted AP (VAP), risk to avoid classifying search results and average precision (AP). It shows FCM based system improves the feedback session's outcome than the normal pseudo documents. Index terms: User search goals, Feedback sessions, pseudo-documents, classified average precision (CAP), Voted AP (VAP), average precision (AP), Fuzzy C means clustering, K-means clustering.

  2. TWO WAY CHAINED PACKETS MARKING TECHNIQUE FOR SECURE COMMUNICATION IN WIRELESS SENSOR NETWORKSDownload Article

    *1Dr.C.Chandrasekar, *2 V.Prabhakaran,M.Phil.,

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    Wireless Sensor Networks, at the outset, used in military applications for sensitive data recordings and transfer which obviously limits the participation of mankind. Today wireless Sensor Networks has entered and proved its efficiency in almost every application. Yet there are some metrics to holdback the security of the same, due to the very own attractive features of flexibility and open nature. Jamming of the medium to deny the service of a legitimate user is one among the many vulnerabilities of a wireless Sensor Network. This paper intends to provide a scheme for detecting the attack by a bidirectional link between the packets and recognizing any off the chain packets at the boundary routers. Prevention of any mishap packets from entering into the network improves the security of the network. A novel approach of marking the neighborhood packets forms a chain of legitimate message will preserve the originality at the other end and any packets to be found without the link information will be eliminated at the perimeter. This approach intends to provide a secure environment which withstands detection and mitigation as the principle. Index terms: Jamming, Bi-directional, Packets, Legitimate, Information.

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