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Titlebook: Computer Networks and Intelligent Computing; 5th International Co K. R. Venugopal,L. M. Patnaik Conference proceedings 2011 Springer-Verlag

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https://doi.org/10.1007/978-3-540-71355-5forts in this direction are susceptible to leakage of valuable information through quasi identifiers. Over the past few years, several algorithms based upon the concept of .-anonymity [1-2] have been developed, to handle such problems. A better privacy model, called .-diversity [3] was proposed to h
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,Testauswahl und prüfgerechter Entwurf,normal or anomaly. In the work classification performance using rules obtained by different methods are applied on network traffic and compared. Classifier is built based on rules of decision table, conjunctive rule, OneR, PART, JRip, NNge, ZeroR, BayesNet, Ridor from WEKA and using rough set theory
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https://doi.org/10.1007/978-3-211-37139-8m. To convert Web into the Semantic Web it is required to add semantic information to Web resources. Previous approaches generate the semantic document statically. This paper provides a model for developers to generate semantic document (RDF) for the request and session data dynamically. It also pro
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Test- und Prüfungsaufgaben Regelungstechnikhs, broad degree distributions and the existence of community structure. Here, an application has been developed in the domain of information propagation which satisfies all the above characteristics, based on some existing social network models. In addition, this model facilitates interaction betwe
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https://doi.org/10.1007/978-3-211-37139-8lved many methods including distance measures, similarity measures and semantic measures. Ontology matching is the process of discovering similarities between ontology and determines the relationships holding two sets of entities that belong to different discrete ontology. The traditional methodolog
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