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Articles by Ranjit Biswas
Total Records ( 4 ) for Ranjit Biswas
  Abdillateef, W. M. , B. H. Nabeel and Ranjit Biswas
  In this paper the authors study a method of clustering of modules of a large software system. An algorithm is presented for doing the clustering, whose complexity is calculated. The method will be very much helpful, as an essential tool, at the developer level in software development organizations for module management.
  Ranjit Biswas , Ranjit Biswas , Zhe Zhang and Deshu Chen
  This paper introduces a kind of compensated fuzzy neural network based on fusion fuzzy theory and neural network technology. The compensated fuzzy neural network have fleet self-study algorithm and can perform compensated fuzzy reasoning. This method overcomes the critical value criterion defection problem that exists in traditional dissolved gas analysis. The method improves fault recognition capability by conversion fuzzy semantic to ration denotation applying features air diagnosis method. The method can resolve the transformer insulation`s fuzzy phenomena. The method realizes fuzzy disposal of transformer fault diagnosis of feature gas by applying fuzzy neural network in the transformer insulation diagnosis knowledge base. The method increases the accuracy of the diagnosis and maneuverability by actual computation.
  Sasikumaran Sreedharan and Ranjit Biswas
  In this paper the authors present a multiphase offline approach for recognition of handwritten Arabic characters called Variance Method. The method is able to recognize a character irrespective of its size ( big or small) and thickness. New methods are proposed for the extraction of feature vectors, and for computing the recognition task.
  Ashit Kumar Dutta , Sahar Idwan and Ranjit Biswas
  In this study, the authors propose an algorithm of intelligent search using vague theory of Gau and Buehrer. The objective of such research is to deal with the imprecise data involved in different kinds of existing searching techniques in a more efficient ways and thus to suggest a new improved version of searching technique under uncertainty which will be helpful in many real life problems of computer science, specially in AI, in Data Mining, in fuzzy DBMS, etc. to list a few only.
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