Uday V. Kulkarni

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In this paper fuzzy hyperline segment neural network (FHLSNN) is proposed for recognition of handwritten characters. The paper describes the architecture, learning algorithm and an example that demonstrates the qualities of FHLSNN algorithm. The FHLSNN utilises fuzzy sets as pattern classes in which each fuzzy set is an union of fuzzy set hyperline(More)
Recommendation systems represent user preferences for the purpose of suggesting items to purchase or examine. They have become important applications in electronic commerce for information access and for providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and(More)
This paper describes modular general fuzzy hypersphere neural network (MGFHSNN) with its learning algorithm, which is an extension of general fuzzy hypersphere neural network (GFHSNN) proposed by Kulkarni, Doye and Sontakke (2002) that combines supervised and unsupervised learning in a single algorithm so that it can be used for pure classification, pure(More)
In this paper fuzzy hypersphere neural network (FHSNN) is proposed with its learning algorithm, which is used for rotation invariant handwaritten character recognition. The FHSNN utilizes fuzzy sets as pattern classes in which each fuzzy set is an union of fuzzy set hyperspheres. The fuzzy set hypersphere is an ndimensional hypersphere defined by a center(More)
This paper proposes the hybrid neuro-fuzzy classification model to perform the supervised classification of the data. In the proposed classification model, artificial neural network is used to learn the membership function for fuzzy classes of an input data set. This learned membership function gives the belongingness of each feature value to all the(More)
With advances in the field of digitization, document analysis and handwriting recognition have emerged as key research areas. Authors present a handwritten character recognition system for Gujrati, an Indian language spoken by 40 million people. The proposed system extracts four features. A unique pattern descriptor and Gabor phase XNOR pattern are the two(More)