Surender Singh

Work place: School of Mathematics, Shri Mata Vaishno Devi University, Sub post office, Katra-182320 (J & K) India



Research Interests: Computer systems and computational processes, Computer Architecture and Organization, Information Systems, Data Structures and Algorithms, Information Theory, Algorithmic Information Theory


Surender Singh, Assistant Professor at School of Mathematics, Shri Mata Vaishno Devi University, Katra (J & K), India. He has taught undergraduate and post graduate students for over ten years. He is also working towards his Ph.D under supervision of Prof. P.K Bhatia and Dr. Vinod Kumar. His research directions include Information, divergence measures and their applications.

Author Articles
On Applications of a Generalized Hyperbolic Measure of Entropy

By P.K Bhatia Surender Singh Vinod Kumar

DOI:, Pub. Date: 8 Jun. 2015

After generalization of Shannon’s entropy measure by Renyi in 1961, many generalized versions of Shannon measure were proposed by different authors. Shannon measure can be obtained from these generalized measures asymptotically. A natural question arises in the parametric generalization of Shannon’s entropy measure. What is the role of the parameter(s) from application point of view? In the present communication, super additivity and fast scalability of generalized hyperbolic measure [Bhatia and Singh, 2013] of probabilistic entropy as compared to some classical measures of entropy has been shown. Application of a generalized hyperbolic measure of probabilistic entropy in certain situations has been discussed. Also, application of generalized hyperbolic measure of fuzzy entropy in multi attribute decision making have been presented where the parameter affects the preference order.

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A New Measure of Fuzzy Directed Divergence and Its Application in Image Segmentation

By P.K Bhatia Surender Singh

DOI:, Pub. Date: 8 Mar. 2013

An approach to develop new measures of fuzzy directed divergence is proposed here. A new measure of fuzzy directed divergence is proposed, and some mathematical properties of this measure are proved. The application of fuzzy directed divergence in image segmentation is explained. The proposed technique minimizes the fuzzy divergence or the separation between the actual and ideal thresholded image.

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