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The book provides a comprehensive knowledge of applications of measures of fuzzy information and directed divergence in decision making and coding theory. The book emphasizes on the introduction of a new generalized measure of fuzzy directed divergence involving two real parameters. A decision making algorithm is developed based on proposed generalized fuzzy divergence measure for solving multiple criteria decision making problems with fuzzy information. A numerical example is also considered to illustrate the flexibility of the proposed decision making approach. The application of Holder´s inequality to Coding theory i.e. Noiseless coding theorem connected with the proposed measure of fuzzy information has been established. New fuzzy code word length has been developed. Further, the mean code word length of order and type beta for 1:1 codes has been proposed and the relationship between average code word length and fuzzy information measures for binary 1:1 codes has been analyzed. The target audience comprises primarily researchers and practitioners in the involved fields but the book may also be beneficial for graduate students.

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Munde, Anjali
Dr Anjali Munde is a Ph.D in Mathematics. She obtained her Post- Graduation and Honours Graduation degree in Mathematics from Hans Raj College, University of Delhi. She is a gold medalist in M.Sc (Mathematics). She has a teaching experience of 13 + years and her areas of research interest include Information theory, Decision Making & Coding Theory.

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