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Entropy 2018, 20(9), 672; https://doi.org/10.3390/e20090672

Entropy, Measures of Distance and Similarity of Q-Neutrosophic Soft Sets and Some Applications

School of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor 43600, Malaysia
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Received: 14 August 2018 / Revised: 29 August 2018 / Accepted: 1 September 2018 / Published: 5 September 2018
(This article belongs to the Section Information Theory)
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Abstract

The idea of the Q-neutrosophic soft set emerges from the neutrosophic soft set by upgrading the membership functions to a two-dimensional entity which indicate uncertainty, indeterminacy and falsity. Hence, it is able to deal with two-dimensional inconsistent, imprecise, and indeterminate information appearing in real life situations. In this study, the tools that measure the similarity, distance and the degree of fuzziness of Q-neutrosophic soft sets are presented. The definitions of distance, similarity and measures of entropy are introduced. Some formulas for Q-neutrosophic soft entropy were presented. The known Hamming, Euclidean and their normalized distances are generalized to make them well matched with the idea of Q-neutrosophic soft set. The distance measure is subsequently used to define the measure of similarity. Lastly, we expound three applications of the measures of Q-neutrosophic soft sets by applying entropy and the similarity measure to a medical diagnosis and decision making problems. View Full-Text
Keywords: decision making; distance measure; entropy measure; Q-neutrosophic soft set; similarity measure decision making; distance measure; entropy measure; Q-neutrosophic soft set; similarity measure
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Abu Qamar, M.; Hassan, N. Entropy, Measures of Distance and Similarity of Q-Neutrosophic Soft Sets and Some Applications. Entropy 2018, 20, 672.

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