Version 1
: Received: 26 March 2018 / Approved: 28 March 2018 / Online: 28 March 2018 (04:19:59 CEST)
How to cite:
Pramanik, S.; Dalapati, S.; Alam, S.; Smarandache, F.; Roy, T.K. NC-Cross Entropy Based MADM under Neutrosophic Cubic Set Environment. Preprints2018, 2018030230. https://doi.org/10.20944/preprints201803.0230.v1.
Pramanik, S.; Dalapati, S.; Alam, S.; Smarandache, F.; Roy, T.K. NC-Cross Entropy Based MADM under Neutrosophic Cubic Set Environment. Preprints 2018, 2018030230. https://doi.org/10.20944/preprints201803.0230.v1.
Cite as:
Pramanik, S.; Dalapati, S.; Alam, S.; Smarandache, F.; Roy, T.K. NC-Cross Entropy Based MADM under Neutrosophic Cubic Set Environment. Preprints2018, 2018030230. https://doi.org/10.20944/preprints201803.0230.v1.
Pramanik, S.; Dalapati, S.; Alam, S.; Smarandache, F.; Roy, T.K. NC-Cross Entropy Based MADM under Neutrosophic Cubic Set Environment. Preprints 2018, 2018030230. https://doi.org/10.20944/preprints201803.0230.v1.
Abstract
Neutrosophic cubic set (NCS) is one of the important family members of neutrosophic hybrid sets. Neutrosophic cubic set has more strength than other family members of neutrosophic hybrid sets to express incomplete information due to the presence of interval valued neutrosophic set (IVNS) and single valued neutrosophic set (SVNS) in its structure. Cross entropy measure is one of the best way to calculate the divergence of any variable from the priori one variable. In this paper we first define a new cross entropy measure under NCSs environment which we call NC- cross entropy measure. We investigate the basic properties of NC-cross entropy. We also propose weighted NC-cross entropy and investigate its basic properties. We develop a novel multi attribute decision making (MADM) strategy based on weighted NC-cross entropy. To show the feasibility and applicability, we solve a MADM problem using the proposed strategy.
Keywords
single valued neutrosophic set; interval neutrosophic set; neutrosophic cubic set; multi attribute decision making; NC-cross entropy
Copyright:
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.
