Intuitionistic fuzzy entropy-based knowledge and accuracy measure with its applications in extended VIKOR approach for solving multi-criteria decision-making

The study of unclear phenomena has been facilitated by fuzzy sets. Fuzzy set extensions have allowed for a more detailed investigation of these kinds of research. Finding quantitative measures for ambiguity and other characteristics of these occurrences thus becomes a challenge. As a fuzzy set extension, several researchers proposed intuitionistic fuzzy (IF) sets and used them in many contexts since they were first described by Atanassov. One such use is to solve multi-criteria decision-making issues. This study measure the amount of knowledge linked with an IF-set. An IF-knowledge measure is proposed. Using numerical examples, its utility and validity are examined. Besides this, the IF-accuracy measure, IF-information measure, similarity measure, and dissimilarity measure, are the four new measures that are derived from the proposed IF-knowledge measure. All these measures are checked for their validation and their properties are discussed. Pattern detection is taken as an application of the proposed accuracy measure. Finally, a modified VIKOR approach depending upon the proposed similarity and dissimilarity measure is proposed to deal with an MCDM issue in an intuitionistic fuzzy environment. The efficiency of the proposed approach is demonstrated by using a numerical example. A comparative study is also provided to assess the feasibility of the proposed approach.