Entropy Measures of Q-Rung Orthopair Fuzzy Sets and its Application to Multicriteria Decision Making and Medical Diagnosis

The notion of generalized q-rung orthopair fuzzy sets (q-ROFSs) was introduced by Yager in 2016, which gives a new way to model uncertainty and vagueness with more precision and correctness than intuitionistic fuzzy sets (IFSs) and Pythagorean fuzzy sets (PFSs) respectively. The idea was concretely designed to characterize uncertainty and vagueness in mathematical approach and to demonstrate a formalized way for modeling uncertainty to real world problems. In this manuscript, we propose novel entropy measures for q-ROFSs and then establish an axiomatic definition for suggested entropy measures for q-ROFSs. This gives the decision makers more confident in conveying their belief about membership grade thus q-ROFSs increases the space of acceptance in the uncertain environment. Several examples are discussed to exhibit suitability and reliability of suggested methods particularly for selecting the superlative one/ones in structured linguistic variables. Moreover, Orthopairian Fuzzy Technique for ordering preference by similarity to ideal solution (OF-TOPSIS) based on the suggested entropy measures is developed. Finally, we utilized our suggested OF-TOPSIS technique to calculate the criteria weights and obtain a raking of alternatives to treat with the problems relating complex multi criteria decision making processes amicably. We also developed an application of proposed entropy measure related to covid-19. Numerical examples illustrate the practicality, validity and applicability of the suggested methods.