Enhanced dissimilarity measurement for pythagorean fuzzy sets in real-world scenarios

Researchers often encounter significant challenges when dealing with the inherent unpredictability of real-world data. Fuzzy sets are widely recognized for their ability to address ambiguity, yet a persistent challenge remains: accurately determining the dissimilarity or similarity between two fuzzy sets. This paper introduces a novel divergence measure specifically designed to assess the dissimilarity between Pythagorean fuzzy sets. A comparative analysis demonstrates that the proposed measure provides a clearer differentiation between Pythagorean fuzzy sets compared to other established popular measures. The suggested measure can be used in various applications, this work explains the use of the measure in three main real-world problems, namely multi-attribute decision-making problems, medical diagnosis problems, and pattern recognition problems. The proposed divergence measure has been integrated with the VIKOR approach, followed by a sensitivity analysis, affirming the stability of this novel methodology. A comparative analysis is conducted to demonstrate the feasibility and practicality of the proposed research.