A reliable correlation coefficient for complicated intuitionistic fuzzy sets and its applications in decision-making (Journal of Advances and Scholarly Researches in Allied Education, 2024)
This paper introduces a reliable correlation coefficient designed for complicated intuitionistic fuzzy sets (CIFS) to enhance the accuracy of decision-making in uncertain and complex environments. Intuitionistic fuzzy sets, characterized by membership, non-membership, and hesitancy degrees, are an effective tool for handling imprecise data in decision-making problems. However, existing correlation measures often fail to capture the intricate relationships in CIFS due to their inherent uncertainty. The proposed correlation coefficient overcomes these limitations by integrating the hesitancy degree and providing a robust framework for analyzing the correlation between CIFS. Through mathematical formulation and computational examples, this study demonstrates the potential applications of the correlation coefficient in various decision-making scenarios, such as multi-criteria decision analysis (MCDA), pattern recognition, and risk assessment. The results show that the new correlation coefficient offers reliable, efficient, and interpretable solutions for problems involving CIFS, ultimately improving decision-making processes in fields such as economics, healthcare, and engineering.