Mansi Bhatia
Department of Mathematics, Amity Institute of Applied Sciences, Amity University, Noida, India.

H. D. Arora
Department of Mathematics, Amity Institute of Applied Sciences Amity University, Noida, India.

Anjali Naithani
Department of Mathematics, Amity Institute of Applied Sciences, Amity University, Noida, India.

DOI https://doi.org/10.33889/IJMEMS.2023.8.2.015

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Abstract

Fermatean fuzzy set (FFS) is an effective tool to depict expert reasoning information in the decision‐making process than fuzzy sets (FS), intuitionistic fuzzy sets (IFS), and Pythagorean fuzzy sets (PFS). Keeping in mind the importance of correlation coefficient and application in medical diagnosis, decision making and pattern recognition, several studies on correlation coefficient measures have been proposed in the literature. As there does not exist any study concerning correlation coefficient measures for FFS, in this communication, we propose novel entropy-correlation measures for Fermatean fuzzy sets and applied it decision making problems of pattern analysis and multi-criteria decision making for supplier selection. With the help of proposed correlation coefficient, we establish some weighted measures for FFS. Using numerical computations, we determine the efficacy of the suggested measures over other measures. The aim of this study is to propose a novel and efficient methodology for evaluation of supplier’s selection with uncertain information. Finally, we establish the comparative study of our developed measures over the existing correlation coefficient measures. The analysis showed that the suggested methodology is reliable, flexible, and consistent with the existing techniques.

Keywords- Pythagorean fuzzy sets, Correlation coefficient, Entropy, Pattern recognition and multi-criteria decision-making.

Citation

Bhatia, M., Arora, H. D. & Naithani, A. (2023). Some New Correlation Coefficient Measures Based on Fermatean Fuzzy Sets using Decision Making Approach in Pattern Analysis and Supplier Selection. International Journal of Mathematical, Engineering and Management Sciences, 8(2), 245-263. https://doi.org/10.33889/IJMEMS.2023.8.2.015.