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Novel Correlation Measure for Generalized Orthopair Fuzzy Sets and Its Decision-Making Applications

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Abstract

A generalized orthopair fuzzy set (GOFS), also known as a q-rung orthopair fuzzy set (q-ROFS), is a higher variant of ordinary fuzzy sets by relaxing restrictions on the degrees of membership and non-membership. In fact, GOFSs generalize intuitionistic fuzzy sets (IFSs), Pythagorean fuzzy sets (PFSs), and Fermatean fuzzy sets (FFSs) with an improved ability to tackle vagueness. On the other hand, correlation analysis measures the statistical relationships between two samples or variables. Certain approaches for measuring the correlation coefficient of GOFSs have been studied, however, with some setbacks. In this paper, we propose a new correlation coefficient that measures the interrelation between any two arbitrary GOFSs with a better rating. Some properties of the novel generalized orthopair correlation coefficient are presented to validate its appropriateness. In addition, the novel correlation coefficient is validated with some numerical examples and adjudged to outperform some existing approaches via comparative analysis. Finally, we discuss the applications of the novel approach in problems involving pattern recognition and medical diagnosis based on simulated data presented as generalized orthopair fuzzy values.

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Authors and Affiliations

  1. Department of Mathematics, University of Agriculture, P.M.B. 2373, Makurdi, Nigeria

    Paul Augustine Ejegwa

  2. Department of Mathematics, Heramba Chandra College, Kolkata, 700029, India

    Arun Sarkar

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  1. Paul Augustine Ejegwa
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  2. Arun Sarkar
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Contributions

Paul Augustine Ejegwa contributed to the conception of the work, the drafting of the work, the computation of the results, and the interpretations. Arun Sarkar contributed to the conception, design, and drafting of the introduction and preliminaries of the article.

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Correspondence to Paul Augustine Ejegwa.

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Ejegwa, P.A., Sarkar, A. Novel Correlation Measure for Generalized Orthopair Fuzzy Sets and Its Decision-Making Applications. Oper. Res. Forum 4, 32 (2023). https://doi.org/10.1007/s43069-023-00213-8

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