New Methods of Computing Correlation Coefficient Based on Pythagorean Fuzzy Information and Their Applications in Disaster Control and Diagnostic Analysis

Paul Augustine Ejegwa (), Arun Sarkar () and Idoko Charles Onyeke ()
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Paul Augustine Ejegwa: Department of Mathematics, University of Agriculture, P.M.B. 2373
Arun Sarkar: Heramba Chandra College, Department of Mathematics
Idoko Charles Onyeke: University of Agriculture, P.M.B. 2373, Department of Computer Science

Chapter Chapter 21 in Fuzzy Optimization, Decision-making and Operations Research, 2023, pp 473-498 from Springer

Abstract: Abstract Pythagorean fuzzy correlation coefficient (PFCC) is a trustworthy information measure to determine sundry real-world decision-making problems. Some authors have worked on methods for the calculation of PFCC, notwithstanding with some limitations, which bother on accuracy and reliability. In this chapter, two methods for the calculating of PFCC are developed in a quest to obtain more reliable methods. The methods are adorned with the traditional attributes of Pythagorean fuzzy set (PFS) to forestall any possibility of exclusive error. Some theoretic results based on the new methods are buttressed in consonant with the attributes of the classical correlation coefficient. To demonstrate the resourcefulness of the new methods, some real-world problems like disaster control and medical diagnosis are resolved using Pythagorean fuzzy data. The attractiveness of the new methods are portrayed in comparative analysis involving other methods of PFCC to justify the relevance of the new methods as reliable PFCC methods.

Keywords: Disaster control; Disease diagnosis; Pythagorean fuzzy correlation coefficient; Pythagorean fuzzy set; Pythagorean fuzzy data (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-35668-1_21

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DOI: 10.1007/978-3-031-35668-1_21

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