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Granular Computing
Published in: Granular Computing 2/2022

09-07-2021 | Original Paper

Some modified Pythagorean fuzzy correlation measures with application in determining some selected decision-making problems

Authors: Paul Augustine Ejegwa, Victoria Adah, Idoko Charles Onyeke

Published in: Granular Computing | Issue 2/2022

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Abstract

Pythagorean fuzzy set (PFS) is an advanced version of generalized fuzzy sets. It has a better applicative expression in decision-making because of its capability in curbing fuzziness embedded in decision science. Correlation coefficient is a reliable measuring operator for the applicability of generalized fuzzy sets in decision-making. Some approaches of estimating correlation of PFSs have been explored, albeit with certain setbacks. This paper introduces some methods of calculating the correlation coefficient of PFSs which resolve the setbacks in the existing methods. Some numerical examples are supplied to confirm the superiority of the novel methods over the existing correlation coefficient measures. In addition, certain decision-making problems such as marital choice-making, classification of building materials, and electioneering process represented in Pythagorean fuzzy values are resolved using the proposed correlation measure. Specifically, the objectives of this work are to (1) introduce some new triparametric methods of computing correlation coefficient of PFSs, (2) characterize their theoretic properties, (3) ascertain their advantages over the existing methods, and (4) explore the application of the proposed methods in certain decision-making problems. From the study, it is observed that the new Pythagorean fuzzy correlation coefficients give reliable outputs compared to the existing ones and, hence, can suitably handle multiple criteria decision-making effectively.
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Metadata
Title
Some modified Pythagorean fuzzy correlation measures with application in determining some selected decision-making problems
Authors
Paul Augustine Ejegwa
Victoria Adah
Idoko Charles Onyeke
Publication date
09-07-2021
Publisher
Springer International Publishing
Published in
Granular Computing / Issue 2/2022
Print ISSN: 2364-4966
Electronic ISSN: 2364-4974
DOI
https://doi.org/10.1007/s41066-021-00272-4

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