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Enhanced intuitionistic fuzzy similarity operators with applications in emergency management and pattern recognition

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Abstract

Intuitionistic fuzzy similarity operators (IFSOs) are integral part of decision-making process under uncertain environment, and as such, some authors have intensively researched on the subject. Several setbacks are spotted with the existing IFSOs such as imprecise results, violations of similarity axioms, inability to measure similarity of similar intuitionistic fuzzy sets (IFSs), and the exclusion of measurable parameter. In this research, two new similarity operators of IFSs are developed with the capacity to resolve the enumerated setbacks. Some properties of the new IFSOs are discussed to show their alliance with the conditions of similarity operator. In terms of application, we discuss some problems of decision making under indeterminate environments involving emergency management and pattern recognition based on the new IFSOs. The applicative processes were decided from the rankings of the computed similarity values. To ascertain the superiority of the new IFSOs, we present comparative analyses in terms of the two applications, and in both cases, the new IFSOs yield the most reasonable results.

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Authors and Affiliations

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

    Paul Augustine Ejegwa & Sesugh Ahemen

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  1. Paul Augustine Ejegwa
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  2. Sesugh Ahemen
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Correspondence to Paul Augustine Ejegwa.

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Ejegwa, P.A., Ahemen, S. Enhanced intuitionistic fuzzy similarity operators with applications in emergency management and pattern recognition. Granul. Comput. 8, 361–372 (2023). https://doi.org/10.1007/s41066-022-00334-1

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