Pattern Recognition Based on an Improved Szmidt and Kacprzyk’s Correlation Coefficient in Pythagorean Fuzzy Environment

Ejegwa, Paul Augustine; Feng, Yuming; Zhang, Wei

doi:10.1007/978-3-030-64221-1_17

Cited by 11 publications

(1 citation statement)

References 44 publications

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“…Peng [24] used various parametric SMs, and Nguyen et al [25] developed the exponential SM. Ejegwa et al [26] applied the PF correlation coefcient for pattern recognition, decision making [27,28], disaster control, and diagnostic analysis [29]. Zulqarnain [30][31][32][33][34][35][36] applied various forms of the FS Einstein aggregation operators, and Kuppusamy et al [37] used a bipolar PF approach and its topological properties in decision making.…”

Section: Introductionmentioning

confidence: 99%

Modern Approach in Pattern Recognition Using Circular Fermatean Fuzzy Similarity Measure for Decision Making with Practical Applications

Aruchsamy,

Velusamy,

Dhandapani

et al. 2024

Journal of Mathematics

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The circular Fermatean fuzzy (CFF) set is an advancement of the Fermatean fuzzy (FF) set and the interval-valued Fermatean fuzzy (IVFF) set which deals with uncertainty. The CFF set is represented as a circle of radius ranging from 0 to 2 with the center at the degree of association (DA) and degree of nonassociation (DNA). If multiple people are involved in making decisions, the CFF set, as an alternative to the FF and IVFF sets, can deal with ambiguity more effectively by encircling the decision values within a circle rather than taking an average. Using algorithms, a pattern can be observed computationally or visually. Machine learning algorithm utilizes pattern recognition as an instrument for identifying patterns and also similarity measure (SM) is a beneficial pattern recognition tool used to classify items, discover variations, and make future predictions for decision making. In this work, we introduce the CFF cosine and Dice similarity measures (CFFDMs and CFFSMs), and their properties are studied. Unlike traditional approaches of decision making, which emphasize a single number, the proposed CFFSMs observe the pattern over the circular region to help in dealing with uncertainty more effectively. We introduce an innovative decision-making method in the FF setting. Available bank loans and applicants’ eligibility levels are represented as CFF set using their FF criteria and are taken as loan patterns and customer eligibility patterns. The loan is allocated to the applicant by measuring the CFFCSM and CFFDSM between the two patterns. Also, laptops are suggested to the customers by measuring the similarity between specification pattern and requirement pattern. The correctness and consistency of the proposed models are ensured by comparison analysis and graphical simulations of the input and similarity CFFNs.

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