Elementary Function-Based Fermatean Fuzzy Similarity Measures: Applications to Medical Pattern Recognition and Multi-Criteria Decision-Making

Abstract

Fermatean fuzzy sets (FFSs) constitute a formidable tool for tackling uncertainty in information, thereby enjoying widespread adoption across multiple domains. Similarity measures play a crucial role in determining the similarity between different FFSs. However, existing similarity measures for FFSs sometimes encounter issues that result in unreasonable outcomes when differentiating between FFSs. Therefore, efficiently quantifying the similarity between FFSs has emerged as a pressing issue demanding immediate resolution. In this paper, we introduce twenty-four novel similarity measures based on elementary function. Some properties of these similarity measures and weighted similarity measures are verified. Furthermore, we implement the proposed similarity measures in medical pattern recognition and multi-criteria decision-making to validate their feasibility in practical applications. Compared with some existing measures for FFSs, the outcomes conclusively demonstrate that the introduced similarity measures lead to significantly efficient outcomes.

Keywords

• Fermatean fuzzy sets
• similarity measures
• elementary function
• medical pattern recognition
• multi-criteria decision-making