Elementary Function-Based Fermatean Fuzzy Similarity Measures: Applications to Medical Pattern Recognition and Multi-Criteria Decision-Making
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...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10741519/ |
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| author | Zhe Liu Donglai Wang Sukumar Letchmunan Sarah Aljohani Nabil Mlaiki |
| author_facet | Zhe Liu Donglai Wang Sukumar Letchmunan Sarah Aljohani Nabil Mlaiki |
| author_sort | Zhe Liu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c0ab012e96ad464dacdf0be5f52b822e |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c0ab012e96ad464dacdf0be5f52b822e2025-01-15T00:01:43ZengIEEEIEEE Access2169-35362024-01-011216345216346410.1109/ACCESS.2024.349060610741519Elementary Function-Based Fermatean Fuzzy Similarity Measures: Applications to Medical Pattern Recognition and Multi-Criteria Decision-MakingZhe Liu0https://orcid.org/0000-0002-8580-9655Donglai Wang1Sukumar Letchmunan2https://orcid.org/0000-0002-3521-7141Sarah Aljohani3https://orcid.org/0009-0008-2035-0880Nabil Mlaiki4https://orcid.org/0000-0002-7986-886XCollege of Mathematics and Computer, Xinyu University, Xinyu, ChinaSchool of Cyberspace Security, Hainan University, Haikou, ChinaSchool of Computer Sciences, Universiti Sains Malaysia, Penang, MalaysiaDepartment of Mathematics and Sciences, Prince Sultan University, Riyadh, Saudi ArabiaDepartment of Mathematics and Sciences, Prince Sultan University, Riyadh, Saudi ArabiaFermatean 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.https://ieeexplore.ieee.org/document/10741519/Fermatean fuzzy setssimilarity measureselementary functionmedical pattern recognitionmulti-criteria decision-making |
| spellingShingle | Zhe Liu Donglai Wang Sukumar Letchmunan Sarah Aljohani Nabil Mlaiki Elementary Function-Based Fermatean Fuzzy Similarity Measures: Applications to Medical Pattern Recognition and Multi-Criteria Decision-Making IEEE Access Fermatean fuzzy sets similarity measures elementary function medical pattern recognition multi-criteria decision-making |
| title | Elementary Function-Based Fermatean Fuzzy Similarity Measures: Applications to Medical Pattern Recognition and Multi-Criteria Decision-Making |
| title_full | Elementary Function-Based Fermatean Fuzzy Similarity Measures: Applications to Medical Pattern Recognition and Multi-Criteria Decision-Making |
| title_fullStr | Elementary Function-Based Fermatean Fuzzy Similarity Measures: Applications to Medical Pattern Recognition and Multi-Criteria Decision-Making |
| title_full_unstemmed | Elementary Function-Based Fermatean Fuzzy Similarity Measures: Applications to Medical Pattern Recognition and Multi-Criteria Decision-Making |
| title_short | Elementary Function-Based Fermatean Fuzzy Similarity Measures: Applications to Medical Pattern Recognition and Multi-Criteria Decision-Making |
| title_sort | elementary function based fermatean fuzzy similarity measures applications to medical pattern recognition and multi criteria decision making |
| topic | Fermatean fuzzy sets similarity measures elementary function medical pattern recognition multi-criteria decision-making |
| url | https://ieeexplore.ieee.org/document/10741519/ |
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