Data analysis for panoramic X-ray selection: Fermatean fuzzy type correlation coefficients approach

Creative Commons License

Kirişci M.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, cilt.126, sa.106824, ss.1-20, 2023 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 126 Sayı: 106824
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1016/j.engappai.2023.106824
  • Dergi Adı: ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1-20
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • İstanbul Üniversitesi-Cerrahpaşa Adresli: Evet

Özet

An interval-valued Fermatean hesitant fuzzy set (IVFHFS) not only can be regarded as the union of some interval-valued Fermatean fuzzy sets (IVFFSs) but also represent the Fermatean hesitant fuzzy elements (FHFEs) in the form of interval values. So IVFHFSs are extensions of FHFSs and IVFFSs, which are powerful tools to represent more complicated, uncertain, and vague information. This paper focuses on the four kinds of correlation coefficients for FHFSs and extends them to the correlation coefficients and the weighted correlation coefficients for IVFHFSs. In the processing, we develop the least common multiple expansion (LCME) methods to solve the problem that the cardinalities of Fermatean hesitant fuzzy elements (FHFEs) (or interval-valued Fermatean hesitant fuzzy elements (IVPHFEs)) are different. In addition, we propose score functions and accuracy functions of FFEs (or IVFFEs) to rank all the FFEs (or IVFFEs) in an FHFE (or an IVFHFE). Especially, score functions and accuracy functions of IVFFEs are both presented as interval numbers. Then use the comparison method of interval numbers to compare two revised IVFHFEs in order to keep the original fuzzy information as far as possible. What is more, we define the local correlations and local informational energies which can depict the similarity between two IVFHFEs more meticulously and completely. At last, the numerical examples show the feasibility and applicability of the proposed methods in multiple criteria decision-making (MCDM) and clustering analysis.