Methods for multi-attribute decision making, pattern recognition and clustering based on T-spherical fuzzy information measures

Article type: Research Article

Authors: Ullah, Kifayat^a | Ali, Zeeshan^b | Mahmood, Tahir^b | Garg, Harish^c | Chinram, Ronnason^{d; *}

Affiliations: [a] Department of Mathematics, Riphah Institute of Computing and Applied Sciences, Riphah International University Lahore, Pakistan | [b] Department of Mathematics & Statistics, International Islamic University Islamabad, Pakistan | [c] School of Mathematics, Thapar Institute of Engineering & Technology, Deemed University, Patiala, Punjab, India | [d] Algebra and Applications Research Unit, Division of Computational Science, Faculty of Science, Prince of Songkla University, Hat Yai, Songkhla, Thailand

Correspondence: [*] Corresponding author. Ronnason Chinram, Algebra and Applications Research Unit, Division of Computational Science, Faculty of Science, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand. E-mail: [email protected].

Abstract: T-spherical fuzzy set (TSFS) is a generalized version of the spherical fuzzy set (SFS) and picture fuzzy set (PFS) to manage awkward and unpredictable information in realistic decision issues. TSFS deals with yes, abstinence, no, and refusal type of fuzzy information. This manuscript aims to observe the drawbacks of some existing dice similarity measures (DSMs) and to propose some new DSMs in the environment of TSFSs. The validation of the new DSMs is proved. The defined DSMs are further extended to introduce some generalized DSMs (GDSMs) and their special cases are studied. Additionally, the TOPSIS method using the entropy measures (EMs) based on TSFSs is also explored and verified with the help of some examples. The proposed new GDSMs and TOPSIS method are applied to the problem of building material recognition, medical diagnosis, clustering, and the results obtained are investigated. A comparison of the new theory is established where the advancement of the proposed DSMs is elaborated under some conditions. The advantages of the new DSMs and the drawbacks of the previous DSMs of IFSs, PyFSs, and PFSs have been studied because of their applicability. The article is comprehensively summarized, and some possible future directions are stated.

Keywords: Information measures, medical diagnosis, pattern recognition, T-spherical fuzzy set, TOPSIS method

DOI: 10.3233/JIFS-210402

Journal: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 2957-2977, 2022