Paper Info
Title
Similarity Measures Based on T-Spherical Fuzzy Information with Applications to Pattern Recognition and Decision Making
Abstract
T-spherical fuzzy set (TSFS) is a fuzzy layout aiming to provide a larger room for the processing of uncertain information-based data where four aspects of unpredictable information are studied. The frame of picture fuzzy sets (PFSs) and intuitionistic fuzzy sets (IFSs) provide limited room for processing such kinds of information. On a scale of zero to one, similarity measures (SMs) are a tool for evaluating the degrees of resemblance between various items or phenomena. The goal of this paper is to investigate the shortcomings of picture fuzzy (PF) SMs in order to introduce a new SM in a T-spherical fuzzy (TSF) environment. The newly improved SM has a larger ground for accommodating the uncertain information with three degrees and is also responsible for the reduction of information loss. The proposed SM's validity is demonstrated mathematically and by examples. To examine the application of the suggested SM two real-life issues are discussed, including the concerns of medical diagnosis and pattern recognition. A comparison of the suggested SMs with current SMs is also made to assess the proposed work's reliability. Since symmetric triangular fuzzy numbers are quite useful in database acquisition, we will consider the proposed SM for symmetric T-spherical triangular fuzzy numbers in the near future.
Year
DOI
Venue
2022
10.3390/sym14020410
SYMMETRY-BASEL
Keywords
DocType
Volume
decision making, pattern recognition, similarity measures (SMs), fuzzy sets, picture fuzzy sets (PFSs), T-spherical fuzzy sets (TSFSs)
Journal
14
Issue
ISSN
Citations 
2
2073-8994
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Muhammad Nabeel Abid100.34
Miin-Shen Yang22025129.12
Hanen Karamti300.34
Kifayat Ullah400.34
Dragan Pamucar503.38