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.

A novel extended Portuguese of Interactive and Multi‐Criteria Decision Making and Archimedean Bonferroni mean operators based on prospect theory to select green supplier with complex q‐rung orthopair fuzzy information

“…In the future, maybe we needed some new ideas such as Tspherical fuzzy sets [42], linear Diophantine fuzzy sets [43,44], fuzzy N-soft sets [45,46], aggregation operators for Tspherical fuzzy sets [47,48], Maclaurin symmetric mean operators [49], sine trigonometric operators [50] and intuitionistic cubic fuzzy sets [51] to use it for evaluating awkward and unreliable problems in machine learning, game theory, artificial intelligence, neural networks, decision-making theory, road signals and clustering analysis to improve the worth and quality of the proposed works.…”

An Uncertain Multiple-Criteria Choice Method on Grounds of T-Spherical Fuzzy Data-Driven Correlation Measures

“…As of the advancement of T-SF theory in uncertain decision circumstances, a variety of valuable multiple-criteria assessment approaches and evaluation techniques have been constructed for facilitating intelligent decision support and aiding. By way of illustration, Abid et al (2022) presented improved T-SF similarity measures to suggest an approach to decision-making and pattern recognition. analysed and addressed threats on social media platforms by employing an uncertain set of the complex cubic T-SF model and put forward a risk-assessing method for cyber-security and social media.…”

Multiple criteria decision analytic methods in management with T-spherical fuzzy information

“…The T-SF set-based framework can create an easy-to-use configuration to assist the decision-maker in manipulating erroneous and ambiguous information regarding multiple criteria assessment tasks in highly uncertain scenarios, thereby making headway at a comprehensive intelligent decision support system (Chen 2022a ; Ozceylan et al 2022 ; Wang and Chen 2021 ). In this connection, the theory in the T-SF framework has been skillfully utilized to manipulate difficult uncertainties present in pragmatic decision-making situations (Abid et al 2022 ; Chen 2022b ; 2022c ; Hussain et al 2022 ; Liu and Wang 2022 ; Yang and Pang 2022 ). As a promising advancement over spherical, picture, q-rung orthopair, Fermatean, Pythagorean, and intuitionistic fuzzy frames, T-SF sets can give decision-makers more room to articulate hazy and muddled judgments and serve as a key analytical instrument for multiple criteria choice analysis.…”

“…Natural language expressions in T-SF settings are also beneficial for identifying the qualitative evaluation of unclear data (Akram et al 2023 ; Naz et al 2022 ; Wang 2022 ). T-SF theory can produce a successful decision-making technique for assessing and choosing options in sophisticated fuzzy environments through varied T-SF decision-analysis tools created by numerous academics (Abid et al 2022 ; Chen 2022b ; 2022d ; Karaaslan and Al-Husseinawi 2022 ; Yang and Pang 2022 ).…”

“…Correlation is a characteristic used to assess the mutual relationship between two objects that are connected to each other, while distance measurement is a property that tests how far apart two objects are (Abid et al 2022 ; Donyatalab et al 2022 ). Considering that distance metrics can determine the comparative disparity between two entities in the discourse domain, some T-SF distance measures have been propounded so far, such as the extended Hamming distance metrics (Garg et al 2021 ; Liu et al 2019 ; Wu et al 2020 ), extended Euclidean distance metrics (Quek et al 2019 ; Wu et al 2020 ), enhanced Minkowski distance metrics (Chen 2022d ; Ju et al 2021 ), and cosine function-focused distance metrics (Ullah et al 2018 ; Wu et al 2020 ) in T-SF contexts.…”