Parametric Similarity Measurement of T-Spherical Fuzzy Sets for Enhanced Decision-Making

Journal Title: International Journal of Knowledge and Innovation Studies - Year 2024, Vol 2, Issue 1

Abstract

The T-spherical fuzzy set (T-SFS), an advancement over the spherical fuzzy set (SFS), offers a refined approach for addressing contradictions and ambiguities in data. In this context, similarity measures (SMs) serve as critical tools for quantifying the resemblance between fuzzy values, traditionally relying on the calculation of distances between these values. Nevertheless, existing methodologies often encounter irrational outcomes due to certain characteristics and complex operations involved. To surmount these challenges, a novel parametric similarity measure is proposed, grounded in three adjustable parameters. This enables decision-makers to tailor the SM to suit diverse decision-making styles, thereby circumventing the aforementioned irrationalities. An analytical comparison with existing SM reveals the superiority of the proposed measure through mathematical validation. Furthermore, the utility of this measure is demonstrated in the resolution of multi-attribute decision-making (MADM) problems, highlighting its efficacy over several existing approaches within the domain of T-SFS. The implementation of the proposed SM not only enhances the precision of similarity assessment in fuzzy sets but also significantly contributes to the optimization of decision-making processes.

Authors and Affiliations

Mehwish Sarfraz, Muhammad Azeem

Keywords

Related Articles

Generalized and Group-Generalized Parameter Based Fermatean Fuzzy Aggregation Operators with Application to Decision-Making

Fermatean fuzzy set (FRFS) is very helpful in representing vague information that occurs in real world circumstances. Their eminent characteristic of FRFS is that the degree of membership ℑℓ and degree of non-membership...

AMBERT-DWPM: An Adaptive Masking and Dynamic Prototype Learning Framework for Few-Shot Text Classification

Transformer-based language models have demonstrated remarkable success in few-shot text classification; however, their effectiveness is often constrained by challenges such as high intraclass diversity and interclass sim...

Gear Fault Detection Based on Convolutional Neural Networks and Support Vector Machines

As a critical component of mechanical transmission systems, gears play a vital role in ensuring industrial production runs smoothly. Undetected gear failures can lead to mechanical breakdowns, production interruptions, a...

Assessing the Environmental Sustainability Performance of the Banking Sector: A Novel Integrated Grey Multi-Criteria Decision-Making (MCDM) Approach

The objective of this work is to analyze the environmental sustainability performance of deposit banks traded in Borsa Istanbul (BIST) through the application a novel integrated grey Multi-Criteria Decision-Making (MCDM)...

Selection of CRM Systems Using Objective Criteria for the Needs of Small Companies

This research examines customer relationship management (CRM) systems using multi-criteria decision-making (MCDM) methods, with the intention of selecting the most suitable solution for small companies. The main goal of...

Download PDF file
  • EP ID EP744649
  • DOI https://doi.org/10.56578/ijkis020104
  • Views 40
  • Downloads 0

How To Cite

Mehwish Sarfraz, Muhammad Azeem (2024). Parametric Similarity Measurement of T-Spherical Fuzzy Sets for Enhanced Decision-Making. International Journal of Knowledge and Innovation Studies, 2(1), -. https://europub.co.uk/articles/-A-744649