Summary of On Globular T-spherical Fuzzy (g-tsf) Sets with Application to G-tsf Multi-criteria Group Decision-making, by Miin-shen Yang et al.
On Globular T-Spherical Fuzzy (G-TSF) Sets with Application to G-TSF Multi-Criteria Group Decision-Making
by Miin-Shen Yang, Yasir Akhtar, Mehboob Ali
First submitted to arxiv on: 9 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces Globular T-Spherical Fuzzy (G-TSF) Sets as an extension of traditional fuzzy sets. G-TSFSs represent membership, indeterminacy, and non-membership degrees using a globular/sphere bound, enabling more accurate portrayal of vague information. This model enhances decision-making by evaluating objects within a flexible region. The paper defines basic set operations and algebraic operations for G-TSF Values, expanding evaluative capabilities. A similarity measure is introduced, as well as Hamming distance and Euclidean distance. Theorems and examples illustrate computational mechanisms. The paper also proposes the G-TSFWA and G-TSFWG operators, and develops a Multi-Criteria Group Decision-Making (MCGDM) scheme for G-TSFSs. This method is applied to solve the selection problem of professional development training sessions in a firm, demonstrating its effectiveness in practical decision-making scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to deal with fuzzy information called Globular T-Spherical Fuzzy Sets. These sets help us make decisions by giving us a better idea of how likely something is or isn’t. It’s like trying to decide which restaurant to go to based on how good the food is, but also considering other factors. The paper shows how this new way can be used to solve real-life problems, like choosing the best place for training sessions in a company. |
Keywords
» Artificial intelligence » Euclidean distance
