Correlation coefficients for T-spherical fuzzy sets and their applications in pattern analysis and multi-attribute...

Area of Science:

• Fuzzy Set Theory
• Decision Science
• Pattern Recognition

Background:

• T-spherical fuzzy sets (TSFS) are effective for handling vagueness and uncertainty, especially with multiple circumstances.
• Correlation coefficients are crucial for quantifying the association between fuzzy sets, with applications in science, management, and engineering.
• Existing methods for TSFS lack comprehensive association analysis.

Purpose of the Study:

• To introduce novel correlation coefficients for T-spherical fuzzy sets.
• To demonstrate the application of these coefficients in pattern recognition and decision-making.
• To highlight the advantages of the proposed coefficients over existing methods.

Main Methods:

• Development of new correlation coefficients specifically for T-spherical fuzzy sets.
• Application of proposed coefficients to pattern analysis tasks.
• Utilizing the coefficients in a multi-attribute decision-making (MADM) problem for COVID-19 mask selection.

Main Results:

• The proposed correlation coefficients provide a complete measure of association between TSFS.
• Demonstrated effectiveness in pattern analysis and a practical MADM problem.
• Comparative analysis shows superiority over existing correlation coefficients for TSFS.

Conclusions:

• The newly introduced correlation coefficients for T-spherical fuzzy sets offer enhanced capabilities for analyzing associations.
• These coefficients are valuable tools for pattern recognition and complex decision-making scenarios, such as selecting appropriate COVID-19 masks.
• The proposed methods present a significant improvement over existing techniques for T-spherical fuzzy set analysis.