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Novel similarity measures under complex pythagorean fuzzy soft matrices and their application in decision making
Muhammad Zeeshan1, Madad Khan2, Ramsha Shafqat3
1Department of Mathematics, The University of Agriculture, Dera Ismail Khan, Pakistan.
This study introduces complex Pythagorean fuzzy soft matrices (CPFSMs) for enhanced uncertainty modeling in decision-making and pattern recognition. CPFSMs offer a robust framework for analyzing complex data with improved flexibility and accuracy.
Area of Science:
- Mathematics
- Computer Science
- Decision Science
Background:
- Complex fuzzy soft matrices are vital for decision-making, pattern recognition, signal, and image processing.
- Existing models may lack the flexibility and accuracy needed for complex uncertainty modeling.
Purpose of the Study:
- Introduce complex Pythagorean fuzzy soft matrices (CPFSMs) for advanced uncertainty modeling.
- Develop novel distance metrics and a decision-making technique using CPFSMs.
Main Methods:
- Defined novel notions of complex Pythagorean fuzzy soft matrices.
- Established fundamental set-theoretic operations and principles for CPFSMs.
- Developed new distance metrics between CPFSMs.
Main Results:
- Introduced a CPFS decision-making technique within the CPFSM framework.
- Demonstrated the effectiveness of CPFSMs through a numerical example and comparative analysis.
- CPFSMs integrate Pythagorean fuzzy sets, soft matrices, and complex numbers for robust modeling.
Conclusions:
- CPFSMs provide a flexible and accurate framework for modeling complex uncertainty.
- The proposed decision-making technique and distance metrics enhance analytical capabilities.
- CPFSMs are suitable for complex decision-making and uncertain data analysis.
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