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What is Information Measures for Complex Fuzzy Sets about?

The article introduces novel information measures for complex intuitionistic fuzzy sets (CIFSs), which incorporate complex-valued membership and nonmembership functions to handle multidimensional data. It proposes formulas for similarity, distance, entropy, and inclusion measures, and demonstrates their application in pattern recognition and multicriteria decision-making problems. The study also develops algorithms based on these measures and illustrates their effectiveness through examples.

Author

ritu24rs

Language

EN