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Intuitionistic Fuzzy Modulus Similarity Measure

Author

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  • Pawan Gora

    (Deenbandhu Chhotu Ram University of Science and Technology, India)

  • V.P. Tomar

    (Deenbandhu Chhotu Ram University of Science and Technology, India)

Abstract

The concept of intuitionistic fuzzy sets (IFSs) is an expected explanation for finding the appropriate information. It originated from concept of fuzzy set (FS) theory, which extends the classical conception of a fuzzy set. This paper examines a number of widely employed similarity measures then proposes an IFSs modulus similarity measure and a weight similarity measure. Initially, the authors have discussed numerous existing similarity measures, some of which are unable to justify the axioms of being a similarity measure. Furthermore, some numerical examples are presented to compare the existing similarity measures with the proposed similarity measure. The proposed similarity measure is a practical and effective method for determining the qualitative similarity between IFSs, which do not have any paradoxical nature. In addition, the proposed similarity measure has been demonstrated practically in pattern recognition and medical diagnosis problem. Suggestions for future research comprise the conclusions of the paper.

Suggested Citation

  • Pawan Gora & V.P. Tomar, 2023. "Intuitionistic Fuzzy Modulus Similarity Measure," International Journal of Decision Support System Technology (IJDSST), IGI Global, vol. 15(1), pages 1-22, January.
    • Handle: RePEc:igg:jdsst0:v:15:y:2023:i:1:p:1-22
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    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJDSST.315757
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    Cited by:

    1. Wajid Ali & Tanzeela Shaheen & Iftikhar Ul Haq & Hamza Ghazanfar Toor & Tmader Alballa & Hamiden Abd El-Wahed Khalifa, 2023. "A Novel Interval-Valued Decision Theoretic Rough Set Model with Intuitionistic Fuzzy Numbers Based on Power Aggregation Operators and Their Application in Medical Diagnosis," Mathematics, MDPI, vol. 11(19), pages 1-18, October.

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