An Intelligent Multiattribute Decision-Support Framework Based on Parameterization of Neutrosophic Hypersoft Set

Area of Science:

• Fuzzy mathematics and set theory.
• Decision-making under uncertainty.
• Computational intelligence.

Background:

• Existing soft set structures are inadequate for real-world scenarios requiring classification of overlapping parameters.
• Hypersoft sets provide a novel framework for handling such complex data structures.
• Integrating fuzzy set theory with hypersoft sets addresses limitations in representing uncertainty.

Purpose of the Study:

• To introduce novel fuzzy parameterized intuitionistic fuzzy hypersoft sets (fpifhs-set) and fuzzy parameterized neutrosophic hypersoft sets (fpnhs-set).
• To develop and apply these new structures for decision-making problems, specifically addressing the COVID-19 pandemic.
• To analyze the theoretical properties and operational characteristics of the proposed set structures.

Main Methods:

• Development of fpifhs-set and fpnhs-set using algebraic and theoretical techniques.
• Characterization of fundamental properties and set-theoretic operations for the novel structures.
• Design of two novel algorithms for decision-making applications, validated against real-world data (COVID-19).

Main Results:

• Successful development and theoretical validation of fpifhs-set and fpnhs-set.
• Demonstrated efficacy of the proposed algorithms in solving a COVID-19 decision-making problem.
• Comparative analysis showing advantageous aspects of the proposed models over existing methods.

Conclusions:

• The proposed fuzzy parameterized hypersoft set structures effectively handle uncertainty and overlapping parameters in complex decision-making.
• The developed algorithms provide a robust and advantageous approach for real-world applications like pandemic response.
• The study establishes a foundation for further research into hypersoft set theory and its applications.