A Similarity Measures-Based TOPSIS Method for Neutrosophic Hypersoft Set with Application in Crop Production

Neutrosophic sets and hypersoft sets are both fuzzy set extensions that deal with various aspects of uncertainty and missing data. Combining these two frameworks within Multi-Criteria Decision-Making (MCDM) allows choosing an optimal solution from a wide range of scenarios and alternatives. TOPSIS is a significant practical method for evaluating and selecting numerous possibilities, which ranks preferences according to how closely they resemble the ideal solution. This study used similarity and distance measures for NHSS and aggregate NHSS decision matrices by employing aggregation operators. The proposed NHSS-TOPSIS technique was used to assess the fertility of the soil for the production of specific crops. Multiple criteria were considered to make decisions regarding crop production based on soil conditions and other factors. This work can be further broadened to various existing hybrids of hypersoft sets, such as Intuitionistic Fuzzy Hypersoft Sets (IFHSS), Pythagorean Fuzzy Hypersoft Sets (PFHSS), Bipolar fuzzy hypersoft sets, Pythagorean Fuzzy Hypersoft Matrices (PFHSM), and neutrosophic hybrids.