Similarity measures of neutrosophic fuzzy soft set and its application to decision making

Complex data analysis should be done in the most accurate way in order to express the uncertain environments in the most ideal way. In addition, mathematical models that can best express the membership degrees that represent the objects are preferred for these data analyses. Taking these situations into account, this study proposes the concept of single-valued neutrosophic fuzzy soft set, which can be expressed with four different membership degrees of each object that provides a parameter, with the contribution of the parameterisation tool. Some important results are obtained by examining some single-valued neutrosophic fuzzy soft set-based theoretical operations. In addition, distance measures between two single-valued neutrosophic fuzzy soft sets. Then, similarity measurements based on these distance measurements are studied. In this way, a determination has been made for the analysis of the decision-making processes for the determination of the interaction between two single-valued neutrosophic fuzzy soft sets.