Distance and Similarity Measures Under Spherical Fuzzy Environment and Their Application to Pattern Recognition

The recently developed three-dimensional spherical fuzzy sets are an extension of the ordinary fuzzy sets, which are effective in handling uncertainty and quantifying expert judgments. The similarity measure is one of the beneficial tools to define the degree of similarity between two objects. It has many vital implementations such as medical diagnosis, and pattern recognition. Some different distance and similarity measures of SFSs have been proposed to literature, but they are limited when compared to other extensions of fuzzy sets. In this study, some novel distances and similarity measures of spherical fuzzy sets are presented. Then, we propose the novel distance measurements such as spherical fuzzy Minkowski k-Chord distance, weighted spherical fuzzy Minkowski k-Chord distance. In addition, f-similarity measures are developed under a spherical fuzzy environment. The newly defined similarity measures are applied to pattern recognition for the COVID-19 virus. In this application, the goal is to determine the main significant group of parameters that cause to spreading of COVID-19 virus in different countries separately. A comparative analysis of new similarity measures is established and some advantages of the proposed study are discussed.