New similarity measures for single-valued neutrosophic sets with applications in pattern recognition and medical diagnosis problems

Abstract

The single-valued neutrosophic set (SVNS) is a well-known model for handling uncertain and indeterminate information. Information measures such as distance measures, similarity measures and entropy measures are very useful tools to be used in many applications such as multi-criteria decision making (MCDM), medical diagnosis, pattern recognition and clustering problems. A lot of such information measures have been proposed for the SVNS model. However, many of these measures have inherent problems that prevent them from producing reasonable or consistent results to the decision makers. In this paper, we propose several new distance and similarity measures for the SVNS model. The proposed measures have been verified and proven to comply with the axiomatic definition of the distance and similarity measure for the SVNS model. A detailed and comprehensive comparative analysis between the proposed similarity measures and other well-known existing similarity measures has been done. Based on the comparison results, it is clearly proven that the proposed similarity measures are able to overcome the shortcomings that are inherent in existing similarity measures. Finally, an extensive set of numerical examples, related to pattern recognition and medical diagnosis, is given to demonstrate the practical applicability of the proposed similarity measures. In all numerical examples, it is proven that the proposed similarity measures are able to produce accurate and reasonable results. To further verify the superiority of the suggested similarity measures, the Spearman’s rank correlation coefficient test is performed on the ranking results that were obtained from the numerical examples, and it was again proven that the proposed similarity measures produced the most consistent ranking results compared to other existing similarity measures.

Context-aware POI Recommendation using Neutrosophic Set for Mobile Edge Computing

“…Neutrosophic set has been widely used in image processing [22,23], pattern recognition [24] and medical diagnosis [25,26,8]. Neutrosophic set is widely used to solve MCDM problems because it can represent fuzzy decision information in multiple dimensions.…”

Multi-attribute Cognitive Decision Making via Convex Combination of Weighted Vector Similarity Measures for Single-Valued Neutrosophic Sets

“…In the recent 5 years, SVNSs have seen widespread applications in the realm of cognitive decision-making. For instance, Chai et al [35] enriched the literature of SVNSs by proposing certain novel similarity measures. Saqlain et al [36] proposed the concept of tangent similarity measure for single and multi-valued hypersoft sets under a neutrosophic setting.…”

A neutrosophic set-based TLBO algorithm for the flexible job-shop scheduling problem with routing flexibility and uncertain processing times

“…For example, Broumi et al in their research suggested a new score function for interval-valued neutrosophic numbers and the neutrosophic shortest path is determined based on the score function [39]; the score function is used to evaluate the paths that have been chosen. Applications of SVNSs can also be found in pattern recognition and medical diagnosis [41,42], taxonomy, and clustering analysis [42] and other areas. Unfortunately, most of the existing studies regarding SVNSs or related applications focus on decision-making issues; to the best of our knowledge, there is no research regarding SVNSs for the uncertain IPPS problem.…”