Cognitive Decision-Making Based on a Non-linear Similarity Measure Using an Intuitionistic Fuzzy Set Framework

Similarity measure (SM) between two intuitionistic fuzzy sets (IFSs) plays a crucial role in cognitive decision-making, for instance, in cognitive medical diagnosis, pattern recognition, criminal investigation, etc., in dealing with uncertainty. An SM between two IFSs signifies the degree of similarity or the exactness of two IFSs. Inherent inadequacies in SMs may lead to erroneous results; therefore, it is vital and significant to use an efficient SM while dealing with cognitive decision-making (DM) problems in uncertain environments. This study proposes a novel SM of IFSs to enhance the capability of producing fair outcomes in cognitive decision-making problems. The proposed SM has provided the similarity values between different pairs of IFSs, describing its advantages and efficiency, whereas many of the existing SMs have produced contradictory results. Furthermore, the proposed SM has been applied to solve certain biologically inspired cognitive medical diagnosis problems, pattern recognition problems, and criminal investigation problems. The results of the comparative study demonstrate how the proposed SM of IFSs conquers and tides over the shortcomings of the previous existing SMs. Many of the existing SMs produced identical similarity values for different pairs of IFSs, thereby serving as unfit for offering the appropriate exactness of information carried by the pairs of IFSs. Against such a backdrop, the proposed SM selects the best alternative for the cognitive decision-making problems and, hence, in a self-evident manner, evinces its applicability and feasibility in such environments. When delving into the biologically inspired multi-criteria decision-making (MCDM) methods under uncertainty with human cognition, SMs of IFSs serve as one of the essential devices. Accordingly, this study merits attention as it accounts for a veritable fundamental research endeavour with the larger goal of making available consistent and proficient SMs in the literature so that the various MCDM methods become more efficient and reliable. This study attempts to define an advanced and novel SM of IFSs to assist and enrich the MCDM methods using a sophisticated approach.