An Intelligent Decision Support System for Selecting Optimal AI-Powered Assistive Technology for Individuals with Disabilities

We introduce a novel extension of conventional fuzzy sets in this paper: called trimorphic fuzzy sets. As per our research, trimorphic fuzzy sets which exhibit greater capability than intuitionistic fuzzy sets, picture fuzzy sets and bipolar fuzzy sets, present a viable approach to address ambiguity and uncertainty in decision-making scenarios. We present a complete characterization of trimorphic fuzzy sets, discuss their properties, and consider applications to real-world decision-making scenarios. We also present a case study to further highlight the practical applications of trimorphic fuzzy sets. We look into a few aggregation strategies for trimorphic fuzzy data in this work. We create the MCDM method using trimorphic fuzzy aggregation operators to help people with disabilities choose AI-Powered Assistive Technologies. We have also presented the extended TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method with trimorphic fuzzy numbers. A numerical example for selection of AI-Powered Assistive Technologies using TOPSIS method is also provided.