LINGUISTIC DUAL HESITANT SUPER HYPERSOFT TOPOLOGICAL SPACE IN MULTI CRITERIA DECISION MAKING

Abstract

In this article presents a topology-based approach using linguistic dual hesitant super hypersoft sets to support medical diagnosis under conditions of vagueness and multiple interacting attributes. The method draws on dual hesitant membership and non-membership values along with topological notions, subbase, interior, and closure to structure and refine the evaluation process. A decision algorithm is proposed that merges the linguistic dual hesitant super hypersoft set assessments with these topological operations to rank alternatives and support decision-making.