查看文章

In the realm of biometric authentication systems, the challenge of optimal selection underlies the necessity for a sophisticated decision-making framework capable of handling the inherent uncertainty and complexity. This paper introduces an advanced Multi-Criteria Decision-Making (MCDM) methodology that leverages Artificial Intelligence (AI) and Machine Learning (ML) principles, contained within the realms of Complex Circular Intuitionistic Fuzzy Set (C-CIFS) and Dombi Aggregation (DA) Operators. By synthesizing C-CIFS with Dombi operators, we propose a novel aggregation approach characterized by its capability to properly navigate through and aggregate the complicated criteria associated with biometric authentication systems. Our method, the Complex Circular Intuitionistic Fuzzy Dombi Weighted Average (C-CIFDWA) and the Complex Circular Intuitionistic Fuzzy Dombi Weighted Geometric (C-CIFDWG) operators are designed to enhance decision accuracy by effectively managing the nuances of circular intuitionistic fuzzy information. Through a series of comparative studies, including the exploration of radius and complex term impacts, our findings illustrate the superior performance and flexibility of our proposed operators against traditional models. The implementation of our approach within an AI-based framework not only paves the way for more secure and reliable biometric authentication systems but also sets a new standard for decision-making processes in uncertain and imprecise environments.