Innovative AI Solutions through the Utilization of Complex Fuzzy Rough Set Theory

The introduction of innovative artificial intelligence (AI) solutions has changed different fields, including diagnostics, autonomous systems, and intelligent decision support systems. However, these advanced AI solutions that are used in real-world applications, have limitations in dealing with complex, uncertain, and rough information in MCDM problems. This chapter presents a new mathematical model that improves the ability of AI systems by incorporating the CFRS theory. We develop several sophisticated operators: There are complex fuzzy rough power averaging (CFRPA), complex fuzzy rough power weighted averaging (CFRPWA), complex fuzzy rough power geometric (CFRPG), and complex fuzzy rough power weighted geometric (CFRPWG). These operators are particularly developed to aggregate CFR in MCDM problems, which offers a stable framework to deal with uncertainty, roughness, and multidimensionality of data. In addition, we present a detailed MCDM approach that can efficiently handle problems with complex fuzzy rough data, especially concerning AI solution selection. The actual usefulness of the proposed theoretical framework is illustrated by a comprehensive case study devoted to the identification of the best AI solutions for enterprise deployment. A comparison of our approach with existing methodologies shows the effectiveness of the proposed approach in dealing with multiple decision parameters and uncertain information. This integrated framework not only improves the decision-making process for selecting AI solutions but also defines a new paradigm for AI solution evaluation and implementation in different domains.