Artificial intelligence-based intelligent computing using circular q-rung orthopair fuzzy information aggregation

Artificial Intelligence (AI) based computing techniques play a transformative role in enhancing the capabilities of modern computing systems by enabling them to learn, adapt, optimize, and make decisions autonomously. These techniques are applied across various fields to improve performance, reduce human effort, and solve complex problems more efficiently. In this article, we explored a potent approach of the circular q-rung orthopair fuzzy for coping with uncertain and vague type information in life-life dilemmas because it covers extensive information in the form of degree of membership value, degree of non-membership value, and radius among both membership functions. Some flexible operations of Frank t-norms were formulated under the circular q-rung orthopair fuzzy (Crq-ROF) context. Based on these operations, we developed novel approaches of circular q-rung orthopair fuzzy Frank weighted average (Crq-ROFFWA) and circular q-rung orthopair fuzzy Frank weighted geometric (Crq-ROFFWG) operators with dominant properties. Additionally, we modified a novel theory of evaluation based on distance from the average solution (EDAS) method for the multi-attribute decision-making (MADM) problem. Later, we discussed an experimental case study related to artificial intelligence for measuring the performance of intelligent computing techniques. Using a numerical example, we explored the worth and compatibility of discussed methodologies and decision support systems. Finally, utilizing a comparison technique, we identified the supremacy and effectiveness of proposed theories.