Bridging Art and Logic: Evolving Artificial Intelligence for Emotionally Intelligent Art Communication Using Integrated PROMETHEE – Complex Circular Intuitionistic Fuzzy Dombi Framework

In modern communication systems, especially in art communication, emotional intelligence is essential in improving engagement and sympathy between audiences and creators. Existing decision-making models fail when faced with the complexity and subjectivity of human emotions, particularly in situations marked by vagueness and cultural diversity. This study aims to introduce the Dombi operational law model based on complex circular intuitionistic fuzzy (C-CrIF) information. The prioritized aggregation operators (AOs) involve assigning multiple levels of significance to the fuzzified criteria, the alternatives, and the expert opinions before combining them. We proposed new AOs, Dombi operational laws under the C-CrIF set (C-CrIFS), including complex circular intuitionistic fuzzy Dombi prioritized weighted averaging and complex circular intuitionistic fuzzy Dombi prioritized weighted geometric operators. These AOs are the improved forms of Dombi and prioritized AOs for fuzzy, complex fuzzy, intuitionistic fuzzy, complex intuitionistic fuzzy, and complex circular intuitionistic fuzzy data. Some essential properties of the proposed operators are also proposed. Also, we proposed the preference ranking organization method for enrichment evaluation (PROMETHEE) method based on the C-CrIF framework. Next, to demonstrate the applicability of established AOs, a case study of emotionally intelligent art communication is conducted in the presence of the proposed operators and using the PROMETHEE method. By combining these methods, the model can identify the most emotionally resonant communication alternatives, offering artists, curators, and artificial intelligence agents a powerful tool to improve audience connection. Lastly, by comparing the existing models, we provide clarifications on the established model to demonstrate the superiority and value of the developed model.