Enhancing Artificial Intelligence Models with Interval-Valued Picture Fuzzy Sets and Sugeno-Weber Triangular Norms

This work aims to improve intelligence decision-making using interval-valued picture fuzzy sets (IVPFS). In particular, it explores using Sugeno-Weber (SW) norms in IVPFS data recording, providing reliable estimates important for decision-making. This paper introduces a new class of aggregation operators such as the interval-valued picture fuzzy Sugeno-Weber power average (IVPFSWPA), interval-valued picture fuzzy Sugeno-Weber power geometric (IVPFSWPG), interval-valued picture fuzzy Sugeno-Weber power weighted average (IVPFSWPWA), and interval-valued picture fuzzy Sugeno-Weber power weighted geometric (IVPFSWPWG) operators. The real-life characteristics and specific situations of these operators are described as well as how they adapt to real-life situations. The new multi-attribute decision-making method suitable for many practical applications with different requirements or functions is proposed. An example of an intelligent selection process is given to demonstrate its effectiveness. In addition, a general comparative method is proposed to demonstrate the effectiveness and suitability of the collective strategy by comparing its results with existing methods. The study concludes by summarizing its findings and discussing its prospects, highlighting the potential contribution of the proposed studies to the advancement of cutting-edge technology in a dynamic and complex environment.