Decision support system for selection of the appropriate tool of artificial intelligence using Diophantine spherical fuzzy normal interval-valued set

Modern society employs an increasing number of robots, notably service robots. Robots will be able to handle everyday objects with simplicity, but only if they are collaborated with a planning and decision-making (DM) strategy that allows them to very quickly recognize how to execute tasks. Robotic activities generate natural-world decisions by aggregating operators (AO) into a Diophantine spherical normal interval-valued fuzzy set (DSIVFS). This article discusses using DSNIVFS to tackle multiple attribute decision-making (MADM) problems. The Diophantine spherical normal fuzzy numbers (DSNFN) were also used in the current investigation. Diophantine spherical normal interval-valued fuzzy numbers (DSNIVFN) were investigated utilizing a variety of algebraic operations, including idempotency, boundedness, commutativity, and monotonicity, as well as the expansion of the Euclidean distance (ED) and Hamming distance (HD). The objectives of this research include DSNIVF weighted averaging (DSNIVFWA), DSNIVF weighted geometric (DSNIVFWG), generalized DSNIVF weighted averaging (GDSNIVFWA), and generalized DSNIVF weighted geometric (GDSNIVFWG). These operators are utilized in the MADM approach to solve DM problems. The distance between DSNIVFNs is turned into the distance between NFNs as a result of the discussion. It was discussed that GDSNIVFWA (GDSNIVFWG) has been modified to DSNIVFWA (DSNIVFWG) for natural number [Formula: see text]. For example, agricultural robotics is a subject of industrial engineering that combines computer science and machine tool technology. Artificial intelligence (AI) allows computers to apply what they have learned to process new inputs. Therefore, five different types of AI robotics were chosen at random: analytical, functional, visual, interactive, and text. Cost, scalability, performance, availability, and durability are among the characteristics used in the selection process. The best robotics can be taken by making choices based on established criteria. The [Formula: see text] serves as the model for the outcome, which was found by comparing proposed models to a number of existing models. To demonstrate the reliability, authenticity, and viability of our theoretical construct, careful comparison analysis, sensitivity analysis and the validity of our proposed methodologies are carried out. The findings will be extremely useful to decision-makers in dealing with unclear and conflicting facts effectively.