Robotic sensor based on score and accuracy values in [formula omitted]-rung complex diophatine neutrosophic normal set with an aggregation operation

Abstract

The multiple-attribute decision-making (MADM) problem is resolved through the q-rung complex diophantine neutrosophic normal set (q-rung CDNNS). An important way to express uncertain information is using q-rung orthopair fuzzy sets (q-ROFs). Yager introduced q-ROFs as a generalization of intuitionistic fuzzy sets in which the sum of membership and non-membership degrees is one. In addition, they have superiority over intuitionistic fuzzy sets and Pythagorean fuzzy sets. Complex diophantine fuzzy sets are generalizations of neutrosophic and diophantine fuzzy sets, respectively. Several aggregating operations (AOs) are discussed here, as well as their respective interpretations. The paper discusses q-rung CDNN weighted averaging (q-rung CDNNWA), q-rung CDNN weighted geometric (q-rung CDNNWG), q-rung generalized CDNN weighted averaging (q-rung GCDNNWA) and q-rung generalized CDNN weighted geometric (q-rung GCDNNWG). We will review several of these sets with important properties in greater detail using algebraic operations. Additionally, we develop an algorithm for solving MADM problems using these operators. Several real-world examples illustrate how enhanced score values can be applied. Sensor robots are said to rely heavily on computer science and machine tool technology. Four factors are to evaluate when determining a sensor robotics system’s quality: resolution, sensitivity, error, and environment. It is possible to compare expert opinions with the criteria and determine the best alternative. Therefore, the value of q significantly impacts the model’s results. To prove that the models considered are valid and useful, we will compare the current and proposed models. Thus, q has a significant impact on the results of the model.