Novel possibility Pythagorean interval valued fuzzy soft set method for a decision making

We discuss the theory of possibility Pythagorean interval valued fuzzy soft set, possibility interval valued fuzzy soft set and define some related the operations namely complement, union, intersection, AND and OR. The possibility Pythagorean interval valued fuzzy soft sets are a generalization of soft sets. Notably, we showed DeMorgan’s laws that are valid in possibility Pythagorean interval valued fuzzy soft set theory. Also, we propose an algorithm to solve the decision making problem based on soft set method. To compare two possibilities Pythagorean interval valued fuzzy soft sets for dealing with decision making problems and find a similarity measure is obtained. Finally, an illustrative example is discussed to prove that they can be effectively used to solve problems with uncertainties.Publisher's Versio

Novel possibility spherical fuzzy soft set model and its application for a decision making

We talk about possibility spherical fuzzy soft set (shortly PSFS set) has much stronger ability than possibility Pythagorean fuzzy soft set (shortly PPFS set) and intuitionistic fuzzy soft set. The PSFS soft set is a generalization of PPFS set and soft set. Here we talk through some operations consisting of complement, union, intersection, AND and OR. We verify that the De Morgan’s laws, associate laws and distributive laws are satisfied in the case of PSFS sets. Also we discuss comparative analysis for the soft set model under the scheme of PSFS sets. Finally, an illustrative example is mentioned for the soft set model using PSFS set.Publisher's Versio

q-rung logarithmic Pythagorean neutrosophic vague normal aggregating operators and their applications in agricultural robotics

The article explores multiple attribute decision making problems through the use of the Pythagorean neutrosophic vague normal set (PyNVNS). The PyNVNS can be generalized to the Pythagorean neutrosophic interval valued normal set (PyNIVNS) and vague set. This study discusses $q$-rung log Pythagorean neutrosophic vague normal weighted averaging ($q$-rung log PyNVNWA), $q$-rung logarithmic Pythagorean neutrosophic vague normal weighted geometric ($q$-rung log PyNVNWG), $q$-rung log generalized Pythagorean neutrosophic vague normal weighted averaging ($q$-rung log GPyNVNWA), and $q$-rung log generalized Pythagorean neutrosophic vague normal weighted geometric ($q$-rung log GPyNVNWG) sets. The properties of $q$-rung log PyNVNSs are discussed based on algebraic operations. The field of agricultural robotics can be described as a fusion of computer science and machine tool technology. In addition to crop harvesting, other agricultural uses are weeding, aerial photography with seed planting, autonomous robot tractors and soil sterilization robots. This study entailed selecting five types of agricultural robotics at random. There are four types of criteria to consider when choosing a robotics system: robot controller features, cheap off-line programming software, safety codes and manufacturer experience and reputation. By comparing expert judgments with the criteria, this study narrows the options down to the most suitable one. Consequently, $q$ has a significant effect on the results of the models

Robot sensors process based on generalized Fermatean normal different aggregation operators framework

Novel methods for multiple attribute decision-making problems are presented in this paper using Type-Ⅱ Fermatean normal numbers. Type-Ⅱ Fermatean fuzzy sets are developed by further generalizing Fermatean fuzzy sets and neutrosophic sets. The Type-Ⅱ Fermatean fuzzy sets with basic aggregation operators are constructed. The concept of a Type-Ⅱ Fermatean normal number is compatible with both commutative and associative rules. This article presents a new proposal for Type-Ⅱ Fermatean normal weighted averaging, Type-Ⅱ Fermatean normal weighted geometric averaging, Type-Ⅱ generalized Fermatean normal weighted averaging, and Type-Ⅱ generalized Fermatean normal weighted geometric averaging. Furthermore, these operators can be used to develop an algorithm that solves MADM problems. Applications for the Euclidean distance and Hamming distances are discussed. Finally, the sets that arise as a result of their connection to algebraic operations are emphasized in our discourse. Examples of real-world applications of enhanced Hamming distances are presented. A sensor robot's most important components are computer science and machine tool technology. Four factors can be used to evaluate the quality of a robotics system: resolution, sensitivity, error and environment. The best alternative can be determined by comparing expert opinions with the criteria. As a result, the proposed models' outcomes are more precise and closer to integer number $\delta$. To demonstrate the applicability and validity of the models under consideration, several existing models are compared with the ones that have been proposed

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

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