Multiple attribute decision-making based on Fermatean fuzzy number

Abstract

<abstract> <p>Multiple attribute decision-making concerns with production significant in our everyday life. To resolve the problems that decision makers might feel uncertain to choose the suitable assessment values among several conceivable ideals in the procedure. Fuzzy model, and its extensions are extensively applied to MADM problems. In this study, we proposed an innovative Schweizer-Sklar t-norm and t-conorm operation of FFNs, Fermatean fuzzy Schweizer-Sklar operators. They were used as a framework for the development of an MCDM method, which was illustrated by an example to demonstrate its effectiveness and applicability. Finally, a complete limitation study, rational examination, and comparative analysis of the presented approaches has been exhibited, we originate that our technique is superior in offering DMs a better decision-making choice and reducing the restrictions on stating individual partialities.</p> </abstract>

Efficient and accurate personalized product recommendations through frequent item set mining fusion algorithm

“…To further validate the performance of this paper's method, the proposed algorithm is compared with the mainstream SOTA algorithm under two metrics, namely, AUC (area under ROC curve) and MEA (mean absolute error). The SOTA algorithm is taken from the literatures [ [40] , [41] , [42] ], and [ 43 ], respectively. The test results on the two datasets are shown in Table 3 .…”