Enhanced TOPSIS-CoCoSo framework for multi-attribute decision-making with triangular fuzzy neutrosophic sets: “effect evaluation of intelligent technology empowering physical education teaching” case
摘要
The history of human education development has proven that there is an interactive relationship between technological development and education and teaching. In the process of promoting education modernization and high-quality development, the widespread application of intelligent technology in the field of education is the trend, and intelligence is driving profound transformation and transformation in the field of education. The effect evaluation of intelligent technology empowering Physical Education teaching could be considered as multiple-attribute decision-making (MADM). Recently, the TOPSIS technique and Combined Compromise Solution (CoCoSo) technique was employed to deal with MADM. The triangular fuzzy neutrosophic sets (TFNSs) are employed as a better tool for expressing uncertain information during the effect evaluation of intelligent technology empowering Physical Education teaching. In this paper, the triangular fuzzy neutrosophic number TOPSIS-CoCoSo (TFNN-TOPSIS-CoCoSo) technique based on the TFNN relative closeness coefficient (TFNNRCC) technique is managed to cope with the MADM under TFNSs. The information entropy technique is employed to manage the weight values based on the TFNNRCC under TFNSs. Finally, a numerical example of effect evaluation of intelligent technology empowering Physical Education teaching is managed and some better comparisons are managed to verify the TFNN-TOPSIS-CoCoSo technique. The main contribution of this paper is outlined: (1)TFNN-TOPSIS-CoCoSo technique based on the TFNNRCC is constructed; (2) Entropy technique is employed to manage weight based on the TFNNRCC under TFNSs. (3) TFNN-TOPSIS-CoCoSo technique is founded to manage the MADM based on the TFNNRCC under TFNSs; (4) numerical example for effect evaluation of intelligent technology empowering Physical Education teaching and some comparative analysis is supplied to verify the proposed TFNN-TOPSIS-CoCoSo technique.