Construction of College Students' Integrity Evaluation Model Based on Bayesian Classifier

Author(s):  
Li Peiliang ◽  
Hu Peipei
2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Xiaolin Yuan

Contemporary young college students are greatly impacted in the aspects of moral cognition and moral choice, which results in the weak moral will of some college students, vague moral concepts, and weak ideals and beliefs, which seriously affect the formation and development of college students’ moral quality. Therefore, the moral education evaluation model based on college students’ quality cultivation is constructed. Firstly, the present situation and defects of college students’ quality training are analyzed. Based on this, association rules in data mining method are constructed and introduced to extract valuable knowledge hidden in the data to assist education managers to make effective decisions and improve management level. Finally, the evaluation index is selected and the weighted principal component TOP-SIS model is constructed to realize the evaluation of moral education based on college students’ quality cultivation. The experimental results show that the evaluation results of the model are consistent with the actual situation, high degree of fit and freedom, and good practical performance.


2009 ◽  
Vol 29 (10) ◽  
pp. 2849-2851
Author(s):  
Li-lun ZHANG ◽  
Jian-ping WU ◽  
Jun-qiang SONG

2020 ◽  
pp. 1-11
Author(s):  
Guo Yunfeng ◽  
Li Jing

In order to improve the effect of the teaching method evaluation model, based on the grid model, this paper constructs an artificial intelligence model based on the grid model. Moreover, this paper proposes a hexahedral grid structure simplification method based on weighted sorting, which comprehensively sorts the elimination order of candidate base complexes in the grid with three sets of sorting items of width, deformation and price improvement. At the same time, for the elimination order of basic complex strings, this paper also proposes a corresponding priority sorting algorithm. In addition, this paper proposes a smoothing regularization method based on the local parameterization method of the improved SLIM algorithm, which uses the regularized unit as the reference unit in the local mapping in the SLIM algorithm. Furthermore, this paper proposes an adaptive refinement method that maintains the uniformity of the grid and reduces the surface error, which can better slow down the occurrence of geometric constraints caused by insufficient number of elements in the process of grid simplification. Finally, this paper designs experiments to study the performance of the model. The research results show that the model constructed in this paper is effective.


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