Education quality detection method based on the probabilistic neural network algorithm
Ping Wang 3,1
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School of Electrical Engineering and Electronic Information, Xihua University
School of information science and Technology, Southwest Jiaotong University
Xihua University
School of information engineering, Chengdu industry and trade college
Submission date: 2020-04-10
Final revision date: 2020-08-26
Acceptance date: 2020-09-02
Online publication date: 2020-11-17
Publication date: 2020-11-17
Corresponding author
Changdong Wu   

School of Electrical Engineering and Electronic Information, Xihua University
Diagnostyka 2020;21(4):79-86
The traditional education quality detection method is too single and unreasonable, which is not suitable to evaluate students' ability comprehensively. In this paper, the probabilistic neural network (PNN) algorithm is used to detect the education quality by considering the important impact between the various achievements of students. PNN algorithm originates from Bayesian decision rule, and it uses the non-linear Gaussian Parzen window as the probability density function. As PNN model has the virtues of strong nonlinear and anti-interfering ability, it is fit to detect the education quality by classifying the students' achievements. Besides, the influences of different evaluation models on classification accuracy and efficiency are also discussed in this paper. Furthermore, the effect of spread value on PNN model is also discussed. Finally, the actual data are used to detect the education quality. Experimental results show that the detection accuracy can reach 95%, and the detection time is only 0.0156s based on the proposed method. That is to say, the method is a very practical detection algorithm with high accuracy and efficiency. Moreover, it also provides a reference for how to further improve the teaching quality.
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