Management Science and Engineering, Liaoning Technical University, Fuxin 123000, Liaoning, China.
College of International Business of Shenyang Normal University, Shenyang 110034, Liaoning, China.
Comput Intell Neurosci. 2021 Nov 16;2021:7097425. doi: 10.1155/2021/7097425. eCollection 2021.
Nowadays, a large number of students' academic registrations change every year in universities, but most of these cases are recorded and mathematically and statistically analysed through forms or systems, which are cumbersome and difficult to find some potential information in them. Therefore, timely and effective prediction of student registration changes and early warning of student registration changes by technical means is an important part of university registration management. At present, relevant research is mostly based on mathematical statistical analysis methods such as students' current credit evaluation or course score averages and seldom uses data mining and other technical methods for in-depth research. In this paper, we propose a mutated fuzzy neural network (MFNN) based prediction model for student registration changes in colleges and universities, which can provide supplementary reference decisions for school registration management for school teaching managers. In this paper, we first construct the corresponding prediction model of academic registration variation, define the relevant parameters, and model the optimization problem and propose the objective optimization function. Second, the proposed model is optimized by adding principal component analysis (PCA) to the original model to improve the efficiency of model training and the correct prediction rate. It is verified that the proposed model can effectively predict individual students' academic registration changes with a prediction accuracy of nearly 92.91%.
如今，高校每年都有大量学生的学业注册信息发生变动，但这些情况大多通过表格或系统进行记录和数理统计分析，过程繁琐，难以从中发现一些潜在信息。因此，及时有效地通过技术手段预测学生的学业注册变动，并对学生的学业注册变动进行预警，是高校注册管理的重要组成部分。目前，相关研究大多基于学生当前学分评估或课程成绩平均分等数理统计分析方法，很少使用数据挖掘等技术方法进行深入研究。本文提出了一种基于突变模糊神经网络（MFNN）的高校学生学业注册变动预测模型，可为学校教学管理人员的学校注册管理提供补充决策参考。本文首先构建了相应的学业注册变动预测模型，定义了相关参数，对优化问题进行建模，并提出了目标优化函数。其次，通过在原始模型中添加主成分分析（PCA）对提出的模型进行优化，提高模型训练的效率和正确预测率。验证表明，该模型可以有效地预测个体学生的学业注册变动，预测准确率接近 92.91%。
