Analysis of College Student Registration Management and Change Prediction Based on Mutated Fuzzy Neural Network Algorithm

• Published in: Computational intelligence and neuroscience Vol. 2021; no. 1; p. 7097425
• Main Authors: Wang, Yao, Jiao, Lie, Liu, Chunzhi
• Format: Journal Article
• Language: English
• Published: United States Hindawi 2021; John Wiley & Sons, Inc
• Subjects: Algorithms; Artificial neural networks; College students; Colleges & universities; Data Mining; Education; Fuzzy logic; Humans; Learning; Neural networks; Neural Networks, Computer; Optimization; Prediction models; Principal components analysis; Registration; Statistical analysis; Statistical methods; Students; Teaching; Tuition; Universities
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2021/7097425

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%.