| Title |
Aerobics Exercise Performance Training for College Students based on Optimized Apriori Algorithm |
| DOI |
https://doi.org/10.5573/IEIESPC.2024.13.2.148 |
| Keywords |
Apriori algorithm; Exercise; Training effect; College students |
| Abstract |
Amazing changes have occurred in college education with the rapid development and popularization of science and technology in the information age. This paper proposes a design method for predicting the effect of college students’ sports performance training based on the optimized Apriori algorithm to develop a more standardized and complete physical education course training plan. The research introduces the gravitational search algorithm (GSA), and the particle swarm optimization algorithm (PSA) combines the two algorithms into a hybrid algorithm that integrates with the Apriori algorithm to form the GSA-PSO-A algorithm to predict the performance training effect of students. The algorithm was used to find valuable associated data in the data and finally conduct application analysis. The GSA-PSO algorithm reached a stable fitness value when the number of iterations was 200 and 100 in the unimodal and multimodal function tests, respectively, and the feasibility was the best. The GSA-PSO-A algorithm proposed by the research institute could effectively mine the training data of college students’ sports performance and provide a feasible path for improving the teaching of physical education courses to college students. |