TSO Algorithm and DBN-Based Comprehensive Evaluation System for University Physical Education

With the rise of fitness technologies and the integration of smart applications in education, improving physical education evaluation methods is essential for better assessing student performance inside and outside the classroom. Traditional evaluation methods often lack precision, fairness, and real-time capabilities. This study aims to develop an integrated evaluation method for university physical education using a combination of the Tuna Swarm Optimization (TSO) algorithm and a Deep Belief Network (DBN) to optimize the accuracy and efficiency of evaluating both in-class and extracurricular physical activities. The evaluation system is built using the Campus Running APP, which tracks and analyzes student performance in various physical education aspects, including in-class participation, extracurricular activities, and fitness tests. The TSO algorithm is employed to optimize the DBN, improving its ability to process complex datasets and avoid local optima. The model is trained and tested on a dataset collected from student activity on the Campus Running APP. Experimental results show that the TSO-DBN model outperforms traditional methods, such as DBN, GWO-DBN, and FTTA-DBN, in terms of evaluation accuracy and processing time. The TSO-DBN model achieves a root mean square error (RMSE) of 0.2-0.3, significantly lower than the comparison models. Additionally, it reaches an R² value of 0.98, indicating high prediction accuracy, and demonstrates the fastest evaluation time of 0.0025 seconds. These results underscore the model’s superior ability to provide accurate, real-time assessments. The integration of the TSO algorithm with the DBN significantly improves the precision, efficiency, and fairness of physical education evaluations. The model offers a comprehensive and objective system for assessing student performance, helping universities better monitor and promote student health and physical activity. This approach paves the way for future research and application of AI-based systems in educational environments.

@article{Yang2024,
  title     = {TSO Algorithm and DBN-Based Comprehensive Evaluation System for University Physical Education},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {10},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Yonghua Yang},
  doi       = {10.14569/IJACSA.2024.0151045},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151045}
}