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Intelligent unsupervised learning method of physical education image resources based on genetic algorithm

  • S.I.: Artificial Intelligence Technologies in Sports and Art Data Applications
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Abstract

In order to improve the effect of intelligent processing of physical education resources, this paper combines genetic algorithms and unsupervised learning methods to study the processing of physical education videos and images, which approximately maintains the neighbor structure relationship of the original sample data and reduces the loss of sample local information. Moreover, this paper constructs a graph model structure between the comprehensive compressed data and the input data. In order to reduce the complexity of the solution method of the graph model and reduce the impact of the method of decomposing the spectrogram into the characteristic function on the hash algorithm, this paper designs an intelligent physical education resource processing method based on genetic algorithms and unsupervised learning methods and constructs system functional modules. Finally, this paper designs experiments to verify the performance of the method proposed in this paper. Through comparative analysis of experiments, it can be known that the method proposed in this paper has good results.

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Authors and Affiliations

  1. Department of Leisure Sports, Paichai University, Daejeon, 302735, Republic of Korea

    Chengbao Li & Kitak Kim

  2. Department of Physical Education, Bengbu College of Technology and Business, Bengbu, 233000, China

    Bowen Liu

Authors
  1. Chengbao Li
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  2. Bowen Liu
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  3. Kitak Kim
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Correspondence to Chengbao Li or Kitak Kim.

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The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

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Li, C., Liu, B. & Kim, K. Intelligent unsupervised learning method of physical education image resources based on genetic algorithm. Neural Comput & Applic 35, 4225–4242 (2023). https://doi.org/10.1007/s00521-022-07021-x

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  • DOI: https://doi.org/10.1007/s00521-022-07021-x

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