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Optimization of Tennis Teaching Resources and Data Visualization Based on Support Vector Machine.

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Machine learning in tennis is advancing beyond simple analysis. This study introduces a new model optimizing network data for faster loading and reduced energy consumption, enhancing visual tennis teaching tools.

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Area of Science:

  • Sports Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Machine learning (ML) has shown success in various fields.
  • Existing ML applications in tennis are limited to basic analysis.
  • There's a need for ML to enable complex tennis movements and new training methods.

Purpose of the Study:

  • To develop a novel data search model using ML for tennis.
  • To optimize network data processing for improved efficiency.
  • To enhance visual teaching tools for tennis defense techniques.

Main Methods:

  • Constructed a data search model by optimizing network data.
  • Downloaded extensive data from network RAM to mitigate environmental impacts.
  • Simulated the model's performance in different network conditions (3G, WiFi, 4G).

Main Results:

  • The model significantly reduced load times and energy consumption in high-quality 3G networks.
  • Demonstrated enhanced efficiency in WiFi and high-quality 4G network environments.
  • The optimized model is suitable for improving the performance of visual tennis teaching tools.

Conclusions:

  • ML offers promising prospects for advanced fuzzy research in sports.
  • The developed model effectively optimizes data processing for tennis-related applications.
  • This research contributes to more efficient and effective visual tennis coaching systems.