Table 1 Summary of relevant studies in the literature review.
From: The optimization of youth football training using deep learning and artificial intelligence
Scholar | Key findings | Related areas |
|---|---|---|
Qian, 2022 | The widespread application of AI across various domains in China has significantly bolstered the nation’s capabilities and improved the standard of living for its residents | Comprehensive AI applications |
Katipoğlu, 2023 | The development of AI can be delineated into five distinct phases, encompassing an analysis of AI’s feasibility and potential | AI development history |
Huang et al., 2021 | The proliferation of AI and intelligent terminals has transformed data into a new resource that can be harnessed to meet diverse needs | Data mining and AI |
Araz et al., 2020 | The significance of data information is underscored, and the development trends of data technologies are proposed | Big data technology |
Najafabadi et al., 2015 | Deep learning, hindered by data and computational power limitations, has not demonstrated a clear advantage over traditional machine learning | Deep learning |
Wani et al., 2022 | In image recognition competitions, deep learning notably outperforms traditional machine learning methods | Image recognition and Deep learning |
Li et al., 2022 | Improvements in soccer teaching quality have been achieved by utilizing 360-degree panoramic VR and AI-based K-means algorithms | Physical education and VR technology |
Zhang et al., 2021 | The successful identification of simultaneous movements of soccer athletes has been accomplished through the application of a multi-layer decision tree identifier | Athlete training and AI |
Zhou et al., 2022 | The construction of the CNNs-based action recognition system, in conjunction with AI and spatial flow networks, provides robust support for soccer training | Sports training and deep learning |
