Open Access

Esearch on the optimization path of campus football teaching based on deep learning mode

   | Jul 29, 2023

Cite

Mankar, S. R., Shenvi, D. N., Hegde, G. R., et al. (2015). Promotion of Stair Climbing Exercise in Medical Students to Achieve Physical Fitness with Student’s Normal Routine. -[J], 5(9), 254-259.Search in Google Scholar

Jia, G., Jia, L., & Chunhsien, S. U. (2016). The analysis of the impact on enhancing physical health from the aspect of Chinese traditional Health Qigong Wuqinxi, 52-52.Search in Google Scholar

Vesile, Yildiz, Kabak, et al. (2018). Short and long-term impairments of cardiopulmonary fitness level in previous childhood cancer cases: a systematic review. Supportive Care in Cancer Official Journal of the Multinational Association of Supportive Care in Cancer, 27(1), 69-86.Search in Google Scholar

Wu, D., Shen, H., & Lv, Z. (2020). An artificial intelligence and multimedia teaching platform based integration path of IPE and IEE in colleges and universities1. Journal of Intelligent and Fuzzy Systems, 40(115), 1-10.Search in Google Scholar

Liu, R. (2021). Design of Ideological and Political Multimedia Network Teaching Resources Integration System Based on Wireless Network. Scientific Programming, 1-15.Search in Google Scholar

Du, X. (2020). Application of deep learning and artificial intelligence algorithm in multimedia music teaching. Journal of Intelligent and Fuzzy Systems, 38(2), 1-11.Search in Google Scholar

Su, Y., Chen, G., Li, M., et al. (2021). Design and Implementation of Web Multimedia Teaching Evaluation System Based on Artificial Intelligence and jQuery. Mobile Information Systems, 2021, 1-11.Search in Google Scholar

Karine, S., Klein, Aline, et al. (2018). Quality of life and levels of physical activity of residents living in therapeutic residential care facilities in Southern Brazil. Ciencia & saude coletiva, 23, 1521-1530.Search in Google Scholar

Agbuga, B., Xiang, P., Mcbride, R. E., et al. (2015). Student Perceptions of Instructional Choices in Middle School Physical Education. Journal of Teaching in Physical Education, 35(2), 138-148.Search in Google Scholar

Pedro, Antonio, Delgado, et al. (2018). Perception of obese schoolchildren regarding their participation in the Physical Education class and their level of self-esteem: comparison according to corporal status. Nutricion Hospitalaria, 1270-1274.Search in Google Scholar

Schmidhuber, J. (2015). Deep learning in neural networks. Neural Netw, 61, 85-117.Search in Google Scholar

Cecilia, S., Lee, et al. (2017). Deep Learning Is Effective for Classifying Normal versus Age-Related Macular Degeneration OCT Images. Ophthalmology Retina, 1(4), 322-327.Search in Google Scholar

O’Shea, T., & Hoydis, J. (2017). An Introduction to Deep Learning for the Physical Layer. IEEE Transactions on Cognitive Communications and Networking, 3(4), 563-575.Search in Google Scholar

Pan, X., & Shen, H. B. (2017). Additional file 1 of RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach. 18(1), 1-14.Search in Google Scholar

F, Wang, Z, et al. (2015). Weakly Semi-Supervised Deep Learning for Multi-Label Image Annotation. Big Data, IEEE Transactions on, 1(3), 109-122.Search in Google Scholar

Dezs?, Ribli, Anna, et al. (2018). Detecting and classifying lesions in mammograms with Deep Learning. Scientific reports, 8(1), 1-7.Search in Google Scholar

Jie Y, Rui Y, Mn A, et al. (2021). PM 2.5 concentrations forecasting in Beijing through deep learning with different inputs, model structures, and forecast time. Atmospheric Pollution Research, 12(9), 101168.Search in Google Scholar

Rogério, Cruz, de, et al. (2018). [Physical and meditative practices: Physical Education on the sidelines?]. Cadernos De Saude Publica, 34(7), e00074718-e00074718.Search in Google Scholar

Popeska B, Jovanovamitkovska S, Sivevska D. (2017). Implementation of technology in physical education teaching process based on teachers’ experiences. 12-107.Search in Google Scholar

Xu Z, Sun J, Xi’an International Academy for Mathematics & Mathematical Technology, et al. (2018). Model-driven deep-learning. National Science Review, 5(1), 22-24.Search in Google Scholar

Sampath, A. K., Gomathi, D. N., Engineering, R., et al. (2017). Decision tree and deep learning based probabilistic model for character recognition. Journal of Central South University, 24(12), 2862-2876.Search in Google Scholar

Sagun, L., Bottou, L., & Lecun, Y. (2016). Eigenvalues of the Hessian in Deep Learning: Singularity and Beyond, 1-8.Search in Google Scholar

Yang, R., Luo, F., Ren, F., et al. (2022). Identifying Urban Wetlands through Remote Sensing Scene Classification Using Deep Learning: A Case Study of Shenzhen, China. ISPRS International Journal of Geo-Information, 11(2), 131.Search in Google Scholar

Li, H., Technologies, N. (2018). Deep learning for natural language processing: advantages and challenges. National Science Review, 24-26.Search in Google Scholar

Wu, B., Zhang, B., Deng, C., et al. (2022). Physics-encoded deep learning in identifying battery parameters without direct knowledge of ground truth. Applied Energy, 321, 119390.Search in Google Scholar

Hesham, Mostafa, Vishwajith, et al. (2018). Deep Supervised Learning Using Local Errors. Frontiers in neuroscience, 12, 608.Search in Google Scholar

Röhr, A. (2016). CHAIN LADDER AND ERROR PROPAGATION. ASTIN Bulletin, 46(2), 293-330.Search in Google Scholar

eISSN:
2444-8656
Language:
English
Publication timeframe:
Volume Open
Journal Subjects:
Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics