{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T14:53:40Z","timestamp":1754146420951,"version":"3.41.2"},"reference-count":54,"publisher":"National Library of Serbia","issue":"3","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ComSIS","COMPUT SCI INF SYST","COMPUT SCI INFORM SY","COMPUTER SCI INFORM","COMSIS J"],"published-print":{"date-parts":[[2025]]},"abstract":"<jats:p>This work aims to optimize college football training using deep learning\n   techniques, addressing the inefficiencies, difficulty in action recognition,\n   and insufficient data analysis present in current training methods. An\n   intelligent optimization system combining Convolutional Neural Networks\n   (CNNs) and Recurrent Neural Networks (RNNs) is proposed to tackle these\n   challenges. Compared to traditional single models, the Convolutional Neural\n   Network-Recurrent Neural Network (CNN-RNN) architecture remarkably improves\n   the efficiency and accuracy of processing training data by leveraging the\n   strengths o spatial features and temporal sequence features. The\n   experimental results show  that CNN-RNN model is significantly superior to\n   the traditional 3D CNN model and other advanced models, such as Transformer,\n   Long Short-Term Memory (LSTM), Bidirectional LSTM and Gated Recurrent Unit\n   (GRU), in key indicators such as accuracy, precision, recall and F1 score.\n   Specifically, CNN-RNN model achieves 92.5% accuracy, 91.2% precision, 93.1%\n   recall and 92.1% F1 score. The lowest training loss rate is 0.24, which is\n   significantly better than other models. In addition, the introduced data\n   balance strategy effectively improves the prediction performance of a few\n   categories (such as foul and yellow card events) through oversampling,\n   undersampling and weighted loss function, and further enhances the\n   generalization ability and practicability of the model. Future research\n   focuses on expanding the dataset, further improving the model?s\n   generalization ability, and exploring its application in real training\n   scenarios.<\/jats:p>","DOI":"10.2298\/csis241121042l","type":"journal-article","created":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T02:50:59Z","timestamp":1747363859000},"page":"1139-1165","source":"Crossref","is-referenced-by-count":0,"title":["The analysis of deep learning-based football training under intelligent optimization technology"],"prefix":"10.2298","volume":"22","author":[{"given":"Kun","family":"Luan","sequence":"first","affiliation":[{"name":"Nanchang University College of Science and Technology, Gongqingcheng, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fan","family":"Wu","sequence":"additional","affiliation":[{"name":"Nanchang University College of Science and Technology, Gongqingcheng, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanyuan","family":"Xu","sequence":"additional","affiliation":[{"name":"Jiangxi University of Finance and Economics, Nanchang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1078","reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"Zhang, J., Oh, Y. 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