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Employing video analysis in training offers substantial advantages, including the potential to train future dancers using innovative technologies. Over time, intricate dance gestures can be honed, reducing the burden on instructors who would, otherwise, need to provide repetitive demonstrations. Recognizing dancers\u2019 movements, evaluating and adjusting their gestures, and extracting cognitive functions for efficient evaluation and classification are pivotal aspects of our model. Deep learning currently stands as one of the most effective approaches for achieving these objectives, particularly with short video clips. However, limited research has focused on automated analysis of dance videos for training purposes and assisting instructors. In addition, assessing the quality and accuracy of performance video recordings presents a complex challenge, especially when judges cannot fully focus on the on\u2010stage performance. This paper proposes an alternative to manual evaluation through a video\u2010based approach for dance assessment. By utilizing short video clips, we conduct dance analysis employing techniques such as fine\u2010grained dance style classification in video frames, convolutional neural networks (CNNs) with channel attention mechanisms (CAMs), and autoencoders (AEs). These methods enable accurate evaluation and data gathering, leading to precise conclusions. Furthermore, utilizing cloud space for real\u2010time processing of video frames is essential for timely analysis of dance styles, enhancing the efficiency of information processing. Experimental results demonstrate the effectiveness of our evaluation method in terms of accuracy and F1\u2010score calculation, with accuracy exceeding 97.24% and the F1\u2010score reaching 97.30%. These findings corroborate the efficacy and precision of our approach in dance evaluation analysis.<\/jats:p>","DOI":"10.1155\/int\/6434673","type":"journal-article","created":{"date-parts":[[2025,4,21]],"date-time":"2025-04-21T21:19:28Z","timestamp":1745270368000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Fine\u2010Grained Dance Style Classification Using an Optimized Hybrid Convolutional Neural Network Architecture for Video Processing Over Multimedia Networks"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-7191-6310","authenticated-orcid":false,"given":"Na","family":"Guo","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3159-4330","authenticated-orcid":false,"given":"Ahong","family":"Yang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-1717-7578","authenticated-orcid":false,"given":"Yan","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2450-1942","authenticated-orcid":false,"given":"Elaheh","family":"Dastbaravardeh","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2025,4,21]]},"reference":[{"key":"e_1_2_10_1_2","volume-title":"Dance Imagery for Technique and Performance","author":"Franklin E. 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