VIDEO DATA FOR TRANSMISSION IN TV CHANNELS
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| Autori principali: | , |
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| Natura: | Recurso digital |
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Zenodo
2025
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| _version_ | 1866901189749112832 |
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| author | Norov, Elnur Tashmetov, Shaxzod |
| author_facet | Norov, Elnur Tashmetov, Shaxzod |
| contents | <p><span lang="EN-US">Intelligent analysis of video data encompasses a variety of techniques, including machine learning, deep learning, computer vision, and real-time processing. These methods are designed to automate the detection, categorization, and enhancement of video content, ensuring seamless transmission and high-quality viewing experiences. For instance, deep learning models, such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, are extensively used for content recognition and anomaly detection in video streams[1].</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15593135 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | VIDEO DATA FOR TRANSMISSION IN TV CHANNELS Norov, Elnur Tashmetov, Shaxzod <p><span lang="EN-US">Intelligent analysis of video data encompasses a variety of techniques, including machine learning, deep learning, computer vision, and real-time processing. These methods are designed to automate the detection, categorization, and enhancement of video content, ensuring seamless transmission and high-quality viewing experiences. For instance, deep learning models, such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, are extensively used for content recognition and anomaly detection in video streams[1].</span></p> |
| title | VIDEO DATA FOR TRANSMISSION IN TV CHANNELS |
| url | https://doi.org/10.5281/zenodo.15593135 |