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YouTube's growth has led to the proliferation of inappropriate content, notably cartoons for children. To address this, a novel deep learning architecture integrates an EfficientNet-B7 CNN and BiLSTM network for real-time video filtering. The model achieves 95.66% accuracy, outperforming traditional classifiers and an attention-based variant. Deep learning excels in capturing intricate patterns, as evident in the architecture's F1 score of 0.9267. Comparative analysis validates BiLSTM's efficacy atop CNN for contextual information extraction, enhancing the detection of inappropriate content. This highlights the necessity of advanced technological solutions in combating harmful online material.
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Cite Article:
"A Deep Learning-Based Approach for Inappropriate Content Detection and Classification of YouTube Videos", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 4, page no.611 - 619, April-2024, Available :http://www.ijrti.org/papers/IJRTI2404086.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator