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- W2981104879 abstract "The Gen Z kids highly rely on internet for various purposes like entertainment, sports, and school projects. There is a demand for parental control systems to monitor the children during their surfing time. Current web page classification approaches are not effective as handcrafted features are extracted from the web content and machine learning techniques are used that need domain knowledge. Hence, a deep learning approach is proposed to perform URL-based web page classification. As the URL is a short text, the model should learn to understand where the important information is present in the URL. The proposed system integrates the strength of attention mechanism with recurrent convolutional neural network for effective learning of context-aware URL features. This enhanced architecture improves the design of kids-relevant URL classification. By conducting various experiments on the benchmark collection Open Directory Project, it is shown that an accuracy of 0.8251 was achieved." @default.
- W2981104879 created "2019-10-25" @default.
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- W2981104879 date "2020-01-01" @default.
- W2981104879 modified "2023-10-17" @default.
- W2981104879 title "Bidirectional GRU-Based Attention Model for Kid-Specific URL Classification" @default.
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- W2981104879 doi "https://doi.org/10.4018/978-1-7998-1192-3.ch005" @default.
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