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Design of Kids-specific URL Classifier using Recurrent Convolutional Neural Network
, H. Tiwari, J. Patel, A. Kumar,
Published in Elsevier B.V.
Volume: 167
Pages: 2124 - 2131
The use of digital devices has increased exponentially in the last decade. Especially, young children spend most of their time surfing for various reasons such as homework. assignments and projects etc. Parental Control is highly important for monitoring the browsing behavior of children. Though several content-based web page classification approaches are available, it requires the entire web page contents for classification purpose. This leads to wastage of bandwidth due to unnecessary downloads. The exponential growth of internet demands URL based classifiers to adapt to the dynamic web, and to make swift decisions on the fly. To address this problem, a deep learning based approach has been proposed in this research that can extract the features only from the URL of a web page. To learn the patterns for Kids-specific web sites automatically, Convolutional Neural Network (CNN) is combined with Bidirectional Gated Recurrent Unit (BGRU) to extract rich context aware features as well as to preserve the sequence information in the URL. By conducting various experiments on the benchmark collection Open Directory Project (ODP), it is shown that an accuracy of 82.04% can be achieved. © 2020 The Authors. Published by Elsevier B.V.
About the journal
JournalData powered by TypesetProcedia Computer Science
PublisherData powered by TypesetElsevier B.V.