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- W3085706036 abstract "In this paper, we present a fast and accurate method for the classification of web content. Our algorithm uses the visual information of the main homepage saved in an image format by means of a full body snapshot. Sliding windows of different sizes and overlaps are used to obtain a large subset of images for each render. For each sub-image, a feature vector is extracted by means of a pre-trained deep learning model. A Extreme Learning Machine (ELM) model is trained for different values of hidden neurons using the large collection of features from a curated dataset of 5979 webpages with different classes: adult, alcohol, dating, gambling, shopping, tobacco and weapons. Our results show that the ELM classifier can be trained without the manual specific object tagging of the sub-images by giving excellent results in comparison to more complex deep learning models. A random forest classifier was trained for the specific class of weapons providing an accuracy of 95% with a F1 score of 0.8." @default.
- W3085706036 created "2020-09-21" @default.
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- W3085706036 date "2020-09-12" @default.
- W3085706036 modified "2023-09-25" @default.
- W3085706036 title "Website Classification from Webpage Renders" @default.
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- W3085706036 doi "https://doi.org/10.1007/978-3-030-58989-9_5" @default.
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