A Convolutional Neural Network (CNN) Classification Model for Web Page: A Tool for Improving Web Page Category Detection Accuracy
Game and Online Video Streaming are the most viewed web pages. Users who spend too much time on these types of web pages may suffer from internet addiction. Access to Game and Online Video Streaming web pages should be restricted to combat internet addiction. A tool is required to recognise the cate...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Department of Information Technology - Politeknik Negeri Padang
2023
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/40032/1/A%20Convolutional%20Neural%20Network%20%28CNN%29%20Classification%20Model.pdf http://umpir.ump.edu.my/id/eprint/40032/ https://doi.org/10.30630/jitsi.4.3.181 https://doi.org/10.30630/jitsi.4.3.181 |
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| Summary: | Game and Online Video Streaming are the most viewed web pages. Users who spend too much time on these types of web pages may suffer from internet addiction. Access to Game and Online Video Streaming web pages should be restricted to combat internet addiction. A tool is required to recognise the category of web pages based on the text content of the web pages. Due to the unavailability of a matrix representation that can handle long web page text content, this study employs a document representation known as word cloud image to visualise the words extracted from the text content web page after data pre-processing. The most popular words are shown in large size and appear in the centre of the word cloud image. The most common words are the words that appear frequently in the text content web page and are related to describing what the web page content is about. The Convolutional Neural Network (CNN) recognises the pattern of words presented in the core portions of the word cloud image to categorise the category to which the web page belongs. The proposed model for web page classification has been compared with the other web page classification models. It shows the good result that achieved an accuracy of 85.6%. It can be used as a tool that helps to make identifying the category of web pages more accurate |
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