Web page classification using convolutional neural network (CNN) towards eliminating internet addiction

Siti Hawa, Apandi and Jamaludin, Sallim and Rozlina, Mohamed and Araby, Madbouly (2021) Web page classification using convolutional neural network (CNN) towards eliminating internet addiction. In: IEEE International Conference on Software Engineering Computer Systems and 4th International Conference on Computational Science and Information Management (ICSECS-ICOCSIM) 2021 , 24-26 Ogos 2021 , Pekan, Pahang, Malaysia. pp. 1-6.. ISBN 978-1-6654-1407-4

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Abstract

In the modern world, everyone has access to the internet as a source of information by surfing the web pages. The most popular web page surf is on Game and Online Video Streaming. Users who are spending too much time on these kinds of web pages may lead to a negative impact on Internet addiction. To overcome the internet addiction problem, access to Game and Online Video Streaming web pages needs to be restricted. Thus, a mechanism that can classify the category of the incoming web page based on the web page content is needed. This paper is proposing a web page classification model using a Convolutional Neural Network (CNN) to classify the web page, then identify whether it is a Game or Online Video Streaming based on the pattern of words in the word cloud image taken from the web page text content. The proposed web page classification model has achieved 82.22 % accuracy to detect the pre-classifled web pages.

Item Type:	Conference or Workshop Item (Lecture)
Uncontrolled Keywords:	Visualization; Scientific computing; Computational modeling; Web pages; Games; Streaming media;Predictive models
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Faculty/Division:	Institute of Postgraduate Studies Faculty of Computing
Depositing User:	Pn. Hazlinda Abd Rahman
Date Deposited:	18 Mar 2022 04:10
Last Modified:	18 Mar 2022 04:10
URI:	http://umpir.ump.edu.my/id/eprint/33378
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