Classification of illicit web pages using neural network

The illicit web contents such as pornography, violence, gambling, etc, have greatly polluted the mind of web users especially children and teenagers. Due to some popular web filtering techniques like Uniform Resource Locator (URL) blocking and Platform for Internet Content Selection (PICS) checking...

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Main Authors: Lee, Zhi Sam, Aizani, Mohd., Selamat, Ali, Shamsuddin, Siti Mariyam
Format: Article
Language:English
Published: Penerbit UTM Press 2007
Subjects:
Online Access:http://eprints.utm.my/8178/1/MohdAizainiMaarof2007_CLassificationofIllicitWebPagesUsingNeural.pdf
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author Lee, Zhi Sam
Aizani, Mohd.
Selamat, Ali
Shamsuddin, Siti Mariyam
author_facet Lee, Zhi Sam
Aizani, Mohd.
Selamat, Ali
Shamsuddin, Siti Mariyam
author_sort Lee, Zhi Sam
collection ePrints
description The illicit web contents such as pornography, violence, gambling, etc, have greatly polluted the mind of web users especially children and teenagers. Due to some popular web filtering techniques like Uniform Resource Locator (URL) blocking and Platform for Internet Content Selection (PICS) checking are limited against today dynamic web content, hence content based analysis techniques with effective model are highly desired In this paper we propose textual content analysis model using entropy term weighting scheme to classify pornography and sex education web pages. We examine the entropy scheme with two other common term weighting schemes which are TFIDF and Glasgow. Those techniques are examined extensively with artificial neural network using small class dataset. In this study, we found that our proposed model archive better performance from the aspects of accuracy, convergence speed and stability.
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spelling utm.eprints-81782017-11-01T04:17:25Z http://eprints.utm.my/8178/ Classification of illicit web pages using neural network Lee, Zhi Sam Aizani, Mohd. Selamat, Ali Shamsuddin, Siti Mariyam ZA4050 Electronic information resources ZA4450 Databases The illicit web contents such as pornography, violence, gambling, etc, have greatly polluted the mind of web users especially children and teenagers. Due to some popular web filtering techniques like Uniform Resource Locator (URL) blocking and Platform for Internet Content Selection (PICS) checking are limited against today dynamic web content, hence content based analysis techniques with effective model are highly desired In this paper we propose textual content analysis model using entropy term weighting scheme to classify pornography and sex education web pages. We examine the entropy scheme with two other common term weighting schemes which are TFIDF and Glasgow. Those techniques are examined extensively with artificial neural network using small class dataset. In this study, we found that our proposed model archive better performance from the aspects of accuracy, convergence speed and stability. Penerbit UTM Press 2007-12 Article PeerReviewed application/pdf en http://eprints.utm.my/8178/1/MohdAizainiMaarof2007_CLassificationofIllicitWebPagesUsingNeural.pdf Lee, Zhi Sam and Aizani, Mohd. and Selamat, Ali and Shamsuddin, Siti Mariyam (2007) Classification of illicit web pages using neural network. Jurnal Teknologi Maklumat, 19 (2). pp. 1-21. ISSN 0128-3790
spellingShingle ZA4050 Electronic information resources
ZA4450 Databases
Lee, Zhi Sam
Aizani, Mohd.
Selamat, Ali
Shamsuddin, Siti Mariyam
Classification of illicit web pages using neural network
title Classification of illicit web pages using neural network
title_full Classification of illicit web pages using neural network
title_fullStr Classification of illicit web pages using neural network
title_full_unstemmed Classification of illicit web pages using neural network
title_short Classification of illicit web pages using neural network
title_sort classification of illicit web pages using neural network
topic ZA4050 Electronic information resources
ZA4450 Databases
url http://eprints.utm.my/8178/1/MohdAizainiMaarof2007_CLassificationofIllicitWebPagesUsingNeural.pdf
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AT selamatali classificationofillicitwebpagesusingneuralnetwork
AT shamsuddinsitimariyam classificationofillicitwebpagesusingneuralnetwork