Simulation of pornography web sites (PWS) classification using principal component analysis with neural network
The explosive growth of objectionable web content such as pornography, terrorist and violence had been a serious threat for internet users especially children. Recently content analysis based filtering is being introduced to overcome this problem. In term of the promising result to satisfy the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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United Kingdom Simulation Society
2008
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| Subjects: | |
| Online Access: | http://eprints.utm.my/8597/ http://eprints.utm.my/8597/3/ZhiSamLee2008_SimulationofPornographyWebSitesClassification.pdf |
| _version_ | 1848891723367841792 |
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| author | Zhi, Sam Lee Maarof, Mohd. Zaini Selamat, Ali Shamsuddin, Siti Mariyam |
| author_facet | Zhi, Sam Lee Maarof, Mohd. Zaini Selamat, Ali Shamsuddin, Siti Mariyam |
| author_sort | Zhi, Sam Lee |
| building | UTeM Institutional Repository |
| collection | Online Access |
| description | The explosive growth of objectionable web content such
as pornography, terrorist and violence had been a serious
threat for internet users especially children. Recently
content analysis based filtering is being introduced to
overcome this problem. In term of the promising result to
satisfy the result of web content analysis, features
extraction techniques play an important role to extract
appropriate features from large volume of web information such as text, image, audio, video etc. In this paper we propose a model of pornography web site classification which mainly based on textual contentbased analysis such as indicative keywords detection. This paper will show that implementation of principal component analysis in back-propagate neural network is capable to classify high similarity illicit web content sufficiently. In this study, we introduce three techniques to implement our Pornography Web Site Classification Model (PWSCM) such as PWSCM with principal component analysis (PWSCM-PCA), PWSCM with only
CPBF (PWSCM-CPBF) and PWSCM with integration of CPBF and PCA (PWSCM-CPBF-PCA). We compare the performance of these three techniques by conducting several simulation experiments. From the experiment results, we have found that the proposed model with three different techniques capable to perform efficient identification for illicit web content. Hence this paper will discuss the simulation results of the model with three techniques. |
| first_indexed | 2025-11-15T21:02:30Z |
| format | Article |
| id | utm-8597 |
| institution | Universiti Teknologi Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T21:02:30Z |
| publishDate | 2008 |
| publisher | United Kingdom Simulation Society |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utm-85972017-02-21T07:39:01Z http://eprints.utm.my/8597/ Simulation of pornography web sites (PWS) classification using principal component analysis with neural network Zhi, Sam Lee Maarof, Mohd. Zaini Selamat, Ali Shamsuddin, Siti Mariyam QA76 Computer software The explosive growth of objectionable web content such as pornography, terrorist and violence had been a serious threat for internet users especially children. Recently content analysis based filtering is being introduced to overcome this problem. In term of the promising result to satisfy the result of web content analysis, features extraction techniques play an important role to extract appropriate features from large volume of web information such as text, image, audio, video etc. In this paper we propose a model of pornography web site classification which mainly based on textual contentbased analysis such as indicative keywords detection. This paper will show that implementation of principal component analysis in back-propagate neural network is capable to classify high similarity illicit web content sufficiently. In this study, we introduce three techniques to implement our Pornography Web Site Classification Model (PWSCM) such as PWSCM with principal component analysis (PWSCM-PCA), PWSCM with only CPBF (PWSCM-CPBF) and PWSCM with integration of CPBF and PCA (PWSCM-CPBF-PCA). We compare the performance of these three techniques by conducting several simulation experiments. From the experiment results, we have found that the proposed model with three different techniques capable to perform efficient identification for illicit web content. Hence this paper will discuss the simulation results of the model with three techniques. United Kingdom Simulation Society 2008-05 Article PeerReviewed application/pdf en http://eprints.utm.my/8597/3/ZhiSamLee2008_SimulationofPornographyWebSitesClassification.pdf Zhi, Sam Lee and Maarof, Mohd. Zaini and Selamat, Ali and Shamsuddin, Siti Mariyam (2008) Simulation of pornography web sites (PWS) classification using principal component analysis with neural network. International Journal of Simulation System, Science and Technology, 9 (2). pp. 43-45. ISSN 1473-804X (online), 1473-8031 (print) http://uk.geocities.com/david.aldabass@btinternet.com/IJSSST/Vol-9/No-2/cover.htm |
| spellingShingle | QA76 Computer software Zhi, Sam Lee Maarof, Mohd. Zaini Selamat, Ali Shamsuddin, Siti Mariyam Simulation of pornography web sites (PWS) classification using principal component analysis with neural network |
| title | Simulation of pornography web sites (PWS) classification using principal component analysis with neural network |
| title_full | Simulation of pornography web sites (PWS) classification using principal component analysis with neural network |
| title_fullStr | Simulation of pornography web sites (PWS) classification using principal component analysis with neural network |
| title_full_unstemmed | Simulation of pornography web sites (PWS) classification using principal component analysis with neural network |
| title_short | Simulation of pornography web sites (PWS) classification using principal component analysis with neural network |
| title_sort | simulation of pornography web sites (pws) classification using principal component analysis with neural network |
| topic | QA76 Computer software |
| url | http://eprints.utm.my/8597/ http://eprints.utm.my/8597/ http://eprints.utm.my/8597/3/ZhiSamLee2008_SimulationofPornographyWebSitesClassification.pdf |