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The exponential increase of information in Internet has raise the issue of information security. Pornography Web content is one of the biggest harmful resource that pollute the mind of children and teenagers. Several Web content based analysis approaches had been proposed to avoiding these illicit Web content accessing by the children. However implementation of each solution still remain as an issue. Most of the approaches are weak against classify the high similarity Web content such as pornography and gynecology Web pages. In this study, we try to solve this issue by propose a modified term weighting scheme which used as term feature selection technique for illicit Web page classification. We examine the performance of this proposed technique via three data sets which represent three critical scenarios and compare it with original term weighting scheme. Based on our observation, the proposed technique had shown its superiority for illicit Web pages classification which averagely achieve higher than 90\% accuracy rate. Meanwhile the experiment result also denote that the proposed technique had improve from original term weighting scheme. We hope that this study would give other researchers an insight especially who work in the similar area.

We analyze the language identification algorithms used to identify the Arabic script Web documents such as Arabic, Jawi, Persian and Urdu using independent component analysis (ICA). We have used a combination of Entropy term weighting scheme and class based feature (CPBF) vectors as feature selection methods for selecting the best features of Arabic script Web documents for Web page language identifications. Then we input the selected features based on the identification of latent semantics of user profiles using singular value decomposition (SVD). The SVD has been used to remove the noises on the documents retrieved before applying the ICA for topic extraction. We assume that the topic on each document is independent from each other. We have used the information retrieval measures that are precision, recall and F\ in order to evaluate the effectiveness of the proposed algorithm. From the experiments, we have found that the proposed method could leads to good Arabic script language identification results with good separations of Arabic, Persian, and Urdu languages using the ICA.