A Fuzzy Ontology and SVM–Based Web Content Classification System

Ali, Farman; Khan, Pervez; Riaz, Kashif; Kwak, Daehan; Abuhmed, Tamer; Park, Daeyoung; Kwak, Kyung Sup

doi:10.1109/access.2017.2768564

Cited by 46 publications

(19 citation statements)

References 47 publications

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“…In our work, we aim to identify the intent to commit crimes in Twitter's posts. Ali et al [38] aimed to filter web content and to identify and block access to pornography. They were not concerned with the use of slang, which is recurrent in pornography.…”

Section: Discussion On Related Workmentioning

confidence: 99%

“…According to the authors, the use of FDO in conjunction with SVM increased the accuracy rates of review classification and of opinion mining. In another study, Ali et al [38] explored an SVM and fuzzy ontology-based semantic knowledge system to systematically filter web content and to identify and block access to pornography. Their work classifies URLs into adult URLs and medical URLs by using a blacklist of censored web pages.…”

Section: Semantic Web and Ontologiesmentioning

confidence: 99%

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A Framework for Detecting Intentions of Criminal Acts in Social Media: A Case Study on Twitter

et al. 2020

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Criminals use online social networks for various activities by including communication, planning, and execution of criminal acts. They often employ ciphered posts using slang expressions, which are restricted to specific groups. Although literature shows advances in analysis of posts in natural language messages, such as hate discourses, threats, and more notably in the sentiment analysis; research enabling intention analysis of posts using slang expressions is still underexplored. We propose a framework and construct software prototypes for the selection of social network posts with criminal slang expressions and automatic classification of these posts according to illocutionary classes. The developed framework explores computational ontologies and machine learning (ML) techniques. Our defined Ontology of Criminal Expressions represents crime concepts in a formal and flexible model, and associates them with criminal slang expressions. This ontology is used for selecting suspicious posts and decipher them. In our solution, the criminal intention in written posts is automatically classified relying on learned models from existing posts. This work carries out a case study to evaluate the framework with 8,835,290 tweets. The obtained results show its viability by demonstrating the benefits in deciphering posts and the effectiveness of detecting user’s intention in written criminal posts based on ML.

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Section: Discussion On Related Workmentioning

confidence: 99%

Section: Semantic Web and Ontologiesmentioning

confidence: 99%

A Framework for Detecting Intentions of Criminal Acts in Social Media: A Case Study on Twitter

et al. 2020

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“…While many studies address ensembles of weak classifiers in the RS ensemble, studies of strong classifiers are lacking. Research shows that the combination of strong classifiers with the RS algorithm, especially integration with an SVM [38], can improve the accuracy and reduce bias and the variance of classifiers [39][40][41]. In this paper, we select the SVM model as the base learner and use the RS algorithm to combine SVM classifiers.…”

Section: ) Base Learner Of Svm Modelmentioning

confidence: 99%

An Improved Selective Ensemble Learning Method for Highway Traffic Flow State Identification

et al. 2020

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“…For instance, some normal websites (underwear sales, medical websites, etc.) often display visual content similar to pornographic websites, leading to them being mistaken for pornographic websites [ 12 ]. Particularly, when the illegal websites conceal themselves via disguising, misleading, blocking, and bypassing, the traditional single-feature-detection methods may not be sufficient for website detection.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Pornographic and Gambling Websites Based on Visual and Textual Content Using a Decision Mechanism

Chen

Zheng

Zhou

et al. 2020

Sensors

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Pornographic and gambling websites become increasingly stubborn via disguising, misleading, blocking, and bypassing, which hinder the construction of a safe and healthy network environment. However, most traditional approaches conduct the detection process through a single aspect of these sites, which would fail to handle the more intricate and challenging situations. To alleviate this problem, this study proposed an automatic detection system for porn and gambling websites based on visual and textual content using a decision mechanism (PG-VTDM). This system can be applied to the intelligent wireless router at home or school to realize the identification, blocking, and warning of ill-suited websites. First, Doc2Vec was employed to learn the textual features that can be used to represent the textual content in the hypertext markup language (HTML) source code of the websites. In addition, the traditional bag-of-visual-words (BoVW) was improved by introducing local spatial relationships of feature points for better representing the visual features of the website screenshot. Then, based on these two types of features, a text classifier and an image classifier were both trained. In the decision mechanism, a data fusion algorithm based on logistic regression (LR) was designed to obtain the final prediction result by measuring the contribution of the two classification results to the final category prediction. The efficiency of this proposed approach was substantiated via comparison experiments using gambling and porn website datasets crawled from the Internet. The proposed approach outperformed the approach based on a single feature and some state-of-the-art approaches, with accuracy, precision, and F-measure all over 99%.

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A Fuzzy Ontology and SVM–Based Web Content Classification System

Cited by 46 publications

References 47 publications

A Framework for Detecting Intentions of Criminal Acts in Social Media: A Case Study on Twitter

A Framework for Detecting Intentions of Criminal Acts in Social Media: A Case Study on Twitter

An Improved Selective Ensemble Learning Method for Highway Traffic Flow State Identification

Automatic Detection of Pornographic and Gambling Websites Based on Visual and Textual Content Using a Decision Mechanism

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