Advanced quantum based neural network classifier and its application for objectionable web content filtering

© 2013 IEEE. In this paper, an Advanced Quantum-based Neural Network Classifier (AQNN) is proposed. The proposed AQNN is used to form an objectionable Web content filtering system (OWF). The aim is to design a neural network with a few numbers of hidden layer neurons with the optimal connection weights and the threshold of neurons. The proposed algorithm uses the concept of quantum computing and genetic concept to evolve connection weights and the threshold of neurons. Quantum computing uses qubit as a probabilistic representation which is the smallest unit of information in the quantum computing concept. In this algorithm, a threshold boundary parameter is also introduced to find the optimal value of the threshold of neurons. The proposed algorithm forms neural network architecture which is used to form an objectionable Web content filtering system which detects objectionable Web request by the user. To judge the performance of the proposed AQNN, a total of 2000 (1000 objectionable + 1000 non-objectionable) Website's contents have been used. The results of AQNN are also compared with QNN-F and well-known classifiers as backpropagation, support vector machine (SVM), multilayer perceptron, decision tree algorithm, and artificial neural network. The results show that the AQNN as classifier performs better than existing classifiers. The performance of the proposed objectionable Web content filtering system (OWF) is also compared with well-known objectionable Web filtering software and existing models. It is found that the proposed OWF performs better than existing solutions in terms of filtering objectionable content

Horror image recognition based on context-aware multi-instance learning

Horror content sharing on the Web is a growing phenomenon that can interfere with our daily life and affect the mental health of those involved. As an important form of expression, horror images have their own characteristics that can evoke extreme emotions. In this paper, we present a novel context-aware multi-instance learning (CMIL) algorithm for horror image recognition. The CMIL algorithm identifies horror images and picks out the regions that cause the sensation of horror in these horror images. It obtains contextual cues among adjacent regions in an image using a random walk on a contextual graph. Borrowing the strength of the Fuzzy Support Vector Machine (FSVM), we define a heuristic optimization procedure based on the FSVM to search for the optimal classifier for the CMIL. To improve the initialization of the CMIL, we propose a novel visual saliency model based on tensor analysis. The average saliency value of each segmented region is set as its initial fuzzy membership in the CMIL. The advantage of the tensor-based visual saliency model is that it not only adaptively selects features, but also dynamically determines fusion weights for saliency value combination from different feature subspaces. The effectiveness of the proposed CMIL model is demonstrated by its use in horror image recognition on two large scale image sets collected from the Internet

Distinguishing Medical Web Pages from Pornographic Ones: An Efficient Pornography Websites Filtering Method

Abstract In this paper, we apply the uncomplicated decision tree data mining algorithm to find association rules about pornographic and medical web pages. On the basis of these association rules, we propose a systematized method of filtering pornographic websites with the following major superiorities: 1) Check only contexts of web pages without scanning pictures to avoid the low operating efficiency in analyzing photographs. Moreover, the error rate is lowered and the accuracy of filtering is enhanced simultaneously. 2) While filtering the pornographic web pages accurately, the misjudgments of identifying medical web pages as pornographic ones will be reduced effectively. 3) A re-learning mechanism is designed to improve our filtering method incrementally. Therefore, the revision information learned from the misjudged web pages can incrementally give feedback to our method and improve its effectiveness. The experimental results showed that each efficacy assessment indexes reached a satisfactory value. Therefore, we can conclude that the proposed method is possessed of outstanding performance and effectivity

An investigation into the efficacy of URL content filtering systems

Content filters are used to restrict to restrict minors from accessing to online content deemed inappropriate. While much research and evaluation has been done on the efficiency of content filters, there is little in the way of empirical research as to their efficacy. The accessing of inappropriate material by minors, and the role content filtering systems can play in preventing the accessing of inappropriate material, is largely assumed with little or no evidence. This thesis investigates if a content filter implemented with the stated aim of restricting specific Internet content from high school students achieved the goal of stopping students from accessing the identified material. The case is of a high school in Western Australia where the logs of a proxy content filter that included all Internet traffic requested by students were examined to determine the efficacy of the content filter. Using text extraction and pattern matching techniques to look for evidence of access to restricted content within this study, the results demonstrate that the belief that content filtering systems reliably prevent access to restricted content is misplaced. in this study there is direct evidence of circumvention of the content filter. This is single case study in one school and as such, the results are not generalisable to all schools or even through subsequent systems that replaced the content filter examined in this study, but it does raise the issue of the ability of these content filter systems to restrict content from high school students. Further studies across multiple schools and more complex circumvention methods would be required to identify if circumvention of content filters is a widespread issue