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Jayne Goh 吴笠绮
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Document segmentation is an essential requirement for automatic transformation of paper documents into electronic documents. However, some restrictions such as variations in character font sizes, different text line spacing, and also non-uniform document layout structures altogether makes designing a generalpurpose document layout analysis algorithm much more sophisticated. Thus in most previously reported methods these parameters are inevitably included. This mentioned issue becomes much more acute and excessively severe, especially in Persian/Arabic documents. Since the Persian/Arabic scripts differ considerably from the English scripts, most of the proposed methods for the English scripts do not render good results for the Persian/Arabic scripts. In this paper, we present a novel parameter-free method based on hybrid method for segmenting the Persian/Arabic document images which also works well for English scripts. The proposed method is capable of document segmentation without considering the character font sizes, text line spacing, document skew, and document layout structures. This algorithm is examined for 150 Persian/Arabic and English documents and document segmentation process are done successfully for 97.3 percent of them at worst condition.
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With the increasing concern for security in the network, many approaches are laid out that try to protect the network from unauthorised access. New methods have been adopted in order to find the potential discrepancies that may damage the network. Most commonly used approach is the vulnerability assessment. By vulnerability, we mean, the potential flaws in the system that make it prone to the attack. Assessment of these system vulnerabilities provide a means to identify and develop new strategies so as to protect the system from the risk of being damaged. This paper focuses on the usage of various vulnerability scanners and their related methodology to detect the various vulnerabilities available in the web applications or the remote host across the network and tries to identify new mechanisms that can be deployed to secure the network.
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As the information proliferates, searching for relevant information has become a primary task. Searching or Information retrieval (IR) aims to help the users in organising as well as retrieving those documents from the documentary collection which are most likely to satisfy information needs of the user. An optimal Information Retrieval System (IRS) is one which retrieves only those documents from the document database which are pertinent to user's information needs, while excluding documents that are not relevant. Genetic Algorithm is described by higher likelihood of finding good solutions to large and complex problems of IR optimisation. The performance of Genetic Algorithm depends upon the decision of underlying operators used namely selection, crossover and mutation. A GA-based algorithm IRIGA (Information Retrieval Improvement using Genetic Algorithm) is developed to improve the performance of Information Retrieval System. This paper presents a comparison of performance of IRIGA when different selection methods are used. The results are analysed by conducting experiments keeping the rest of the GA parameters as constant and varying only the selection strategy.
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As the information proliferates, searching for relevant information has become a primary task. Searching or Information retrieval (IR) aims to help the users in organising as well as retrieving those documents from the documentary collection which are most likely to satisfy information needs of the user. An optimal Information Retrieval System (IRS) is one which retrieves only those documents from the document database which are pertinent to user's information needs, while excluding documents that are not relevant. Genetic Algorithm is described by higher likelihood of finding good solutions to large and complex problems of IR optimisation. The performance of Genetic Algorithm depends upon the decision of underlying operators used namely selection, crossover and mutation. A GA-based algorithm IRIGA (Information Retrieval Improvement using Genetic Algorithm) is developed to improve the performance of Information Retrieval System. This paper presents a comparison of performance of IRIGA when different selection methods are used. The results are analysed by conducting experiments keeping the rest of the GA parameters as constant and varying only the selection strategy.
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