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Full-Text Articles in Computer Sciences
Automatically Extract Information From Web Documents, Dipesh Sharma
Automatically Extract Information From Web Documents, Dipesh Sharma
Masters Theses & Specialist Projects
The Internet could be considered to be a reservoir of useful information in textual form — product catalogs, airline schedules, stock market quotations, weather forecast etc. There has been much interest in building systems that gather such information on a user's behalf. But because these information resources are formatted differently, mechanically extracting their content is difficult. Systems using such resources typically use hand-coded wrappers, customized procedures for information extraction. Structured data objects are a very important type of information on the Web. Such data objects are often records from underlying databases and displayed in Web pages with some fixed templates. …
Predicting Coronary Artery Disease With Medical Profile And Gene Polymorphisms Data, Qiongyu Chen, Guoliang Li, Tze-Yun Leong, Chew-Kiat Heng
Predicting Coronary Artery Disease With Medical Profile And Gene Polymorphisms Data, Qiongyu Chen, Guoliang Li, Tze-Yun Leong, Chew-Kiat Heng
Research Collection School Of Computing and Information Systems
Coronary artery disease (CAD) is a main cause of death in the world. Finding cost-effective methods to predict CAD is a major challenge in public health. In this paper, we investigate the combined effects of genetic polymorphisms and non-genetic factors on predicting the risk of CAD by applying well known classification methods, such as Bayesian networks, naïve Bayes, support vector machine, k-nearest neighbor, neural networks and decision trees. Our experiments show that all these classifiers are comparable in terms of accuracy, while Bayesian networks have the additional advantage of being able to provide insights into the relationships among the variables. …
Multi-Class Classification Averaging Fusion For Detecting Steganography, Benjamin M. Rodriguez, Gilbert L. Peterson, Sos S. Agaian
Multi-Class Classification Averaging Fusion For Detecting Steganography, Benjamin M. Rodriguez, Gilbert L. Peterson, Sos S. Agaian
Faculty Publications
Multiple classifier fusion has the capability of increasing classification accuracy over individual classifier systems. This paper focuses on the development of a multi-class classification fusion based on weighted averaging of posterior class probabilities. This fusion system is applied to the steganography fingerprint domain, in which the classifier identifies the statistical patterns in an image which distinguish one steganography algorithm from another. Specifically we focus on algorithms in which jpeg images provide the cover in order to communicate covertly. The embedding methods targeted are F5, JSteg, Model Based, OutGuess, and StegHide. The developed multi-class steganalvsis system consists of three levels: (1) …