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Phishing is one of the serious web threats that involves mimicking authenticated websites to deceive users in order to obtain their financial information. Phishing has caused financial damage to the different online stakeholders. It is massive in the magnitude of hundreds of millions; hence it is essential to minimize this risk. Classifying websites into “phishy” and legitimate types is a primary task in data mining that security experts and decision makers are hoping to improve particularly with respect to the detection rate and reliability of the results. One way to ensure the reliability of the results and to enhance performance is to identify a set of related features early on so the data dimensionality reduces and irrelevant features are discarded. To increase reliability of preprocessing, this article proposes a new feature selection method that combines the scores of multiple known methods to minimize discrepancies in feature selection results. The proposed method has been applied to the problem of website phishing classification to show its pros and cons in identifying relevant features. Results against a security dataset reveal that the proposed preprocessing method was able to derive new features datasets which when mined generate high competitive classifiers with reference to detection rate when compared to results obtained from other features selection methods.

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This paper deals with the challenging task of computing accurate contours from CT and MRI scans using B-spline curve approximation. To date bio-modeling and visualization have been performed primarily on voxel and facet (triangle) based models. On the other hand, traditional CAD has reached a level of sophistication where just about any object can be designed, prototyped and manufactured using well-refined CAD modeling and manufacturing tools. NURBS, the de facto standard to represent geometry in CAD systems, have been the building blocks of CAD modeling and will be used in this paper to perform a critical task for bio-fabrication of human components. Although at first glance it may seem that contour fitting is a trivial task, the details presented in this paper reveal that traditional techniques are not readily adaptable to medical data and several fundamental algorithms had to be completely rewritten to account for the characteristics of human organs. The emphasis of this research is placed on accuracy and shape fidelity (precise surgical operation), speed (real-time simulation), smoothness (the discrete volumetric or faceted data should be replaced by smooth curves), and data reduction (the large amount of image data must be reduced by at least 60-80%).