In this paper a new kind of splines, called quadratic non‐uniform algebraic trigonometric B‐splines (quadratic NUAT B‐splines) with multiple shape parameters are constructed over the space spanned by { }. As each piece of the curves is generated by three consecutive control points, they possess many properties of the quadratic B‐spline curves and quadratic trigonometric B‐spline curves. These curves have continuity with non‐uniform knot vector. These curves are closer to the control polygon than the quadratic B‐spline curves and quadratic trigonometric B‐spline curves when choosing special shape parameters. The shape parameters serve to local control in the curves. The changes of one shape parameter will only affect two curve segments. Taking different values of the shape parameters, one can globally or locally adjust the shapes of the curves. The generation of tensor product surfaces by these new splines is straightforward.

In today’s digital world new technologies are designed for customers. Once this technology becomes a comfort there are risk associated. Online fraud is one of the high risks that are targeting customers, organizations and any individual who wants to be associated with ecommerce transactions. Self Propagating Codes are targeting Personal Identifiable Information of customers during authentication or authorization of any transaction. In this paper emphasis has been given on possible online frauds such as Phishing, Carding and BIN attacks. What are the different attack vectors associated to online frauds and what precautions need to be taken is also briefed in the current article.

This study presents a new visualization tool using unsupervised classification of LUCL satellite imagery. Visualization of feature space allows exploration of patterns in the image data and insight into the classification process and related uncertainty. Visual Data Mining provides added value to image classifications as the user can be involved in the classification process providing increased confidence in and understanding of the results. In this study, we present a prototype visualization tool for visual data mining (VDM) of satellite imagery into volume visualization. This volume based representation divides feature space into cubes or voxels. The visualization tool is showcased in an unsupervised classification study of high-resolution imageries of Latur district in Maharashtra state of India.