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The aim of Image fusion is to combine the information from number of images of the same scene from different images with focus on different objects. The result of image is more informative and of better quality often the entropy content of the resultant image is poor due to improper local fusing. In this paper a comparative study and modified spatial domain approach is presented by fixing the contrast values of pixel in between the Average -minimum, and Average-Maximum to get better fusion capabilities. Experimental results demonstrates that the few of the proposed techniques outperform the existing techniques in terms of SNR and PSNR. [ABSTRACT FROM AUTHOR]

Data mining methods are used to mine voluminous data to find useful information from data. The data that is to be mined may have a large number of dimensions, so the mining process will take a lot of time. In general, the computation time is an exponential function of the number of dimensions. It is in this context that we use dimensionality reduction techniques to speed up the decision-making process. Dimensionality reduction techniques can be categorized as Feature Selection and Feature Extraction Techniques. In this paper we compare the two categories of dimensionality reduction techniques. Feature selection has been implemented using the Information Gain and Goodman-Kruskal measure. Principal Component Analysis has been used for Feature Extraction. In order to compare the accuracy of the methods, we have also implemented a classifier using back-propagation neural network. In general, it is found that feature extraction methods are more accurate than feature selection methods in the framework of credit risk analysis. [ABSTRACT FROM AUTHOR]

In this paper, Principal Component Analysis is used for feature extraction, and a statistical learning based Support Vector Machine is designed for functional classification of clinical data. Appendicitis data collected from BHEL Hospital, Trichy is taken and classified under three classes. Feature extraction transforms the data in the high-dimensional space to a space of fewer dimensions. The classification is done by constructing an optimal hyperplane that separates the members from the nonmembers of the class. For linearly nonseparable data, Kernel functions are used to map data to a higher dimensional space and there the optimal hyperplane is found. This paper works with different SVMs based on radial basis and polynomial kernels, and their performances are compared. [ABSTRACT FROM AUTHOR]