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Mammography is a medical imaging technique that combines, low-dose radiation and high-contrast, highresolution film for examination of the breast and screening for breast cancer. This paper proposes a Random Forest Decision Classifier (RFDC) for classifying mammograms. Results of screening the mammograms are organised by classification and finally grouped into three categories i.e., Normal, Cancerous and Benign. Experimental results show that this method performs well with the classification accuracy reaching nearly 90% in comparison with the already existing algorithms.

Abstract---Web Page access prediction is a challenging task in the current scenario, which draws the attention of many researchers. Predictions need to keep track of history data to analyze the usage behavior of the users. Web Usage behavior of a user can be analyzed using the web log file of a specific website. User behavior can be analyzed by observing the navigation patterns. This approach requires user session identification, clustering the sessions into similar clusters and developing a model for prediction using the current and earlier accesses. Most of the previous works in this field have used K-Means clustering technique with Euclidean distance for computation. The drawbacks of K-Means is that deciding on the number of clusters, choosing the initial random center are difficult and the order of page visits are not considered. The proposed research work uses hierarchical clustering technique with modified Levenshtein distance, Page Rank using access time length, frequency and higher order Markov model for prediction. Experimental results prove that the proposed approach for prediction gives better accuracy over the existing techniques.

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Abstract:Macular edema ensues when there is abnormal pile-up of fluid and results in swelling of the macula part of the retina. It is commonly associated with diabetes. It can be diagnosed by identifying exudates in the retinal images. In the proposed work, macular the retinal image is pre-processed, enhanced and segmented using morphological operations. The optic disc and macula are segmented. Various statistical features are extracted. Optimal features are selected using Haar wavelets. The selected features are classified using Random tree classifier to detect the severity of the disease in to three stages namely, normal, mild and critical. The accuracy obtained is 98.4%.