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This experimental study addresses the problem of predicting the direction of stocks and the movement of stock price indices for three major stocks and stock indices. The proposed approach for processing input data involves the computation of ten technical indicators using stock trading data. The dataset used for the evaluation of all the prediction models consists of 11 years of historical data from January 2007 to December 2017. The study comprises four prediction models which are Long Short-Term Memory, XGBoost, Support Vector Machine ( and Random forests. Accuracy scores and F1 scores for each of the prediction models have been evaluated using this input approach. Experimental results reveal that a continuous data approach using ten technical indicators gives the best performance in the case of the Random Forest classifier model with the highest accuracy of 84.89% (average wise 83.74%) and highest F1 score of 89.33% (average wise 83.74%). The experiments also give us an insight into why a Naïve Bayes Classification model is not a suitable prediction model for the above task.

Breast Cancer is still one of the leading cancers in women. Mammography is the best tool for early detection of breast cancer. In this work methods for automatic detection and classification of masses into benign or malignant has been proposed. The suspicious masses are detected automatically by performing image segmentation with Otsu's global thresholding technique, morphological operations and watershed transformation. Twenty-five features based on intensity, texture and shape are extracted from each of the 651 mammograms obtained from Database of Digitized Screen-film Mammograms. The Eight most significant features selected by step-wise Linear Discriminate Analysis are used to classify masses using Fisher's Linear Discriminate Analysis, Support Vector Machine and Multilayer Perceptron with two training algorithms Levenberg-Marquardt and Bayesian Regularization. The performance evaluation of classifiers indicates that MLP is better than both LDA and SVM. MLP-RBF has 98.9% accuracy with area under Receiver Operating Characteristics curve AZ=0.98±0.007, MLP-LM 96.0% accuracy with AZ=0.97±0.007, SVM 91.4% accuracy with AZ=0.956±0.009 and LDA 90.3% accuracy with AZ=0.956±0.009. All the results achieved are promising when compared with some existing work.