Stock prices are volatile due to different factors that are involved in the stock market, such as geopolitical tension, company earnings, and commodity prices, affecting stock price. Sometimes stock prices react to domestic uncertainty such as reserve bank policy, government policy, inflation, and global market uncertainty. The volatility estimation of stock is one of the challenging tasks for traders. Accurate prediction of stock price helps investors to reduce the risk in portfolio or investment. Stock prices are nonlinear. To deal with nonlinearity in data, we propose a hybrid stock prediction model using the prediction rule ensembles (PRE) technique and deep neural network (DNN). First, stock technical indicators are considered to identify the uptrend in stock prices. We considered moving average technical indicators: moving average 20 days, moving average 50 days, and moving average 200 days. Second, using the PRE technique-computed different rules for stock prediction, we selected the rules with the lowest root mean square error (RMSE) score. Third, the three-layer DNN is considered for stock prediction. We have fine-tuned the hyperparameters of DNN, such as the number of layers, learning rate, neurons, and number of epochs in the model. Fourth, the average results of the PRE and DNN prediction model are combined. The hybrid stock prediction model results are computed using the mean absolute error (MAE) and RMSE metric. The performance of the hybrid stock prediction model is better than the single prediction model, namely DNN and ANN, with a 5% to 7% improvement in RMSE score. The Indian stock price data are considered for the work.

Prostate cancer is a major cause of concern in male population as it is said to affect 1 in every 7 men in their lifetime. The number of cases being registered for prostate cancer and its mortality rates are increasing yearly at an alarming rate. Due to the high resolution and multidimensionality of the Magnetic Resonance Imaging (MRI) images, proper diagnostic system and tools are required. In this study, multiclass Support Vector Machines (SVM) classifier has been used, which is a well-known machine learning technique to classify the prostate images into 3 categories namely normal, benign and malign. This study has also made use of the Scale Invariant Feature Transform (SIFT) feature extraction method which is well known for its high rotation invariant nature. A SIFT-SVM approach has been introduced for the first time in prostate cancer detection. The performance of the system is computed in terms of sensitivity, specificity and accuracy. Our approach achieved high performance with an accuracy rate of about 99.95% when 40% of the training data was considered for obtaining our result.

In recent years, investors and traders have used Technical Indicators (TIs) to forecast the stock market. An accurate classification model is required in the stock market to gain more profit. Selecting relevant TIs for the stock market remains a hot research topic. The proposed work aims to identify important technical indicators. Therefore, the proposed work considers a hybrid feature selection method to identify the relevant TIs. The hybrid feature selection combines two individual feature selection methods, such as Boruta and the Random Forest (RF) feature importance method. The work considers 20 TIs. The regression power of TIs is computed using the hybrid feature selection method. Using the hybrid feature selection method, the selected relevant TIs are given as input to the classification model, namely Naive Bayes (NB) and Deep Learning. The classification model aims to classify the stock price as up or down. The Hybrid Feature selection-based Deep Learning H2O model performs better than the hybrid feature selectionbased NB model in the experimental work. The accuracy of the hybrid feature selection of the Deep Learning H2O model is around 86 to 89%. The work considers the National Stock Exchange (NSE) in India for the experimental work.