Hybrid Optimization Mechanism to Predict Share Price using Deep Learning Approach

There have been several areas of research for optimization that uses the deep learning approach. The present study is focused on predicting and forecasting the share price using hybrid optimization mechanism. The hybrid optimization mechanism considers the integration of ACO and black hole-based optimization that filters the shared dataset before training to improve the accuracy. After getting filtered content, a deep learning approach is applied to predict the results. In the previous research, it has been observed that there is a lack of accuracy. Moreover, previous research took a lot of time during training and testing operations. The proposed research is a filtered dataset to reduce the training and testing time, and after the elimination of irrelevant records, the accuracy of prediction gets increased.