An LSTM Based Forecasting for Major Stock Sectors Using COVID Sentiment

Stock market forecasting is an important research area,especially for better business decision making.Efficient stock predictions continue to be significant for business intelligence.Traditional short-term stock market forecasting is usually based on historical market data analysis such as stock prices,moving averages,or daily returns.However,major events’news also contains significant information regarding market drivers.An effective stock market forecasting system helps investors and analysts to use supportive information regarding the future direction of the stock market.This research proposes an efficient model for stock market prediction.The current proposed study explores the positive and negative effects of coronavirus events on major stock sectors like the airline,pharmaceutical,e-commerce,technology,and hospitality.We use the Twitter dataset for calculating the coronavirus sentiment with a Long Short-Term Memory(LSTM)model to improve stock prediction.The LSTM has the advantage of analyzing relationship between time-series data through memory functions.The performance of the system is evaluated by Mean Absolute Error(MAE),Mean Squared Error(MSE),and Root Mean Squared Error(RMSE).The results show that performance improves by using coronavirus event sentiments along with the LSTM prediction model.

An Efficient False-Positive Reduction System for Cerebral Microbleeds Detection

Cerebral Microbleeds(CMBs)are microhemorrhages caused by certain abnormalities of brain vessels.CMBs can be found in people with Traumatic Brain Injury(TBI),Alzheimer’s disease,and in old individuals having a brain injury.Current research reveals that CMBs can be highly dangerous for individuals having dementia and stroke.The CMBs seriously impact individuals’life which makes it crucial to recognize the CMBs in its initial phase to stop deterioration and to assist individuals to have a normal life.The existing work report good results but often ignores false-positive’s perspective for this research area.In this paper,an efficient approach is presented to detect CMBs from the Susceptibility Weighted Images(SWI).The proposed framework consists of four main phases(i)making clusters of brain Magnetic Resonance Imaging(MRI)using k-mean classifier(ii)reduce false positives for better classification results(iii)discriminative feature extraction specific to CMBs(iv)classification using a five layers convolutional neural network(CNN).The proposed method is evaluated on a public dataset available for 20 subjects.The proposed system shows an accuracy of 98.9%and a 1.1%false-positive rate value.The results show the superiority of the proposed work as compared to existing states of the art methods.