This paper deals with the new segmentation techniques for retinal blood vessels on fundus images. This technique aims at extracting thin vessels to reduce the intensity difference between thick and thin vessels. This paper proposes the modified UNet model by incorporating ResNet blocks into it which includes structured prediction. In this work we generate the visualization of blood vessels from retinal fundus image for two loss functions namely cross entropy loss and Dice loss where the network classifies several pixels simultaneously. The results shows higher accuracy by considering a much more expressive UNet algorithm and outperforms the past algorithms for Retinal Vessel Segmentation. The benefits of this approach will be demonstrated empirically.
The stock market is a widely used investment scheme promising high returns, but it has some risks. It is an act to forecast the future value of the stock market. The change in the stock market is explosive, and there are multiple sophisticated financial indicators. Still, the enhancement in technology provides a chance to grow constant fortune from the stock market and so helps experts to detect the most enlightening indicators to produce better predictions. Machine learning algorithms have made a magnificent effect in determining stocks precisely. This paper proposed a multiple regression algorithm for determining the future value of a stock. The first thing that was taken into account is the dataset of the companies Apple and Microsoft. Live historical data has been collected from yahoo finance. The dataset was preprocessed and tuned up for real analysis. Hence, this paper also focused on preprocessing of the raw dataset. After preprocessing the data, some forecasting measures are suggested, such as momentum, volatility, index volatility, and stock.
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