Stock market prediction is an important topic in ?nancial engineering especially since new techniques and approaches on this matter are gaining value constantly. In this project, we investigate the impact of sentiment expressed through Twitter tweets on stock price prediction. Twitter is the social media platform which provides a free platform for each individual to express their thoughts publicly. Specifically, we fetch the live twitter tweets of the particular company using the API. All the stop words, special characters are extracted from the dataset. The filtered data is used for sentiment analysis using Naïve bayes classifier. Thus, the tweets are classified into positive, negative and neutral tweets. To predict the stock price, the stock dataset is fetched from yahoo finance API. The stock data along with the tweets data are given as input to the machine learning model to obtain the result. XGBoost classifier is used as a model to predict the stock market price. The obtained prediction value is compared with the actual stock market value. The effectiveness of the proposed project on stock price prediction is demonstrated through experiments on several companies like Apple, Amazon, Microsoft using live twitter data and daily stock data. The goal of the project is to use historical stock data in conjunction with sentiment analysis of news headlines and Twitter posts, to predict the future price of a stock of interest. The headlines were obtained by scraping the website, FinViz, while tweets were taken using Tweepy. Both were analyzed using the Vader Sentiment Analyzer.

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Brain tumor, U-Net, Random Forest

Diabetic retinopathy (DR) is an eye disease, which is caused by the development of retinal microvascularization following diabetes. It is a problem of diabetes mellitus, which produces lesions in the surface of the retina due to which eye vision gets affected. Severe, uncontrolled cases of diabetic retinopathy will result in blindness. Since DR cannot be reversed, it can lead to blindness, and only early treatment maintains vision. Early diagnosis and treatment of DR can significantly reduce The risk of losing the vision. Fundus images are manually examined for morphological changes in retinal lesions such as micro aneurysms, exudates, blood vessels, hemorrhages. They are a tedious and time-consuming job. It is often easily accomplished with the help of a computer-assisted system. The identification and classification of the severity of diabetic retinopathy requires adequate segmentation of the retinal lesions. In this article, various techniques for detecting retinal lesions are discussed for the final detection and classification of nonproliferative diabetic retinopathy. Blood vessel detection techniques for diagnosing proliferative diabetic retinopathy are also discussed. In addition, the available datasets for the fundus colored retina were also examined. This work will be useful for researchers and technicians who wish to use ongoing research in this area. Several challenging topics are also discussed that require further investigation.