Agricultural productivity is that thing on which Indian Economy highly depends. This is the one of the reasons that disease detection in plants plays an important role in agriculture field, as having disease in plants are quite natural. If proper care is not taken in this area then it causes serious effects on plants and due to which respective product quality, quantity or productivity is affected. Detection of plant disease through some automatic technique is beneficial as it reduces a large work of monitoring in big farms of crops, and at very early stage itself it detects the symptoms of diseases means when they appear on plant leaves. This paper presents an algorithm for image segmentation technique used for automatic detection as well as classification of plant leaf diseases and survey on different diseases classification techniques that can be used for plant leaf disease detection. Image segmentation, which is an important aspect for disease detection in plant leaf disease, is done by using genetic algorithm.
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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