Forecasting share performance becomes more challenging issue due to the enormous amount of valuable trading data stored in the stock database. Currently, existing forecasting methods are insufficient to analyze the share performance accurately. There are two main reasons for that: First, the study of existing forecasting methods is still insufficient to identify the most suitable methods for share price prediction. Second, the lack of investigations made on the factors affecting the share performance. In this regard, this study presents a systematic review of the last fifteen years on various machine learning techniques in order to analyze share performance accurately. The only objective of this study is to provide an overview of the machine learning techniques that have been used to forecast share performance. This paper also highlights a how the prediction algorithms can be used to identify the most important variables in a share market dataset. Finally, we could have succeeded to analyze share performance effectively. It could bring benefits and impacts to researchers, society, brokers and financial analysts.
The major intention of higher education institutions is to supply quality education to its students. One approach to get maximum level of quality in higher education system is by discovering knowledge for prediction regarding the internal assessment and end semester examination. The projected work intends to approach this objective by taking the advantage of fuzzy inference technique to classify student scores data according to the level of their performance. In this paper, student's performance is evaluated using fuzzy association rule mining that describes Prediction of performance of the students at the end of the semester, on the basis of previous database like Attendance, Midsem Marks, Previous semester marks and Previous Academic Records were collected from the student's previous database, to identify those students which needed individual attention to decrease fail ration and taking suitable action for the next semester examination.
Stock market collects huge amount of data which is uncertain, insufficient or fuzzy in nature.To make predictions for such data is very complicated task and one of the biggest challenges to the AI community. Various traditional and statistical indicators have been proposed for this. However, combination of these tools and techniques requires highly human expertise and so much justification in the area. Stock market behavior is highly suspecible. To increase performance of prediction there is a need of method which can accurately predict stock price and can train multiple records simultaneously. Neural Network is very important tool for stock market prediction. This paper mainly highlights the Neural Network based approach to predict stock market behavior and also helps the stock brokers and investors to invest money in stock market business at the right time.
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