A Study of the Applications of Data Mining Techniques in Higher Education

Data mining is used to extract meaningful information and to develop significant relationships among variables stored in large data set. Few years ago, the information flow in education field was relatively simple and the application of technology was limited. However, as we progress into a more integrated world where technology has become an integral part of the business processes, the process of transfer of information has become more complicated. Today, one of the biggest challenges that educational institutions face is the explosive growth of educational data and to use this data to improve the quality of managerial decisions and student’s performance. The main objective of higher education institutions is to provide quality education to its students. One way to achieve highest level of quality in higher education system is by discovering knowledge for prediction regarding enrolment of students in a particular course, alienation of traditional classroom teaching model, detection of Unfair means used in online examination, detection of abnormal values in the result sheets of the students, prediction about students’ performance. The paper aims to purpose the use of Data mining techniques to improve the efficiency of higher educational institutions. If data mining techniques such as clustering, dicision tree and association can be applied to higher education processes, it can help improve student’s performance

Application of Data Mining Technique in Stock Market : An Analysis

Stock market prediction with data mining technique is one of the most important issues to be investigated and it is one of the fascinating issues of stock market research over the past decade. Many attempts have been made to predict stock market data using statistical and traditional methods, but these methods are no longer adequate for analyzing this huge amount of data. Data mining is one of most important powerful information technology tool in today’s competitive business world, it is able to uncover hidden patterns and predict future trends and behavior in stock market. This paper also highlights the application of association rule in stock market and their future movement direction

Machine learning for estimation of building energy consumption and performance:a review

Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eciency at a very early design stage. On the other hand, ecient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, articial intelligence (AI) in general and machine learning (ML) techniques in specic terms have been proposed for forecasting of building energy consumption and performance. This paperprovides a substantial review on the four main ML approaches including articial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance