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Attracting more students into science and engineering disciplines concerned many researchers for decades. Literature used traditional statistical methods and qualitative techniques to identify factors that affect student retention up most and predict their persistence. In this paper we developed two neural network models using a feed-forward backpropagation network to predict retention for students...
Aiming at the disadvantages of prediction model of single BP neural network, a prediction model was presented by combining AdaBoost algorithm and BP neural network for improving the forecasting accuracy of single BP neural network. A new updating method is proposed for the characters of ensemble BP neural network based on AdaBoost. The new method can update the model effectively and overcome the disadvantage...
The price of crude oil is tied to major economic activities in all nations of the world, as a change in the price of crude oil invariably affects the cost of other goods and services. This has made the prediction of crude oil price a top priority for researchers and scientists alike. In this paper we present an intelligent system that predicts the price of crude oil. This system is based on Support...
The volatility of crude oil market and its chain effects to the world economy augmented the interest and fear of individuals, public and private sectors. Previous statistical and econometric techniques used for prediction, offer good results when dealing with linear data. Nevertheless, crude oil price series deal with high nonlinearity and irregular events. The continuous usage of statistical and...
This paper is connected with the problem of selecting architectural parameters and learning rate of BP artificial neural network. The self-adapting algorithm of BP artificial neural network has been proposed, and the corresponding C language procedure is programmed. It can make the selection of input units, hidden units and learning rate easily in the course of training, reduce external interference...
Exchange rate time series is often characterized as chaotic in nature. The prediction using conventional statistical techniques and neural network with back propagation algorithm, which is most widely applied, do not give reliable prediction results. Exchange-rate time series is also a dynamic non-linear system, whose characteristics cannot be reflected by the static neutral network. The Nonlinear...
In this paper, we apply data mining technology to Chinese stock market in order to research the trend of price, it aims to predict the future trend of the stock market and the fluctuation of price. This paper points out the shortage that exists in current traditional statistical analysis in the stock, then makes use of BP neural network algorithm to predict the stock market by establishing a three-tier...
Forecasting exchange rate is very important for many international agents e.g. investors, money managers, investment banks, funds makers and others. We forecasted the daily Bangladeshi exchange rate series for the period of January 1992 to March 2009 using popular non-linear forecasting models, namely Markov switching autoregressive model, fuzzy extension of artificial neural network model (ANFIS)...
Companies in financial distress make the creditors, shareholders, employees, investors and other participants of the related firms suffer great losses. In order to prevent the companies run into bankruptcy, financial distress prediction has been a useful tool for distinguishing companies in financial distress from those healthy. Statistical methods and artificial intelligence techniques have been...
Political and social issues play a big role in economical systems. Macroeconomic variables which are affected by the above mentioned factors can be used in economical forecasting. Time series are used as a very powerful tool in economical systems for short time predicting. As time series predict the future output according to the past behaviors of the system, therefore they can not sense sudden changes...
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