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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...
Data mining (DM) is the extraction of hidden predictive information from large databases that has becoming a powerful new technology with great potential to help companies to focus on the most important information in their data warehouses. A predictive model makes a prediction about values of data using known results found from historical data where the best possible outcome based on the previous...
This paper presents a neural-network-based active learning procedure for computer network intrusion detection. Applying data mining and machine learning techniques to network intrusion detection often faces the problem of very large training dataset size. For example, the training dataset commonly used for the DARPA KDD-1999 offline intrusion detection project contained approximately five hundred...
Clustering is a method of unsupervised learning, and a common technique for statistical data analysis used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics a novel algorithm based on clustering to extract rules from neural networks is proposed. After neural networks have been trained and pruned successfully, inner-rules are generated by...
Negotiations are one of the most common ways that agents in a multi-agent system use to reach agreements. As negotiations commonly are multi-lateral and multi-issue, these processes become more difficult. Moreover, in real-world applications in which real-time agents are needed, this issue becomes more important. Autonomous agents should be able to decide what to propose in each round of negotiations...
Trend-following (TF) strategies use fixed trading mechanism in order to take advantages from the long-term market moves without regards to the past price performance. In contrast with most prediction tools that stemmed from soft computing such as neural networks to predict a future trend, TF just rides on the current trend pattern to decide on buying or selling. While TF is widely applied in currency...
Credit scoring model development became a very important issue as the credit industry has many competitions. Therefore, most credit scoring models have been widely studied in the areas of statistics to improve the accuracy of credit scoring models during the past few years. This study constructs a hybrid SVM-based credit scoring models to evaluate the applicantpsilas credit score from the applicantpsilas...
The neural networks may play an important role in statistical model building. As the basic model building tool of the mathematics and economics neural networks can help specialist and researcher. The neural networks will improve the financial research work. The expropriation is a kind of extra interest, which exceeds the income of the biggest share-holdings normally, illegally occupied by the biggest...
In this paper, we propose a new DSS for dealing stocks which utilizes intelligently predictions (obtained by NNs) concerning the occurrence of Golden Cross (GC) & Dead Cross (DC), those concerning the increase (decrease) rate of future stock price several weeks ahead, and that regarding the relative position of the future stock price versus crossing point of GC (DC). Computer simulation results...
The methodology of developing fuzzy cognitive map (FCM) still exhibited weaknesses. This paper investigated a hybrid framework for learning FCM, which was combined of the real-coded genetic (RCGA) algorithm and nonlinear Hebbian learning (NHL) algorithm. This approach combined the synergistic theories of neural networks and fuzzy logic. The hybrid algorithm is introduced, presented and applied successfully...
This paper presents rough sets generating prediction rules scheme for stock price movement. The scheme was able to extract knowledge in the form of rules from daily stock movements. These rules then could be used to guide investors whether to buy, sell or hold a stock. To increase the efficiency of the prediction process, rough sets with Boolean reasoning discretization algorithm is used to discretize...
The performance of speech recognition systems is commonly degraded by either speech-related disabilities or by real-world factors such as the environmentpsilas noise level and reverberation. In this work, we propose a subvocal speech recognition system based on EMG signal for subvocal acquisition, Independent Component Analysis (ICA) for feature extraction and Neural Networks for classification. We...
Iterative learning control is a method of determining the control signal required to drive a plant to a desired state. This paper gives a brief description of the development of learning control and gives one solution using a two neural network system. One network emulates the plant, the other is the controller. The controller learns to control the plant by learning to control the plant emulator....
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