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In this paper, we present a model based on the Neural Network (NN) for classifying Arabic texts. We propose the use of Singular Value Decomposition (SVD) as a preprocessor of NN with the aim of further reducing data in terms of both size and dimensionality. Indeed, the use of SVD makes data more amenable to classification and the convergence training process faster. Specifically, the effectiveness...
Neural networks have been widely used in nonlinear time series prediction. They have generated lot of interest due to their comprehensive adaptive and learning abilities. Neural networks have been used in Medical forecasting, Exchange rate forecasting, stock index prediction, and other areas, which show a practical value of neural networks. This paper presents a novel application of the Self-organised...
This paper describes research work in developing an intelligent model for classifying selected rubber tree series clones based on shape features using image processing techniques. Sample of rubber tree seeds are captured using digital camera where the RGB color image are processed involving segmentation algorithm which includes thresholding and morphological technique. Shape features such as area,...
This paper presents a novel application of the self-organised multilayer perceptrons inspired by the immune algorithm in financial time series prediction. The simulation results were compared with the multilayer perceptrons and the functional link neural networks. The prediction capability of the various neural networks was tested on ten different data sets; the US/UK exchange rates, the JP/US exchange...
This paper presents the prediction of financial time series using an adaptive neural network, which is called the self-organised multilayer perceptrons inspired by the immune algorithm. The simulation results were compared with the multilayer perceptrons and the functional link neural networks. The prediction capability of the various neural networks was tested on ten different data sets; the US/UK...
This paper presents an approach for solving WCCI 2008's Ford Classification Challenge Problem. The solution is based on the creation of new input variables through temporal feature extraction and on the combination via bagging of an ensemble of 30 multi-layer perceptrons trained on sets divided by multiple random sampling of the labeled data. Signal power, signal to noise ratio and signal frequency...
This work presents an award winning approach for solving the NN3 forecasting competition problem. It consisted of predicting 18 future values of 111 monthly short time series. This approach consists of applying the median value of a 15-MLP ensemble for predicting each time series. The system performed very well on test data, finishing as the second best solution of the competition with a SMAPE=16...
This paper presents dynamic voltage collapse prediction on an actual power system using support vector machines. Dynamic voltage collapse prediction is first determined based on the PTSI calculated from information in dynamic simulation output. Simulations were carried out on a practical 87 bus test system by considering load increase as the contingency. The data collected from the time domain simulation...
This paper presents a new method of analyzing time-frequency plots of heart rate variability to detect sleep disordered breathing from nocturnal ECG. Data is collected from 12 normal subjects (7 males, 5 females; age 46 plusmn 9.38 years, AHI 3.75 plusmn 3.11) and 14 apneic subjects (8 males, 6 females; age 50.28 plusmn 9.60 years; AHI 31.21 plusmn 23.89). The proposed algorithm uses textural features...
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