E. Zarei; N. Barimani; G. Nazari Golpayegani
Abstract
Cardiac Arrhythmias are known as one of the most dangerous cardiac diseases. Applying intelligent algorithms in this area, leads into the reduction of the ECG signal processing time by the physician as well as reducing the probable mistakes caused by fatigue of the specialist. The purpose of this study ...
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Cardiac Arrhythmias are known as one of the most dangerous cardiac diseases. Applying intelligent algorithms in this area, leads into the reduction of the ECG signal processing time by the physician as well as reducing the probable mistakes caused by fatigue of the specialist. The purpose of this study is to introduce an intelligent algorithm for the separation of three cardiac arrhythmias by using chaos features of ECG signal and combining three types of the most common classifiers in these signal’s processing area. First, ECG signals related to three cardiac arrhythmias of Atrial Fibrillation, Ventricular Tachycardia and Post Supra Ventricular Tachycardia along with the normal cardiac signal from the arrhythmia database of MIT-BIH were gathered. Then, chaos features describing non-linear dynamic of ECG signal were extracted by calculating the Lyapunov exponent values and signal’s fractal dimension. finally, the compound classifier was used by combining of multilayer perceptron neural network, support vector machine network and K-Nearest Neighbor. Obtained results were compared to the classifying method based on features of time-domain and time-frequency domain, as a proof for the efficacy of the chaos features of the ECG signal. Likewise, to evaluate the efficacy of the compound classifier, each network was used both as separately and also as combined and the results were compared. The obtained results showed that Using the chaos features of ECG signal and the compound classifier, can classify cardiac arrhythmias with 99.1% ± 0.2 accuracy and 99.6% ± 0.1 sensitivity and specificity rate of 99.3 % ± 0.1
M. Gordan; Saeed R. Sabbagh-Yazdi; Z. Ismail; Kh. Ghaedi; H. Hamad Ghayeb
Abstract
A structural health monitoring system contains two components, i.e. a data collection approach comprising a network of sensors for recording the structural responses as well as an extraction methodology in order to achieve beneficial information on the structural health condition. In this regard, data ...
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A structural health monitoring system contains two components, i.e. a data collection approach comprising a network of sensors for recording the structural responses as well as an extraction methodology in order to achieve beneficial information on the structural health condition. In this regard, data mining which is one of the emerging computer-based technologies, can be employed for extraction of valuable information from obtained sensor databases. On the other hand, data inverse analysis scheme as a problem-based procedure has been developing rapidly. Therefore, the aforesaid scheme and data mining should be combined in order to satisfy increasing demand of data analysis, especially in complex systems such as bridges. Consequently, this study develops a damage detection methodology based on these strategies. To this end, an inverse analysis approach using data mining is applied for a composite bridge. To aid the aim, the support vector machine (SVM) algorithm is utilized to generate the patterns by means of vibration characteristics dataset. To compare the robustness and accuracy of the predicted outputs, four kernel functions, including linear, polynomial, sigmoid, and radial basis function (RBF) are applied to build the patterns. The results point out the feasibility of the proposed method for detecting damage in composite slab-on-girder bridges.
H.6.2.5. Statistical
M. Mohammadi; M. Sarmad
Abstract
The purpose of this paper is to identify the effective points on the performance of one of the important algorithm of data mining namely support vector machine. The final classification decision has been made based on the small portion of data called support vectors. So, existence of the atypical observations ...
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The purpose of this paper is to identify the effective points on the performance of one of the important algorithm of data mining namely support vector machine. The final classification decision has been made based on the small portion of data called support vectors. So, existence of the atypical observations in the aforementioned points, will result in deviation from the correct decision. Thus, the idea of Debruyne’s “outlier map” is employed in this paper to identify the outlying points in the SVM classification problem. However, due to the computational reasons such as convenience and rapidity, a robust Mahalanobis distance based on the minimum covariance determinant estimator is utilized. This method has a good compatibility by the data with low dimensional structure. In addition to the classification accuracy, the margin width is used as the criterion for the performance assessment. The larger margin is more desired, due to the higher generalization ability. It should be noted that, by omission of the detected outliers using the suggested outlier map the generalization ability and accuracy of SVM are increased. This leads to the conclusion that the proposed method is very efficient in identifying the outliers. The capability of recognizing the outlying and misclassified observations for this new version of outlier map has been retained similar to the older version, which is tested on the simulated and real world data.
H.5. Image Processing and Computer Vision
A. Asilian Bidgoli; H. Ebrahimpour-Komle; M. Askari; Seyed J. Mousavirad
Abstract
This paper parallelizes the spatial pyramid match kernel (SPK) implementation. SPK is one of the most usable kernel methods, along with support vector machine classifier, with high accuracy in object recognition. MATLAB parallel computing toolbox has been used to parallelize SPK. In this implementation, ...
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This paper parallelizes the spatial pyramid match kernel (SPK) implementation. SPK is one of the most usable kernel methods, along with support vector machine classifier, with high accuracy in object recognition. MATLAB parallel computing toolbox has been used to parallelize SPK. In this implementation, MATLAB Message Passing Interface (MPI) functions and features included in the toolbox help us obtain good performance by two schemes of task-parallelization and dataparallelization models. Parallel SPK algorithm ran over a cluster of computers and achieved less run time. A speedup value equal to 13 is obtained for a configuration with up to 5 Quad processors.
H.6. Pattern Recognition
S. Ahmadkhani; P. Adibi; A. ahmadkhani
Abstract
In this paper, several two-dimensional extensions of principal component analysis (PCA) and linear discriminant analysis (LDA) techniques has been applied in a lossless dimensionality reduction framework, for face recognition application. In this framework, the benefits of dimensionality reduction were ...
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In this paper, several two-dimensional extensions of principal component analysis (PCA) and linear discriminant analysis (LDA) techniques has been applied in a lossless dimensionality reduction framework, for face recognition application. In this framework, the benefits of dimensionality reduction were used to improve the performance of its predictive model, which was a support vector machine (SVM) classifier. At the same time, the loss of the useful information was minimized using the projection penalty idea. The well-known face databases were used to train and evaluate the proposed methods. The experimental results indicated that the proposed methods had a higher average classification accuracy in general compared to the classification based on Euclidean distance, and also compared to the methods which first extracted features based on dimensionality reduction technics, and then used SVM classifier as the predictive model.
