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The increase of malware that are exploiting the Internet daily has become a serious threat. The manual heuristic inspection of malware analysis is no longer considered effective and efficient compared against the high spreading rate of malware. Hence, automated behavior-based malware detection using machine learning techniques is considered a profound solution. The behavior of each malware on an emulated...
In pattern detection systems, the general techniques of feature extraction and selection perform linear transformations from primitive feature vectors to new vectors of lower dimensionality. At times, new extracted features might be linear combinations of some primitive features that are not able to provide better classification accuracy. To solve this problem, we propose the integration of genetic...
In this paper, computer aided diagnosis is applied to the brain CT image processing. We compared performance of morphological operations in extracting three types of features i.e. gray scale, symmetry and texture. SVM, MLPNN and RBFNN are used to build classifiers for normal and abnormal brain CT image. It shows that morphological operation can improve the accuracy. Moreover, method of SVM has better...
This paper describes electrocardiogram (ECG) pattern classification using QRS morphological features and the artificial neural network. Four types of ECG patterns were chosen from the MIT-BIH database to be classified, including normal sinus rhythm, premature ventricular contraction, atrial premature beat and left bundle branch block beat. Authors propose a set of six ECG morphological features to...
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