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Regret is a negative social affect arising from upward counterfactual thinking for adverse outcomes. The neurophysiologic markers of identifying regret based on feature extraction from EEG signal has yet to be investigated. The EEG signals were recorded while 25 subjects performed a gambling task involving feedback for the subjects' chosen selection, the alternative selection, or for both. After the...
With the advent of high-efficiency technology of digital image processing and pattern classification, the research on classification for tool marks is catching forensic scientist's eyes. It is crucial for classification to extract and select the features from tool marks. In the practical situation, the geometrical shapes and the textures of tool marks are complex, irregular and stochastic. It is difficult...
In this study, a novel classifier ensemble method named adaptive nonparametric weighted feature extraction (AdaNWFE) is proposed. This new concept is deduced from AdaBoost and NWFE. The main idea of AdaNWFE is adaptive in the sense that subsequent feature spaces are tweaked in favor of those instances misclassified by classifiers in the previous feature space. All training samples are projected to...
Accurate identification and classification of network traffic according to application type is an important element of many network management tasks. In this paper, a P2P network traffic classification method using SVM classifier is proposed. By this method, the P2P network traffic can been classified according to application types with statistical characteristics of network traffic. This paper mainly...
Currently, optical device, such as microscopes and CCD cameras, are utilized for identification of tool marks in the field of forensic science which mainly depend on the experience of forensic scientists. A new approach using extended fractal analysis technology to classify tool marks such as striation patterns is presented. it computes four directional multi-scale extended fractal parameters and...
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