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Surface electromyogram (sEMG) signal has been applied to gesture recognition successfully. However, performance of gesture recognition degrades due to noise, which is unavoidable in practical environment. This study aims to propose a gesture recognition system using a multiple classifier system (MCS) based on random subspace. Our method makes a decision using a signal which is less sensitive to noise...
Electrode shift of a prosthetic device is one of most challengeable problems in surface Electromyography (sEMG) based hand gesture recognition. Electrode shift is usually caused by repositioning, donning or doffing of a prosthetic device. Accuracy of gesture recognition may significantly drop since a pattern of collected signals may change after electrode shift. Although re-training a recognition...
Pattern recognition and machine learning techniques have been increasingly adopted in adversarial settings such as spam, intrusion, and malware detection, although their security against well-crafted attacks that aim to evade detection by manipulating data at test time has not yet been thoroughly assessed. While previous work has been mainly focused on devising adversary-aware classification algorithms...
Many studies have shown that Multiple Classifier Systems (MCSs) are more robust than single classifiers to evasion attacks for linear classifiers. However, to the best of our knowledge, the robustness of MCSs for non-linear classifiers has not been inves-tigated. This paper attempts to discuss two issues experimentally including a MCS is still more robust than a single classifier for non-linear classifiers,...
Malicious websites provide a platform supporting diverse Internet crimes. They do not only steal the sensitive information but also let the hacker to control the computer of users. Malicious website detection with the machine learning technique achieves satisfying result. However, the characteristics of the malicious website may be modified to evade the detection. In this paper, the exploratory attack...
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