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—The purpose of this research is to develop recommendation system reflecting individual user's Kansei model. In the target contents have some keywords. The proposed method has following features. A recommendation problem is formulated as an optimization problem. The design space is defined with keywords of contents. The distance of each keyword is(More)
In this study, using Deep Learning, the gender of subjects is classified the cerebral blood flow changes that are measured by fNIRS. It is reported that cerebral blood flow changes are triggered by brain activities. Thus, if this classification has a high searching accuracy, gender classification should be related to brain activities. In the experiment,(More)
Recently, systems using motor imagery (MI) have been developed as practical examples of brain-computer interface (BCI). Electroencephalography (EEG) was used to generate an electroencephalogram of elbow flexion. In addition, a method was proposed to extract the feature values that would enable the recognition right- or left-handed elbow flexion MI. In the(More)
In this paper, a feature transformation method for two-class classification using genetic programming (GP) is proposed. GP derives a transformation formula to improve the classification accuracy of Support Vector Machine, SVM. In this paper, we propose a weight function to evaluate converted feature space and the proposed function is used to evaluate the(More)
In this study, a system is proposed to help physicians perform processing on images taken with a magnifying endoscopy with narrow band imaging. In our proposed system, the transition from lesion to normal zone is quantitatively analyzed and presented by texture analysis. Eleven feature values are calculated, i.e., six from a co-occurrence matrix and five(More)
Independent component analysis (ICA) is one of the most preferred methods for removing motion artifacts from functional near-infrared spectroscopy (fNIRS) data. In this method, the fNIRS signal is separated into components by ICA and the component that shows high correlation between the fNIRS signal and motion artifact is determined. This component is(More)