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Now the classification of different tumor types is of great importance in cancer diagnosis and drug discovery. It is more desirable to create an optimal ensemble for data analysis that deals with few samples and large features. In this paper, a new ensemble method for cancer data classification is proposed. The gene expression data is firstly preprocessed for normalization. Kernel Independent Component...
In this paper, a new approach using independent component analysis (ica) and hybrid Flexible Neural Tree (FNT) is put forward for face recognition. To improve the quality of the face images, a series of image pre-processing techniques, which include histogram equalization, edge detection and geometrical transformation are used. The ICA based on Kernel principal component analysis (KPCA) and FastICA...
The electroencephalogram (EEG) is a set of data measured by electrodes placed on the scalp and is often under the influences of artifacts. Mental EEG is recorded when a person performs different mental tasks. In this article, we separated the mental EEG signals into independent components with individual meanings based on independent component analysis (ICA) method, and the EEG was reconstructed by...
The presence of different artifacts has long been a problem for the analysis and interpretation of electroencephalographic (EEG) recordings. Independent component analysis (ICA) is a technique for blind source separation (BSS) and has been used to remove biomedical artifacts. In order to subtract the power line noise effectively and robustly, we use two additional channels of artificial power line...
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