KEYWORDS: Eye, Virtual reality, 3D modeling, Eye models, Data modeling, RGB color model, Linear filtering, Feature extraction, Data corrections, Optical tracking
With the rise of virtual reality technology and eye movement tracking, the cognitive classification of eye movement emotion combined with virtual reality technology was a research hotspot recently. In this research, 96 pictures were selected from the Chinese Affective Picture System (CAPS), and 35 subjects were stimulated with pictures in virtual scene. The corresponding eye movement data were tracked synchronously. Eye movement data were progressed with blink data reconstruction and baseline correction, after which time and frequency domain features were extracted. Finally, for three emotion model dimensions including valence, arousal and dominance expressed as high, medium and low, models are trained with Support Vector Machine (SVM), with the highest classification accuracy of single person reaches 78.1%. The results of this paper are instructive for the processing of eye movement data, and the eye tracking data combined with virtual reality technology can be used for emotion recognition research through the proposed preprocessing methods and feature extraction.
Deep neural networks are frequently used to automate the examination of radiographic images in medical. These approaches may be used to train on huge datasets or extract features from small datasets using pre-trained networks. Due to the lack of large pulmonary tuberculosis datasets, it is possible to diagnose tuberculosis using pre-trained deep convolutional neural networks. Thus, this article aims to detect and diagnose tuberculosis in chest X-rays by combining a pre-trained deep convolutional neural network with a machine learning model. Combined the deep pre-trained DenseNet201 network with the machine learning XGBoost classifier to create a hybrid model for classifying patients as tuberculosis infected or not. The proposed model extracts feature using the pre-trained DenseNet201 neural networks and classify them employing the XGBoost classifier. We performed extensive experiments to assess the performance of the proposed DenseNet201-XGBoost model using tuberculosis chest x-ray images. Comparative study shows that the proposed DenseNet201-XGBoost-based tuberculosis classification model outperforms other competing approaches.
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