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Qing-Ming Liu

Bio: Qing-Ming Liu is an academic researcher from Nanjing Normal University. The author has contributed to research in topics: Support vector machine & Facial expression. The author has an hindex of 1, co-authored 1 publications receiving 194 citations.

Papers
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Journal ArticleDOI
TL;DR: A new emotion recognition system based on facial expression images that is superior to three state-of-the-art methods is proposed and achieved an overall accuracy of 96.77±0.10%.
Abstract: Emotion recognition represents the position and motion of facial muscles. It contributes significantly in many fields. Current approaches have not obtained good results. This paper aimed to propose a new emotion recognition system based on facial expression images. We enrolled 20 subjects and let each subject pose seven different emotions: happy, sadness, surprise, anger, disgust, fear, and neutral. Afterward, we employed biorthogonal wavelet entropy to extract multiscale features, and used fuzzy multiclass support vector machine to be the classifier. The stratified cross validation was employed as a strict validation model. The statistical analysis showed our method achieved an overall accuracy of 96.77±0.10%. Besides, our method is superior to three state-of-the-art methods. In all, this proposed method is efficient.

242 citations


Cited by
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Journal ArticleDOI
TL;DR: This paper streamline machine learning algorithms for effective prediction of chronic disease outbreak in disease-frequent communities by proposing a new convolutional neural network (CNN)-based multimodal disease risk prediction algorithm using structured and unstructured data from hospital.
Abstract: With big data growth in biomedical and healthcare communities, accurate analysis of medical data benefits early disease detection, patient care, and community services. However, the analysis accuracy is reduced when the quality of medical data is incomplete. Moreover, different regions exhibit unique characteristics of certain regional diseases, which may weaken the prediction of disease outbreaks. In this paper, we streamline machine learning algorithms for effective prediction of chronic disease outbreak in disease-frequent communities. We experiment the modified prediction models over real-life hospital data collected from central China in 2013–2015. To overcome the difficulty of incomplete data, we use a latent factor model to reconstruct the missing data. We experiment on a regional chronic disease of cerebral infarction. We propose a new convolutional neural network (CNN)-based multimodal disease risk prediction algorithm using structured and unstructured data from hospital. To the best of our knowledge, none of the existing work focused on both data types in the area of medical big data analytics. Compared with several typical prediction algorithms, the prediction accuracy of our proposed algorithm reaches 94.8% with a convergence speed, which is faster than that of the CNN-based unimodal disease risk prediction algorithm.

764 citations

Journal ArticleDOI
28 Jun 2018-Sensors
TL;DR: A comprehensive review on physiological signal-based emotion recognition, including emotion models, emotion elicitation methods, the published emotional physiological datasets, features, classifiers, and the whole framework for emotion recognition based on the physiological signals is presented.
Abstract: Emotion recognition based on physiological signals has been a hot topic and applied in many areas such as safe driving, health care and social security. In this paper, we present a comprehensive review on physiological signal-based emotion recognition, including emotion models, emotion elicitation methods, the published emotional physiological datasets, features, classifiers, and the whole framework for emotion recognition based on the physiological signals. A summary and comparation among the recent studies has been conducted, which reveals the current existing problems and the future work has been discussed.

484 citations

Journal ArticleDOI
TL;DR: The emotion recognition methods based on multi-channel EEG signals as well as multi-modal physiological signals are reviewed and the correlation between different brain areas and emotions is discussed.

281 citations

Journal ArticleDOI
TL;DR: A novel Deep Learning based approach to detect emotions - Happy, Sad and Angry in textual dialogues using semi-automated techniques to gather large scale training data with diverse ways of expressing emotions to train the model.

244 citations

Journal ArticleDOI
TL;DR: The goal of this study is to provide a new computer-vision based technique to detect Alzheimer's disease in an efficient way using convolutional neural network and increased the classification accuracy by approximately 5% compared to state-of-the-art methods.
Abstract: Alzheimer’s disease (AD) is a progressive brain disease. The goal of this study is to provide a new computer-vision based technique to detect it in an efficient way. The brain-imaging data of 98 AD patients and 98 healthy controls was collected using data augmentation method. Then, convolutional neural network (CNN) was used, CNN is the most successful tool in deep learning. An 8-layer CNN was created with optimal structure obtained by experiences. Three activation functions (AFs): sigmoid, rectified linear unit (ReLU), and leaky ReLU. The three pooling-functions were also tested: average pooling, max pooling, and stochastic pooling. The numerical experiments demonstrated that leaky ReLU and max pooling gave the greatest result in terms of performance. It achieved a sensitivity of 97.96%, a specificity of 97.35%, and an accuracy of 97.65%, respectively. In addition, the proposed approach was compared with eight state-of-the-art approaches. The method increased the classification accuracy by approximately 5% compared to state-of-the-art methods.

229 citations