A5100419847- Face and Expression Recognition
- Emotion and Mood Recognition
- EEG and Brain-Computer Interfaces
- Advanced Clustering Algorithms Research
- Anomaly Detection Techniques and Applications
- Image Retrieval and Classification Techniques
- Face recognition and analysis
- Advanced Image and Video Retrieval Techniques
- Energy Efficient Wireless Sensor Networks
- Gaze Tracking and Assistive Technology
- Video Surveillance and Tracking Methods
- Complex Network Analysis Techniques
- Advanced Algorithms and Applications
- Network Security and Intrusion Detection
- Peer-to-Peer Network Technologies
- Data Management and Algorithms
- Time Series Analysis and Forecasting
- Remote Sensing and Land Use
- Access Control and Trust
- Remote-Sensing Image Classification
- Blind Source Separation Techniques
- Cloud Computing and Resource Management
- Advanced Neural Network Applications
- Fuzzy Logic and Control Systems
- Advanced Computational Techniques and Applications
Liaoning Normal University
2015-2024
Huzhou University
2022-2024
Second Affiliated Hospital of Xi'an Jiaotong University
2022-2024
Nanjing University of Science and Technology
2024
Third People's Hospital of Hangzhou
2024
Liaoning Cancer Hospital & Institute
2023
University of Science and Technology Beijing
2023
Jishou University
2023
First Affiliated Hospital of Zhengzhou University
2021-2023
UNSW Sydney
2023
The dynamic uncertain relationship among each brain region is a necessary factor that limits EEG-based emotion recognition. It thought-provoking problem to availably employ time-varying spatial and temporal characteristics from multi-channel electroencephalogram (EEG) signals. Although deep learning has made remarkable achievements in recognition, the biological topological information regions does not fully exploit, which vital for In response this problem, we design hybrid model called...
Emotion recognition has attracted a lot of attention in recent years. It is widely used health care, teaching, human-computer interaction, and other fields. Various human emotional features are often to recognize different emotions. Currently, there more research on multimodal emotion based the fusion multiple features. This paper proposes deep learning model electroencephalogram (EEG) signals facial expressions achieve an excellent classification effect. First, pre-trained convolution...
The electroencephalogram (EEG) signal has become a highly effective decoding target for emotion recognition and garnered significant attention from researchers. Its spatial topological time-dependent characteristics make it crucial to explore both information temporal accurate recognition. However, existing studies often focus on either or aspects of EEG signals, neglecting the joint consideration perspectives. To this end, article proposes hybrid network consisting dynamic graph convolution...
In imbalanced learning methods, resampling methods modify an dataset to form a balanced dataset. Balanced data sets perform better than datasets for many base classifiers. This paper proposes cost-sensitive ensemble method based on support vector machine (SVM), and query-by-committee (QBC) solve classification. The proposed first divides the majority-class into several subdatasets according proportion of samples trains subclassifiers using AdaBoost method. Then, generates candidate training...
Abstract In the context of big data, exploration application effect machine learning in intelligent encryption for real-time image text digital information aims to improve privacy security people. Aiming at problem leakage text, convolutional neural network is introduced and improved by adding a preprocessing module form AlexNet, encrypt text. Besides, take into account both performance system, encrypted chaotic sequence generated one-dimensional system called Logistic-Sine multi-dimensional...
Traditional random forest has slow convergence in network intrusion detection and its learning performance is not perfect. In order to eliminate the redundant information original data, this paper proposes a method based on combination of gain ReliefF algorithm. The proposed first uses calculate value each feature. Then, algorithm used weight According feature weight, final subset obtained. Finally, classifier for classification. experiment compares three selection methods, including method,...
The purpose of the study was to investigate abnormality both static spontaneous brain activity and dynamic temporal variances following a pontine infarction.Forty-six patients with chronic left infarction (LPI), thirty-two right (RPI), fifty healthy controls (HCs) were recruited for study. amplitude low-frequency fluctuations (sALFF), regional homogeneity (sReHo), ALFF (dALFF), ReHo (dReHo) employed detect alterations in induced by an infarction. Rey Auditory Verbal Learning Test Flanker...
The common goal of the studies is to map any emotional states encoded from electroencephalogram (EEG) into 2-dimensional arousal-valance scores. It still challenging due each emotion having its specific spatial structure and dynamic dependence over distinct time segments among EEG signals. This paper aims model human behavior by considering location connectivity context dependency brain electrodes. Thus, we designed a hybrid modeling method that mainly adopts attention mechanism, combining...
Microbiome datasets are often comprised of different representations or views which provide complementary information, such as genes, functions, and taxonomic assignments. Integration multi-view information for clustering microbiome samples could create a comprehensive view given study. Similarity network fusion (SNF) can efficiently integrate similarities built from each data into unique that represents the full spectrum underlying data. Based on this method, we develop Robust Network...
Emotion recognition has a prominent status in the applications of brain-machine interface. An approach on recognizing Electroencephalography (EEG) emotion using empirical wavelet transform (EWT) and autoregressive (AR) model is given this paper. The proposed method chooses two channels certain time segment to perform feature extraction. EWT first used decompose EEG-based data into several modes, then AR coefficients are calculated based selected modes. Furthermore, these features constitute...
It is well established that epilepsy characterized by the destruction of information capacity brain network and interference with processing in regions outside epileptogenic focus. However, potential mechanism remains poorly understood. In current study, we applied a recently proposed approach on basis resting-state fMRI data to measure altered local neural dynamics mesial temporal lobe (mTLE), which represents how long stored area reflect an ability integration. Using resting-state-fMRI...
Summary With an explosive growth of the datacenter research, virtual machines migration aiming at optimization placement is a major technology improving power efficiency and resource utilization in datacenter. While recent studies have primarily focused on maximizing or minimizing cost separately, there has been little attention jointly taking these two objectives into account. In this paper, we present model minimum maximum with multi‐resources such as storage, bandwidth, CPU, disk space...