Feng Zhao

1.9k total citations
93 papers, 1.2k citations indexed

About

Feng Zhao is a scholar working on Cognitive Neuroscience, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Feng Zhao has authored 93 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Cognitive Neuroscience, 22 papers in Computer Vision and Pattern Recognition and 19 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Feng Zhao's work include Functional Brain Connectivity Studies (28 papers), EEG and Brain-Computer Interfaces (11 papers) and Advanced Neuroimaging Techniques and Applications (8 papers). Feng Zhao is often cited by papers focused on Functional Brain Connectivity Studies (28 papers), EEG and Brain-Computer Interfaces (11 papers) and Advanced Neuroimaging Techniques and Applications (8 papers). Feng Zhao collaborates with scholars based in China, United States and Singapore. Feng Zhao's co-authors include Xiaobo Chen, Anthony K. H. Tung, Dinggang Shen, Islem Rekik, Jian Yang, Ning Mao, Zhiyong An, Qiaolin Ye, Han Zhang and Yingfeng Cai and has published in prestigious journals such as Scientific Reports, Biological Psychiatry and IEEE Transactions on Image Processing.

In The Last Decade

Feng Zhao

84 papers receiving 1.1k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Feng Zhao China 18 305 264 237 182 147 93 1.2k
Michael Eichler Netherlands 22 610 2.0× 252 1.0× 51 0.2× 90 0.5× 51 0.3× 51 1.8k
John Berkowitz United States 3 78 0.3× 519 2.0× 254 1.1× 61 0.3× 48 0.3× 3 1.4k
S. Maouche France 11 121 0.4× 658 2.5× 392 1.7× 41 0.2× 102 0.7× 40 1.5k
Sidan Du China 24 70 0.2× 414 1.6× 644 2.7× 39 0.2× 243 1.7× 115 2.0k
Ming Liang China 21 68 0.2× 357 1.4× 808 3.4× 214 1.2× 65 0.4× 73 1.8k
Jin Xie China 31 213 0.7× 427 1.6× 1.9k 8.0× 119 0.7× 51 0.3× 117 2.8k
Tomoyuki Hiroyasu Japan 18 84 0.3× 310 1.2× 152 0.6× 26 0.1× 65 0.4× 162 1.1k
Saeed Mian Qaisar Saudi Arabia 25 512 1.7× 299 1.1× 156 0.7× 164 0.9× 62 0.4× 172 2.0k
Feiwei Qin China 20 248 0.8× 279 1.1× 502 2.1× 45 0.2× 124 0.8× 91 1.3k
David Windridge United Kingdom 18 81 0.3× 259 1.0× 477 2.0× 71 0.4× 88 0.6× 91 1.1k

Countries citing papers authored by Feng Zhao

Since Specialization
Citations

This map shows the geographic impact of Feng Zhao's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Feng Zhao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Feng Zhao more than expected).

Fields of papers citing papers by Feng Zhao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Feng Zhao. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Feng Zhao. The network helps show where Feng Zhao may publish in the future.

Co-authorship network of co-authors of Feng Zhao

This figure shows the co-authorship network connecting the top 25 collaborators of Feng Zhao. A scholar is included among the top collaborators of Feng Zhao based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Feng Zhao. Feng Zhao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Zhao, Feng, et al.. (2025). A deep-transfer-learning fault diagnosis method for gearboxes based on discriminative feature extraction and improved domain adversarial neural networks. Nondestructive Testing And Evaluation. 41(3). 1722–1743. 3 indexed citations
2.
Glamuzina, Branko, et al.. (2024). Supervised learning-based artificial senses for non-destructive fish quality classification. Biosensors and Bioelectronics. 267. 116770–116770. 6 indexed citations
3.
Jiang, Wen G., et al.. (2024). New reinforcement learning based on representation transfer for portfolio management. Knowledge-Based Systems. 293. 111697–111697. 4 indexed citations
4.
Zhao, Feng, Fan Feng, Xiaobo Chen, et al.. (2024). Multi-head self-attention mechanism-based global feature learning model for ASD diagnosis. Biomedical Signal Processing and Control. 91. 106090–106090. 15 indexed citations
5.
Liu, Jing, et al.. (2024). Dual Graph Convolutional Network for Hyperspectral Images With Spatial Graph and Spectral Multigraph. IEEE Geoscience and Remote Sensing Letters. 21. 1–5. 1 indexed citations
6.
Guo, Yuting, Tongpeng Chu, Heng Ma, et al.. (2024). Diagnosis of Major Depressive Disorder Based on Individualized Brain Functional and Structural Connectivity. Journal of Magnetic Resonance Imaging. 61(4). 1712–1725. 5 indexed citations
7.
Liu, Yepeng, et al.. (2023). A stock series prediction model based on variational mode decomposition and dual-channel attention network. Expert Systems with Applications. 238. 121708–121708. 31 indexed citations
8.
Che, Kaili, Heng Ma, Feng Zhao, et al.. (2023). Diagnosis of Major Depressive Disorder Using Machine Learning Based on Multisequence MRI Neuroimaging Features. Journal of Magnetic Resonance Imaging. 58(5). 1420–1430. 13 indexed citations
9.
10.
Yang, Yang, et al.. (2023). Short-Term Forecasting of Dockless Bike-Sharing Demand with the Built Environment and Weather. Journal of Advanced Transportation. 2023. 1–13. 6 indexed citations
11.
Liu, Jing, et al.. (2023). Hyperspectral Remote Sensing Images Feature Extraction Based on Spectral Fractional Differentiation. Remote Sensing. 15(11). 2879–2879. 3 indexed citations
12.
Chen, Xiaobo, et al.. (2023). Goal-Guided and Interaction-Aware State Refinement Graph Attention Network for Multi-Agent Trajectory Prediction. IEEE Robotics and Automation Letters. 9(1). 57–64. 18 indexed citations
13.
Li, Bo, et al.. (2022). A novel risk-control model for the online portfolio selection of high-frequency transactions. Knowledge-Based Systems. 240. 108176–108176. 6 indexed citations
14.
Chen, Xiaobo, et al.. (2022). Vehicle Trajectory Prediction Based on Intention-Aware Non-Autoregressive Transformer With Multi-Attention Learning for Internet of Vehicles. IEEE Transactions on Instrumentation and Measurement. 71. 1–12. 92 indexed citations
15.
Chen, Xiaobo, et al.. (2022). Intention-Aware Vehicle Trajectory Prediction Based on Spatial-Temporal Dynamic Attention Network for Internet of Vehicles. IEEE Transactions on Intelligent Transportation Systems. 23(10). 19471–19483. 135 indexed citations
16.
Zhao, Feng, Yan Lu, Xinning Li, et al.. (2022). Multiple imputation method of missing credit risk assessment data based on generative adversarial networks. Applied Soft Computing. 126. 109273–109273. 32 indexed citations
17.
Zhao, Feng, et al.. (2021). Constructing Multi-View High-Order Functional Connectivity Networks for Diagnosis of Autism Spectrum Disorder. IEEE Transactions on Biomedical Engineering. 69(3). 1237–1250. 20 indexed citations
18.
Zhao, Feng, et al.. (2020). Topic identification of text‐based expert stock comments using multi‐level information fusion. Expert Systems. 40(2). 3 indexed citations
19.
Zhao, Feng, et al.. (2019). Two‐Phase Incremental Kernel PCA for Learning Massive or Online Datasets. Complexity. 2019(1). 8 indexed citations
20.
Chen, Heng, et al.. (2015). A Novel Vector Representation Model for Text Mining Based on Enhancing Features. 網際網路技術學刊. 16(3). 475–484.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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