Jun Wang
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- Face and Expression Recognition 32
- Artificial Intelligence top 0.5%
- Domain Adaptation and Few-Shot Learning 23
- Machine Learning and ELM 20
- Advanced Clustering Algorithms Research 19
- Neural Networks and Applications 17
- Health Informatics top 2%
- Signal Processing top 1%
- Cognitive Neuroscience top 2%
- Functional Brain Connectivity Studies 21
- EEG and Brain-Computer Interfaces 18
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- Radiomics and Machine Learning in Medical Imaging 21
- Journals
- IEEE Journal of Biomedical and Health Informatics (9 papers)IEEE Transactions on Medical Imaging (7 papers)Neurocomputing (6 papers)
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Jun Wang
295 papers receiving 6.3k citations
Hit Papers
Peers
Comparison fields: 5 of 200
- Computer Vision and Pattern Recognition 1.9k
- Artificial Intelligence 2.5k
- Health Informatics 69
- Signal Processing 514
- Cognitive Neuroscience 815
Countries citing papers authored by Jun Wang
This map shows the geographic impact of Jun Wang'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 Jun Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Wang more than expected).
Fields of papers citing papers by Jun Wang
This network shows the impact of papers produced by Jun Wang. 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 Jun Wang. The network helps show where Jun Wang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Jun Wang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 1 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 2 | |
| 4 | 2024 | 1 | |
| 5 | 2024 | 1 | |
| 6 | 2024 | 2 | |
| 7 | 2024 | 15 | |
| 8 | 2024 | 2 | |
| 9 | 2024 | 0 | |
| 10 | Black-winged kite algorithm: a nature-inspired meta-heuristic for solving benchmark functions and engineering problemsbreakdown → | 2024 | 197 |
| 11 | 2024 | 3 | |
| 12 | 2024 | 0 | |
| 13 | 2023 | 1 | |
| 14 | 2023 | 13 | |
| 15 | 2023 | 15 | |
| 16 | 2023 | 3 | |
| 17 | 2023 | 0 | |
| 18 | 2019 | 3 | |
| 19 | 2019 | 98 | |
| 20 | Duplex Pressure Condenser Vacuum Target Value Determining Method Based on History Optimal Conditions | 2009 | 0 |
About Jun Wang
Jun Wang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Signal Processing and Cognitive Neuroscience, having authored 327 papers that have together received 6.5k indexed citations. Recurring topics across this work include Face and Expression Recognition (32 papers), Domain Adaptation and Few-Shot Learning (23 papers), Functional Brain Connectivity Studies (21 papers), Radiomics and Machine Learning in Medical Imaging (21 papers), Machine Learning and ELM (20 papers), Advanced Clustering Algorithms Research (19 papers), EEG and Brain-Computer Interfaces (18 papers) and Neural Networks and Applications (17 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.9k citations), Artificial Intelligence (2.5k citations), Health Informatics (69 citations), Signal Processing (514 citations) and Cognitive Neuroscience (815 citations). Jun Wang has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Weinan Zhang, Yong Yu, Lantao Yu, Shitong Wang, Zhaohong Deng, Fu-Lai Chung, Zheng Hai, Jun Shi, Kup‐Sze Choi and Pengjiang Qian. Their work appears in journals such as IEEE Journal of Biomedical and Health Informatics, IEEE Transactions on Medical Imaging, Neurocomputing, Information Sciences and IEEE Transactions on Fuzzy Systems.
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.