Hai-Jun Rong
Impact in
- Artificial Intelligence top 1%
- Machine Learning and ELM
- Neural Networks and Applications
- Fuzzy Logic and Control Systems
- Domain Adaptation and Few-Shot Learning
- Data Stream Mining Techniques
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- Fault Detection and Control Systems
Papers in
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- Neural Networks and Applications 26
- Machine Learning and ELM 19
- Fuzzy Logic and Control Systems 15
- Evolutionary Algorithms and Applications 4
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- Fault Detection and Control Systems 6
- Co-authors
- Guang-Bin Huang (8 shared papers)N. Sundararajan (6 shared papers)P. Saratchandran (5 shared papers)Yew-Soon Ong (2 shared papers)Zexuan Zhu (1 shared paper)Ah‐Hwee Tan (1 shared paper)Guangshe Zhao (10 shared papers)Plamen Angelov (6 shared papers)
In The Last Decade
Hai-Jun Rong
45 papers receiving 1.5k citations
Peers
Comparison fields: 5 of 83
- Artificial Intelligence 1.3k
- Control and Systems Engineering 330
- Computer Vision and Pattern Recognition 249
- Statistics and Probability 77
- Signal Processing 93
Countries citing papers authored by Hai-Jun Rong
This map shows the geographic impact of Hai-Jun Rong'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 Hai-Jun Rong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hai-Jun Rong more than expected).
Fields of papers citing papers by Hai-Jun Rong
This network shows the impact of papers produced by Hai-Jun Rong. 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 Hai-Jun Rong. The network helps show where Hai-Jun Rong may publish in the future.
Co-authors
The 25 scholars most cited alongside Hai-Jun Rong, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 49 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2006 | 308 | |
| 2 | 2008 | 279 | |
| 3 | 2009 | 277 | |
| 4 | 2005 | 147 | |
| 5 | 2010 | 71 | |
| 6 | 2017 | 57 | |
| 7 | 2008 | 38 | |
| 8 | 2016 | 37 | |
| 9 | 2011 | 33 | |
| 10 | 2013 | 32 | |
| 11 | 2007 | 29 | |
| 12 | 2019 | 26 | |
| 13 | 2013 | 25 | |
| 14 | 2018 | 23 | |
| 15 | 2021 | 21 | |
| 16 | 2019 | 19 | |
| 17 | 2020 | 13 | |
| 18 | 2021 | 13 | |
| 19 | 2020 | 13 | |
| 20 | 2017 | 11 |
About Hai-Jun Rong
Hai-Jun Rong is a scholar working on Artificial Intelligence, Control and Systems Engineering, Computer Vision and Pattern Recognition, Automotive Engineering and Computational Mechanics, having authored 49 papers that have together received 1.6k indexed citations. Recurring topics across this work include Neural Networks and Applications (26 papers), Machine Learning and ELM (19 papers), Fuzzy Logic and Control Systems (15 papers), Face and Expression Recognition (7 papers), Fault Detection and Control Systems (6 papers), Advanced Battery Technologies Research (6 papers), Evolutionary Algorithms and Applications (4 papers) and Advanced Adaptive Filtering Techniques (4 papers). The work is most often cited by research in Artificial Intelligence (1.3k citations), Control and Systems Engineering (330 citations), Computer Vision and Pattern Recognition (249 citations), Statistics and Probability (77 citations) and Signal Processing (93 citations). Hai-Jun Rong has collaborated with scholars based in China, Macao and Singapore. Frequent co-authors include Guang-Bin Huang, N. Sundararajan, P. Saratchandran, Yew-Soon Ong, Zexuan Zhu, Ah‐Hwee Tan, Guangshe Zhao, Plamen Angelov, Badong Chen and Nanying Liang. Their work appears in journals such as IEEE Transactions on Fuzzy Systems, Neurocomputing, Information Sciences, IEEE Access and Evolving 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.