Minghui Hu
Impact in
- Geochemistry and Petrology top 2%
- Groundwater and Isotope Geochemistry
- Oceanography top 5%
- Marine and coastal ecosystems
- Marine Biology and Ecology Research
Papers in
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- Machine Learning and ELM 9
- Neural Networks and Applications 7
- Domain Adaptation and Few-Shot Learning 6
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- Face and Expression Recognition 5
- Robotic Path Planning Algorithms 3
- Co-authors
- Ponnuthurai Nagaratnam Suganthan (17 shared papers)M. Tanveer (1 shared paper)A. K. Malik (1 shared paper)M. A. Ganaie (1 shared paper)John M. Edmond (2 shared papers)Robert F. Stallard (1 shared paper)Arthur J. Spivack (1 shared paper)B. Grant (1 shared paper)
In The Last Decade
Minghui Hu
54 papers receiving 2.3k citations
Minghui Hu's Hit Papers
Peers
Comparison fields: 5 of 180
- Geochemistry and Petrology 267
- Oceanography 376
- Artificial Intelligence 543
- Environmental Chemistry 131
- Environmental Engineering 181
Countries citing papers authored by Minghui Hu
This map shows the geographic impact of Minghui Hu'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 Minghui Hu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Minghui Hu more than expected).
Fields of papers citing papers by Minghui Hu
This network shows the impact of papers produced by Minghui Hu. 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 Minghui Hu. The network helps show where Minghui Hu may publish in the future.
Co-authors
The 25 scholars most cited alongside Minghui Hu, 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 63 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Ensemble deep learning: A review Hit paper breakdown → | 2022 | 1196 |
| 2 | 1985 | 297 | |
| 3 | 1982 | 214 | |
| 4 | 2007 | 129 | |
| 5 | 2022 | 62 | |
| 6 | 2020 | 44 | |
| 7 | 2022 | 43 | |
| 8 | 2023 | 34 | |
| 9 | 2010 | 33 | |
| 10 | 2022 | 32 | |
| 11 | 2022 | 26 | |
| 12 | 2024 | 25 | |
| 13 | 2022 | 21 | |
| 14 | 2011 | 21 | |
| 15 | 2010 | 18 | |
| 16 | 2022 | 18 | |
| 17 | 2005 | 16 | |
| 18 | 2021 | 13 | |
| 19 | 2023 | 12 | |
| 20 | 2019 | 12 |
About Minghui Hu
Minghui Hu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology, Electrical and Electronic Engineering and Oceanography, having authored 63 papers that have together received 2.4k indexed citations. Recurring topics across this work include Machine Learning and ELM (9 papers), Neural Networks and Applications (7 papers), Domain Adaptation and Few-Shot Learning (6 papers), Face and Expression Recognition (5 papers), Marine and coastal ecosystems (5 papers), Robotic Path Planning Algorithms (3 papers), Marine Biology and Ecology Research (3 papers) and Chinese history and philosophy (3 papers). The work is most often cited by research in Geochemistry and Petrology (267 citations), Oceanography (376 citations), Artificial Intelligence (543 citations), Environmental Chemistry (131 citations) and Environmental Engineering (181 citations). Minghui Hu has collaborated with scholars based in China, Singapore and Qatar. Frequent co-authors include Ponnuthurai Nagaratnam Suganthan, M. Tanveer, A. K. Malik, M. A. Ganaie, John M. Edmond, Robert F. Stallard, Arthur J. Spivack, B. Grant, Ruobin Gao and Ruilin Li. Their work appears in journals such as Engineering Applications of Artificial Intelligence, IEEE Transactions on Neural Networks and Learning Systems, Pattern Recognition, Blood and Applied Energy.
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.