Chun‐Na Li
Impact in
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- Face and Expression Recognition
- Media Technology top 2%
- Remote-Sensing Image Classification
Papers in
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- Face and Expression Recognition 54
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- Machine Learning and ELM 19
- Neural Networks and Applications 8
- Co-authors
- Yuan‐Hai Shao (62 shared papers)Nai-Yang Deng (18 shared papers)Wei-Jie Chen (11 shared papers)Zhen Wang (15 shared papers)Shenglan Chen (1 shared paper)Yanru Guo (10 shared papers)Lan Bai (7 shared papers)Wotao Yin (1 shared paper)
- Journals
- Information Sciences (7 papers)Knowledge-Based Systems (6 papers)Applied Soft Computing (6 papers)IEEE Access (5 papers)Engineering Applications of Artificial Intelligence (4 papers)
- Partner nations
- ChinaAustraliaUnited States
In The Last Decade
Chun‐Na Li
62 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 111
- Computer Vision and Pattern Recognition 695
- Media Technology 161
- Artificial Intelligence 561
- Computational Mathematics 8
- Signal Processing 118
Countries citing papers authored by Chun‐Na Li
This map shows the geographic impact of Chun‐Na Li'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 Chun‐Na Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chun‐Na Li more than expected).
Fields of papers citing papers by Chun‐Na Li
This network shows the impact of papers produced by Chun‐Na Li. 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 Chun‐Na Li. The network helps show where Chun‐Na Li may publish in the future.
Co-authors
The 25 scholars most cited alongside Chun‐Na Li, 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 71 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2015 | 110 | |
| 2 | 2015 | 90 | |
| 3 | 2022 | 69 | |
| 4 | 2014 | 66 | |
| 5 | 2019 | 60 | |
| 6 | 2015 | 51 | |
| 7 | 2015 | 49 | |
| 8 | 2018 | 39 | |
| 9 | 2018 | 38 | |
| 10 | 2020 | 34 | |
| 11 | 2020 | 33 | |
| 12 | 2017 | 28 | |
| 13 | 2018 | 28 | |
| 14 | 2014 | 24 | |
| 15 | 2019 | 24 | |
| 16 | 2020 | 23 | |
| 17 | 2019 | 23 | |
| 18 | 2019 | 20 | |
| 19 | 2014 | 20 | |
| 20 | 2021 | 17 |
About Chun‐Na Li
Chun‐Na Li is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Computational Mechanics, Control and Systems Engineering and Signal Processing, having authored 71 papers that have together received 1.1k indexed citations. Recurring topics across this work include Face and Expression Recognition (54 papers), Machine Learning and ELM (19 papers), Sparse and Compressive Sensing Techniques (17 papers), Advanced Algorithms and Applications (14 papers), Blind Source Separation Techniques (12 papers), Remote-Sensing Image Classification (9 papers), Neural Networks and Applications (8 papers) and Advanced Statistical Methods and Models (8 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (695 citations), Media Technology (161 citations), Artificial Intelligence (561 citations), Computational Mathematics (8 citations) and Signal Processing (118 citations). Chun‐Na Li has collaborated with scholars based in China, Australia and United States. Frequent co-authors include Yuan‐Hai Shao, Nai-Yang Deng, Wei-Jie Chen, Zhen Wang, Shenglan Chen, Yanru Guo, Lan Bai, Wotao Yin, Liming Liu and Zhimin Yang. Their work appears in journals such as Information Sciences, Knowledge-Based Systems, Applied Soft Computing, IEEE Access and Engineering Applications of Artificial Intelligence.
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.