Jun Kit Chaw
Impact in
- Microbiology top 1%
- Reproductive tract infections research
- Bacterial Infections and Vaccines
- Medical Laboratory Technology top 10%
Papers in
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- Face and Expression Recognition 2
- Augmented Reality Applications 1
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- Color perception and design 2
- Co-authors
- G. Baker Hubbard (1 shared paper)Jeffrey D. Klausner (1 shared paper)Simone Gibson (1 shared paper)William Chi Wai Wong (1 shared paper)Sally Liska (1 shared paper)Charlotte Kent (1 shared paper)Mei Choo Ang (13 shared papers)Ting Tin Tin (8 shared papers)
- Journals
- IEEE Access (2 papers)Expert Systems with Applications (1 paper)Artificial Intelligence in Medicine (1 paper)Sensors (1 paper)Technological Forecasting and Social Change (1 paper)
- Partner nations
- MalaysiaChinaUnited Kingdom
In The Last Decade
Jun Kit Chaw
23 papers receiving 534 citations
Peers
Comparison fields: 5 of 91
- Microbiology 313
- Medical Laboratory Technology 9
- Infectious Diseases 80
- Physiology 109
- Health Informatics 5
Countries citing papers authored by Jun Kit Chaw
This map shows the geographic impact of Jun Kit Chaw'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 Kit Chaw with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Kit Chaw more than expected).
Fields of papers citing papers by Jun Kit Chaw
This network shows the impact of papers produced by Jun Kit Chaw. 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 Kit Chaw. The network helps show where Jun Kit Chaw may publish in the future.
Co-authors
The 25 scholars most cited alongside Jun Kit Chaw, 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 30 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2005 | 437 | |
| 2 | 2022 | 32 | |
| 3 | 2023 | 22 | |
| 4 | 2023 | 19 | |
| 5 | 2024 | 4 | |
| 6 | 2024 | 4 | |
| 7 | 2022 | 3 | |
| 8 | 2016 | 3 | |
| 9 | 2023 | 3 | |
| 10 | 2025 | 3 | |
| 11 | 2025 | 3 | |
| 12 | 2016 | 3 | |
| 13 | 2024 | 2 | |
| 14 | 2025 | 2 | |
| 15 | 2024 | 2 | |
| 16 | 2020 | 2 | |
| 17 | 2024 | 2 | |
| 18 | 2023 | 1 | |
| 19 | 2023 | 1 | |
| 20 | 2024 | 1 |
About Jun Kit Chaw
Jun Kit Chaw is a scholar working on Computer Vision and Pattern Recognition, Social Psychology, Artificial Intelligence, Experimental and Cognitive Psychology and Plant Science, having authored 30 papers that have together received 553 indexed citations. Recurring topics across this work include Emotion and Mood Recognition (3 papers), Smart Agriculture and AI (3 papers), Color perception and design (2 papers), Face and Expression Recognition (2 papers), Spectroscopy and Chemometric Analyses (2 papers), Artificial Intelligence in Healthcare (2 papers), Scheduling and Optimization Algorithms (2 papers) and Augmented Reality Applications (1 paper). The work is most often cited by research in Microbiology (313 citations), Medical Laboratory Technology (9 citations), Infectious Diseases (80 citations), Physiology (109 citations) and Health Informatics (5 citations). Jun Kit Chaw has collaborated with scholars based in Malaysia, China and United Kingdom. Frequent co-authors include G. Baker Hubbard, Jeffrey D. Klausner, Simone Gibson, William Chi Wai Wong, Sally Liska, Charlotte Kent, Mei Choo Ang, Ting Tin Tin, Yanfeng Zhao and Ah-Lian Kor. Their work appears in journals such as IEEE Access, Expert Systems with Applications, Artificial Intelligence in Medicine, Sensors and Technological Forecasting and Social Change.
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.