Kok Swee Sim
Impact in
- Neurology top 5%
- Brain Tumor Detection and Classification
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- Radiomics and Machine Learning in Medical Imaging
- Infrared Thermography in Medicine
- COVID-19 diagnosis using AI
Papers in
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- AI in cancer detection 7
- Co-authors
- Fung Fung Ting (6 shared papers)Shing Chiang Tan (17 shared papers)Bruno A. Latella (2 shared papers)I.M. Low (2 shared papers)J. Lane (1 shared paper)Martin P. McGrath (1 shared paper)David Lawrence (1 shared paper)Patrick Schmidt (1 shared paper)
In The Last Decade
Kok Swee Sim
61 papers receiving 928 citations
Kok Swee Sim's Hit Papers
Peers
Comparison fields: 5 of 117
- Neurology 162
- Radiology, Nuclear Medicine and Imaging 282
- Artificial Intelligence 451
- Computer Vision and Pattern Recognition 185
- Structural Biology 12
Countries citing papers authored by Kok Swee Sim
This map shows the geographic impact of Kok Swee Sim'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 Kok Swee Sim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kok Swee Sim more than expected).
Fields of papers citing papers by Kok Swee Sim
This network shows the impact of papers produced by Kok Swee Sim. 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 Kok Swee Sim. The network helps show where Kok Swee Sim may publish in the future.
Co-authors
The 25 scholars most cited alongside Kok Swee Sim, 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 74 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Convolutional neural network improvement for breast cancer classification Hit paper breakdown → | 2018 | 355 |
| 2 | 2006 | 103 | |
| 3 | 2017 | 94 | |
| 4 | 2010 | 39 | |
| 5 | 2022 | 33 | |
| 6 | 2006 | 32 | |
| 7 | 2004 | 28 | |
| 8 | 2021 | 25 | |
| 9 | 2017 | 22 | |
| 10 | 2020 | 16 | |
| 11 | 2020 | 15 | |
| 12 | 2017 | 14 | |
| 13 | 2023 | 14 | |
| 14 | 2023 | 13 | |
| 15 | 2005 | 12 | |
| 16 | 2014 | 11 | |
| 17 | 2010 | 10 | |
| 18 | 2007 | 9 | |
| 19 | 2015 | 9 | |
| 20 | 2023 | 8 |
About Kok Swee Sim
Kok Swee Sim is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Surfaces, Coatings and Films, Electrical and Electronic Engineering and Cognitive Neuroscience, having authored 74 papers that have together received 968 indexed citations. Recurring topics across this work include Electron and X-Ray Spectroscopy Techniques (12 papers), EEG and Brain-Computer Interfaces (10 papers), Industrial Vision Systems and Defect Detection (7 papers), AI in cancer detection (7 papers), Brain Tumor Detection and Classification (6 papers), Integrated Circuits and Semiconductor Failure Analysis (5 papers), Advancements in Photolithography Techniques (4 papers) and Advanced Malware Detection Techniques (4 papers). The work is most often cited by research in Neurology (162 citations), Radiology, Nuclear Medicine and Imaging (282 citations), Artificial Intelligence (451 citations), Computer Vision and Pattern Recognition (185 citations) and Structural Biology (12 citations). Kok Swee Sim has collaborated with scholars based in Malaysia, Australia and Singapore. Frequent co-authors include Fung Fung Ting, Shing Chiang Tan, Bruno A. Latella, I.M. Low, J. Lane, Martin P. McGrath, David Lawrence, Patrick Schmidt, Mohammed Nasser Al-Andoli and Chee Peng Lim. Their work appears in journals such as IEEE Access, Journal of Microscopy, Rubber Chemistry and Technology, Applied Physics Letters and Microscopy Research and Technique.
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.