Phyo Phyo San
- Computer Vision and Pattern Recognition top 2%
- Artificial Intelligence top 5%
- Biomedical Engineering top 10%
- Control and Systems Engineering top 5%
- Computer Networks and Communications top 5%
- Co-authors
- Jian YangMinh Nhut NguyenShonali KrishnaswamyXiaoli LiSai Ho LingHung T. NguyenBeibei RenShuzhi Sam Ge
- Topics
- Diabetes Management and Research (12 papers)ECG Monitoring and Analysis (11 papers)Heart Rate Variability and Autonomic Control (7 papers)
- Journals
- IEEE Transactions on Industrial ElectronicsIEEE Transactions on CyberneticsIEEE Transactions on Neural Networks and Learning Systems
- Partner nations
- AustraliaSingaporeUnited Kingdom
In The Last Decade
Phyo Phyo San
24 papers receiving 1.2k citations
Hit Papers
Peers
Comparison fields: 5 of 110
- Computer Vision and Pattern Recognition 466
- Artificial Intelligence 319
- Biomedical Engineering 284
- Control and Systems Engineering 183
- Computer Networks and Communications 174
Countries citing papers authored by Phyo Phyo San
This map shows the geographic impact of Phyo Phyo San'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 Phyo Phyo San with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Phyo Phyo San more than expected).
Fields of papers citing papers by Phyo Phyo San
This network shows the impact of papers produced by Phyo Phyo San. 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 Phyo Phyo San. The network helps show where Phyo Phyo San may publish in the future.
Co-authorship network of co-authors of Phyo Phyo San
This figure shows the co-authorship network connecting the top 25 collaborators of Phyo Phyo San. A scholar is included among the top collaborators of Phyo Phyo San based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Phyo Phyo San. Phyo Phyo San is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 121 | |
| 2 | 53 | |
| 3 | 27 | |
| 4 | 33 | |
| 5 | Deep convolutional neural networks on multichannel time series for human activity recognitionbreakdown → | 668 |
| 6 | 12 | |
| 7 | 14 | |
| 8 | 4 | |
| 9 | 7 | |
| 10 | 9 | |
| 11 | 7 | |
| 12 | 2 | |
| 13 | 10 | |
| 14 | 13 | |
| 15 | 1 | |
| 16 | 4 | |
| 17 | 15 | |
| 18 | 33 | |
| 19 | 94 | |
| 20 | 2 |
About Phyo Phyo San
Phyo Phyo San is a scholar working on Endocrinology, Diabetes and Metabolism, Cardiology and Cardiovascular Medicine and Health Information Management, having authored 24 papers that have together received 1.2k indexed citations. Recurring topics across this work include Diabetes Management and Research (12 papers), ECG Monitoring and Analysis (11 papers) and Heart Rate Variability and Autonomic Control (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (466 citations), Signal Processing (113 citations) and Artificial Intelligence (319 citations). Phyo Phyo San has collaborated with scholars based in Australia, Singapore and United Kingdom. Frequent co-authors include Jian Yang, Minh Nhut Nguyen, Shonali Krishnaswamy, Xiaoli Li, Sai Ho Ling, Hung T. Nguyen, Beibei Ren, Shuzhi Sam Ge, Tong Heng Lee and Rifai Chai. Their work appears in journals such as IEEE Transactions on Industrial Electronics, IEEE Transactions on Cybernetics and IEEE Transactions on Neural Networks and Learning 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.