John Sum
Impact in
- Artificial Intelligence top 5%
- Neural Networks and Applications
- Machine Learning and ELM
- Neural Networks and Reservoir Computing
- Signal Processing top 10%
- Blind Source Separation Techniques
Papers in
-
- Neural Networks and Applications 37
- Machine Learning and ELM 15
-
- Optimization and Search Problems 6
- Network Security and Intrusion Detection 5
- Co-authors
- Chi-Sing Leung (43 shared papers)Kevin Ho (17 shared papers)Gilbert H. Young (10 shared papers)Laiwan Chan (7 shared papers)Yi Xiao (4 shared papers)Hong Shen (6 shared papers)P.K.S. Tam (2 shared papers)Hongjiang Wang (1 shared paper)
- Journals
- IEEE Transactions on Neural Networks and Learning Systems (15 papers)Neural Computing and Applications (4 papers)Neurocomputing (4 papers)Neural Computation (3 papers)Cognitive Computation (1 paper)
- Partner nations
- Hong KongTaiwanUnited States
In The Last Decade
John Sum
67 papers receiving 652 citations
Peers
Comparison fields: 5 of 81
- Artificial Intelligence 411
- Signal Processing 80
- Computer Networks and Communications 150
- Computer Vision and Pattern Recognition 134
- Control and Systems Engineering 105
Countries citing papers authored by John Sum
This map shows the geographic impact of John Sum'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 John Sum with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Sum more than expected).
Fields of papers citing papers by John Sum
This network shows the impact of papers produced by John Sum. 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 John Sum. The network helps show where John Sum may publish in the future.
Co-authors
The 25 scholars most cited alongside John Sum, 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 | 1999 | 47 | |
| 2 | 2010 | 40 | |
| 3 | 1999 | 39 | |
| 4 | 2008 | 35 | |
| 5 | 2010 | 28 | |
| 6 | 2017 | 27 | |
| 7 | 2008 | 26 | |
| 8 | 2012 | 24 | |
| 9 | 2014 | 24 | |
| 10 | 1999 | 23 | |
| 11 | 2012 | 23 | |
| 12 | 1999 | 22 | |
| 13 | 2013 | 22 | |
| 14 | 1998 | 18 | |
| 15 | 1997 | 16 | |
| 16 | 2003 | 15 | |
| 17 | 2011 | 14 | |
| 18 | 2010 | 13 | |
| 19 | 2021 | 12 | |
| 20 | 2013 | 12 |
About John Sum
John Sum is a scholar working on Artificial Intelligence, Computer Networks and Communications, Computer Vision and Pattern Recognition, Signal Processing and Electrical and Electronic Engineering, having authored 71 papers that have together received 678 indexed citations. Recurring topics across this work include Neural Networks and Applications (37 papers), Machine Learning and ELM (15 papers), Face and Expression Recognition (12 papers), Blind Source Separation Techniques (8 papers), Advanced Memory and Neural Computing (7 papers), Optimization and Search Problems (6 papers), Fault Detection and Control Systems (5 papers) and Network Security and Intrusion Detection (5 papers). The work is most often cited by research in Artificial Intelligence (411 citations), Signal Processing (80 citations), Computer Networks and Communications (150 citations), Computer Vision and Pattern Recognition (134 citations) and Control and Systems Engineering (105 citations). John Sum has collaborated with scholars based in Hong Kong, Taiwan and United States. Frequent co-authors include Chi-Sing Leung, Kevin Ho, Gilbert H. Young, Laiwan Chan, Yi Xiao, Hong Shen, P.K.S. Tam, Hongjiang Wang, A.G. Constantinides and Lei Xu. Their work appears in journals such as IEEE Transactions on Neural Networks and Learning Systems, Neural Computing and Applications, Neurocomputing, Neural Computation and Cognitive Computation.
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.