Chee‐Kheong Siew
- Artificial Intelligence top 0.1%
- Electrical and Electronic Engineering top 2%
- Computer Vision and Pattern Recognition top 1%
- Control and Systems Engineering top 2%
- Environmental Engineering top 2%
- Co-authors
- Guang-Bin HuangQin‐Yu ZhuLei ChenQinyu ZhuN. SundararajanKezhi MaoP. SaratchandranJunhua Tang
- Topics
- Neural Networks and Applications (9 papers)Machine Learning and ELM (9 papers)Advanced Memory and Neural Computing (5 papers)
In The Last Decade
Chee‐Kheong Siew
15 papers receiving 5.7k citations
Hit Papers
Peers
Comparison fields: 5 of 151
- Artificial Intelligence 4.5k
- Electrical and Electronic Engineering 1.7k
- Computer Vision and Pattern Recognition 1.1k
- Control and Systems Engineering 676
- Environmental Engineering 363
Countries citing papers authored by Chee‐Kheong Siew
This map shows the geographic impact of Chee‐Kheong Siew'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 Chee‐Kheong Siew with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chee‐Kheong Siew more than expected).
Fields of papers citing papers by Chee‐Kheong Siew
This network shows the impact of papers produced by Chee‐Kheong Siew. 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 Chee‐Kheong Siew. The network helps show where Chee‐Kheong Siew may publish in the future.
Co-authorship network of co-authors of Chee‐Kheong Siew
This figure shows the co-authorship network connecting the top 25 collaborators of Chee‐Kheong Siew. A scholar is included among the top collaborators of Chee‐Kheong Siew 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 Chee‐Kheong Siew. Chee‐Kheong Siew is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 5 | |
| 3 | 196 | |
| 4 | Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodesbreakdown → | 1979 |
| 5 | 184 | |
| 6 | 4 | |
| 7 | 196 | |
| 8 | Extreme learning machine: a new learning scheme of feedforward neural networksbreakdown → | 3093 |
| 9 | Fast Modular Network Implementation for Support | 3 |
| 10 | 5 | |
| 11 | 3 | |
| 12 | 210 | |
| 13 | 8 | |
| 14 | 1 | |
| 15 | 1 | |
| 16 | 0 |
About Chee‐Kheong Siew
Chee‐Kheong Siew is a scholar working on Artificial Intelligence, Computer Networks and Communications and Electrical and Electronic Engineering, having authored 16 papers that have together received 5.9k indexed citations. Recurring topics across this work include Neural Networks and Applications (9 papers), Machine Learning and ELM (9 papers) and Advanced Memory and Neural Computing (5 papers). The work is most often cited by research in Artificial Intelligence (4.5k citations), Computer Vision and Pattern Recognition (1.1k citations) and Electrical and Electronic Engineering (1.7k citations). Chee‐Kheong Siew has collaborated with scholars based in Singapore, Australia and China. Frequent co-authors include Guang-Bin Huang, Qin‐Yu Zhu, Lei Chen, Qinyu Zhu, Lei Chen, N. Sundararajan, Kezhi Mao, P. Saratchandran, Junhua Tang and Liren Zhang. Their work appears in journals such as IEEE Transactions on Vehicular Technology, Neurocomputing and Computer Communications.
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.