Chee‐Kheong Siew

7.8k total citations · 2 hit papers
16 papers, 5.9k citations indexed

About

Chee‐Kheong Siew is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Chee‐Kheong Siew has authored 16 papers receiving a total of 5.9k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Electrical and Electronic Engineering, 9 papers in Artificial Intelligence and 5 papers in Computer Networks and Communications. Recurrent topics in Chee‐Kheong Siew's work include Neural Networks and Applications (9 papers), Machine Learning and ELM (9 papers) and Advanced Memory and Neural Computing (5 papers). Chee‐Kheong Siew is often cited by papers focused on Neural Networks and Applications (9 papers), Machine Learning and ELM (9 papers) and Advanced Memory and Neural Computing (5 papers). Chee‐Kheong Siew collaborates with scholars based in Singapore, Australia and China. Chee‐Kheong Siew's 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 and has published in prestigious journals such as IEEE Transactions on Vehicular Technology, Neurocomputing and Computer Communications.

In The Last Decade

Chee‐Kheong Siew

15 papers receiving 5.7k citations

Hit Papers

Extreme learning machine: a new learning scheme of feedfo... 2005 2026 2012 2019 2005 2006 1000 2.0k 3.0k

Peers

Chee‐Kheong Siew
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
Replace Hongming Zhou with:
Hongming Zhou China
Xiaojian Ding China
Rui Zhang China
P. Saratchandran Singapore
Amaury Lendasse Finland
Qinyu Zhu China
Zhihua Cai China
Shiji Song China
Gao Huang China
Ke Yan China
Hongming Zhou China View profile →
Citations per field, relative to Chee‐Kheong Siew
Chee‐Kheong Siew · 1×
Citations per year, relative to Chee‐Kheong Siew
Chee‐Kheong Siew · 1×

Countries citing papers authored by Chee‐Kheong Siew

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

16 of 16 papers shown
# Work Indexed citations
1 3
2 5
3 196
4
Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes breakdown →
1979
5 184
6 4
7 196
8
Extreme learning machine: a new learning scheme of feedforward neural networks breakdown →
3093
9
Fast Modular Network Implementation for Support
3
10 5
11 3
12 210
13 8
14 1
15 1
16 0

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.

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