S.C. Chiam

680 citations
16 papers · 560 · h-index 9

Impact in

Papers in

S.C. Chiam

16 papers receiving 543 citations

Peers

S.C. Chiam
Comparison fields: 5 of 71
  • Computational Theory and Mathematics 253
  • Management Science and Operations Research 148
  • Artificial Intelligence 344
  • Finance 54
  • Industrial and Manufacturing Engineering 49
Replace Rubén Saborido with:
Rubén Saborido Canada
Enlu Zhou United States
Claudio Gentile Italy
Antonio López Jáimes Mexico
Ivona Brajević Serbia
Tunçhan Cura Türkiye
Arnab Nilim United States
Jui-Fang Chang Taiwan
Sana Ben Hamida France
Douglas J. White United Kingdom
S.C. Chiam relative to Rubén Saborido Canada Rubén Saborido's profile →
Citations per field
00.5×2.7×
Rubén Saborido · 1×
Citations per year

Countries citing papers authored by S.C. Chiam

Since Specialization
Citations

This map shows the geographic impact of S.C. Chiam'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 S.C. Chiam with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites S.C. Chiam more than expected).

Fields of papers citing papers by S.C. Chiam

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by S.C. Chiam. 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 S.C. Chiam. The network helps show where S.C. Chiam may publish in the future.

Co-authors

The 4 scholars most cited alongside S.C. Chiam, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with S.C. Chiam Line = papers co-authored together S.C. Chiam links everyone, so they are left out of the graph.

All Works

16 of 16 papers shown
#Work
1 2009207
2 2008138
3 200869
4 200839
5 201330
6 200719
7 200916
8 200615
9 200710
10 20065
11 20053
12 20072
13 20082
14 20072
15 20072
16 20101

About S.C. Chiam

S.C. Chiam is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research, Control and Systems Engineering and Finance, having authored 16 papers that have together received 560 indexed citations. Recurring topics across this work include Advanced Multi-Objective Optimization Algorithms (13 papers), Metaheuristic Optimization Algorithms Research (13 papers), Evolutionary Algorithms and Applications (10 papers), Process Optimization and Integration (3 papers), Stock Market Forecasting Methods (3 papers), Advanced Control Systems Optimization (1 paper), Risk and Portfolio Optimization (1 paper) and Forecasting Techniques and Applications (1 paper). The work is most often cited by research in Computational Theory and Mathematics (253 citations), Management Science and Operations Research (148 citations), Artificial Intelligence (344 citations), Finance (54 citations) and Industrial and Manufacturing Engineering (49 citations). S.C. Chiam has collaborated with scholars based in Singapore. Frequent co-authors include Kay Chen Tan, Chi-Keong Goh, Abdullah Al Mamun and C. Y. Cheong. Their work appears in journals such as Expert Systems with Applications, European Journal of Operational Research, IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), Applied Soft Computing and Lecture notes in computer science.

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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