Liam Cervante
Impact in
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- Advanced Multi-Objective Optimization Algorithms
- Rough Sets and Fuzzy Logic
- Artificial Intelligence top 5%
- Metaheuristic Optimization Algorithms Research
- Evolutionary Algorithms and Applications
- Machine Learning and Data Classification
Papers in
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- Metaheuristic Optimization Algorithms Research 3
- Evolutionary Algorithms and Applications 2
- Imbalanced Data Classification Techniques 1
- Text and Document Classification Technologies 1
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- Advanced Multi-Objective Optimization Algorithms 2
- Rough Sets and Fuzzy Logic 2
- Co-authors
- Lin Shang (5 shared papers)Bing Xue (5 shared papers)Mengjie Zhang (3 shared papers)Will N. Browne (3 shared papers)
- Journals
- Connection Science (1 paper)International Journal of Artificial Intelligence Tools (1 paper)International Journal of Computational Intelligence and Applications (1 paper)Zenodo (CERN European Organization for Nuclear Research) (1 paper)
- Partner nations
- New ZealandChina
In The Last Decade
Liam Cervante
5 papers receiving 289 citations
Peers
Comparison fields: 5 of 52
- Computational Theory and Mathematics 118
- Artificial Intelligence 237
- Computer Vision and Pattern Recognition 48
- Modeling and Simulation 6
- Control and Systems Engineering 26
Countries citing papers authored by Liam Cervante
This map shows the geographic impact of Liam Cervante'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 Liam Cervante with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Liam Cervante more than expected).
Fields of papers citing papers by Liam Cervante
This network shows the impact of papers produced by Liam Cervante. 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 Liam Cervante. The network helps show where Liam Cervante may publish in the future.
Co-authors
The 4 scholars most cited alongside Liam Cervante, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2012 | 108 | |
| 2 | 2012 | 79 | |
| 3 | 2013 | 62 | |
| 4 | 2014 | 40 | |
| 5 | 2013 | 12 |
About Liam Cervante
Liam Cervante is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Control and Systems Engineering, Computer Vision and Pattern Recognition and Management Science and Operations Research, having authored 5 papers that have together received 301 indexed citations. Recurring topics across this work include Metaheuristic Optimization Algorithms Research (3 papers), Advanced Multi-Objective Optimization Algorithms (2 papers), Evolutionary Algorithms and Applications (2 papers), Rough Sets and Fuzzy Logic (2 papers), Imbalanced Data Classification Techniques (1 paper), Grey System Theory Applications (1 paper), Advanced Algorithms and Applications (1 paper) and Text and Document Classification Technologies (1 paper). The work is most often cited by research in Computational Theory and Mathematics (118 citations), Artificial Intelligence (237 citations), Computer Vision and Pattern Recognition (48 citations), Modeling and Simulation (6 citations) and Control and Systems Engineering (26 citations). Liam Cervante has collaborated with scholars based in New Zealand and China. Frequent co-authors include Lin Shang, Bing Xue, Mengjie Zhang and Will N. Browne. Their work appears in journals such as Connection Science, International Journal of Artificial Intelligence Tools, International Journal of Computational Intelligence and Applications and Zenodo (CERN European Organization for Nuclear Research).
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.