James Smith
Impact in
- Artificial Intelligence top 10%
- Domain Adaptation and Few-Shot Learning
- Machine Learning and ELM
- Geochemistry and Geologic Mapping
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- Multimodal Machine Learning Applications
Papers in
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- Domain Adaptation and Few-Shot Learning 3
- Machine Learning and ELM 2
- Neural Networks and Applications 2
- Advanced Clustering Algorithms Research 1
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- Multimodal Machine Learning Applications 2
- Co-authors
- Bogdan M. Wilamowski (3 shared papers)Bo Wu (2 shared papers)Zsolt Kira (3 shared papers)Yen-Chang Hsu (2 shared papers)Hongxia Jin (1 shared paper)Yilin Shen (1 shared paper)Michael E. Baginski (1 shared paper)Daniel R. Chavas (1 shared paper)
- Journals
- Australian and New Zealand Journal of Public Health (1 paper)ISLE Interdisciplinary Studies in Literature and Environment (1 paper)The Medical Journal of Australia (1 paper)Journal of Anatomy (1 paper)IEEE Transactions on Neural Networks and Learning Systems (1 paper)
- Partner nations
- United StatesAustraliaPoland
In The Last Decade
James Smith
17 papers receiving 416 citations
Peers
Comparison fields: 5 of 108
- Artificial Intelligence 179
- Computer Vision and Pattern Recognition 95
- Atmospheric Science 67
- Geophysics 36
- Global and Planetary Change 53
Countries citing papers authored by James Smith
This map shows the geographic impact of James Smith'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 James Smith with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James Smith more than expected).
Fields of papers citing papers by James Smith
This network shows the impact of papers produced by James Smith. 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 James Smith. The network helps show where James Smith may publish in the future.
Co-authors
The 25 scholars most cited alongside James Smith, 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 | 2021 | 109 | |
| 2 | 2018 | 79 | |
| 3 | 2018 | 61 | |
| 4 | 2006 | 38 | |
| 5 | Summary of results - Joint NTGS - AGSO Age Determination Program 1999-2001 | 2001 | 38 |
| 6 | 2019 | 32 | |
| 7 | 2001 | 23 | |
| 8 | 2021 | 21 | |
| 9 | 2023 | 13 | |
| 10 | 2015 | 6 | |
| 11 | 2018 | 6 | |
| 12 | Unsupervised Continual Learning and Self-Taught Associative Memory Hierarchies | 2019 | 3 |
| 13 | 2024 | 2 | |
| 14 | 2018 | 2 | |
| 15 | 2011 | 2 | |
| 16 | 2023 | 1 | |
| 17 | 1979 | 1 | |
| 18 | 2023 | 0 |
About James Smith
James Smith is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Public Health, Environmental and Occupational Health, Atmospheric Science and Astronomy and Astrophysics, having authored 18 papers that have together received 437 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (3 papers), Global Health and Surgery (2 papers), Health and Medical Research Impacts (2 papers), Machine Learning and ELM (2 papers), Multimodal Machine Learning Applications (2 papers), Neural Networks and Applications (2 papers), Advanced Clustering Algorithms Research (1 paper) and Human-Animal Interaction Studies (1 paper). The work is most often cited by research in Artificial Intelligence (179 citations), Computer Vision and Pattern Recognition (95 citations), Atmospheric Science (67 citations), Geophysics (36 citations) and Global and Planetary Change (53 citations). James Smith has collaborated with scholars based in United States, Australia and Poland. Frequent co-authors include Bogdan M. Wilamowski, Bo Wu, Zsolt Kira, Yen-Chang Hsu, Hongxia Jin, Yilin Shen, Michael E. Baginski, Daniel R. Chavas, Ning Lin and Kerry Emanuel. Their work appears in journals such as Australian and New Zealand Journal of Public Health, ISLE Interdisciplinary Studies in Literature and Environment, The Medical Journal of Australia, Journal of Anatomy and IEEE Transactions on Neural Networks and Learning Systems.
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.