Simon Bernard
Impact in
- Artificial Intelligence top 5%
- Machine Learning and Data Classification
- Imbalanced Data Classification Techniques
- Anomaly Detection Techniques and Applications
- AI in cancer detection
- Statistics and Probability top 5%
- Statistical Methods and Inference
Papers in
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- Machine Learning and Data Classification 10
- Imbalanced Data Classification Techniques 7
- Anomaly Detection Techniques and Applications 3
- AI in cancer detection 1
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- Statistical Methods and Inference 8
- Co-authors
- Laurent Heutte (17 shared papers)Sébastien Adam (10 shared papers)Caroline Petitjean (3 shared papers)Robert Sabourin (7 shared papers)Clément Chatelain (3 shared papers)Christian Baron (1 shared paper)Danny Lo Seen (1 shared paper)Agnès Bégué (1 shared paper)
In The Last Decade
Simon Bernard
22 papers receiving 748 citations
Peers
Comparison fields: 5 of 90
- Artificial Intelligence 382
- Statistics and Probability 77
- Information Systems 129
- Health Information Management 39
- Computer Vision and Pattern Recognition 78
Countries citing papers authored by Simon Bernard
This map shows the geographic impact of Simon Bernard'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 Simon Bernard with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Simon Bernard more than expected).
Fields of papers citing papers by Simon Bernard
This network shows the impact of papers produced by Simon Bernard. 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 Simon Bernard. The network helps show where Simon Bernard may publish in the future.
Co-authors
The 11 scholars most cited alongside Simon Bernard, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 22 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2013 | 125 | |
| 2 | 2009 | 122 | |
| 3 | 2012 | 99 | |
| 4 | 2009 | 97 | |
| 5 | 2007 | 66 | |
| 6 | 2018 | 54 | |
| 7 | 2018 | 45 | |
| 8 | 2008 | 42 | |
| 9 | 2015 | 28 | |
| 10 | 2012 | 19 | |
| 11 | 2012 | 17 | |
| 12 | 2024 | 16 | |
| 13 | 2009 | 15 | |
| 14 | 2010 | 14 | |
| 15 | 2023 | 9 | |
| 16 | 2012 | 7 | |
| 17 | 2016 | 4 | |
| 18 | 2021 | 4 | |
| 19 | 2018 | 3 | |
| 20 | 2020 | 3 |
About Simon Bernard
Simon Bernard is a scholar working on Artificial Intelligence, Statistics and Probability, Computer Vision and Pattern Recognition, Information Systems and Radiology, Nuclear Medicine and Imaging, having authored 22 papers that have together received 791 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (10 papers), Statistical Methods and Inference (8 papers), Imbalanced Data Classification Techniques (7 papers), Face and Expression Recognition (3 papers), Anomaly Detection Techniques and Applications (3 papers), Data Mining Algorithms and Applications (2 papers), Radiomics and Machine Learning in Medical Imaging (2 papers) and AI in cancer detection (1 paper). The work is most often cited by research in Artificial Intelligence (382 citations), Statistics and Probability (77 citations), Information Systems (129 citations), Health Information Management (39 citations) and Computer Vision and Pattern Recognition (78 citations). Simon Bernard has collaborated with scholars based in France, Canada and Belgium. Frequent co-authors include Laurent Heutte, Sébastien Adam, Caroline Petitjean, Robert Sabourin, Clément Chatelain, Christian Baron, Danny Lo Seen, Agnès Bégué, Élodie Vintrou and Jean Paul Barddal. Their work appears in journals such as Pattern Recognition, Statistics and Computing, IEEE Access, Photogrammetric Engineering & Remote Sensing and Pattern Recognition Letters.
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.