Damien Dablain
Impact in
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- Artificial Intelligence in Healthcare
- Artificial Intelligence top 10%
- Imbalanced Data Classification Techniques
- Anomaly Detection Techniques and Applications
- Text and Document Classification Technologies
Papers in
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- Imbalanced Data Classification Techniques 4
- Anomaly Detection Techniques and Applications 2
- Domain Adaptation and Few-Shot Learning 1
- Machine Learning in Healthcare 1
- Explainable Artificial Intelligence (XAI) 1
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- Digital Media Forensic Detection 1
- Co-authors
- Nitesh V. Chawla (4 shared papers)Bartosz Krawczyk (3 shared papers)Colin Bellinger (2 shared papers)Mark Roberts (1 shared paper)David W. Aha (1 shared paper)
- Journals
- Machine Learning (2 papers)IEEE Transactions on Neural Networks and Learning Systems (1 paper)
- Partner nations
- United StatesCanada
In The Last Decade
Damien Dablain
4 papers receiving 365 citations
Damien Dablain's Hit Papers
Peers
Comparison fields: 5 of 88
- Health Information Management 29
- Artificial Intelligence 188
- Health Informatics 4
- Media Technology 22
- Computer Vision and Pattern Recognition 45
Countries citing papers authored by Damien Dablain
This map shows the geographic impact of Damien Dablain'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 Damien Dablain with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Damien Dablain more than expected).
Fields of papers citing papers by Damien Dablain
This network shows the impact of papers produced by Damien Dablain. 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 Damien Dablain. The network helps show where Damien Dablain may publish in the future.
Co-authors
The 5 scholars most cited alongside Damien Dablain, 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 | DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data Hit paper breakdown → | 2022 | 325 |
| 2 | 2023 | 36 | |
| 3 | 2024 | 6 | |
| 4 | 2024 | 5 |
About Damien Dablain
Damien Dablain is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Safety Research, Economics and Econometrics and Infectious Diseases, having authored 4 papers that have together received 372 indexed citations. Recurring topics across this work include Imbalanced Data Classification Techniques (4 papers), Anomaly Detection Techniques and Applications (2 papers), Domain Adaptation and Few-Shot Learning (1 paper), Digital Media Forensic Detection (1 paper), Law, Economics, and Judicial Systems (1 paper), Machine Learning in Healthcare (1 paper), Ethics and Social Impacts of AI (1 paper) and Explainable Artificial Intelligence (XAI) (1 paper). The work is most often cited by research in Health Information Management (29 citations), Artificial Intelligence (188 citations), Health Informatics (4 citations), Media Technology (22 citations) and Computer Vision and Pattern Recognition (45 citations). Damien Dablain has collaborated with scholars based in United States and Canada. Frequent co-authors include Nitesh V. Chawla, Bartosz Krawczyk, Colin Bellinger, Mark Roberts and David W. Aha. Their work appears in journals such as Machine Learning 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.