Jesús Maillo
Impact in
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- Artificial Intelligence in Healthcare
- Artificial Intelligence top 5%
- Machine Learning and Data Classification
- Imbalanced Data Classification Techniques
- Data Stream Mining Techniques
- Anomaly Detection Techniques and Applications
- Text and Document Classification Technologies
Papers in
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- Machine Learning and Data Classification 9
- Imbalanced Data Classification Techniques 5
- Data Stream Mining Techniques 5
- Anomaly Detection Techniques and Applications 1
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- Data Mining Algorithms and Applications 6
- Co-authors
- Francisco Herrera (10 shared papers)Isaac Triguero (9 shared papers)Sergio Andrés Osuna Ramírez (1 shared paper)Julián Luengo (6 shared papers)Salvador García (6 shared papers)Diego García‐Gil (2 shared papers)Mikel Galar (1 shared paper)Humberto Bustince (1 shared paper)
- Journals
- Knowledge-Based Systems (1 paper)IEEE Access (1 paper)Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery (1 paper)IEEE Transactions on Fuzzy Systems (1 paper)2015 IEEE Trustcom/BigDataSE/ISPA (1 paper)
- Partner nations
- SpainUnited KingdomBelgium
In The Last Decade
Jesús Maillo
10 papers receiving 558 citations
Peers
Comparison fields: 5 of 115
- Health Information Management 48
- Artificial Intelligence 328
- Signal Processing 54
- Information Systems 101
- Computer Vision and Pattern Recognition 81
Countries citing papers authored by Jesús Maillo
This map shows the geographic impact of Jesús Maillo'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 Jesús Maillo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jesús Maillo more than expected).
Fields of papers citing papers by Jesús Maillo
This network shows the impact of papers produced by Jesús Maillo. 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 Jesús Maillo. The network helps show where Jesús Maillo may publish in the future.
Co-authors
The 8 scholars most cited alongside Jesús Maillo, 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 | 2016 | 227 | |
| 2 | 2018 | 141 | |
| 3 | 2015 | 59 | |
| 4 | 2016 | 43 | |
| 5 | 2019 | 41 | |
| 6 | 2020 | 22 | |
| 7 | 2017 | 17 | |
| 8 | 2016 | 14 | |
| 9 | 2018 | 7 | |
| 10 | 2019 | 1 |
About Jesús Maillo
Jesús Maillo is a scholar working on Artificial Intelligence, Information Systems, Health Information Management, Management Science and Operations Research and Infectious Diseases, having authored 10 papers that have together received 572 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (9 papers), Data Mining Algorithms and Applications (6 papers), Imbalanced Data Classification Techniques (5 papers), Data Stream Mining Techniques (5 papers), Artificial Intelligence in Healthcare (3 papers), Data Quality and Management (1 paper) and Anomaly Detection Techniques and Applications (1 paper). The work is most often cited by research in Health Information Management (48 citations), Artificial Intelligence (328 citations), Signal Processing (54 citations), Information Systems (101 citations) and Computer Vision and Pattern Recognition (81 citations). Jesús Maillo has collaborated with scholars based in Spain, United Kingdom and Belgium. Frequent co-authors include Francisco Herrera, Isaac Triguero, Sergio Andrés Osuna Ramírez, Julián Luengo, Salvador García, Diego García‐Gil, Mikel Galar and Humberto Bustince. Their work appears in journals such as Knowledge-Based Systems, IEEE Access, Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, IEEE Transactions on Fuzzy Systems and 2015 IEEE Trustcom/BigDataSE/ISPA.
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.