David Charte
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
-
- COVID-19 diagnosis using AI
- Radiomics and Machine Learning in Medical Imaging
Papers in
-
- Anomaly Detection Techniques and Applications 3
- Machine Learning and Data Classification 2
- Text and Document Classification Technologies 2
- Imbalanced Data Classification Techniques 1
-
- Generative Adversarial Networks and Image Synthesis 2
- Co-authors
- Francisco Charte (5 shared papers)Francisco Herrera (6 shared papers)Julián Luengo (2 shared papers)Juan Luis Suárez (1 shared paper)José Luis Martín Rodríguez (1 shared paper)Siham Tabik (1 shared paper)Anabel Gómez-Ríos (1 shared paper)Emilio Guirado (1 shared paper)
- Journals
- Neurocomputing (2 papers)The R Journal (1 paper)Information Fusion (1 paper)IEEE Journal of Biomedical and Health Informatics (1 paper)Knowledge-Based Systems (1 paper)
- Partner nations
- SpainSaudi Arabia
In The Last Decade
David Charte
8 papers receiving 417 citations
Peers
Comparison fields: 5 of 79
- Health Informatics 44
- Radiology, Nuclear Medicine and Imaging 232
- Artificial Intelligence 247
- Computer Vision and Pattern Recognition 66
- Health Information Management 13
Countries citing papers authored by David Charte
This map shows the geographic impact of David Charte'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 David Charte with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Charte more than expected).
Fields of papers citing papers by David Charte
This network shows the impact of papers produced by David Charte. 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 David Charte. The network helps show where David Charte may publish in the future.
Co-authors
The 15 scholars most cited alongside David Charte, 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 | 2020 | 253 | |
| 2 | 2020 | 44 | |
| 3 | 2021 | 41 | |
| 4 | 2015 | 40 | |
| 5 | 2018 | 25 | |
| 6 | 2020 | 16 | |
| 7 | 2021 | 12 | |
| 8 | 2019 | 4 |
About David Charte
David Charte is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Information Systems and Health Informatics, having authored 8 papers that have together received 435 indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (3 papers), Generative Adversarial Networks and Image Synthesis (2 papers), Machine Learning and Data Classification (2 papers), Time Series Analysis and Forecasting (2 papers), Text and Document Classification Technologies (2 papers), Imbalanced Data Classification Techniques (1 paper), Industrial Vision Systems and Defect Detection (1 paper) and Rough Sets and Fuzzy Logic (1 paper). The work is most often cited by research in Health Informatics (44 citations), Radiology, Nuclear Medicine and Imaging (232 citations), Artificial Intelligence (247 citations), Computer Vision and Pattern Recognition (66 citations) and Health Information Management (13 citations). David Charte has collaborated with scholars based in Spain and Saudi Arabia. Frequent co-authors include Francisco Charte, Francisco Herrera, Julián Luengo, Juan Luis Suárez, José Luis Martín Rodríguez, Siham Tabik, Anabel Gómez-Ríos, Emilio Guirado, Iván Sevillano-García and Fabián Herrera. Their work appears in journals such as Neurocomputing, The R Journal, Information Fusion, IEEE Journal of Biomedical and Health Informatics and Knowledge-Based 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.