David Charte

995 total citations
8 papers, 418 citations indexed

About

David Charte is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, David Charte has authored 8 papers receiving a total of 418 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 2 papers in Signal Processing. Recurrent topics in David Charte's work include Anomaly Detection Techniques and Applications (3 papers), Generative Adversarial Networks and Image Synthesis (2 papers) and Time Series Analysis and Forecasting (2 papers). David Charte is often cited by papers focused on Anomaly Detection Techniques and Applications (3 papers), Generative Adversarial Networks and Image Synthesis (2 papers) and Time Series Analysis and Forecasting (2 papers). David Charte collaborates with scholars based in Spain and Saudi Arabia. David Charte's co-authors include Francisco Charte, Francisco Herrera, Julián Luengo, Anabel Gómez-Ríos, Fabián Herrera, Siham Tabik, Juan Luis Suárez, Iván Sevillano-García, José Luis Martín Rodríguez and Emilio Guirado and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Neurocomputing and Knowledge-Based Systems.

In The Last Decade

David Charte

8 papers receiving 398 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
David Charte Spain 7 251 230 65 47 34 8 418
Neha Gianchandani Canada 4 317 1.3× 427 1.9× 91 1.4× 79 1.7× 56 1.6× 5 549
Pedram Lalbakhsh Iran 4 170 0.7× 247 1.1× 28 0.4× 57 1.2× 28 0.8× 16 429
Afshar Shamsi Australia 8 139 0.6× 111 0.5× 39 0.6× 20 0.4× 14 0.4× 13 287
Rajeev Kumar Singh India 9 132 0.5× 138 0.6× 60 0.9× 25 0.5× 16 0.5× 25 380
Juan Luis Suárez Spain 6 211 0.8× 229 1.0× 71 1.1× 46 1.0× 34 1.0× 10 359
Hamzeh Asgharnezhad Australia 6 125 0.5× 111 0.5× 34 0.5× 20 0.4× 14 0.4× 8 249
Abolfazl Zargari Khuzani United States 6 296 1.2× 400 1.7× 95 1.5× 55 1.2× 67 2.0× 13 503
Seyed Vahid Moravvej Iran 12 202 0.8× 71 0.3× 62 1.0× 9 0.2× 27 0.8× 13 406
Shailendra Tiwari India 11 131 0.5× 119 0.5× 150 2.3× 9 0.2× 25 0.7× 37 387
Utkarsh Sinha India 6 196 0.8× 295 1.3× 66 1.0× 44 0.9× 41 1.2× 13 368

Countries citing papers authored by David Charte

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of David Charte

This figure shows the co-authorship network connecting the top 25 collaborators of David Charte. A scholar is included among the top collaborators of David Charte based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with David Charte. David Charte is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

8 of 8 papers shown
1.
Charte, David, Francisco Charte, & Francisco Herrera. (2021). Reducing Data Complexity Using Autoencoders With Class-Informed Loss Functions. IEEE Transactions on Pattern Analysis and Machine Intelligence. 44(12). 9549–9560. 12 indexed citations
2.
3.
Charte, David, Francisco Charte, María José del Jesús, & Francisco Herrera. (2020). An analysis on the use of autoencoders for representation learning: Fundamentals, learning task case studies, explainability and challenges. Neurocomputing. 404. 93–107. 43 indexed citations
4.
5.
Tabik, Siham, Anabel Gómez-Ríos, José Luis Martín Rodríguez, et al.. (2020). COVIDGR Dataset and COVID-SDNet Methodology for Predicting COVID-19 Based on Chest X-Ray Images. IEEE Journal of Biomedical and Health Informatics. 24(12). 3595–3605. 244 indexed citations
6.
Charte, David, Francisco Herrera, & Francisco Charte. (2019). Ruta: Implementations of neural autoencoders in R. Knowledge-Based Systems. 174. 4–8. 4 indexed citations
7.
Charte, Francisco, Antonio J. Rivera, David Charte, María José del Jesús, & Francisco Herrera. (2018). Tips, guidelines and tools for managing multi-label datasets: The mldr.datasets R package and the Cometa data repository. Neurocomputing. 289. 68–85. 24 indexed citations
8.
Charte, Francisco & David Charte. (2015). Working with Multilabel Datasets in R: The mldr Package. The R Journal. 7(2). 149–149. 39 indexed citations

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.

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2026