José M. Puerta
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 5%
- Information Systems top 5%
- Computational Theory and Mathematics top 5%
- Management Science and Operations Research top 5%
- Co-authors
- José A. GámezPablo BermejoLuis de la OssaJuan L. MateoLuis M. de CamposJuan M. Fernández‐LunaM. Julia FloresFrancisco Ayala
- Topics
- Bayesian Modeling and Causal Inference (25 papers)Machine Learning and Data Classification (12 papers)Data Mining Algorithms and Applications (10 papers)
- Partner nations
- SpainUnited KingdomBelgium
In The Last Decade
José M. Puerta
68 papers receiving 1.4k citations
Peers
Comparison fields: 5 of 135
- Artificial Intelligence 819
- Computer Vision and Pattern Recognition 247
- Information Systems 221
- Computational Theory and Mathematics 171
- Management Science and Operations Research 148
Countries citing papers authored by José M. Puerta
This map shows the geographic impact of José M. Puerta'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 José M. Puerta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites José M. Puerta more than expected).
Fields of papers citing papers by José M. Puerta
This network shows the impact of papers produced by José M. Puerta. 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 José M. Puerta. The network helps show where José M. Puerta may publish in the future.
Co-authorship network of co-authors of José M. Puerta
This figure shows the co-authorship network connecting the top 25 collaborators of José M. Puerta. A scholar is included among the top collaborators of José M. Puerta 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 José M. Puerta. José M. Puerta is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 12 | |
| 5 | 21 | |
| 6 | 19 | |
| 7 | 4 | |
| 8 | 6 | |
| 9 | 83 | |
| 10 | 19 | |
| 11 | 27 | |
| 12 | 27 | |
| 13 | 3 | |
| 14 | 4 | |
| 15 | SUPPORTING INTELLIGENT SYSTEMS TEACHING BY MEANS OF COMPUTER GAMES PROGRAMMING | 2 |
| 16 | 1 | |
| 17 | 12 | |
| 18 | 2 | |
| 19 | Dependency networks based classifiers: learning models by using independence. | 4 |
| 20 | Graphical Models to Causal Discovery from Data. | 0 |
About José M. Puerta
José M. Puerta is a scholar working on Artificial Intelligence, Management Science and Operations Research and Computer Vision and Pattern Recognition, having authored 75 papers that have together received 1.4k indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (25 papers), Machine Learning and Data Classification (12 papers) and Data Mining Algorithms and Applications (10 papers). The work is most often cited by research in Artificial Intelligence (819 citations), Signal Processing (135 citations) and Orthopedics and Sports Medicine (109 citations). José M. Puerta has collaborated with scholars based in Spain, United Kingdom and Belgium. Frequent co-authors include José A. Gámez, Pablo Bermejo, Luis de la Ossa, Juan L. Mateo, Luis M. de Campos, Juan M. Fernández‐Luna, M. Julia Flores, Francisco Ayala, Iñaki Ruiz‐Pérez and Sergio Hernández-Sánchez. Their work appears in journals such as PLoS ONE, Medicine & Science in Sports & Exercise and Expert Systems with Applications.
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.