Andrés Bueno-Crespo
- Health Informatics top 10%
- Environmental Engineering top 10%
- Water Science and Technology top 10%
- Modeling and Simulation top 10%
- Artificial Intelligence top 10%
- Machine Learning and ELM 10
- AI in cancer detection 3
- Neural Networks and Applications 3
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- Smart Agriculture and AI 7
- Greenhouse Technology and Climate Control 5
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- Advanced Memory and Neural Computing 4
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- Computational Drug Discovery Methods 4
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- Plant Water Relations and Carbon Dynamics 3
- Co-authors
- Raquel Martínez‐EspañaJosé M. CeciliaJosé‐Luis Sancho‐GómezJ. SotoAndrés MuñozPatricia Jimeno‐SáezJulio Pérez‐SánchezJavier Senent‐Aparicio
- Partner nations
- SpainUnited KingdomAustralia
In The Last Decade
Andrés Bueno-Crespo
41 papers receiving 622 citations
Peers
Comparison fields: 5 of 117
- Health Informatics 16
- Environmental Engineering 143
- Water Science and Technology 90
- Modeling and Simulation 21
- Artificial Intelligence 136
Countries citing papers authored by Andrés Bueno-Crespo
This map shows the geographic impact of Andrés Bueno-Crespo'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 Andrés Bueno-Crespo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrés Bueno-Crespo more than expected).
Fields of papers citing papers by Andrés Bueno-Crespo
This network shows the impact of papers produced by Andrés Bueno-Crespo. 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 Andrés Bueno-Crespo. The network helps show where Andrés Bueno-Crespo may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Andrés Bueno-Crespo, 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 | 2024 | 4 | |
| 2 | 2024 | 1 | |
| 3 | 2023 | 10 | |
| 4 | 2023 | 11 | |
| 5 | 2023 | 7 | |
| 6 | 2023 | 2 | |
| 7 | 2022 | 7 | |
| 8 | 2022 | 1 | |
| 9 | 2021 | 27 | |
| 10 | 2020 | 76 | |
| 11 | 2020 | 14 | |
| 12 | 2020 | 8 | |
| 13 | 2020 | 17 | |
| 14 | 2020 | 29 | |
| 15 | 2019 | 51 | |
| 16 | 2018 | 41 | |
| 17 | 2016 | 3 | |
| 18 | 2016 | 1 | |
| 19 | 2014 | 15 | |
| 20 | 2013 | 26 |
About Andrés Bueno-Crespo
Andrés Bueno-Crespo is a scholar working on Artificial Intelligence, Developmental Biology, Environmental Engineering, Computational Theory and Mathematics and Building and Construction, having authored 42 papers that have together received 637 indexed citations. Recurring topics across this work include Machine Learning and ELM (10 papers), Smart Agriculture and AI (7 papers), Greenhouse Technology and Climate Control (5 papers), Advanced Memory and Neural Computing (4 papers), Computational Drug Discovery Methods (4 papers), Plant Water Relations and Carbon Dynamics (3 papers), AI in cancer detection (3 papers) and Neural Networks and Applications (3 papers). The work is most often cited by research in Health Informatics (16 citations), Environmental Engineering (143 citations), Water Science and Technology (90 citations), Modeling and Simulation (21 citations) and Artificial Intelligence (136 citations). Andrés Bueno-Crespo has collaborated with scholars based in Spain, United Kingdom and Australia. Frequent co-authors include Raquel Martínez‐España, José M. Cecilia, José‐Luis Sancho‐Gómez, J. Soto, Andrés Muñoz, Patricia Jimeno‐Sáez, Julio Pérez‐Sánchez, Javier Senent‐Aparicio, Rosa-María Menchón-Lara and David Pulido‐Velazquez. Their work appears in journals such as Applied Soft Computing, Biosystems Engineering, Sensors, Scientific Reports and Electronics.
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.