Daniel Rivero
Impact in
- Cognitive Neuroscience top 2%
- EEG and Brain-Computer Interfaces
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- Context-Aware Activity Recognition Systems
- Robotic Path Planning Algorithms
Papers in
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- Evolutionary Algorithms and Applications 28
- Neural Networks and Applications 11
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- EEG and Brain-Computer Interfaces 12
- Co-authors
- Alejandro Pazos (46 shared papers)Ling Guo (4 shared papers)Julián Dorado (39 shared papers)Enrique Fernández-Blanco (30 shared papers)Juan R. Rabuñal (32 shared papers)Alejandro Puente-Castro (4 shared papers)Cristian Robert Munteanu (7 shared papers)José Antonio Seoane (6 shared papers)
In The Last Decade
Daniel Rivero
64 papers receiving 2.2k citations
Peers
Comparison fields: 5 of 100
- Cognitive Neuroscience 1.1k
- Computer Vision and Pattern Recognition 307
- Artificial Intelligence 378
- Analytical Chemistry 99
- Environmental Engineering 104
Countries citing papers authored by Daniel Rivero
This map shows the geographic impact of Daniel Rivero'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 Daniel Rivero with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Rivero more than expected).
Fields of papers citing papers by Daniel Rivero
This network shows the impact of papers produced by Daniel Rivero. 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 Daniel Rivero. The network helps show where Daniel Rivero may publish in the future.
Co-authors
The 25 scholars most cited alongside Daniel Rivero, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 73 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2010 | 373 | |
| 2 | 2010 | 352 | |
| 3 | 2011 | 231 | |
| 4 | 2009 | 167 | |
| 5 | 2021 | 163 | |
| 6 | 2020 | 132 | |
| 7 | 2017 | 72 | |
| 8 | 2006 | 71 | |
| 9 | 2003 | 69 | |
| 10 | 2023 | 62 | |
| 11 | 2022 | 45 | |
| 12 | 2016 | 38 | |
| 13 | 2023 | 36 | |
| 14 | 2020 | 31 | |
| 15 | 2004 | 29 | |
| 16 | 2010 | 26 | |
| 17 | 2011 | 20 | |
| 18 | 2002 | 19 | |
| 19 | 2008 | 19 | |
| 20 | 2013 | 19 |
About Daniel Rivero
Daniel Rivero is a scholar working on Artificial Intelligence, Cognitive Neuroscience, Computer Vision and Pattern Recognition, Environmental Engineering and Environmental Chemistry, having authored 73 papers that have together received 2.3k indexed citations. Recurring topics across this work include Evolutionary Algorithms and Applications (28 papers), EEG and Brain-Computer Interfaces (12 papers), Neural Networks and Applications (11 papers), Hydrological Forecasting Using AI (6 papers), Marine Toxins and Detection Methods (5 papers), Spectroscopy and Chemometric Analyses (5 papers), Robotic Path Planning Algorithms (4 papers) and Biomedical Text Mining and Ontologies (3 papers). The work is most often cited by research in Cognitive Neuroscience (1.1k citations), Computer Vision and Pattern Recognition (307 citations), Artificial Intelligence (378 citations), Analytical Chemistry (99 citations) and Environmental Engineering (104 citations). Daniel Rivero has collaborated with scholars based in Spain, Ecuador and Portugal. Frequent co-authors include Alejandro Pazos, Ling Guo, Julián Dorado, Enrique Fernández-Blanco, Juan R. Rabuñal, Alejandro Puente-Castro, Cristian Robert Munteanu, José Antonio Seoane, Miguel Rodríguez Luaces and Jerónimo Puertas. Their work appears in journals such as Computers and Electronics in Agriculture, Expert Systems with Applications, Applied Sciences, Soft Computing and Neurocomputing.
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.