Daniel Rivero
- Signal Processing top 1%
- Blind Source Separation Techniques 7
- Cognitive Neuroscience top 2%
- EEG and Brain-Computer Interfaces 11
- Artificial Intelligence top 5%
- Evolutionary Algorithms and Applications 19
- Neural Networks and Applications 13
- Metaheuristic Optimization Algorithms Research 12
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- Force Microscopy Techniques and Applications 5
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- Spectroscopy and Chemometric Analyses 5
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- Fault Detection and Control Systems 4
- Co-authors
- Alejandro PazosLing GuoJulián DoradoEnrique Fernández-BlancoJuan R. RabuñalAlejandro Puente-CastroCristian R. MunteanuJosé A. Seoane
- Journals
- Computers and Electronics in Agriculture (4 papers)Journal of Chemical Theory and Computation (4 papers)Expert Systems with Applications (3 papers)
- Partner nations
- SpainUnited StatesEcuador
In The Last Decade
Daniel Rivero
62 papers receiving 2.1k citations
Peers
Comparison fields: 5 of 138
- Signal Processing 681
- Cognitive Neuroscience 976
- Computer Vision and Pattern Recognition 338
- Artificial Intelligence 459
- Health, Toxicology and Mutagenesis 154
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-authorship network
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
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2022 | 15 | |
| 3 | 2022 | 6 | |
| 4 | 2021 | 8 | |
| 5 | 2021 | 3 | |
| 6 | 2018 | 118 | |
| 7 | 2018 | 22 | |
| 8 | 2017 | 14 | |
| 9 | 2017 | 13 | |
| 10 | 2013 | 3 | |
| 11 | 2012 | 14 | |
| 12 | 2012 | 10 | |
| 13 | 2011 | 204 | |
| 14 | 2010 | 329 | |
| 15 | 2010 | 21 | |
| 16 | 2010 | 312 | |
| 17 | 2010 | 16 | |
| 18 | 2009 | 1 | |
| 19 | Using genetic programming for artificial neural network development and simplification | 2006 | 3 |
| 20 | Distributed genetic programming by an object oriented system using java and corba. | 2004 | 0 |
About Daniel Rivero
Daniel Rivero is a scholar working on Artificial Intelligence, Signal Processing, Cognitive Neuroscience, Biophysics and Analytical Chemistry, having authored 65 papers that have together received 2.2k indexed citations. Recurring topics across this work include Evolutionary Algorithms and Applications (19 papers), Neural Networks and Applications (13 papers), Metaheuristic Optimization Algorithms Research (12 papers), EEG and Brain-Computer Interfaces (11 papers), Blind Source Separation Techniques (7 papers), Force Microscopy Techniques and Applications (5 papers), Spectroscopy and Chemometric Analyses (5 papers) and Fault Detection and Control Systems (4 papers). The work is most often cited by research in Signal Processing (681 citations), Cognitive Neuroscience (976 citations), Computer Vision and Pattern Recognition (338 citations), Artificial Intelligence (459 citations) and Health, Toxicology and Mutagenesis (154 citations). Daniel Rivero has collaborated with scholars based in Spain, United States and Ecuador. Frequent co-authors include Alejandro Pazos, Ling Guo, Julián Dorado, Enrique Fernández-Blanco, Juan R. Rabuñal, Alejandro Puente-Castro, Cristian R. Munteanu, José A. Seoane, Miguel R. Luaces and Jerónimo Puertas. Their work appears in journals such as Computers and Electronics in Agriculture, Journal of Chemical Theory and Computation, Expert Systems with Applications, 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.