Daniel Rivero

3.1k citations
65 papers · 2.2k · h-index 21

Impact in

Papers in

Daniel Rivero

62 papers receiving 2.1k citations

Peers

Daniel Rivero
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
Replace Hualou Liang with:
Hualou Liang United States
Haiping Lu United States
Julián Dorado Spain
Carlos G. Puntonet Spain
H. Troy Nagle United States
Ravinder Agarwal India
Shasha Yuan China
Petros Xanthopoulos United States
Biao Sun China
Huimin Zhao China
Daniel Rivero relative to Hualou Liang United States Hualou Liang's profile →
Citations per field
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Hualou Liang · 1×
Citations per year

Countries citing papers authored by Daniel Rivero

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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.

Border = papers with Daniel Rivero Line = papers co-authored together Daniel Rivero links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 65 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2010329
2 2010312
3 2011204
4 2009151
5 2021133
6 2018118
7 2020111
8 200667
9 201764
10 200361
11 201946
12 201632
13 202231
14 202330
15 202327
16 202027
17 200423
18 201023
19 201822
20 201021

About Daniel Rivero

Daniel Rivero is a scholar working on Artificial Intelligence, Cognitive Neuroscience, Molecular Biology, Computer Vision and Pattern Recognition and Signal Processing, 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), Spectroscopy and Chemometric Analyses (5 papers), Force Microscopy Techniques and Applications (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, Cristian R. Munteanu, Alejandro Puente-Castro, 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.

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