Juan R. Rabuñal

1.8k citations
67 papers · 1.2k indexed · h-index 19
Topics
Evolutionary Algorithms and Applications (11 papers)Metaheuristic Optimization Algorithms Research (7 papers)Water Quality Monitoring Technologies (7 papers)
Journals
SHILAP Revista de lepidopterologíaAnalytical BiochemistryPolymer
Partner nations
SpainPortugalNorway

In The Last Decade

Juan R. Rabuñal

64 papers receiving 1.2k citations

Peers

Juan R. Rabuñal
Comparison fields: 5 of 136
  • Cognitive Neuroscience 343
  • Signal Processing 218
  • Artificial Intelligence 212
  • Civil and Structural Engineering 146
  • Environmental Engineering 146
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Citations per field
00.5×2.8×
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Citations per year

Countries citing papers authored by Juan R. Rabuñal

Since Specialization
Citations

This map shows the geographic impact of Juan R. Rabuñal'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 Juan R. Rabuñal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Juan R. Rabuñal more than expected).

Fields of papers citing papers by Juan R. Rabuñal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Juan R. Rabuñal. 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 Juan R. Rabuñal. The network helps show where Juan R. Rabuñal may publish in the future.

Co-authorship network of co-authors of Juan R. Rabuñal

This figure shows the co-authorship network connecting the top 25 collaborators of Juan R. Rabuñal. A scholar is included among the top collaborators of Juan R. Rabuñal 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 Juan R. Rabuñal. Juan R. Rabuñal is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 1
2 5
3 15
4 1
5 1
6 1
7 2
8 10
9 11
10 48
11 24
12 14
13 17
14 329
15 30
16
Uso de técnicas de inteligencia artificial en ingeniería civil
1
17
Aceleración del entrenamiento de Redes de Neuronas Artificiales mediante Algoritmos Genéticos utilizando la GPU
1
18
Using genetic programming for artificial neural network development and simplification
3
19
Distributed genetic programming by an object oriented system using java and corba.
0
20
Optimization of GA parameters to train recurrent ANN through weight adjustment and selection of activation functions
1

About Juan R. Rabuñal

Juan R. Rabuñal is a scholar working on Radiological and Ultrasound Technology, Earth-Surface Processes and Ocean Engineering, having authored 67 papers that have together received 1.2k indexed citations. Recurring topics across this work include Evolutionary Algorithms and Applications (11 papers), Metaheuristic Optimization Algorithms Research (7 papers) and Water Quality Monitoring Technologies (7 papers). The work is most often cited by research in Signal Processing (218 citations), Cognitive Neuroscience (343 citations) and Environmental Engineering (146 citations). Juan R. Rabuñal has collaborated with scholars based in Spain, Portugal and Norway. Frequent co-authors include Julián Dorado, Alejandro Pazos, Daniel Rivero, Ling Guo, Jerónimo Puertas, Álvaro Rodríguez, Gregório Iglesias, Alberte Castro, Juan Luis Pérez-Ordóñez and Fernando Martínez‐Abella. Their work appears in journals such as SHILAP Revista de lepidopterología, Analytical Biochemistry and Polymer.

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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