Inés M. Galván

1.2k total citations
57 papers, 808 citations indexed

About

Inés M. Galván is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering and Computational Theory and Mathematics. According to data from OpenAlex, Inés M. Galván has authored 57 papers receiving a total of 808 indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Artificial Intelligence, 9 papers in Electrical and Electronic Engineering and 8 papers in Computational Theory and Mathematics. Recurrent topics in Inés M. Galván's work include Neural Networks and Applications (17 papers), Solar Radiation and Photovoltaics (15 papers) and Evolutionary Algorithms and Applications (10 papers). Inés M. Galván is often cited by papers focused on Neural Networks and Applications (17 papers), Solar Radiation and Photovoltaics (15 papers) and Evolutionary Algorithms and Applications (10 papers). Inés M. Galván collaborates with scholars based in Spain, Italy and Poland. Inés M. Galván's co-authors include Ricardo Aler, Pedro Isasi, Alejandro Cervantes, José M. Valls, J.M. Zaldı́var, David Pozo‐Vázquez, Javier Huertas‐Tato, Clara Arbizu‐Barrena, Christian A. Gueymard and José A. Ruiz‐Arias and has published in prestigious journals such as Expert Systems with Applications, Renewable Energy and Solar Energy.

In The Last Decade

Inés M. Galván

53 papers receiving 770 citations

Peers

Inés M. Galván

EEE

RESE

CSE

MSOR

Manoj Duhan India

Ian T. Nabney United Kingdom

Jiabin Yuan China

Chaoli Zhang China

Antonello Rosato Italy

William Lefebvre France

Ranjeeta Bisoi India

Pingping Zhu United States

Jian Xiao China

Kevin L. Priddy United States

Manoj Duhan India

Inés M. Galván

331 ×0.8

302 ×1.3

EEE

102 ×0.8

RESE

36 ×0.4

CSE

28 ×0.3

MSOR

Citations per year, relative to Inés M. Galván Inés M. Galván (= 1×) peers Manoj Duhan

Countries citing papers authored by Inés M. Galván

Since Specialization

Citations

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

Fields of papers citing papers by Inés M. Galván

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Inés M. Galván. 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 Inés M. Galván. The network helps show where Inés M. Galván may publish in the future.

Co-authorship network of co-authors of Inés M. Galván

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

All Works

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20 of 20 papers shown

Galván, Inés M., et al.. (2024). A novel method for modeling renewable power production using ERA5: Spanish solar PV energy. Renewable Energy. 240. 122120–122120. 2 indexed citations

García-Ramos, R., Fátima Carrillo, Diego Santos‐García, et al.. (2024). Artificial intelligence for identification of candidates for device-aided therapy in Parkinson's disease: DELIST-PD study. Computers in Biology and Medicine. 185. 109504–109504. 2 indexed citations

Aler, Ricardo, et al.. (2023). Improving Solar Radiation Nowcasts by Blending Data-Driven, Satellite-Images-Based and All-Sky-Imagers-Based Models Using Machine Learning Techniques. Remote Sensing. 15(9). 2328–2328. 9 indexed citations

Galván, Inés M., et al.. (2022). Direct estimation of prediction intervals for solar and wind regional energy forecasting with deep neural networks. Engineering Applications of Artificial Intelligence. 114. 105128–105128. 27 indexed citations

Huertas‐Tato, Javier, et al.. (2021). Using a Multi-view Convolutional Neural Network to monitor solar irradiance. Neural Computing and Applications. 34(13). 10295–10307. 6 indexed citations

Arbizu‐Barrena, Clara, et al.. (2019). A short-term solar radiation forecasting system for the Iberian Peninsula. Part 1: Models description and performance assessment. Solar Energy. 195. 396–412. 37 indexed citations

Huertas‐Tato, Javier, et al.. (2019). A short-term solar radiation forecasting system for the Iberian Peninsula. Part 2: Model blending approaches based on machine learning. Solar Energy. 195. 685–696. 42 indexed citations

Aler, Ricardo, et al.. (2015). Machine learning techniques for daily solar energy prediction and interpolation using numerical weather models. Concurrency and Computation Practice and Experience. 28(4). 1261–1274. 21 indexed citations

Quintana, David, et al.. (2014). Multiobjective Algorithms with Resampling for Portfolio Optimization. Computing and Informatics / Computers and Artificial Intelligence. 32(4). 777–796. 8 indexed citations

10.

García-Cuesta, Esteban, Antonio Arjona Castro, Inés M. Galván, & F. López. (2014). Temperature Profile Retrieval in Axisymmetric Combustion Plumes Using Multilayer Perceptron Modeling and Spectral Feature Selection in the Infrared CO₂ Emission Band. Applied Spectroscopy. 68(8). 900–908. 8 indexed citations

11.

Quintana, David, et al.. (2012). Time-stamped resampling for robust evolutionary portfolio optimization. Expert Systems with Applications. 39(12). 10722–10730. 12 indexed citations

12.

Aler, Ricardo, Inés M. Galván, & José M. Valls. (2010). Evolving spatial and frequency selection filters for Brain-Computer Interfaces. e-Archivo (Carlos III University of Madrid). 1–7. 16 indexed citations

13.

Cervantes, Alejandro, Inés M. Galván, & Pedro Isasi. (2009). AMPSO: A New Particle Swarm Method for Nearest Neighborhood Classification. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics). 39(5). 1082–1091. 82 indexed citations

14.

Valls, José M., Inés M. Galván, & Pedro Isasi. (2006). Improving the Generalization Ability of RBNN Using a Selective Strategy Based on the Gaussian Kernel Function.. Computing and Informatics / Computers and Artificial Intelligence. 25. 1–15. 3 indexed citations

15.

Gutiérrez, Germán, Araceli Sanchis, Pedro Isasi, José M. Molina, & Inés M. Galván. (2005). NON-DIRECT ENCODING METHOD BASED ON CELLULAR AUTOMATA TO DESIGN NEURAL NETWORK ARCHITECTURES. Computing and Informatics / Computers and Artificial Intelligence. 24(3). 225–247. 5 indexed citations

16.

Valls, José M., Inés M. Galván, & Pedro Isasi. (2003). How the selection of training patterns can improve the generalization capability in Radial Basis Neural Networks. e-Archivo (Carlos III University of Madrid). 1 indexed citations

17.

Galván, Inés M., et al.. (2003). Modified Self-organizing Maps for Line Extraction in Digitized Text Documents.. Applied Informatics. 281–286. 1 indexed citations

18.

Molina, José M., et al.. (2001). Optimizing the Number of Learning Cycles in the Design of Radial Basis Neural Networks Using a Multi-Agent System. Computing and Informatics / Computers and Artificial Intelligence. 20(5). 429–449. 1 indexed citations

19.

Zaldı́var, J.M., Eduardo M. Gutiérrez, Inés M. Galván, Fernanda Strozzi, & Alberto Tomasin. (2000). Forecasting high waters at Venice Lagoon using chaotic time series analysis and nonlinear neural networks. Journal of Hydroinformatics. 2(1). 61–84. 40 indexed citations

20.

Galván, Inés M. & J.M. Zaldı́var. (1998). Application of recurrent neural networks in batch reactors. Chemical Engineering and Processing - Process Intensification. 37(2). 149–161. 12 indexed citations

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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