Abel A. Cuadrado

597 total citations
33 papers, 391 citations indexed

About

Abel A. Cuadrado is a scholar working on Control and Systems Engineering, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Abel A. Cuadrado has authored 33 papers receiving a total of 391 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Control and Systems Engineering, 12 papers in Artificial Intelligence and 10 papers in Computer Vision and Pattern Recognition. Recurrent topics in Abel A. Cuadrado's work include Fault Detection and Control Systems (11 papers), Neural Networks and Applications (9 papers) and Data Visualization and Analytics (8 papers). Abel A. Cuadrado is often cited by papers focused on Fault Detection and Control Systems (11 papers), Neural Networks and Applications (9 papers) and Data Visualization and Analytics (8 papers). Abel A. Cuadrado collaborates with scholars based in Spain, Belgium and Sweden. Abel A. Cuadrado's co-authors include Ignacio Díaz, Manuel Domí­nguez, Daniel Pérez, Alberto B. Díez, Juan-Francisco Martı́n, Zahra Salehi-Najafabadi, Carlos García‐Estrada, Mohammad‐Saeid Jami, Daniel Pérez and Carlos Barreiro and has published in prestigious journals such as Bioinformatics, Energy and Buildings and IEEE Transactions on Smart Grid.

In The Last Decade

Abel A. Cuadrado

31 papers receiving 376 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Abel A. Cuadrado Spain 10 181 109 84 50 40 33 391
Fredy Ruíz Italy 18 278 1.5× 449 4.1× 54 0.6× 77 1.5× 16 0.4× 103 830
Jingtao Hu China 16 157 0.9× 107 1.0× 29 0.3× 78 1.6× 205 5.1× 63 668
Shihao Zhao China 9 38 0.2× 95 0.9× 174 2.1× 13 0.3× 21 0.5× 24 488
Apurva Narayan Canada 11 86 0.5× 188 1.7× 104 1.2× 31 0.6× 75 1.9× 59 482
Zhenyu Wang China 13 290 1.6× 111 1.0× 46 0.5× 71 1.4× 29 0.7× 76 538
Xiaoying Tian China 12 128 0.7× 119 1.1× 57 0.7× 17 0.3× 38 0.9× 32 421
Xin Shi China 11 99 0.5× 140 1.3× 57 0.7× 33 0.7× 19 0.5× 40 338

Countries citing papers authored by Abel A. Cuadrado

Since Specialization
Citations

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

Fields of papers citing papers by Abel A. Cuadrado

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Abel A. Cuadrado. 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 Abel A. Cuadrado. The network helps show where Abel A. Cuadrado may publish in the future.

Co-authorship network of co-authors of Abel A. Cuadrado

This figure shows the co-authorship network connecting the top 25 collaborators of Abel A. Cuadrado. A scholar is included among the top collaborators of Abel A. Cuadrado 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 Abel A. Cuadrado. Abel A. Cuadrado 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
1.
Pérez, Daniel, et al.. (2024). Conditioned fully convolutional denoising autoencoder for multi-target NILM. Neural Computing and Applications. 37(17). 10491–10505. 3 indexed citations
2.
Díaz, Ignacio, et al.. (2023). Exploratory Analysis of the Gene Expression Matrix Based on Dual Conditional Dimensionality Reduction. IEEE Journal of Biomedical and Health Informatics. 27(6). 3083–3092. 2 indexed citations
3.
Díaz, Ignacio, et al.. (2022). Health indicator for machine condition monitoring built in the latent space of a deep autoencoder. Reliability Engineering & System Safety. 224. 108482–108482. 67 indexed citations
4.
Díaz, Ignacio, et al.. (2020). DCNN for condition monitoring and fault detection in rotating machines and its contribution to the understanding of machine nature. Heliyon. 6(2). e03395–e03395. 24 indexed citations
5.
Pérez, Daniel, et al.. (2020). Fully-Convolutional Denoising Auto-Encoders for NILM in Large Non-Residential Buildings. IEEE Transactions on Smart Grid. 12(3). 2722–2731. 53 indexed citations
6.
Pérez, Daniel, et al.. (2018). Interactive data visualization of chatter conditions in a cold rolling mill. Computers in Industry. 103. 86–96. 7 indexed citations
7.
Díaz, Ignacio, et al.. (2018). Interactive visualization for NILM in large buildings using non-negative matrix factorization. Energy and Buildings. 176. 95–108. 13 indexed citations
8.
Díaz, Ignacio, Abel A. Cuadrado, & Michel Verleysen. (2016). A state-space model on interactive dimensionality reduction.. The European Symposium on Artificial Neural Networks. 1 indexed citations
9.
Díaz, Ignacio, Abel A. Cuadrado, Daniel Pérez, et al.. (2016). Energy analytics in public buildings using interactive histograms. Energy and Buildings. 134. 94–104. 9 indexed citations
10.
Díaz, Ignacio, et al.. (2014). Interactive Dimensionality Reduction for Visual Analytics. Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)). 4 indexed citations
11.
Domí­nguez, Manuel, Juan J. Fuertes, Ignacio Díaz, et al.. (2011). Analysis of electric power consumption using Self-Organizing Maps.. IFAC Proceedings Volumes. 44(1). 12213–12218. 8 indexed citations
12.
Jami, Mohammad‐Saeid, Carlos García‐Estrada, Carlos Barreiro, et al.. (2010). The Penicillium Chrysogenum Extracellular Proteome. Conversion from a Food-rotting Strain to a Versatile Cell Factory for White Biotechnology. Molecular & Cellular Proteomics. 9(12). 2729–2744. 53 indexed citations
13.
Fuertes, Juan J., et al.. (2009). Visual dynamic model based on self-organizing maps for supervision and fault detection in industrial processes. Engineering Applications of Artificial Intelligence. 23(1). 8–17. 24 indexed citations
14.
Fraga, César G., et al.. (2006). Thickness quality control. IEEE Industry Applications Magazine. 12(2). 12–20. 3 indexed citations
15.
Cuadrado, Abel A., et al.. (2005). Modelado Visual de Procesos Industriales. Revista Iberoamericana de Automática e Informática Industrial RIAI. 2(4). 101–112. 4 indexed citations
16.
Díaz, Ignacio, Abel A. Cuadrado, & Alberto B. Díez. (2005). Visualizing knowledge for data mining using dimension reduction mappings. 290. 235–240. 2 indexed citations
17.
Fraga, César G., et al.. (2004). Thickness quality control and diagnosis system for tinplate rolling mills. Conference Record of the 2004 IEEE Industry Applications Conference, 2004. 39th IAS Annual Meeting.. 2. 919–926. 1 indexed citations
18.
Díaz, Ignacio, et al.. (2004). A visual approach for fuzzy rule induction. 2. 761–767. 3 indexed citations
19.
Díaz, Ignacio, Alberto B. Díez, Abel A. Cuadrado, & Manuel Domí­nguez. (2002). PRIOR KNOWLEDGE INTEGRATION IN SELFORGANIZING MAPS FOR COMPLEX PROCESS SUPERVISION. IFAC Proceedings Volumes. 35(1). 79–84. 3 indexed citations
20.
Cuadrado, Abel A., et al.. (2001). Fuzzy inference maps for condition monitoring with self-organizing maps.. European Society for Fuzzy Logic and Technology Conference. 55–58. 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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