Ignacio Díaz

1.5k total citations
66 papers, 929 citations indexed

About

Ignacio Díaz is a scholar working on Artificial Intelligence, Control and Systems Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Ignacio Díaz has authored 66 papers receiving a total of 929 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Artificial Intelligence, 21 papers in Control and Systems Engineering and 15 papers in Computer Vision and Pattern Recognition. Recurrent topics in Ignacio Díaz's work include Neural Networks and Applications (17 papers), Fault Detection and Control Systems (15 papers) and Data Visualization and Analytics (11 papers). Ignacio Díaz is often cited by papers focused on Neural Networks and Applications (17 papers), Fault Detection and Control Systems (15 papers) and Data Visualization and Analytics (11 papers). Ignacio Díaz collaborates with scholars based in Spain, Belgium and United States. Ignacio Díaz's co-authors include Abel A. Cuadrado, Tarik Smani, Manuel Domí­nguez, Antonio Ordóñez, Eva Calderón-Sánchez, Juan J. Fuertes, Juan A. Rosado, Daniel Pérez, Alejandro Domínguez‐Rodríguez and Jaakko Hollmén and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and PLoS ONE.

In The Last Decade

Ignacio Díaz

62 papers receiving 900 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ignacio Díaz Spain 17 196 190 173 112 89 66 929
Ying Yang China 24 310 1.6× 210 1.1× 637 3.7× 124 1.1× 78 0.9× 127 1.8k
Lihong Ren China 17 109 0.6× 211 1.1× 117 0.7× 127 1.1× 45 0.5× 103 1.1k
Yuanlong Li China 25 93 0.5× 493 2.6× 314 1.8× 229 2.0× 247 2.8× 77 1.7k
Gang Gao China 19 144 0.7× 202 1.1× 22 0.1× 37 0.3× 46 0.5× 77 1.1k
Juan Zhang China 20 51 0.3× 355 1.9× 293 1.7× 142 1.3× 133 1.5× 127 1.6k
Yingying Wang China 17 223 1.1× 131 0.7× 40 0.2× 82 0.7× 29 0.3× 73 978
Quanyi Li China 21 57 0.3× 479 2.5× 77 0.4× 67 0.6× 131 1.5× 38 1.5k
Hao Ran Portugal 19 111 0.6× 166 0.9× 140 0.8× 424 3.8× 23 0.3× 80 1.5k
Pin Lv China 22 59 0.3× 176 0.9× 249 1.4× 243 2.2× 24 0.3× 142 1.4k

Countries citing papers authored by Ignacio Díaz

Since Specialization
Citations

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

Fields of papers citing papers by Ignacio Díaz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ignacio Díaz. 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 Ignacio Díaz. The network helps show where Ignacio Díaz may publish in the future.

Co-authorship network of co-authors of Ignacio Díaz

This figure shows the co-authorship network connecting the top 25 collaborators of Ignacio Díaz. A scholar is included among the top collaborators of Ignacio Díaz 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 Ignacio Díaz. Ignacio Díaz 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.
Morán, Antonio, et al.. (2025). Adaptive model based on ESN for anomaly detection in industrial systems. Evolving Systems. 16(1).
2.
3.
Díaz, Ignacio, et al.. (2024). Machine Learning for Inverter-Fed Motors Monitoring and Fault Detection: An Overview. IEEE Access. 12. 27167–27179. 5 indexed citations
4.
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
5.
Díaz, Ignacio, et al.. (2024). Exploring deep fully convolutional neural networks for surface defect detection in complex geometries. The International Journal of Advanced Manufacturing Technology. 134(1-2). 97–111. 3 indexed citations
6.
Fernández, Daniel, et al.. (2024). Insulation Condition Assessment in Inverter-Fed Motors Using the High-Frequency Common Mode Current: A Case Study. Energies. 17(2). 470–470. 3 indexed citations
7.
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
8.
Valdés, Nuria, Paula Jiménez‐Fonseca, Ignacio Díaz, et al.. (2022). Differential HIF2α Protein Expression in Human Carotid Body and Adrenal Medulla under Physiologic and Tumorigenic Conditions. Cancers. 14(12). 2986–2986. 6 indexed citations
9.
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
10.
Domínguez‐Rodríguez, Alejandro, Eva Calderón-Sánchez, Ignacio Díaz, et al.. (2018). Urocortin-2 Prevents Dysregulation of Ca2+ Homeostasis and Improves Early Cardiac Remodeling After Ischemia and Reperfusion. Frontiers in Physiology. 9. 813–813. 26 indexed citations
11.
Díaz, Ignacio, Eva Calderón-Sánchez, R. Toro, et al.. (2017). miR-125a, miR-139 and miR-324 contribute to Urocortin protection against myocardial ischemia-reperfusion injury. Scientific Reports. 7(1). 8898–8898. 53 indexed citations
12.
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
13.
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
14.
Verleysen, Michel, et al.. (2013). Sensitivity to parameter and data variations in dimensionality reduction techniques. The European Symposium on Artificial Neural Networks. 2 indexed citations
15.
Díaz, Ignacio & Tarik Smani. (2013). New Insights into the Mechanisms Underlying Vascular and Cardiac Effects of Urocortin. Current Vascular Pharmacology. 11(4). 457–464. 13 indexed citations
16.
Sáez, María Eugenia, Tarik Smani, Reposo Ramírez‐Lorca, et al.. (2011). Association Analysis of Urotensin II Gene (UTS2) and Flanking Regions with Biochemical Parameters Related to Insulin Resistance. PLoS ONE. 6(4). e19327–e19327. 14 indexed citations
17.
Smani, Tarik, et al.. (2010). Urocortin‐2 induces vasorelaxation of coronary arteries isolated from patients with heart failure. Clinical and Experimental Pharmacology and Physiology. 38(1). 71–76. 25 indexed citations
18.
Bernal-Morales, Carolina, Francisco Aguayo, Macarena Vargas, et al.. (2008). Reprimo as a Potential Biomarker for Early Detection in Gastric Cancer. Clinical Cancer Research. 14(19). 6264–6269. 99 indexed citations
19.
Moreno, Luís A., et al.. (2004). Extended coasting duration exerts a negative impact on IVF cycle outcome due to premature luteinization. Reproductive BioMedicine Online. 9(5). 500–504. 21 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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