Aníbal Pedraza

1.4k total citations
23 papers, 376 citations indexed

About

Aníbal Pedraza is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Aníbal Pedraza has authored 23 papers receiving a total of 376 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 9 papers in Computer Vision and Pattern Recognition and 7 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Aníbal Pedraza's work include Adversarial Robustness in Machine Learning (10 papers), Anomaly Detection Techniques and Applications (6 papers) and Digital Imaging for Blood Diseases (4 papers). Aníbal Pedraza is often cited by papers focused on Adversarial Robustness in Machine Learning (10 papers), Anomaly Detection Techniques and Applications (6 papers) and Digital Imaging for Blood Diseases (4 papers). Aníbal Pedraza collaborates with scholars based in Spain, India and Germany. Aníbal Pedraza's co-authors include Gloria Bueno, Óscar Déniz, Gabriel Cristóbal, Saúl Blanco, Jesús Ruiz-Santaquiteria, Jesús Salido, Jaime Gallego, Arvydas Laurinavičius, Georg Steiner and Noelia Vállez and has published in prestigious journals such as Chaos Solitons & Fractals, Applied Sciences and Neural Computing and Applications.

In The Last Decade

Aníbal Pedraza

18 papers receiving 369 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Aníbal Pedraza Spain 10 135 129 51 48 43 23 376
Jesús Ruiz-Santaquiteria Spain 12 126 0.9× 115 0.9× 55 1.1× 35 0.7× 26 0.6× 23 384
Jose L. Pech-Pacheco Spain 7 169 1.3× 31 0.2× 34 0.7× 137 2.9× 28 0.7× 13 401
Jiawen Lin China 10 102 0.8× 53 0.4× 33 0.6× 108 2.3× 70 1.6× 50 506
Guangrong Ji China 9 215 1.6× 74 0.6× 39 0.8× 17 0.4× 122 2.8× 52 465
Mohammad Reza Ahmadzadeh Iran 12 191 1.4× 127 1.0× 12 0.2× 62 1.3× 51 1.2× 38 492
Sergey Kosov Germany 5 130 1.0× 115 0.9× 20 0.4× 74 1.5× 63 1.5× 9 307
Xiwang Xie China 10 253 1.9× 111 0.9× 17 0.3× 87 1.8× 62 1.4× 18 466
Nikita Moshkov Hungary 7 92 0.7× 89 0.7× 71 1.4× 57 1.2× 45 1.0× 12 340
Jianan Chen China 10 254 1.9× 138 1.1× 11 0.2× 66 1.4× 224 5.2× 32 580
Qiwei Wang China 9 104 0.8× 62 0.5× 15 0.3× 36 0.8× 44 1.0× 18 342

Countries citing papers authored by Aníbal Pedraza

Since Specialization
Citations

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

Fields of papers citing papers by Aníbal Pedraza

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Aníbal Pedraza

This figure shows the co-authorship network connecting the top 25 collaborators of Aníbal Pedraza. A scholar is included among the top collaborators of Aníbal Pedraza 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 Aníbal Pedraza. Aníbal Pedraza 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.
Singh, Harbinder, Aníbal Pedraza, Óscar Déniz, & Gloria Bueno. (2025). Detection of adversarial examples through chaos quantification in time-series analysis. International Journal of Machine Learning and Cybernetics. 16(10). 8041–8062.
2.
Pedraza, Aníbal, et al.. (2025). Characterizing Natural Adversarial Examples Through Activation Map Analysis. IET Image Processing. 19(1).
3.
Singh, Harbinder, Aníbal Pedraza, Óscar Déniz, & Gloria Bueno. (2025). Adversarial Examples Detection with Chaos-Based Multivariate Features. International Journal of Machine Learning and Cybernetics. 16(10). 7331–7342.
4.
Ruiz-Santaquiteria, Jesús, Aníbal Pedraza, Óscar Déniz, & Gloria Bueno. (2025). DT4PEIS: detection transformers for parasitic egg instance segmentation. Applied Intelligence. 55(4).
5.
Pedraza, Aníbal, et al.. (2024). GNCnn: A QuPath extension for glomerulosclerosis and glomerulonephritis characterization based on deep learning. Computational and Structural Biotechnology Journal. 27. 35–47.
6.
Pedraza, Aníbal, Óscar Déniz, Harbinder Singh, & Gloria Bueno. (2024). Leveraging AutoEncoders and chaos theory to improve adversarial example detection. Neural Computing and Applications. 36(29). 18265–18275. 1 indexed citations
7.
Pedraza, Aníbal, et al.. (2024). Deep Neural Networks for HER2 Grading of Whole Slide Images with Subclasses Levels. Algorithms. 17(3). 97–97. 6 indexed citations
8.
Singh, Simrandeep, Harbinder Singh, Gloria Bueno, et al.. (2023). A review of image fusion: Methods, applications and performance metrics. Digital Signal Processing. 137. 104020–104020. 46 indexed citations
9.
Vállez, Noelia, José Luís Espinosa-Aranda, Aníbal Pedraza, Óscar Déniz, & Gloria Bueno. (2023). Deep Learning within a DICOM WSI Viewer for Histopathology. Applied Sciences. 13(17). 9527–9527. 3 indexed citations
10.
Pedraza, Aníbal, Óscar Déniz, & Gloria Bueno. (2022). Lyapunov stability for detecting adversarial image examples. Chaos Solitons & Fractals. 155. 111745–111745. 3 indexed citations
11.
Ruiz-Santaquiteria, Jesús, Aníbal Pedraza, Noelia Vállez, & Alberto Velasco. (2022). Parasitic Egg Detection with a Deep Learning Ensemble. 2022 IEEE International Conference on Image Processing (ICIP). 4283–4286. 11 indexed citations
12.
Vállez, Noelia, et al.. (2022). Hyperdeep: Comparison of Ai-Based Methods for Predicting Chemical Components in Hyperspectral Images. 2022 IEEE International Conference on Image Processing (ICIP). 4287–4291. 1 indexed citations
13.
Pedraza, Aníbal, Jesús Ruiz-Santaquiteria, Óscar Déniz, & Gloria Bueno. (2022). Parasitic Egg Detection and Classification with Transformer-Based Architectures. 2022 IEEE International Conference on Image Processing (ICIP). 4301–4305. 8 indexed citations
14.
Pedraza, Aníbal, Óscar Déniz, & Gloria Bueno. (2021). On the Relationship between Generalization and Robustness to Adversarial Examples. Symmetry. 13(5). 817–817. 9 indexed citations
15.
Pedraza, Aníbal, Óscar Déniz, & Gloria Bueno. (2021). Really natural adversarial examples. International Journal of Machine Learning and Cybernetics. 13(4). 1065–1077. 9 indexed citations
16.
Pedraza, Aníbal, Óscar Déniz, & Gloria Bueno. (2020). Approaching Adversarial Example Classification with Chaos Theory. Entropy. 22(11). 1201–1201. 9 indexed citations
17.
Bueno, Gloria, Óscar Déniz, Jesús Ruiz-Santaquiteria, et al.. (2018). Lights and pitfalls of convolutional neural networks for diatom identification. Buleria (Universidad de León). 22 indexed citations
18.
Gallego, Jaime, et al.. (2018). Glomerulus Classification and Detection Based on Convolutional Neural Networks. Journal of Imaging. 4(1). 20–20. 66 indexed citations
19.
Sánchez, Carlos, Gabriel Cristóbal, Gloria Bueno, et al.. (2017). Oblique illumination in microscopy: A quantitative evaluation. Micron. 105. 47–54. 20 indexed citations
20.
Bueno, Gloria, Óscar Déniz, Aníbal Pedraza, et al.. (2017). Automated Diatom Classification (Part A): Handcrafted Feature Approaches. Applied Sciences. 7(8). 753–753. 47 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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