Fernando Rojas

1.4k total citations
68 papers, 866 citations indexed

About

Fernando Rojas is a scholar working on Artificial Intelligence, Signal Processing and Computer Vision and Pattern Recognition. According to data from OpenAlex, Fernando Rojas has authored 68 papers receiving a total of 866 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Artificial Intelligence, 14 papers in Signal Processing and 11 papers in Computer Vision and Pattern Recognition. Recurrent topics in Fernando Rojas's work include Neural Networks and Applications (18 papers), Fuzzy Logic and Control Systems (14 papers) and Blind Source Separation Techniques (9 papers). Fernando Rojas is often cited by papers focused on Neural Networks and Applications (18 papers), Fuzzy Logic and Control Systems (14 papers) and Blind Source Separation Techniques (9 papers). Fernando Rojas collaborates with scholars based in Spain, Colombia and Cuba. Fernando Rojas's co-authors include Ignacio Rojas, Olga Valenzuela, H. Pomares, Luis Javier Herrera, Alberto Guillén, M. Pasadas, Jesús González, Miguel Damas, Daniel Castillo-Secilla and Juan Manuel Gálvez and has published in prestigious journals such as Nucleic Acids Research, SHILAP Revista de lepidopterología and Bioinformatics.

In The Last Decade

Fernando Rojas

55 papers receiving 827 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Fernando Rojas Spain 15 353 145 125 122 113 68 866
Olga Valenzuela Spain 18 462 1.3× 177 1.2× 157 1.3× 133 1.1× 163 1.4× 84 1.1k
Junkai Ji China 20 742 2.1× 141 1.0× 227 1.8× 76 0.6× 122 1.1× 68 1.2k
Jiwen Dong China 12 474 1.3× 139 1.0× 133 1.1× 43 0.4× 233 2.1× 53 1.1k
Derya Avcı Türkiye 19 500 1.4× 199 1.4× 104 0.8× 60 0.5× 305 2.7× 47 1.3k
Rodrigo Fernandes de Mello Brazil 20 441 1.2× 54 0.4× 141 1.1× 52 0.4× 113 1.0× 106 1.2k
Zhisong Pan China 16 558 1.6× 175 1.2× 134 1.1× 49 0.4× 287 2.5× 123 1.1k
Adriaan Lambrechts Belgium 4 256 0.7× 166 1.1× 111 0.9× 26 0.2× 134 1.2× 7 677
Qun Song China 17 983 2.8× 150 1.0× 140 1.1× 81 0.7× 137 1.2× 49 1.5k
Şeyda Ertekin United States 12 549 1.6× 132 0.9× 207 1.7× 45 0.4× 132 1.2× 32 912
Qun Dai China 20 676 1.9× 161 1.1× 220 1.8× 40 0.3× 218 1.9× 80 1.2k

Countries citing papers authored by Fernando Rojas

Since Specialization
Citations

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

Fields of papers citing papers by Fernando Rojas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fernando Rojas

This figure shows the co-authorship network connecting the top 25 collaborators of Fernando Rojas. A scholar is included among the top collaborators of Fernando Rojas 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 Fernando Rojas. Fernando Rojas 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.
2.
Valenzuela, Olga, Fernando Rojas, Luis Javier Herrera, H. Pomares, & Ignacio Rojas. (2023). New Developments in Time Series and Forecasting, ITISE-2023. SHILAP Revista de lepidopterología. 101–101.
3.
Rojas, Ignacio, et al.. (2023). Novel methodology for detecting and localizing cancer area in histopathological images based on overlapping patches. Computers in Biology and Medicine. 168. 107713–107713. 5 indexed citations
4.
Valenzuela, Olga, Fernando Rojas, Luis Javier Herrera, H. Pomares, & Ignacio Rojas. (2023). Theory and Applications of Time Series Analysis and Forecasting. 1 indexed citations
5.
6.
Rojas, Fernando, et al.. (2022). Determination of the Severity and Percentage of COVID-19 Infection through a Hierarchical Deep Learning System. Journal of Personalized Medicine. 12(4). 535–535. 9 indexed citations
7.
Suárez, Esther, Beatriz Rodríguez Lozano, Antonio Saiz Ayala, et al.. (2022). Design and Application of Automated Algorithms for Diagnosis and Treatment Optimization in Neurodegenerative Diseases. Neuroinformatics. 20(3). 765–775. 2 indexed citations
8.
Rojas, Fernando, et al.. (2021). Snapshot compressive spectral depth imaging from coded aberrations. Optics Express. 29(6). 8142–8142. 7 indexed citations
9.
Rojas, Fernando, et al.. (2021). Compressive spectral image fusion via a single aperture high throughput imaging system. Scientific Reports. 11(1). 10311–10311. 8 indexed citations
10.
Valenzuela, Olga, Fernando Rojas, Ignacio Rojas, & Peter Glösekötter. (2020). Main findings and advances in bioinformatics and biomedical engineering- IWBBIO 2018. BMC Bioinformatics. 21(S7). 153–153.
11.
Rojas, Fernando, et al.. (2020). Demonstration of a Compressive Hyperspectral Image Fusion Optical Imager. Imaging and Applied Optics Congress. CW4B.6–CW4B.6. 2 indexed citations
12.
Rojas, Fernando, et al.. (2020). Fusion of Hyperspectral and Multispectral Images Based on a Centralized Non-local Sparsity Model of Abundance Maps. SHILAP Revista de lepidopterología. 24(66). 62–75. 1 indexed citations
13.
Castillo-Secilla, Daniel, Juan Manuel Gálvez, Luis Javier Herrera, et al.. (2019). Leukemia multiclass assessment and classification from Microarray and RNA-seq technologies integration at gene expression level. PLoS ONE. 14(2). e0212127–e0212127. 30 indexed citations
14.
Rojas, Fernando, et al.. (2019). Primary Conjunctival Rhabdomyosarcoma in a Pediatric Patient. Cureus. 11(12). e6310–e6310. 4 indexed citations
15.
Castillo-Secilla, Daniel, et al.. (2017). Integration of RNA-Seq data with heterogeneous microarray data for breast cancer profiling. BMC Bioinformatics. 18(1). 506–506. 41 indexed citations
16.
González, Jesús, Ignacio Rojas, H. Pomares, et al.. (2006). Improving the accuracy while preserving the interpretability of fuzzy function approximators by means of multi-objective evolutionary algorithms. International Journal of Approximate Reasoning. 44(1). 32–44. 34 indexed citations
17.
Valenzuela, Olga, et al.. (2005). Automatic classification of prostate cancer using pseudo-gaussian radial basis function neural network. The European Symposium on Artificial Neural Networks. 145–150. 3 indexed citations
18.
Álvarez, M., et al.. (2004). Lattice ICA for the separation of speech signals. University of Regensburg Publication Server (University of Regensburg). 337–342.
19.
Pomares, H., Ignacio Rojas, Jesús González, et al.. (2002). A two-stage approach to self-learning direct fuzzy controllers. International Journal of Approximate Reasoning. 29(3). 267–289. 19 indexed citations
20.
Rojas, Ignacio, et al.. (2001). The synergy between multideme genetic algorithms and fuzzy systems. The European Symposium on Artificial Neural Networks. 199–204. 1 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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