Eva Cernadas

3.6k total citations · 1 hit paper
59 papers, 2.7k citations indexed

About

Eva Cernadas is a scholar working on Artificial Intelligence, Molecular Biology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Eva Cernadas has authored 59 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 13 papers in Molecular Biology and 12 papers in Computer Vision and Pattern Recognition. Recurrent topics in Eva Cernadas's work include Spectroscopy and Chemometric Analyses (11 papers), Identification and Quantification in Food (9 papers) and Meat and Animal Product Quality (8 papers). Eva Cernadas is often cited by papers focused on Spectroscopy and Chemometric Analyses (11 papers), Identification and Quantification in Food (9 papers) and Meat and Animal Product Quality (8 papers). Eva Cernadas collaborates with scholars based in Spain, Portugal and Jordan. Eva Cernadas's co-authors include Manuel Fernández-Delgado, Senén Barro, Manisha Sirsat, Sadi Alawadi, Manuel Febrero–Bande, Teresa Antequera, Arno Formella, Rehanullah Khan, Pablo G. Rodríguez and Rosario Domínguez‐Petit and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Scientific Reports and Expert Systems with Applications.

In The Last Decade

Eva Cernadas

51 papers receiving 2.6k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Do we need hundreds of classifiers to solve real world classification problems

2014 1.7k citations Manuel Fernández-Delgado, Eva Cernadas et al. Journal of Machine Learning Research profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Eva Cernadas Spain	16	832	333	227	188	181	59	2.7k
Manuel Fernández-Delgado Spain	21	915 1.1×	353 1.1×	222 1.0×	127 0.7×	242 1.3×	86	3.3k
Erwan Scornet France	11	861 1.0×	207 0.6×	177 0.8×	100 0.5×	180 1.0×	18	3.4k
Jair Cervantes Mexico	15	688 0.8×	458 1.4×	126 0.6×	139 0.7×	200 1.1×	48	2.3k
Asdrúbal López‐Chau Mexico	12	639 0.8×	374 1.1×	113 0.5×	139 0.7×	176 1.0×	61	2.1k
Tzu-Tsung Wong Taiwan	12	593 0.7×	191 0.6×	207 0.9×	91 0.5×	145 0.8×	19	2.2k
Emilio Soria‐Olivas Spain	29	654 0.8×	267 0.8×	109 0.5×	284 1.5×	79 0.4×	122	2.5k
Yunqian Ma United States	15	1.0k 1.2×	417 1.3×	148 0.7×	144 0.8×	174 1.0×	46	3.4k
Gérard Biau France	12	796 1.0×	187 0.6×	221 1.0×	85 0.5×	172 1.0×	24	3.2k
Farid García‐Lamont Mexico	11	546 0.7×	358 1.1×	113 0.5×	136 0.7×	129 0.7×	38	1.9k
Nicola L. C. Talbot United Kingdom	13	824 1.0×	404 1.2×	314 1.4×	137 0.7×	97 0.5×	32	2.7k

Countries citing papers authored by Eva Cernadas

Since Specialization

Citations

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

Fields of papers citing papers by Eva Cernadas

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Eva Cernadas

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Tarawneh, Ahmad S., et al.. (2025). Cell Detection in Biomedical Immunohistochemical Images Using Unsupervised Segmentation and Deep Learning. Electronics. 14(18). 3705–3705.

Carracedo, Ãngel, et al.. (2025). Predicting autistic traits, anxiety and depression symptoms using camouflaging autistic traits questionnaire (CAT-Q-ES): A machine learning study. 128. 202712–202712.

Sirsat, Manisha, et al.. (2025). Machine and deep learning for the prediction of nutrient deficiency in wheat leaf images. Knowledge-Based Systems. 317. 113400–113400. 4 indexed citations

Rocha, Cátia Vieira, Eva Cernadas, Manuel Fernández-Delgado, et al.. (2025). Dual intelligent multiplexing sensor for accurate disease management with portable NMR. Biosensors and Bioelectronics. 287. 117700–117700. 1 indexed citations

Fernández-Delgado, Manuel, et al.. (2024). Closed-Form Gaussian Spread Estimation for Small and Large Support Vector Classification. IEEE Transactions on Neural Networks and Learning Systems. 36(3). 4336–4344. 1 indexed citations

Cernadas, Eva, Manuel Fernández-Delgado, Manisha Sirsat, E. Fulladosa, & Israel Muñoz. (2024). MarblingPredictor: A software to analyze the quality of dry-cured ham slices. Meat Science. 221. 109713–109713.

Cernadas, Eva, et al.. (2024). OralImmunoAnalyser: a software tool for immunohistochemical assessment of oral leukoplakia using image segmentation and classification models. Frontiers in Artificial Intelligence. 7. 1324410–1324410. 2 indexed citations

Segalàs, Cinto, Eva Cernadas, Manuel Fernández-Delgado, et al.. (2024). Cognitive and clinical predictors of a long-term course in obsessive compulsive disorder: A machine learning approach in a prospective cohort study. Journal of Affective Disorders. 350. 648–655. 4 indexed citations

Galán‐Moya, Eva María, Manuel Fernández-Delgado, Joaquín Mosquera, et al.. (2024). Software BreastAnalyser for the semi-automatic analysis of breast cancer immunohistochemical images. Scientific Reports. 14(1). 2995–2995. 4 indexed citations

10.

Cernadas, Eva & Encina Calvo Iglesias. (2023). Crawford, Kate (2023). Atlas de IA. Poder, política y costes planetarios de la inteligencia artificial Barcelona: Ned Ediciones. 1. 105–108.

11.

Cernadas, Eva, et al.. (2023). MSCF: Multi-Scale Canny Filter to Recognize Cells in Microscopic Images. Sustainability. 15(18). 13693–13693. 6 indexed citations

12.

Cernadas, Eva, et al.. (2023). Promoting Equality in Higher Education Computer Programming Courses Through Cooperative Learning. 1–4. 1 indexed citations

13.

Fernández-Delgado, Manuel, et al.. (2021). Fast Support Vector Classification for Large-Scale Problems. IEEE Transactions on Pattern Analysis and Machine Intelligence. 44(10). 6184–6195. 35 indexed citations

14.

Domínguez‐Petit, Rosario, et al.. (2021). STERapp: Semiautomatic Software for Stereological Analysis. Application in the Estimation of Fish Fecundity. Electronics. 10(12). 1432–1432. 8 indexed citations

15.

Cernadas, Eva, et al.. (2020). CystAnalyser: A new software tool for the automatic detection and quantification of cysts in Polycystic Kidney and Liver Disease, and other cystic disorders. PLoS Computational Biology. 16(10). e1008337–e1008337. 14 indexed citations

16.

Sirsat, Manisha, Eva Cernadas, Manuel Fernández-Delgado, & Rehanullah Khan. (2017). Classification of agricultural soil parameters in India. Computers and Electronics in Agriculture. 135. 269–279. 69 indexed citations

17.

Fernández-Delgado, Manuel, et al.. (2014). Do we need hundreds of classifiers to solve real world classification problems. Journal of Machine Learning Research. 1709 indexed citations breakdown →

18.

Fernández-Delgado, Manuel, Eva Cernadas, Senén Barro, Jorge Ribeiro, & José Neves. (2013). Direct Kernel Perceptron (DKP): Ultra-fast kernel ELM-based classification with non-iterative closed-form weight calculation. Neural Networks. 50. 60–71. 42 indexed citations

19.

Fernández-Delgado, Manuel, et al.. (2011). Direct Parallel Perceptrons (DPPs): Fast Analytical Calculation of the Parallel Perceptrons Weights With Margin Control for Classification Tasks. IEEE Transactions on Neural Networks. 22(11). 1837–1848. 14 indexed citations

20.

Cernadas, Eva, et al.. (2004). Pollen classification using brightness-based and shape-based descriptors. Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.. 212–215 Vol.2. 17 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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