Jerónimo Hernández-González

536 total citations
24 papers, 257 citations indexed

About

Jerónimo Hernández-González is a scholar working on Artificial Intelligence, Computer Science Applications and Information Systems. According to data from OpenAlex, Jerónimo Hernández-González has authored 24 papers receiving a total of 257 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 5 papers in Computer Science Applications and 3 papers in Information Systems. Recurrent topics in Jerónimo Hernández-González's work include Machine Learning and Data Classification (5 papers), Mobile Crowdsensing and Crowdsourcing (5 papers) and Imbalanced Data Classification Techniques (5 papers). Jerónimo Hernández-González is often cited by papers focused on Machine Learning and Data Classification (5 papers), Mobile Crowdsensing and Crowdsourcing (5 papers) and Imbalanced Data Classification Techniques (5 papers). Jerónimo Hernández-González collaborates with scholars based in Spain, United Kingdom and United States. Jerónimo Hernández-González's co-authors include José A. Lozano, Iñaki Inza, Karim Lekadir, Rachel Harrison, Daniel Rodríguez, Jordi Mur-Petit, Marina Camacho, Jordi Vitrià, Polyxeni Gkontra and Marc Chadeau‐Hyam and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Pattern Recognition.

In The Last Decade

Jerónimo Hernández-González

21 papers receiving 253 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jerónimo Hernández-González Spain 8 127 32 32 18 16 24 257
Xiajiong Shen China 7 102 0.8× 51 1.6× 34 1.1× 15 0.8× 48 3.0× 49 271
Selim Buyrukoğlu Türkiye 9 113 0.9× 28 0.9× 14 0.4× 8 0.4× 15 0.9× 30 288
Chee Keong Chan Singapore 10 150 1.2× 60 1.9× 27 0.8× 33 1.8× 2 0.1× 18 306
Robert L. Joseph United States 4 134 1.1× 25 0.8× 46 1.4× 6 0.3× 4 0.3× 12 231
Manal A. Ismail Egypt 8 87 0.7× 39 1.2× 36 1.1× 22 1.2× 1 0.1× 30 278
Sudhakar Tripathi India 10 102 0.8× 46 1.4× 29 0.9× 3 0.2× 50 3.1× 34 345
Fangyuan Wang China 5 197 1.6× 58 1.8× 26 0.8× 12 0.7× 2 0.1× 15 269
Plamen Mateev Bulgaria 9 80 0.6× 16 0.5× 12 0.4× 3 0.2× 14 0.9× 22 297
Huirui Han China 6 114 0.9× 56 1.8× 90 2.8× 3 0.2× 25 1.6× 16 268
Lambodar Jena India 7 46 0.4× 19 0.6× 28 0.9× 4 0.2× 6 0.4× 17 147

Countries citing papers authored by Jerónimo Hernández-González

Since Specialization
Citations

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

Fields of papers citing papers by Jerónimo Hernández-González

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jerónimo Hernández-González. 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 Jerónimo Hernández-González. The network helps show where Jerónimo Hernández-González may publish in the future.

Co-authorship network of co-authors of Jerónimo Hernández-González

This figure shows the co-authorship network connecting the top 25 collaborators of Jerónimo Hernández-González. A scholar is included among the top collaborators of Jerónimo Hernández-González 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 Jerónimo Hernández-González. Jerónimo Hernández-González 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.
Campello, Víctor M., et al.. (2025). Empirical Comparison of Post-processing Debiasing Methods for Machine Learning Classifiers in Healthcare. PubMed. 9(3). 465–493. 1 indexed citations
2.
Díaz‐Caneja, Covadonga M., Dejan Stevanović, Carlos Martín-Isla, et al.. (2025). Longitudinal Prediction of Mental Health Outcomes in Vulnerable Youth using Machine Learning. Cognitive Computation. 17(5).
3.
Mulder, Rosa H., et al.. (2024). Fairness and bias correction in machine learning for depression prediction across four study populations. Scientific Reports. 14(1). 7848–7848. 7 indexed citations
4.
Salamó, María, et al.. (2024). Modeling river flow for flood forecasting: A case study on the Ter river. SHILAP Revista de lepidopterología. 23. 100181–100181. 2 indexed citations
5.
Mur-Petit, Jordi, Jerónimo Hernández-González, Marina Camacho, et al.. (2023). Machine and deep learning for longitudinal biomedical data: a review of methods and applications. Artificial Intelligence Review. 56(S2). 1711–1771. 40 indexed citations
6.
Hernández-González, Jerónimo & Pedro Javier Herrera. (2023). On the Supervision of Peer Assessment Tasks: An Efficient Instructor Guidance Technique. IEEE Transactions on Learning Technologies. 16(6). 926–939. 3 indexed citations
7.
Hernández-González, Jerónimo, et al.. (2022). Modeling three sources of uncertainty in assisted reproductive technologies with probabilistic graphical models. Computers in Biology and Medicine. 150. 106160–106160. 2 indexed citations
8.
Hernández-González, Jerónimo & Aritz Pérez. (2022). On the relative value of weak information of supervision for learning generative models: An empirical study. International Journal of Approximate Reasoning. 150. 258–272.
9.
Cerquides, Jesús, et al.. (2021). A Conceptual Probabilistic Framework for Annotation Aggregation of Citizen Science Data. Mathematics. 9(8). 875–875. 3 indexed citations
10.
Basurko, Oihane C., et al.. (2019). Beach litter forecasting on the south-eastern coast of the Bay of Biscay: A bayesian networks approach. Continental Shelf Research. 180. 14–23. 11 indexed citations
11.
Hernández-González, Jerónimo, et al.. (2019). Aggregated outputs by linear models: An application on marine litter beaching prediction. Information Sciences. 481. 381–393. 7 indexed citations
12.
Hernández-González, Jerónimo, Daniel Rodríguez, Iñaki Inza, Rachel Harrison, & José A. Lozano. (2018). Two datasets of defect reports labeled by a crowd of annotators of unknown reliability. Data in Brief. 18. 840–845.
13.
Hernández-González, Jerónimo, et al.. (2018). Output Feedback Self-tuning Wavenet Control for Underactuated Euler-Lagrange Systems. IFAC-PapersOnLine. 51(13). 633–638. 2 indexed citations
14.
Hernández-González, Jerónimo, Iñaki Inza, & José A. Lozano. (2018). A Note on the Behavior of Majority Voting in Multi-Class Domains with Biased Annotators. IEEE Transactions on Knowledge and Data Engineering. 31(1). 195–200. 10 indexed citations
15.
Hernández-González, Jerónimo, Estevam Hruschka, & Tom M. Mitchell. (2017). Merging knowledge bases in different languages. 21–29. 2 indexed citations
16.
Hernández-González, Jerónimo, Daniel Rodríguez, Iñaki Inza, Rachel Harrison, & José A. Lozano. (2017). Learning to classify software defects from crowds: A novel approach. Applied Soft Computing. 62. 579–591. 24 indexed citations
17.
Hernández-González, Jerónimo, et al.. (2016). Fitting the data from embryo implantation prediction: Learning from label proportions. Statistical Methods in Medical Research. 27(4). 1056–1066. 21 indexed citations
18.
Hernández-González, Jerónimo, Iñaki Inza, & José A. Lozano. (2015). Weak supervision and other non-standard classification problems: A taxonomy. Pattern Recognition Letters. 69. 49–55. 57 indexed citations
19.
Hernández-González, Jerónimo, Iñaki Inza, & José A. Lozano. (2015). Multidimensional Learning from Crowds: Usefulness and Application of Expertise Detection. International Journal of Intelligent Systems. 30(3). 326–354. 8 indexed citations
20.
Hernández-González, Jerónimo, Iñaki Inza, & José A. Lozano. (2013). Learning Bayesian network classifiers from label proportions. Pattern Recognition. 46(12). 3425–3440. 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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