Jónathan Heras

2.1k total citations
58 papers, 1.1k citations indexed

About

Jónathan Heras is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Theory and Mathematics. According to data from OpenAlex, Jónathan Heras has authored 58 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Artificial Intelligence, 14 papers in Computer Vision and Pattern Recognition and 11 papers in Computational Theory and Mathematics. Recurrent topics in Jónathan Heras's work include Cell Image Analysis Techniques (8 papers), Logic, programming, and type systems (8 papers) and Smart Agriculture and AI (6 papers). Jónathan Heras is often cited by papers focused on Cell Image Analysis Techniques (8 papers), Logic, programming, and type systems (8 papers) and Smart Agriculture and AI (6 papers). Jónathan Heras collaborates with scholars based in Spain, United Kingdom and Italy. Jónathan Heras's co-authors include César Domínguez, Vico Pascual, Eloy Mata, Cármen Torres, Myriam Zarazaga, Carmen Lozano, J. M. Górriz, Afshin Shoeibi, Parisa Moridian and Saeid Nahavandi and has published in prestigious journals such as SHILAP Revista de lepidopterología, Computers in Human Behavior and IEEE Access.

In The Last Decade

Jónathan Heras

51 papers receiving 1.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jónathan Heras Spain 16 179 146 141 117 115 58 1.1k
Pratik Shah United States 16 83 0.5× 67 0.5× 382 2.7× 44 0.4× 38 0.3× 34 991
David J. Hamilton United States 23 60 0.3× 36 0.2× 305 2.2× 93 0.8× 192 1.7× 63 1.6k
Sergio A. Álvarez United States 16 152 0.8× 47 0.3× 147 1.0× 20 0.2× 115 1.0× 60 1.2k
César Domínguez Spain 15 82 0.5× 59 0.4× 145 1.0× 58 0.5× 10 0.1× 59 919
Qizhong Zhang China 24 76 0.4× 106 0.7× 246 1.7× 10 0.1× 188 1.6× 114 1.5k
Jing Wu China 23 241 1.3× 517 3.5× 395 2.8× 21 0.2× 17 0.1× 87 1.4k
Yizhen Zhang United States 15 139 0.8× 183 1.3× 464 3.3× 47 0.4× 349 3.0× 41 1.9k
Hyunjin Yoon South Korea 24 111 0.6× 59 0.4× 780 5.5× 24 0.2× 64 0.6× 95 2.2k
François Laviolette Canada 23 549 3.1× 134 0.9× 921 6.5× 135 1.2× 483 4.2× 70 2.9k
Qing Wang China 23 169 0.9× 291 2.0× 154 1.1× 11 0.1× 37 0.3× 119 1.7k

Countries citing papers authored by Jónathan Heras

Since Specialization
Citations

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

Fields of papers citing papers by Jónathan Heras

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jónathan Heras

This figure shows the co-authorship network connecting the top 25 collaborators of Jónathan Heras. A scholar is included among the top collaborators of Jónathan Heras 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 Jónathan Heras. Jónathan Heras 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.
Doblaré, M., et al.. (2025). Anomaly detection applied to the classification of cytology images. Biomedical Signal Processing and Control. 105. 107625–107625. 1 indexed citations
2.
Baraloto, Christopher, Benoît Burban, Géraldine Derroire, et al.. (2024). Shifting trait coordination along a soil‐moisture‐nutrient gradient in tropical forests. Functional Ecology. 39(1). 21–37. 6 indexed citations
3.
Heras, Jónathan, et al.. (2024). Deep Learning Models for Justified Referral in AI Glaucoma Screening. 1–3. 1 indexed citations
4.
Ghassemi, Navid, Afshin Shoeibi, Marjane Khodatars, et al.. (2023). Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning. Applied Soft Computing. 144. 110511–110511. 35 indexed citations
5.
Yarahmady, Allan, et al.. (2023). Disease-Associated Mutations in Tau Encode for Changes in Aggregate Structure Conformation. ACS Chemical Neuroscience. 14(24). 4282–4297. 5 indexed citations
6.
Domínguez, César, et al.. (2023). Deep style transfer to deal with the domain shift problem on spheroid segmentation. Neurocomputing. 569. 127105–127105. 9 indexed citations
7.
Heras, Jónathan, et al.. (2023). Estimation of Crop Production by Fusing Images and Crop Features. 525–530.
8.
Heras, Jónathan, et al.. (2023). The Role of Mechanical Properties and Structure of Type I Collagen Hydrogels on Colorectal Cancer Cell Migration. Macromolecular Bioscience. 23(10). e2300108–e2300108. 11 indexed citations
9.
Domínguez, César, et al.. (2023). Analysing semi-supervised learning for image classification using compact networks in the biomedical context. Soft Computing. 28(15-16). 8931–8943. 2 indexed citations
10.
Cicirelli, Grazia, et al.. (2022). The HA4M dataset: Multi-Modal Monitoring of an assembly task for Human Action recognition in Manufacturing. Scientific Data. 9(1). 745–745. 26 indexed citations
11.
Heras, Jónathan, et al.. (2022). Semi-supervised deep learning and low-cost cameras for the semantic segmentation of natural images in viticulture. Precision Agriculture. 23(6). 2001–2026. 25 indexed citations
12.
Domínguez, César, et al.. (2022). Binary and multi-class automated detection of age-related macular degeneration using convolutional- and transformer-based architectures. Computer Methods and Programs in Biomedicine. 229. 107302–107302. 19 indexed citations
13.
Shoeibi, Afshin, Delaram Sadeghi, Parisa Moridian, et al.. (2021). Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models. Frontiers in Neuroinformatics. 15. 777977–777977. 113 indexed citations
14.
Shoeibi, Afshin, Marjane Khodatars, Mahboobeh Jafari, et al.. (2021). Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review. Computers in Biology and Medicine. 136. 104697–104697. 127 indexed citations
15.
Domínguez, César, et al.. (2019). CLoDSA: a tool for augmentation in classification, localization, detection, semantic segmentation and instance segmentation tasks. BMC Bioinformatics. 20(1). 323–323. 55 indexed citations
16.
Domínguez, César, Jónathan Heras, Eloy Mata, & Vico Pascual. (2018). DecoFungi: a web application for automatic characterisation of dye decolorisation in fungal strains. BMC Bioinformatics. 19(1). 66–66. 2 indexed citations
17.
Domínguez, César, Jónathan Heras, & Vico Pascual. (2017). IJ-OpenCV: Combining ImageJ and OpenCV for processing images in biomedicine. Computers in Biology and Medicine. 84. 189–194. 38 indexed citations
18.
Alonso, Carla Andrea, César Domínguez, Jónathan Heras, et al.. (2017). Antibiogramj: A tool for analysing images from disk diffusion tests. Computer Methods and Programs in Biomedicine. 143. 159–169. 35 indexed citations
19.
Heras, Jónathan & Ekaterina Komendantskaya. (2013). Statistical Proof-Patterns in Coq/SSReflect. arXiv (Cornell University).
20.
Heras, Jónathan, et al.. (2010). Integrating multiple sources to answer questions in algebraic topology. arXiv (Cornell University). 331–335.

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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