Francesco Isgrò

2.3k total citations · 1 hit paper
66 papers, 903 citations indexed

About

Francesco Isgrò is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Aerospace Engineering. According to data from OpenAlex, Francesco Isgrò has authored 66 papers receiving a total of 903 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Computer Vision and Pattern Recognition, 23 papers in Artificial Intelligence and 9 papers in Aerospace Engineering. Recurrent topics in Francesco Isgrò's work include Advanced Vision and Imaging (15 papers), Advanced Image and Video Retrieval Techniques (9 papers) and Robotics and Sensor-Based Localization (6 papers). Francesco Isgrò is often cited by papers focused on Advanced Vision and Imaging (15 papers), Advanced Image and Video Retrieval Techniques (9 papers) and Robotics and Sensor-Based Localization (6 papers). Francesco Isgrò collaborates with scholars based in Italy, United Kingdom and Germany. Francesco Isgrò's co-authors include Andrea Apicella, Roberto Prevete, Francesco Donnarumma, Emanuele Trucco, Domenico Tegolo, Francesca Odone, Alessandro Verri, Oliver Schreer, Peter Kauff and M. Pilu and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Geoscience and Remote Sensing and IEEE Access.

In The Last Decade

Francesco Isgrò

57 papers receiving 861 citations

Hit Papers

A survey on modern trainable activation functions 2021 2026 2022 2024 2021 100 200 300
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.

  1. A survey on modern trainable activation functions
    2021 322 citations Andrea Apicella, Francesco Donnarumma et al. Neural Networks profile →

Peers

Francesco Isgrò
Hao Zhou China
Hong Bao China
Hong Zhou China
Stefan Elfwing Japan
Olac Fuentes United States
Nidhal Bouaynaya United States
Zunlei Feng China
Zhongchao Shi China
Luping Ji China
Hao Zhou China
Francesco Isgrò
Citations per year, relative to Francesco Isgrò Francesco Isgrò (= 1×) peers Hao Zhou

Countries citing papers authored by Francesco Isgrò

Since Specialization
Citations

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

Fields of papers citing papers by Francesco Isgrò

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Francesco Isgrò

This figure shows the co-authorship network connecting the top 25 collaborators of Francesco Isgrò. A scholar is included among the top collaborators of Francesco Isgrò 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 Francesco Isgrò. Francesco Isgrò 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.
Isgrò, Francesco, et al.. (2025). SincVAE: A new semi-supervised approach to improve anomaly detection on EEG data using SincNet and variational autoencoder. SHILAP Revista de lepidopterología. 8. 100213–100213.
2.
Jomaa, Seifeddine, et al.. (2025). Technical note: Image processing for continuous river turbidity monitoring – full-scale tests and potential applications. Hydrology and earth system sciences. 29(17). 4133–4151.
3.
4.
Brancato, Valentina, et al.. (2025). Unveiling key pathomic features for automated diagnosis and Gleason grade estimation in prostate cancer. BMC Medical Imaging. 25(1). 299–299.
5.
Luca, Vincenzo De, Claudia Vetrani, Sara Aprano, et al.. (2025). Digital interventions for weight control to prevent obesity in adolescents: a systematic review. Frontiers in Public Health. 13. 1584595–1584595. 3 indexed citations
6.
Arpaïa, Pasquale, Luca Capobianco, Antonio Espósito, et al.. (2024). Accurate Energy Measurements for Tiny Machine Learning Workloads. 831–836. 2 indexed citations
7.
Apicella, Andrea, et al.. (2023). Adaptive filters in Graph Convolutional Neural Networks. Pattern Recognition. 144. 109867–109867. 17 indexed citations
8.
Apicella, Andrea, Francesco Isgrò, & Roberto Prevete. (2023). Hidden classification layers: Enhancing linear separability between classes in neural networks layers. Pattern Recognition Letters. 177. 69–74. 2 indexed citations
9.
Apicella, Andrea, et al.. (2023). On the effects of data normalization for domain adaptation on EEG data. Engineering Applications of Artificial Intelligence. 123. 106205–106205. 35 indexed citations
10.
Brancato, Valentina, et al.. (2023). A pathomic approach for tumor-infiltrating lymphocytes classification on breast cancer digital pathology images. Heliyon. 9(3). e14371–e14371. 18 indexed citations
11.
Jomaa, Seifeddine, et al.. (2022). Monitoring Water Turbidity Using Remote Sensing Techniques. SHILAP Revista de lepidopterología. 63–63. 8 indexed citations
12.
Apicella, Andrea, et al.. (2022). A Survey on EEG-Based Solutions for Emotion Recognition With a Low Number of Channels. IEEE Access. 10. 117411–117428. 24 indexed citations
13.
Apicella, Andrea, et al.. (2022). Exploiting auto-encoders and segmentation methods for middle-level explanations of image classification systems. Knowledge-Based Systems. 255. 109725–109725. 11 indexed citations
14.
Brancato, Valentina, et al.. (2022). The relationship between radiomics and pathomics in Glioblastoma patients: Preliminary results from a cross-scale association study. Frontiers in Oncology. 12. 1005805–1005805. 20 indexed citations
15.
Apicella, Andrea, Francesco Donnarumma, Francesco Isgrò, & Roberto Prevete. (2021). A survey on modern trainable activation functions. Neural Networks. 138. 14–32. 322 indexed citations breakdown →
16.
Apicella, Andrea, et al.. (2019). Explaining classification systems using sparse dictionaries.. The European Symposium on Artificial Neural Networks. 2 indexed citations
17.
Anzalone, A., М. Бертаина, Susana Briz, et al.. (2018). Methods to Retrieve the Cloud-Top Height in the Frame of the JEM-EUSO Mission. IEEE Transactions on Geoscience and Remote Sensing. 57(1). 304–318. 13 indexed citations
18.
Corazza, Anna, et al.. (2016). Unsupervised entity and relation extraction from clinical records in Italian. Computers in Biology and Medicine. 72. 263–275. 32 indexed citations
19.
Isgrò, Francesco, et al.. (2005). Clustering for Surface Reconstruction. Max Planck Institute for Plasma Physics. 156–162. 1 indexed citations
20.
Isgrò, Francesco, Emanuele Trucco, Peter Kauff, & Oliver Schreer. (2004). 3-D image processing in the future of immersive media. IEEE Transactions on Multimedia. 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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