Edoardo Ragusa

687 total citations
52 papers, 426 citations indexed

About

Edoardo Ragusa is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Edoardo Ragusa has authored 52 papers receiving a total of 426 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Artificial Intelligence, 22 papers in Electrical and Electronic Engineering and 14 papers in Computer Vision and Pattern Recognition. Recurrent topics in Edoardo Ragusa's work include Machine Learning and ELM (13 papers), Advanced Memory and Neural Computing (9 papers) and Neural Networks and Applications (9 papers). Edoardo Ragusa is often cited by papers focused on Machine Learning and ELM (13 papers), Advanced Memory and Neural Computing (9 papers) and Neural Networks and Applications (9 papers). Edoardo Ragusa collaborates with scholars based in Italy, Singapore and Denmark. Edoardo Ragusa's co-authors include Paolo Gastaldo, Rodolfo Zunino, Erik Cambria, Christian Gianoglio, Iti Chaturvedi, F. Guastavino, Maurizio Valle, Strahinja Došen, Luca De Marchi and Federica Zonzini and has published in prestigious journals such as IEEE Access, Sensors and IEEE Transactions on Industrial Informatics.

In The Last Decade

Edoardo Ragusa

46 papers receiving 418 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Edoardo Ragusa Italy 11 234 96 91 38 34 52 426
R. Manjula Devi India 12 157 0.7× 37 0.4× 72 0.8× 24 0.6× 26 0.8× 41 390
Suan Lee South Korea 11 113 0.5× 32 0.3× 118 1.3× 27 0.7× 38 1.1× 56 423
Ye-Hoon Kim South Korea 10 161 0.7× 26 0.3× 130 1.4× 62 1.6× 65 1.9× 19 508
Tatiana Baidyk Mexico 9 118 0.5× 86 0.9× 96 1.1× 20 0.5× 14 0.4× 21 308
Jiadui Chen China 11 108 0.5× 60 0.6× 47 0.5× 24 0.6× 57 1.7× 29 385
Sina Mohseni Iran 11 210 0.9× 70 0.7× 81 0.9× 14 0.4× 36 1.1× 23 365
Liangqi Yuan United States 9 176 0.8× 95 1.0× 57 0.6× 48 1.3× 19 0.6× 18 372
Mrinal Bachute India 7 93 0.4× 41 0.4× 69 0.8× 22 0.6× 40 1.2× 30 320
Haowen Fang United States 11 148 0.6× 260 2.7× 80 0.9× 25 0.7× 27 0.8× 26 467

Countries citing papers authored by Edoardo Ragusa

Since Specialization
Citations

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

Fields of papers citing papers by Edoardo Ragusa

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Edoardo Ragusa

This figure shows the co-authorship network connecting the top 25 collaborators of Edoardo Ragusa. A scholar is included among the top collaborators of Edoardo Ragusa 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 Edoardo Ragusa. Edoardo Ragusa 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
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Ragusa, Edoardo, et al.. (2025). Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras. IEEE Sensors Journal. 25(14). 27467–27477. 1 indexed citations
3.
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Gastaldo, Paolo, Edoardo Ragusa, Strahinja Došen, & Francesco Palmieri. (2024). Special Issue on integration of machine learning and edge computing for next generation of smart wearable systems. Future Generation Computer Systems. 164. 107574–107574. 1 indexed citations
5.
Ragusa, Edoardo, et al.. (2024). Design and Implementation of Tiny Deep Neural Networks for Landing Pad Detection on UAVs. IEEE Access. 12. 124009–124020.
6.
Ragusa, Edoardo, Federica Zonzini, Luca De Marchi, & Rodolfo Zunino. (2024). Compression–Accuracy Co-Optimization Through Hardware-Aware Neural Architecture Search for Vibration Damage Detection. IEEE Internet of Things Journal. 11(19). 31745–31757. 5 indexed citations
7.
Ragusa, Edoardo, Strahinja Došen, Rodolfo Zunino, & Paolo Gastaldo. (2023). Affordance Segmentation Using Tiny Networks for Sensing Systems in Wearable Robotic Devices. IEEE Sensors Journal. 23(19). 23916–23926. 6 indexed citations
8.
Gianoglio, Christian, Edoardo Ragusa, Paolo Gastaldo, & Maurizio Valle. (2023). Trade-off Between Accuracy and Computational Cost With Neural Architecture Search: A Novel Strategy for Tactile Sensing Design. IEEE Sensors Letters. 7(5). 1–4. 7 indexed citations
9.
Ragusa, Edoardo, Christian Gianoglio, Rodolfo Zunino, & Paolo Gastaldo. (2022). An approximate randomization-based neural network with dedicated digital architecture for energy-constrained devices. Neural Computing and Applications. 35(9). 6753–6766. 2 indexed citations
10.
Mastronardi, Valentina, Mauro Moglianetti, Edoardo Ragusa, Rodolfo Zunino, & Pier Paolo Pompa. (2022). From a Chemotherapeutic Drug to a High-Performance Nanocatalyst: A Fast Colorimetric Test for Cisplatin Detection at ppb Level. Biosensors. 12(6). 375–375. 9 indexed citations
11.
Ragusa, Edoardo, Christian Gianoglio, Strahinja Došen, & Paolo Gastaldo. (2021). Hardware-Aware Affordance Detection for Application in Portable Embedded Systems. IEEE Access. 9. 123178–123193. 8 indexed citations
12.
Pandelea, Vlad, Edoardo Ragusa, Tom Young, Paolo Gastaldo, & Erik Cambria. (2021). Toward hardware-aware deep-learning-based dialogue systems. Neural Computing and Applications. 34(13). 10397–10408. 6 indexed citations
13.
Ragusa, Edoardo, et al.. (2021). Design and Deployment of an Image Polarity Detector with Visual Attention. Cognitive Computation. 14(1). 261–273. 14 indexed citations
14.
Gianoglio, Christian, et al.. (2020). Unsupervised Monitoring System for Predictive Maintenance of High Voltage Apparatus. Energies. 13(5). 1109–1109. 7 indexed citations
15.
Ragusa, Edoardo, Christian Gianoglio, Rodolfo Zunino, & Paolo Gastaldo. (2019). Data-Driven Video Grasping Classification for Low-Power Embedded System. CINECA IRIS Institutial Research Information System (University of Genoa). 5. 871–874. 3 indexed citations
16.
Ragusa, Edoardo, Christian Gianoglio, Paolo Gastaldo, & Rodolfo Zunino. (2018). A Digital Implementation of Extreme Learning Machines for Resource-Constrained Devices. IEEE Transactions on Circuits & Systems II Express Briefs. 65(8). 1104–1108. 20 indexed citations
17.
Gianoglio, Christian, et al.. (2018). Hardware Friendly Neural Network for the PD Classification. CINECA IRIS Institutial Research Information System (University of Genoa). 29. 538–541. 4 indexed citations
18.
Gastaldo, Paolo, Federica Bisio, Christian Gianoglio, Edoardo Ragusa, & Rodolfo Zunino. (2017). Learning with similarity functions: A novel design for the extreme learning machine. Neurocomputing. 261. 37–49. 14 indexed citations
19.
Ragusa, Edoardo, et al.. (2016). Spam detection of Twitter traffic: A framework based on random forests and non-uniform feature sampling. CINECA IRIS Institutial Research Information System (University of Genoa). 8 indexed citations
20.
Oliveri, Alberto, Christian Gianoglio, Edoardo Ragusa, & Marco Storace. (2015). Low-complexity digital architecture for solving the point location problem in explicit Model Predictive Control. Journal of the Franklin Institute. 352(6). 2249–2258. 5 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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