Luca Ghelardoni
- Electrical and Electronic Engineering
- Artificial Intelligence
- Management Science and Operations Research top 10%
- Control and Systems Engineering
- Computer Vision and Pattern Recognition
- Co-authors
- Davide AnguitaAlessandro GhioSandro RidellaLuca OnetoAndrea CeccarelliMatteo PastorinoGiovanni BozzaDaniele D. Caviglia
- Topics
- Machine Learning and Algorithms (3 papers)Image and Signal Denoising Methods (2 papers)Neural Networks and Applications (2 papers)
- Cited by
- Management Science and Operations ResearchElectrical and Electronic EngineeringArtificial Intelligence
In The Last Decade
Luca Ghelardoni
8 papers receiving 291 citations
Peers
Comparison fields: 5 of 98
- Electrical and Electronic Engineering 166
- Artificial Intelligence 69
- Management Science and Operations Research 58
- Control and Systems Engineering 43
- Computer Vision and Pattern Recognition 32
Countries citing papers authored by Luca Ghelardoni
This map shows the geographic impact of Luca Ghelardoni'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 Luca Ghelardoni with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Luca Ghelardoni more than expected).
Fields of papers citing papers by Luca Ghelardoni
This network shows the impact of papers produced by Luca Ghelardoni. 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 Luca Ghelardoni. The network helps show where Luca Ghelardoni may publish in the future.
Co-authorship network of co-authors of Luca Ghelardoni
This figure shows the co-authorship network connecting the top 25 collaborators of Luca Ghelardoni. A scholar is included among the top collaborators of Luca Ghelardoni 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 Luca Ghelardoni. Luca Ghelardoni is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 170 | |
| 3 | The 'K' in K-fold Cross Validation | 93 |
| 4 | 3 | |
| 5 | 13 | |
| 6 | 9 | |
| 7 | 5 | |
| 8 | 5 |
About Luca Ghelardoni
Luca Ghelardoni is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Management Science and Operations Research, having authored 8 papers that have together received 300 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (3 papers), Image and Signal Denoising Methods (2 papers) and Neural Networks and Applications (2 papers). The work is most often cited by research in Management Science and Operations Research (58 citations), Electrical and Electronic Engineering (166 citations) and Artificial Intelligence (69 citations). Luca Ghelardoni has collaborated with scholars based in Italy, Austria and Germany. Frequent co-authors include Davide Anguita, Alessandro Ghio, Sandro Ridella, Luca Oneto, Andrea Ceccarelli, Matteo Pastorino, Giovanni Bozza and Daniele D. Caviglia. Their work appears in journals such as IEEE Transactions on Smart Grid, IEEE Microwave and Wireless Components Letters and Journal of Artificial Intelligence and Soft Computing Research.
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.