Francesco Piccialli
- Artificial Intelligence top 0.5%
- Computer Networks and Communications top 1%
- Computer Vision and Pattern Recognition top 1%
- Statistical and Nonlinear Physics top 1%
- Electrical and Electronic Engineering top 5%
- Co-authors
- Salvatore CuomoGwanggil JeonFabio GiampaoloVincenzo Schiano Di ColaImran AhmedAngelo ChianeseGianluigi RozzaMaziar Raissi
- Topics
- IoT and Edge/Fog Computing (28 papers)Privacy-Preserving Technologies in Data (15 papers)Video Surveillance and Tracking Methods (14 papers)
- Partner nations
- ItalySouth KoreaChina
In The Last Decade
Francesco Piccialli
173 papers receiving 5.4k citations
Hit Papers
Peers
Comparison fields: 5 of 187
- Artificial Intelligence 1.5k
- Computer Networks and Communications 985
- Computer Vision and Pattern Recognition 984
- Statistical and Nonlinear Physics 715
- Electrical and Electronic Engineering 668
Countries citing papers authored by Francesco Piccialli
This map shows the geographic impact of Francesco Piccialli'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 Piccialli with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Francesco Piccialli more than expected).
Fields of papers citing papers by Francesco Piccialli
This network shows the impact of papers produced by Francesco Piccialli. 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 Piccialli. The network helps show where Francesco Piccialli may publish in the future.
Co-authorship network of co-authors of Francesco Piccialli
This figure shows the co-authorship network connecting the top 25 collaborators of Francesco Piccialli. A scholar is included among the top collaborators of Francesco Piccialli 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 Piccialli. Francesco Piccialli is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 11 | |
| 3 | 22 | |
| 4 | 3 | |
| 5 | 2 | |
| 6 | 3 | |
| 7 | 6 | |
| 8 | 38 | |
| 9 | 1 | |
| 10 | 10 | |
| 11 | From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Wherebreakdown → | 430 |
| 12 | 10 | |
| 13 | 33 | |
| 14 | 78 | |
| 15 | 3 | |
| 16 | 66 | |
| 17 | 70 | |
| 18 | 62 | |
| 19 | 21 | |
| 20 | 81 |
About Francesco Piccialli
Francesco Piccialli is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Computer Networks and Communications, having authored 180 papers that have together received 5.6k indexed citations. Recurring topics across this work include IoT and Edge/Fog Computing (28 papers), Privacy-Preserving Technologies in Data (15 papers) and Video Surveillance and Tracking Methods (14 papers). The work is most often cited by research in Health Informatics (128 citations), Statistical and Nonlinear Physics (715 citations) and Computer Vision and Pattern Recognition (984 citations). Francesco Piccialli has collaborated with scholars based in Italy, South Korea and China. Frequent co-authors include Salvatore Cuomo, Gwanggil Jeon, Fabio Giampaolo, Vincenzo Schiano Di Cola, Imran Ahmed, Angelo Chianese, Gianluigi Rozza, Maziar Raissi, Kashif Naseer Qureshi and Gang Mei. Their work appears in journals such as Analytical Chemistry, Scientific Reports and Expert Systems with Applications.
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.