G. Soda
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 2%
- Molecular Biology
- Signal Processing top 5%
- Surgery
- Co-authors
- Paolo FrasconiMarco GoriSimone MarinaiF. CesariniPierre BaldiGianluca PollastriSøren BrunakMarco Maggini
- Topics
- Image Retrieval and Classification Techniques (20 papers)Handwritten Text Recognition Techniques (18 papers)Algorithms and Data Compression (11 papers)
- Journals
- Journal of Clinical OncologyBioinformaticsIEEE Transactions on Pattern Analysis and Machine Intelligence
- Partner nations
- ItalyUnited KingdomAustralia
In The Last Decade
G. Soda
104 papers receiving 1.7k citations
Peers
Comparison fields: 5 of 160
- Artificial Intelligence 622
- Computer Vision and Pattern Recognition 538
- Molecular Biology 372
- Signal Processing 127
- Surgery 126
Countries citing papers authored by G. Soda
This map shows the geographic impact of G. Soda'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 G. Soda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites G. Soda more than expected).
Fields of papers citing papers by G. Soda
This network shows the impact of papers produced by G. Soda. 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 G. Soda. The network helps show where G. Soda may publish in the future.
Co-authorship network of co-authors of G. Soda
This figure shows the co-authorship network connecting the top 25 collaborators of G. Soda. A scholar is included among the top collaborators of G. Soda 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 G. Soda. G. Soda 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 | 3 | |
| 3 | 3 | |
| 4 | 3 | |
| 5 | 1 | |
| 6 | 12 | |
| 7 | 38 | |
| 8 | 46 | |
| 9 | 3 | |
| 10 | 4 | |
| 11 | 6 | |
| 12 | 10 | |
| 13 | 7 | |
| 14 | 19 | |
| 15 | Word retrieval in document images without OCR. | 1 |
| 16 | Enhancing first-pass attachment prediction | 2 |
| 17 | A topological transformation for hidden recursive modelsarchitecture networks. | 2 |
| 18 | Expression of bcl-2, c-erbB-2, p53, and p21 (waf1-cip1) protein in thyroid carcinomas. | 33 |
| 19 | 346 | |
| 20 | 0 |
About G. Soda
G. Soda is a scholar working on Computer Vision and Pattern Recognition, Dermatology and Signal Processing, having authored 110 papers that have together received 1.9k indexed citations. Recurring topics across this work include Image Retrieval and Classification Techniques (20 papers), Handwritten Text Recognition Techniques (18 papers) and Algorithms and Data Compression (11 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (538 citations), Artificial Intelligence (622 citations) and Signal Processing (127 citations). G. Soda has collaborated with scholars based in Italy, United Kingdom and Australia. Frequent co-authors include Paolo Frasconi, Marco Gori, Simone Marinai, F. Cesarini, Pierre Baldi, Gianluca Pollastri, Søren Brunak, Marco Maggini, Emanuele Marino and Daniela Bosco. Their work appears in journals such as Journal of Clinical Oncology, Bioinformatics and IEEE Transactions on Pattern Analysis and Machine Intelligence.
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.