Ricardo Sousa
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition
- Control and Systems Engineering
- Management Science and Operations Research
- Signal Processing
- Co-authors
- Jaime S. CardosoJoaquim Pinto da CostaAjalmar R. Rocha NetoJorge M. SantosLuís M. SilvaInês DominguesLuı́s A. AlexandreGuilherme A. Barreto
- Topics
- Imbalanced Data Classification Techniques (6 papers)Data Mining Algorithms and Applications (4 papers)AI in cancer detection (3 papers)
- Journals
- Neural Computing and ApplicationsMachine Vision and ApplicationsInternational Journal of Pattern Recognition and Artificial Intelligence
- Partner nations
- PortugalUnited StatesBrazil
In The Last Decade
Ricardo Sousa
19 papers receiving 189 citations
Peers
Comparison fields: 5 of 83
- Artificial Intelligence 109
- Computer Vision and Pattern Recognition 50
- Control and Systems Engineering 15
- Management Science and Operations Research 15
- Signal Processing 14
Countries citing papers authored by Ricardo Sousa
This map shows the geographic impact of Ricardo Sousa'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 Ricardo Sousa with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ricardo Sousa more than expected).
Fields of papers citing papers by Ricardo Sousa
This network shows the impact of papers produced by Ricardo Sousa. 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 Ricardo Sousa. The network helps show where Ricardo Sousa may publish in the future.
Co-authorship network of co-authors of Ricardo Sousa
This figure shows the co-authorship network connecting the top 25 collaborators of Ricardo Sousa. A scholar is included among the top collaborators of Ricardo Sousa 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 Ricardo Sousa. Ricardo Sousa is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 2 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 13 | |
| 6 | 24 | |
| 7 | 2 | |
| 8 | 7 | |
| 9 | 1 | |
| 10 | 11 | |
| 11 | 11 | |
| 12 | 6 | |
| 13 | 3 | |
| 14 | 73 | |
| 15 | 6 | |
| 16 | A Preliminary Model to the Automatic Prediction of Aesthetic Results in Breast Cancer Conservative Treatment | 0 |
| 17 | Prediction Model of Asymmetry in Breast Cancer Conservative Treatment (BCCT) | 0 |
| 18 | 10 | |
| 19 | Is Asymmetry Enough for the Aesthetic Evaluation of Breast Cancer Conservative Treatment (BCCT) | 0 |
| 20 | 7 |
About Ricardo Sousa
Ricardo Sousa is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Graphics and Computer-Aided Design, having authored 23 papers that have together received 191 indexed citations. Recurring topics across this work include Imbalanced Data Classification Techniques (6 papers), Data Mining Algorithms and Applications (4 papers) and AI in cancer detection (3 papers). The work is most often cited by research in Artificial Intelligence (109 citations), Computer Vision and Pattern Recognition (50 citations) and Signal Processing (14 citations). Ricardo Sousa has collaborated with scholars based in Portugal, United States and Brazil. Frequent co-authors include Jaime S. Cardoso, Joaquim Pinto da Costa, Ajalmar R. Rocha Neto, Jorge M. Santos, Luís M. Silva, Inês Domingues, Luı́s A. Alexandre, Guilherme A. Barreto, Iryna Yevseyeva and Maria João Cardoso. Their work appears in journals such as Neural Computing and Applications, Machine Vision and Applications and International Journal of Pattern Recognition and Artificial 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.