Soufiane Hamida
- Artificial Intelligence top 5%
- Radiology, Nuclear Medicine and Imaging top 5%
- Computer Vision and Pattern Recognition top 5%
- Health Information Management top 1%
- Neurology top 10%
- Co-authors
- Bouchaib CherradiAbdelhadi RaihaniOussama El GannourHassan OuajjiOumaima TerradaOmar BouattaneAmal TmiriLhoussain Bahatti
- Topics
- AI in cancer detection (13 papers)Radiomics and Machine Learning in Medical Imaging (12 papers)COVID-19 diagnosis using AI (11 papers)
- Journals
- SHILAP Revista de lepidopterologíaIEEE AccessHeliyon
- Partner nations
- MoroccoSaudi ArabiaYemen
In The Last Decade
Soufiane Hamida
61 papers receiving 868 citations
Peers
Comparison fields: 5 of 95
- Artificial Intelligence 450
- Radiology, Nuclear Medicine and Imaging 236
- Computer Vision and Pattern Recognition 171
- Health Information Management 133
- Neurology 108
Countries citing papers authored by Soufiane Hamida
This map shows the geographic impact of Soufiane Hamida'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 Soufiane Hamida with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Soufiane Hamida more than expected).
Fields of papers citing papers by Soufiane Hamida
This network shows the impact of papers produced by Soufiane Hamida. 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 Soufiane Hamida. The network helps show where Soufiane Hamida may publish in the future.
Co-authorship network of co-authors of Soufiane Hamida
This figure shows the co-authorship network connecting the top 25 collaborators of Soufiane Hamida. A scholar is included among the top collaborators of Soufiane Hamida 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 Soufiane Hamida. Soufiane Hamida 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 | 1 | |
| 3 | 4 | |
| 4 | 2 | |
| 5 | 1 | |
| 6 | 3 | |
| 7 | 11 | |
| 8 | 7 | |
| 9 | 3 | |
| 10 | 5 | |
| 11 | 1 | |
| 12 | 8 | |
| 13 | 48 | |
| 14 | 1 | |
| 15 | 19 | |
| 16 | 14 | |
| 17 | 11 | |
| 18 | 25 | |
| 19 | 31 | |
| 20 | 36 |
About Soufiane Hamida
Soufiane Hamida is a scholar working on Health Information Management, Human-Computer Interaction and Artificial Intelligence, having authored 66 papers that have together received 886 indexed citations. Recurring topics across this work include AI in cancer detection (13 papers), Radiomics and Machine Learning in Medical Imaging (12 papers) and COVID-19 diagnosis using AI (11 papers). The work is most often cited by research in Health Information Management (133 citations), Health Informatics (22 citations) and Artificial Intelligence (450 citations). Soufiane Hamida has collaborated with scholars based in Morocco, Saudi Arabia and Yemen. Frequent co-authors include Bouchaib Cherradi, Abdelhadi Raihani, Oussama El Gannour, Hassan Ouajji, Oumaima Terrada, Omar Bouattane, Amal Tmiri, Lhoussain Bahatti, Mohammed Al-Sarem and Faisal Saeed. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Access and Heliyon.
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.