Younes Akbari
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Media Technology top 5%
- Human-Computer Interaction top 10%
- Radiology, Nuclear Medicine and Imaging
- Co-authors
- Somaya Al-MáadeedOmar ElharroussNoor AlmaadeedJavad SadriKazem NouriChawki DjeddiImran SiddiqiMoutaz Saleh
- Topics
- Handwritten Text Recognition Techniques (15 papers)Digital Media Forensic Detection (12 papers)AI in cancer detection (9 papers)
- Journals
- SHILAP Revista de lepidopterologíaScientific ReportsExpert Systems with Applications
- Partner nations
- QatarUnited Arab EmiratesIran
In The Last Decade
Younes Akbari
41 papers receiving 429 citations
Peers
Comparison fields: 5 of 83
- Computer Vision and Pattern Recognition 291
- Artificial Intelligence 110
- Media Technology 74
- Human-Computer Interaction 47
- Radiology, Nuclear Medicine and Imaging 32
Countries citing papers authored by Younes Akbari
This map shows the geographic impact of Younes Akbari'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 Younes Akbari with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Younes Akbari more than expected).
Fields of papers citing papers by Younes Akbari
This network shows the impact of papers produced by Younes Akbari. 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 Younes Akbari. The network helps show where Younes Akbari may publish in the future.
Co-authorship network of co-authors of Younes Akbari
This figure shows the co-authorship network connecting the top 25 collaborators of Younes Akbari. A scholar is included among the top collaborators of Younes Akbari 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 Younes Akbari. Younes Akbari 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 | 0 | |
| 3 | 0 | |
| 4 | 8 | |
| 5 | 2 | |
| 6 | 2 | |
| 7 | 12 | |
| 8 | 31 | |
| 9 | 4 | |
| 10 | 18 | |
| 11 | 1 | |
| 12 | 1 | |
| 13 | 1 | |
| 14 | 3 | |
| 15 | 2 | |
| 16 | 5 | |
| 17 | 1 | |
| 18 | 70 | |
| 19 | 21 | |
| 20 | 8 |
About Younes Akbari
Younes Akbari is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Artificial Intelligence, having authored 44 papers that have together received 449 indexed citations. Recurring topics across this work include Handwritten Text Recognition Techniques (15 papers), Digital Media Forensic Detection (12 papers) and AI in cancer detection (9 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (291 citations), Human-Computer Interaction (47 citations) and Media Technology (74 citations). Younes Akbari has collaborated with scholars based in Qatar, United Arab Emirates and Iran. Frequent co-authors include Somaya Al-Máadeed, Omar Elharrouss, Noor Almaadeed, Javad Sadri, Kazem Nouri, Chawki Djeddi, Imran Siddiqi, Moutaz Saleh, Fouad Khelifi and Ahmed Bouridane. Their work appears in journals such as SHILAP Revista de lepidopterología, 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.