Robail Yasrab
- Plant Science
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence
- Ecology
- Radiology, Nuclear Medicine and Imaging
- Co-authors
- Michael P. PoundNaijie GuDarren M. WellsTony PridmoreJonathan A. AtkinsonAndrew P. FrenchJincheng ZhangJ. Alison Noble
- Topics
- Fetal and Pediatric Neurological Disorders (9 papers)Domain Adaptation and Few-Shot Learning (7 papers)Advanced Neural Network Applications (6 papers)
- Journals
- SHILAP Revista de lepidopterologíaIEEE Transactions on Medical ImagingRemote Sensing
- Partner nations
- United KingdomChinaIsrael
In The Last Decade
Robail Yasrab
21 papers receiving 287 citations
Peers
Comparison fields: 5 of 78
- Plant Science 125
- Computer Vision and Pattern Recognition 63
- Artificial Intelligence 59
- Ecology 45
- Radiology, Nuclear Medicine and Imaging 33
Countries citing papers authored by Robail Yasrab
This map shows the geographic impact of Robail Yasrab'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 Robail Yasrab with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Robail Yasrab more than expected).
Fields of papers citing papers by Robail Yasrab
This network shows the impact of papers produced by Robail Yasrab. 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 Robail Yasrab. The network helps show where Robail Yasrab may publish in the future.
Co-authorship network of co-authors of Robail Yasrab
This figure shows the co-authorship network connecting the top 25 collaborators of Robail Yasrab. A scholar is included among the top collaborators of Robail Yasrab 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 Robail Yasrab. Robail Yasrab 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 | 1 | |
| 4 | 1 | |
| 5 | 7 | |
| 6 | 8 | |
| 7 | 1 | |
| 8 | 16 | |
| 9 | 2 | |
| 10 | 5 | |
| 11 | 54 | |
| 12 | 11 | |
| 13 | 90 | |
| 14 | 11 | |
| 15 | 15 | |
| 16 | 30 | |
| 17 | 1 | |
| 18 | 6 | |
| 19 | 8 | |
| 20 | 9 |
About Robail Yasrab
Robail Yasrab is a scholar working on Computer Vision and Pattern Recognition, Pediatrics, Perinatology and Child Health and Artificial Intelligence, having authored 24 papers that have together received 296 indexed citations. Recurring topics across this work include Fetal and Pediatric Neurological Disorders (9 papers), Domain Adaptation and Few-Shot Learning (7 papers) and Advanced Neural Network Applications (6 papers). The work is most often cited by research in Health Informatics (6 citations), Plant Science (125 citations) and Computer Vision and Pattern Recognition (63 citations). Robail Yasrab has collaborated with scholars based in United Kingdom, China and Israel. Frequent co-authors include Michael P. Pound, Naijie Gu, Darren M. Wells, Tony Pridmore, Jonathan A. Atkinson, Andrew P. French, Jincheng Zhang, J. Alison Noble, Lior Drukker and He Zhao. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Medical Imaging and Remote Sensing.
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.