Abdullah Y. Muaad
- Artificial Intelligence top 5%
- Radiology, Nuclear Medicine and Imaging top 10%
- Computer Vision and Pattern Recognition top 10%
- Cardiology and Cardiovascular Medicine
- Cognitive Neuroscience
- Co-authors
- Md Belal Bin HeyatFaijan AkhtarMugahed A. Al–antariChiagoziem C. UkwuomaChannabasava CholaJ. V. Bibal BenifaDakun LaiKaishun Wu
- Topics
- Text and Document Classification Technologies (10 papers)COVID-19 diagnosis using AI (9 papers)Radiomics and Machine Learning in Medical Imaging (5 papers)
- Journals
- SHILAP Revista de lepidopterologíaIEEE AccessSensors
In The Last Decade
Abdullah Y. Muaad
33 papers receiving 688 citations
Peers
Comparison fields: 5 of 121
- Artificial Intelligence 279
- Radiology, Nuclear Medicine and Imaging 177
- Computer Vision and Pattern Recognition 100
- Cardiology and Cardiovascular Medicine 79
- Cognitive Neuroscience 79
Countries citing papers authored by Abdullah Y. Muaad
This map shows the geographic impact of Abdullah Y. Muaad'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 Abdullah Y. Muaad with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Abdullah Y. Muaad more than expected).
Fields of papers citing papers by Abdullah Y. Muaad
This network shows the impact of papers produced by Abdullah Y. Muaad. 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 Abdullah Y. Muaad. The network helps show where Abdullah Y. Muaad may publish in the future.
Co-authorship network of co-authors of Abdullah Y. Muaad
This figure shows the co-authorship network connecting the top 25 collaborators of Abdullah Y. Muaad. A scholar is included among the top collaborators of Abdullah Y. Muaad 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 Abdullah Y. Muaad. Abdullah Y. Muaad 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 | 2 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 26 | |
| 6 | 1 | |
| 7 | 1 | |
| 8 | 8 | |
| 9 | 15 | |
| 10 | 10 | |
| 11 | 16 | |
| 12 | 7 | |
| 13 | 4 | |
| 14 | 41 | |
| 15 | 34 | |
| 16 | 94 | |
| 17 | 35 | |
| 18 | 49 | |
| 19 | 11 | |
| 20 | 12 |
About Abdullah Y. Muaad
Abdullah Y. Muaad is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging and Information Systems, having authored 37 papers that have together received 715 indexed citations. Recurring topics across this work include Text and Document Classification Technologies (10 papers), COVID-19 diagnosis using AI (9 papers) and Radiomics and Machine Learning in Medical Imaging (5 papers). The work is most often cited by research in Health Informatics (20 citations), Artificial Intelligence (279 citations) and Radiology, Nuclear Medicine and Imaging (177 citations). Abdullah Y. Muaad has collaborated with scholars based in India, Yemen and China. Frequent co-authors include Md Belal Bin Heyat, Faijan Akhtar, Mugahed A. Al–antari, Chiagoziem C. Ukwuoma, Channabasava Chola, J. V. Bibal Benifa, Dakun Lai, Kaishun Wu, Zhiguang Qin and Arshiya Sultana. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Access and Sensors.
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.