Abbas Jafar
Impact in
- Analytical Chemistry top 10%
- Spectroscopy and Chemometric Analyses
- Health Information Management top 10%
- Artificial Intelligence in Healthcare
Papers in
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- Adversarial Robustness in Machine Learning 2
- Machine Learning in Healthcare 2
- Machine Learning and Data Classification 2
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- Radiomics and Machine Learning in Medical Imaging 3
- COVID-19 diagnosis using AI 2
- Co-authors
- Rizwan Ali Naqvi (5 shared papers)Abolghasem Sadeghi‐Niaraki (1 shared paper)Daesik Jeong (2 shared papers)Myungho Lee (5 shared papers)Hyung Seok Kim (3 shared papers)Zain Ul Abıdın (1 shared paper)Seung Won Lee (2 shared papers)
- Journals
- IEEE Access (2 papers)Journal of Personalized Medicine (1 paper)Frontiers in Plant Science (1 paper)Engineering Applications of Artificial Intelligence (1 paper)Neurocomputing (1 paper)
- Partner nations
- South KoreaPakistan
In The Last Decade
Abbas Jafar
10 papers receiving 203 citations
Abbas Jafar's Hit Papers
Peers
Comparison fields: 5 of 73
- Analytical Chemistry 35
- Health Information Management 14
- Health Informatics 4
- Plant Science 100
- Radiology, Nuclear Medicine and Imaging 28
Countries citing papers authored by Abbas Jafar
This map shows the geographic impact of Abbas Jafar'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 Abbas Jafar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Abbas Jafar more than expected).
Fields of papers citing papers by Abbas Jafar
This network shows the impact of papers produced by Abbas Jafar. 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 Abbas Jafar. The network helps show where Abbas Jafar may publish in the future.
Co-authors
The 7 scholars most cited alongside Abbas Jafar, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Revolutionizing agriculture with artificial intelligence: plant disease detection methods, applications, and their limitations Hit paper breakdown → | 2024 | 113 |
| 2 | 2022 | 28 | |
| 3 | 2021 | 16 | |
| 4 | 2020 | 13 | |
| 5 | 2024 | 13 | |
| 6 | 2023 | 12 | |
| 7 | 2023 | 7 | |
| 8 | 2024 | 3 | |
| 9 | 2025 | 2 | |
| 10 | 2024 | 1 | |
| 11 | 2025 | 0 |
About Abbas Jafar
Abbas Jafar is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Computer Vision and Pattern Recognition, Health Information Management and Oncology, having authored 11 papers that have together received 208 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (3 papers), Advanced Neural Network Applications (3 papers), Artificial Intelligence in Healthcare (2 papers), Adversarial Robustness in Machine Learning (2 papers), Colorectal Cancer Screening and Detection (2 papers), Machine Learning in Healthcare (2 papers), Machine Learning and Data Classification (2 papers) and COVID-19 diagnosis using AI (2 papers). The work is most often cited by research in Analytical Chemistry (35 citations), Health Information Management (14 citations), Health Informatics (4 citations), Plant Science (100 citations) and Radiology, Nuclear Medicine and Imaging (28 citations). Abbas Jafar has collaborated with scholars based in South Korea and Pakistan. Frequent co-authors include Rizwan Ali Naqvi, Abolghasem Sadeghi‐Niaraki, Daesik Jeong, Myungho Lee, Hyung Seok Kim, Zain Ul Abıdın and Seung Won Lee. Their work appears in journals such as IEEE Access, Journal of Personalized Medicine, Frontiers in Plant Science, Engineering Applications of Artificial Intelligence and Neurocomputing.
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.