Amir Mosavi
- Artificial Intelligence top 10%
- Neurology top 5%
- Computer Vision and Pattern Recognition top 10%
- Radiology, Nuclear Medicine and Imaging
- Information Systems top 10%
- Co-authors
- Muhammad Adnan KhanSagheer AbbasShahab S. BandAbdur RehmanTaher M. GhazalMd. Razaul KarimAnichur RahmanMd. Saikat Islam Khan
- Topics
- Radiomics and Machine Learning in Medical Imaging (3 papers)AI in cancer detection (3 papers)COVID-19 epidemiological studies (2 papers)
In The Last Decade
Amir Mosavi
18 papers receiving 481 citations
Hit Papers
Peers
Comparison fields: 5 of 89
- Artificial Intelligence 200
- Neurology 172
- Computer Vision and Pattern Recognition 115
- Radiology, Nuclear Medicine and Imaging 106
- Information Systems 74
Countries citing papers authored by Amir Mosavi
This map shows the geographic impact of Amir Mosavi'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 Amir Mosavi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Amir Mosavi more than expected).
Fields of papers citing papers by Amir Mosavi
This network shows the impact of papers produced by Amir Mosavi. 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 Amir Mosavi. The network helps show where Amir Mosavi may publish in the future.
Co-authorship network of co-authors of Amir Mosavi
This figure shows the co-authorship network connecting the top 25 collaborators of Amir Mosavi. A scholar is included among the top collaborators of Amir Mosavi 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 Amir Mosavi. Amir Mosavi 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 | 12 | |
| 3 | 2 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 18 | |
| 8 | Accurate brain tumor detection using deep convolutional neural networkbreakdown → | 181 |
| 9 | 21 | |
| 10 | 50 | |
| 11 | 28 | |
| 12 | 6 | |
| 13 | 17 | |
| 14 | A secure healthcare 5.0 system based on blockchain technology entangled with federated learning techniquebreakdown → | 120 |
| 15 | 14 | |
| 16 | 29 | |
| 17 | 5 | |
| 18 | 2 | |
| 19 | 0 | |
| 20 | 1 |
About Amir Mosavi
Amir Mosavi is a scholar working on Modeling and Simulation, Energy Engineering and Power Technology and Neurology, having authored 20 papers that have together received 509 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (3 papers), AI in cancer detection (3 papers) and COVID-19 epidemiological studies (2 papers). The work is most often cited by research in Neurology (172 citations), Health Informatics (18 citations) and Artificial Intelligence (200 citations). Amir Mosavi has collaborated with scholars based in Hungary, Slovakia and Germany. Frequent co-authors include Muhammad Adnan Khan, Sagheer Abbas, Shahab S. Band, Abdur Rehman, Taher M. Ghazal, Md. Razaul Karim, Anichur Rahman, Md. Saikat Islam Khan, Tanoy Debnath and Mostofa Kamal Nasir. Their work appears in journals such as Energy Conversion and Management, IEEE Access and Sustainability.
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.