Muhammad Assam
Impact in
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- Brain Tumor Detection and Classification
Papers in
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- Face recognition and analysis 3
- Generative Adversarial Networks and Image Synthesis 3
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- AI in cancer detection 4
- Sentiment Analysis and Opinion Mining 2
- Co-authors
- Yazeed Yasin Ghadi (12 shared papers)Javed Ali Khan (5 shared papers)Hanan Aljuaid (2 shared papers)Asaf Raza (1 shared paper)Heba G. Mohamed (4 shared papers)Antonella Guzzo (1 shared paper)Naeem Ullah (1 shared paper)Aftab Ahmed (2 shared papers)
- Journals
- IEEE Access (12 papers)Applied Sciences (6 papers)Sensors (4 papers)PLoS ONE (1 paper)Big Data Research (1 paper)
- Partner nations
- PakistanSaudi ArabiaChina
In The Last Decade
Muhammad Assam
31 papers receiving 385 citations
Peers
Comparison fields: 5 of 112
- Health Informatics 11
- Neurology 57
- Computer Vision and Pattern Recognition 112
- Artificial Intelligence 134
- Health Information Management 18
Countries citing papers authored by Muhammad Assam
This map shows the geographic impact of Muhammad Assam'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 Muhammad Assam with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Muhammad Assam more than expected).
Fields of papers citing papers by Muhammad Assam
This network shows the impact of papers produced by Muhammad Assam. 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 Muhammad Assam. The network helps show where Muhammad Assam may publish in the future.
Co-authors
The 25 scholars most cited alongside Muhammad Assam, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 35 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2023 | 65 | |
| 2 | 2023 | 34 | |
| 3 | 2023 | 33 | |
| 4 | 2022 | 32 | |
| 5 | 2021 | 31 | |
| 6 | 2024 | 24 | |
| 7 | 2023 | 22 | |
| 8 | 2022 | 20 | |
| 9 | 2022 | 19 | |
| 10 | 2022 | 15 | |
| 11 | 2022 | 15 | |
| 12 | 2022 | 10 | |
| 13 | 2022 | 10 | |
| 14 | 2022 | 8 | |
| 15 | 2024 | 7 | |
| 16 | 2023 | 7 | |
| 17 | 2021 | 6 | |
| 18 | 2024 | 6 | |
| 19 | 2022 | 5 | |
| 20 | 2022 | 5 |
About Muhammad Assam
Muhammad Assam is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Computer Networks and Communications, Information Systems and Radiology, Nuclear Medicine and Imaging, having authored 35 papers that have together received 401 indexed citations. Recurring topics across this work include IoT and Edge/Fog Computing (4 papers), AI in cancer detection (4 papers), COVID-19 diagnosis using AI (3 papers), Face recognition and analysis (3 papers), Generative Adversarial Networks and Image Synthesis (3 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), Sentiment Analysis and Opinion Mining (2 papers) and Smart Grid Security and Resilience (2 papers). The work is most often cited by research in Health Informatics (11 citations), Neurology (57 citations), Computer Vision and Pattern Recognition (112 citations), Artificial Intelligence (134 citations) and Health Information Management (18 citations). Muhammad Assam has collaborated with scholars based in Pakistan, Saudi Arabia and China. Frequent co-authors include Yazeed Yasin Ghadi, Javed Ali Khan, Hanan Aljuaid, Asaf Raza, Heba G. Mohamed, Antonella Guzzo, Naeem Ullah, Aftab Ahmed, Hashim Ali and Fahd N. Al‐Wesabi. Their work appears in journals such as IEEE Access, Applied Sciences, Sensors, PLoS ONE and Big Data Research.
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.