Muddasar Naeem
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
-
- Artificial Intelligence in Healthcare
Papers in
-
- Machine Learning in Healthcare 6
- Reinforcement Learning in Robotics 5
-
- Pharmaceutical Practices and Patient Outcomes 5
- Co-authors
- Antonio Coronato (26 shared papers)Giovanni Paragliola (7 shared papers)Giuseppe De Pietro (6 shared papers)Syed Tahir Hussain Rizvi (3 shared papers)Zaib Ullah (5 shared papers)Patrizia Ribino (1 shared paper)Fabrizio Stasolla (1 shared paper)Sajid Bashir (5 shared papers)
In The Last Decade
Muddasar Naeem
33 papers receiving 808 citations
Muddasar Naeem's Hit Papers
Peers
Comparison fields: 5 of 119
- Health Informatics 61
- Health Information Management 63
- Family Practice 17
- Artificial Intelligence 304
- Geriatrics and Gerontology 23
Countries citing papers authored by Muddasar Naeem
This map shows the geographic impact of Muddasar Naeem'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 Muddasar Naeem with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Muddasar Naeem more than expected).
Fields of papers citing papers by Muddasar Naeem
This network shows the impact of papers produced by Muddasar Naeem. 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 Muddasar Naeem. The network helps show where Muddasar Naeem may publish in the future.
Co-authors
The 25 scholars most cited alongside Muddasar Naeem, 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 38 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 233 | |
| 2 | 2020 | 173 | |
| 3 | Impact of AI-Powered Solutions in Rehabilitation Process: Recent Improvements and Future Trends Hit paper breakdown → | 2024 | 74 |
| 4 | 2023 | 65 | |
| 5 | 2020 | 36 | |
| 6 | 2021 | 35 | |
| 7 | 2019 | 19 | |
| 8 | 2022 | 18 | |
| 9 | 2022 | 18 | |
| 10 | 2023 | 18 | |
| 11 | 2019 | 17 | |
| 12 | 2020 | 17 | |
| 13 | 2022 | 17 | |
| 14 | 2018 | 13 | |
| 15 | 2022 | 13 | |
| 16 | 2019 | 11 | |
| 17 | 2019 | 11 | |
| 18 | 2024 | 10 | |
| 19 | 2016 | 9 | |
| 20 | 2021 | 8 |
About Muddasar Naeem
Muddasar Naeem is a scholar working on Artificial Intelligence, Geriatrics and Gerontology, Cognitive Neuroscience, Software and Computer Networks and Communications, having authored 38 papers that have together received 855 indexed citations. Recurring topics across this work include Advanced MIMO Systems Optimization (7 papers), EEG and Brain-Computer Interfaces (6 papers), Machine Learning in Healthcare (6 papers), Pharmaceutical Practices and Patient Outcomes (5 papers), Reinforcement Learning in Robotics (5 papers), Advanced Wireless Network Optimization (4 papers), Cooperative Communication and Network Coding (4 papers) and COVID-19 diagnosis using AI (3 papers). The work is most often cited by research in Health Informatics (61 citations), Health Information Management (63 citations), Family Practice (17 citations), Artificial Intelligence (304 citations) and Geriatrics and Gerontology (23 citations). Muddasar Naeem has collaborated with scholars based in Italy, Pakistan and Zimbabwe. Frequent co-authors include Antonio Coronato, Giovanni Paragliola, Giuseppe De Pietro, Syed Tahir Hussain Rizvi, Zaib Ullah, Patrizia Ribino, Fabrizio Stasolla, Sajid Bashir, Aqeel Abbas Syed and Mario J. Ciampi. Their work appears in journals such as Expert Systems with Applications, Journal of Reliable Intelligent Environments, IEEE Access, Sensors and Journal of Sensor and Actuator Networks.
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.