Muhammad Umer Sarwar
Impact in
-
- Artificial Intelligence in Healthcare
- Artificial Intelligence top 10%
- Imbalanced Data Classification Techniques
- Algorithms and Data Compression
- Internet Traffic Analysis and Secure E-voting
Papers in
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- Text and Document Classification Technologies 2
- Imbalanced Data Classification Techniques 2
- Co-authors
- Suhuai Luo (4 shared papers)Kamran Shaukat (4 shared papers)Talha Mahboob Alam (4 shared papers)Ibrahim A. Hameed (4 shared papers)Zaheer Ahmed (1 shared paper)Matloob Khushi (1 shared paper)Jiaming Li (1 shared paper)Farhat Iqbal (2 shared papers)
In The Last Decade
Muhammad Umer Sarwar
19 papers receiving 413 citations
Peers
Comparison fields: 5 of 101
- Health Information Management 36
- Artificial Intelligence 197
- Computer Science Applications 26
- Accounting 51
- Computer Networks and Communications 91
Countries citing papers authored by Muhammad Umer Sarwar
This map shows the geographic impact of Muhammad Umer Sarwar'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 Umer Sarwar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Muhammad Umer Sarwar more than expected).
Fields of papers citing papers by Muhammad Umer Sarwar
This network shows the impact of papers produced by Muhammad Umer Sarwar. 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 Umer Sarwar. The network helps show where Muhammad Umer Sarwar may publish in the future.
Co-authors
The 25 scholars most cited alongside Muhammad Umer Sarwar, 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 | 2020 | 122 | |
| 2 | Agrep -- A Fast Approximate Pattern-Matching Tool | 2006 | 56 |
| 3 | 2021 | 54 | |
| 4 | 2021 | 38 | |
| 5 | 2021 | 32 | |
| 6 | 2021 | 28 | |
| 7 | 2021 | 26 | |
| 8 | 2021 | 24 | |
| 9 | 2019 | 13 | |
| 10 | 2021 | 13 | |
| 11 | 2022 | 12 | |
| 12 | 2022 | 10 | |
| 13 | 2024 | 7 | |
| 14 | 2021 | 5 | |
| 15 | 2022 | 4 | |
| 16 | 2025 | 3 | |
| 17 | 2023 | 3 | |
| 18 | 2023 | 2 | |
| 19 | 2022 | 1 | |
| 20 | 2025 | 0 |
About Muhammad Umer Sarwar
Muhammad Umer Sarwar is a scholar working on Artificial Intelligence, Computer Networks and Communications, Information Systems, Sociology and Political Science and Management Information Systems, having authored 20 papers that have together received 453 indexed citations. Recurring topics across this work include Big Data and Business Intelligence (2 papers), Text and Document Classification Technologies (2 papers), Imbalanced Data Classification Techniques (2 papers), Occupational and environmental lung diseases (2 papers), Crime Patterns and Interventions (1 paper), Social Media and Politics (1 paper), Machine Learning in Bioinformatics (1 paper) and Online Learning and Analytics (1 paper). The work is most often cited by research in Health Information Management (36 citations), Artificial Intelligence (197 citations), Computer Science Applications (26 citations), Accounting (51 citations) and Computer Networks and Communications (91 citations). Muhammad Umer Sarwar has collaborated with scholars based in Pakistan, Norway and Australia. Frequent co-authors include Suhuai Luo, Kamran Shaukat, Talha Mahboob Alam, Ibrahim A. Hameed, Zaheer Ahmed, Matloob Khushi, Jiaming Li, Farhat Iqbal, Muhammad Kashif Hanif and Muhammad Younas. Their work appears in journals such as IEEE Access, Complexity, Applied Sciences, Journal of Advanced Transportation and Applied Soft Computing.
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.