Ali Samad
Impact in
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- Artificial Intelligence in Healthcare
- Medical Laboratory Technology top 10%
Papers in
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- Sentiment Analysis and Opinion Mining 3
- Machine Learning in Healthcare 2
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- Software Engineering Research 2
- Co-authors
- Sikandar Ali (10 shared papers)Muhammad Faisal Abrar (1 shared paper)Najeeb Ullah (1 shared paper)Muhammad Usman (1 shared paper)Mujtaba Husnain (6 shared papers)Mukhtaj Khan (4 shared papers)Muhammad Aamir (2 shared papers)Norhalina Senan (1 shared paper)
- Journals
- IEEE Access (3 papers)Complexity (1 paper)Mathematical Problems in Engineering (4 papers)Mobile Information Systems (4 papers)Scientific Programming (2 papers)
- Partner nations
- PakistanMalaysiaSaudi Arabia
In The Last Decade
Ali Samad
21 papers receiving 301 citations
Peers
Comparison fields: 5 of 71
- Health Information Management 117
- Medical Laboratory Technology 12
- Neurology 25
- Artificial Intelligence 106
- Health Informatics 4
Countries citing papers authored by Ali Samad
This map shows the geographic impact of Ali Samad'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 Ali Samad with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ali Samad more than expected).
Fields of papers citing papers by Ali Samad
This network shows the impact of papers produced by Ali Samad. 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 Ali Samad. The network helps show where Ali Samad may publish in the future.
Co-authors
The 25 scholars most cited alongside Ali Samad, 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 22 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 95 | |
| 2 | 2022 | 49 | |
| 3 | 2022 | 40 | |
| 4 | 2021 | 26 | |
| 5 | 2022 | 25 | |
| 6 | 2022 | 13 | |
| 7 | 2020 | 12 | |
| 8 | 2009 | 11 | |
| 9 | 2021 | 7 | |
| 10 | 2021 | 7 | |
| 11 | 2021 | 6 | |
| 12 | 2022 | 5 | |
| 13 | 2022 | 3 | |
| 14 | 2022 | 3 | |
| 15 | 2024 | 3 | |
| 16 | 2022 | 2 | |
| 17 | 2022 | 2 | |
| 18 | 2020 | 2 | |
| 19 | 2022 | 1 | |
| 20 | 2024 | 1 |
About Ali Samad
Ali Samad is a scholar working on Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Computer Networks and Communications and Radiology, Nuclear Medicine and Imaging, having authored 22 papers that have together received 314 indexed citations. Recurring topics across this work include Sentiment Analysis and Opinion Mining (3 papers), Software System Performance and Reliability (2 papers), Brain Tumor Detection and Classification (2 papers), Machine Learning in Healthcare (2 papers), Software Engineering Research (2 papers), Advanced Neural Network Applications (2 papers), Artificial Intelligence in Healthcare (2 papers) and IoT and Edge/Fog Computing (2 papers). The work is most often cited by research in Health Information Management (117 citations), Medical Laboratory Technology (12 citations), Neurology (25 citations), Artificial Intelligence (106 citations) and Health Informatics (4 citations). Ali Samad has collaborated with scholars based in Pakistan, Malaysia and Saudi Arabia. Frequent co-authors include Sikandar Ali, Muhammad Faisal Abrar, Najeeb Ullah, Muhammad Usman, Mujtaba Husnain, Mukhtaj Khan, Muhammad Aamir, Norhalina Senan, Fazli Wahid and Muhammad Faheem Mushtaq. Their work appears in journals such as IEEE Access, Complexity, Mathematical Problems in Engineering, Mobile Information Systems and Scientific Programming.
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.