Majed Alhaisoni
- Neurology top 1%
- Brain Tumor Detection and Classification 16
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- Advanced Neural Network Applications 10
- Human Pose and Action Recognition 9
- Image and Video Quality Assessment 9
- Health Informatics top 5%
- Artificial Intelligence top 1%
- AI in cancer detection 18
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- COVID-19 diagnosis using AI 13
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- Caching and Content Delivery 16
- Peer-to-Peer Network Technologies 15
- Co-authors
- Muhammad Attique KhanRobertas DamaševičiusUsman TariqAmjad RehmanImran AshrafSyed Ahmad Chan BukhariYudong ZhangSeifedine Kadry
- Journals
- SHILAP Revista de lepidopterología (1 paper)Scientific Reports (1 paper)IEEE Access (5 papers)
- Partner nations
- Saudi ArabiaPakistanUnited Kingdom
In The Last Decade
Majed Alhaisoni
102 papers receiving 2.3k citations
Hit Papers
Peers
Comparison fields: 5 of 135
- Neurology 574
- Computer Vision and Pattern Recognition 875
- Health Informatics 45
- Artificial Intelligence 1.0k
- Radiology, Nuclear Medicine and Imaging 671
Countries citing papers authored by Majed Alhaisoni
This map shows the geographic impact of Majed Alhaisoni'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 Majed Alhaisoni with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Majed Alhaisoni more than expected).
Fields of papers citing papers by Majed Alhaisoni
This network shows the impact of papers produced by Majed Alhaisoni. 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 Majed Alhaisoni. The network helps show where Majed Alhaisoni may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Majed Alhaisoni, 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 | 2024 | 25 | |
| 2 | 2024 | 5 | |
| 3 | 2023 | 40 | |
| 4 | 2023 | 4 | |
| 5 | 2023 | 5 | |
| 6 | 2023 | 12 | |
| 7 | 2023 | 6 | |
| 8 | 2023 | 6 | |
| 9 | 2023 | 9 | |
| 10 | 2023 | 1 | |
| 11 | 2023 | 3 | |
| 12 | 2023 | 23 | |
| 13 | 2023 | 30 | |
| 14 | 2023 | 8 | |
| 15 | 2023 | 1 | |
| 16 | 2022 | 55 | |
| 17 | 2022 | 32 | |
| 18 | Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologistsbreakdown → | 2020 | 306 |
| 19 | 2020 | 11 | |
| 20 | An Empirical Study of MPI over PC Clusters | 2011 | 1 |
About Majed Alhaisoni
Majed Alhaisoni is a scholar working on Computer Vision and Pattern Recognition, Neurology and Human-Computer Interaction, having authored 104 papers that have together received 2.5k indexed citations. Recurring topics across this work include AI in cancer detection (18 papers), Caching and Content Delivery (16 papers), Brain Tumor Detection and Classification (16 papers), Peer-to-Peer Network Technologies (15 papers), COVID-19 diagnosis using AI (13 papers), Advanced Neural Network Applications (10 papers), Human Pose and Action Recognition (9 papers) and Image and Video Quality Assessment (9 papers). The work is most often cited by research in Neurology (574 citations), Computer Vision and Pattern Recognition (875 citations) and Health Informatics (45 citations). Majed Alhaisoni has collaborated with scholars based in Saudi Arabia, Pakistan and United Kingdom. Frequent co-authors include Muhammad Attique Khan, Robertas Damaševičius, Usman Tariq, Amjad Rehman, Imran Ashraf, Syed Ahmad Chan Bukhari, Yudong Zhang, Seifedine Kadry, Yunyoung Nam and Tallha Akram. Their work appears in journals such as SHILAP Revista de lepidopterología, Scientific Reports and IEEE Access.
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.