Ebrahim Hamid Sumiea
- Artificial Intelligence
- Computer Vision and Pattern Recognition top 10%
- Electrical and Electronic Engineering
- Control and Systems Engineering
- Biomedical Engineering
- Co-authors
- Said Jadid AbdulkadirSafwan Mahmood Al-SelwiAlawi AlqushaibiMohammed Gamal RagabAmgad MuneerHitham AlhussianMohd Fadzil HassanRizwan Qureshi
- Topics
- Brain Tumor Detection and Classification (2 papers)AI in cancer detection (2 papers)COVID-19 diagnosis using AI (2 papers)
- Partner nations
- MalaysiaPakistanSaudi Arabia
In The Last Decade
Ebrahim Hamid Sumiea
10 papers receiving 316 citations
Hit Papers
Peers
Comparison fields: 5 of 103
- Artificial Intelligence 70
- Computer Vision and Pattern Recognition 61
- Electrical and Electronic Engineering 58
- Control and Systems Engineering 37
- Biomedical Engineering 35
Countries citing papers authored by Ebrahim Hamid Sumiea
This map shows the geographic impact of Ebrahim Hamid Sumiea'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 Ebrahim Hamid Sumiea with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ebrahim Hamid Sumiea more than expected).
Fields of papers citing papers by Ebrahim Hamid Sumiea
This network shows the impact of papers produced by Ebrahim Hamid Sumiea. 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 Ebrahim Hamid Sumiea. The network helps show where Ebrahim Hamid Sumiea may publish in the future.
Co-authorship network of co-authors of Ebrahim Hamid Sumiea
This figure shows the co-authorship network connecting the top 25 collaborators of Ebrahim Hamid Sumiea. A scholar is included among the top collaborators of Ebrahim Hamid Sumiea based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ebrahim Hamid Sumiea. Ebrahim Hamid Sumiea is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 8 | |
| 5 | A Comprehensive Systematic Review of YOLO for Medical Object Detection (2018 to 2023)breakdown → | 95 |
| 6 | 10 | |
| 7 | RNN-LSTM: From applications to modeling techniques and beyond—Systematic reviewbreakdown → | 144 |
| 8 | 1 | |
| 9 | Deep deterministic policy gradient algorithm: A systematic reviewbreakdown → | 66 |
| 10 | 11 | |
| 11 | 2 |
About Ebrahim Hamid Sumiea
Ebrahim Hamid Sumiea is a scholar working on Neurology, Artificial Intelligence and Health Information Management, having authored 11 papers that have together received 339 indexed citations. Recurring topics across this work include Brain Tumor Detection and Classification (2 papers), AI in cancer detection (2 papers) and COVID-19 diagnosis using AI (2 papers). The work is most often cited by research in Health Informatics (5 citations), Computer Vision and Pattern Recognition (61 citations) and Artificial Intelligence (70 citations). Ebrahim Hamid Sumiea has collaborated with scholars based in Malaysia, Pakistan and Saudi Arabia. Frequent co-authors include Said Jadid Abdulkadir, Safwan Mahmood Al-Selwi, Alawi Alqushaibi, Mohammed Gamal Ragab, Amgad Muneer, Hitham Alhussian, Mohd Fadzil Hassan, Rizwan Qureshi, Suliman Mohamed Fati and Kamaluddeen Usman Danyaro. Their work appears in journals such as IEEE Access, Heliyon and Journal of King Saud University - Computer and Information Sciences.
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.