Maha Sharkas
- Artificial Intelligence top 2%
- Radiology, Nuclear Medicine and Imaging top 2%
- Computer Vision and Pattern Recognition top 5%
- Neurology top 5%
- Signal Processing top 5%
- Co-authors
- Omneya AttallahDina A. RagabStephen MarshallJinchang RenMohamed TamazinMohamed KhedrEhab F. BadranIbrahim El Rube
- Topics
- AI in cancer detection (8 papers)Image Retrieval and Classification Techniques (7 papers)Advanced Steganography and Watermarking Techniques (6 papers)
- Partner nations
- EgyptUnited KingdomCanada
In The Last Decade
Maha Sharkas
39 papers receiving 1.1k citations
Hit Papers
Peers
Comparison fields: 5 of 100
- Artificial Intelligence 636
- Radiology, Nuclear Medicine and Imaging 523
- Computer Vision and Pattern Recognition 318
- Neurology 168
- Signal Processing 132
Countries citing papers authored by Maha Sharkas
This map shows the geographic impact of Maha Sharkas'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 Maha Sharkas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maha Sharkas more than expected).
Fields of papers citing papers by Maha Sharkas
This network shows the impact of papers produced by Maha Sharkas. 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 Maha Sharkas. The network helps show where Maha Sharkas may publish in the future.
Co-authorship network of co-authors of Maha Sharkas
This figure shows the co-authorship network connecting the top 25 collaborators of Maha Sharkas. A scholar is included among the top collaborators of Maha Sharkas 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 Maha Sharkas. Maha Sharkas is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 25 | |
| 3 | 39 | |
| 4 | 20 | |
| 5 | 137 | |
| 6 | 35 | |
| 7 | 46 | |
| 8 | 31 | |
| 9 | 46 | |
| 10 | 10 | |
| 11 | 2 | |
| 12 | 2 | |
| 13 | 111 | |
| 14 | 10 | |
| 15 | 0 | |
| 16 | 3 | |
| 17 | Multiple watermark embedding scheme in wavelet-spatial domains based on ROI of medical images | 17 |
| 18 | 2 | |
| 19 | 5 | |
| 20 | Application of DCT blocks with principal component analysis for face recognition | 10 |
About Maha Sharkas
Maha Sharkas is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Artificial Intelligence, having authored 40 papers that have together received 1.1k indexed citations. Recurring topics across this work include AI in cancer detection (8 papers), Image Retrieval and Classification Techniques (7 papers) and Advanced Steganography and Watermarking Techniques (6 papers). The work is most often cited by research in Health Informatics (41 citations), Radiology, Nuclear Medicine and Imaging (523 citations) and Neurology (168 citations). Maha Sharkas has collaborated with scholars based in Egypt, United Kingdom and Canada. Frequent co-authors include Omneya Attallah, Dina A. Ragab, Stephen Marshall, Jinchang Ren, Mohamed Tamazin, Mohamed Khedr, Ehab F. Badran, Ibrahim El Rube, Onsy Abdel Alim and Mohamed Mahmoud. Their work appears in journals such as Scientific Reports, Sensors and Computers in Biology and Medicine.
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.