Muhammad A. Rushdi

685 citations
46 papers · 473 · h-index 13

Impact in

Papers in

Muhammad A. Rushdi

43 papers receiving 463 citations

Peers

Muhammad A. Rushdi
Comparison fields: 5 of 102
  • Computer Vision and Pattern Recognition 194
  • Industrial and Manufacturing Engineering 65
  • Media Technology 43
  • Neurology 37
  • Artificial Intelligence 136
Replace Hiroharu Kawanaka with:
Hiroharu Kawanaka Japan
Xuenan Cui South Korea
Dandan Zhu China
Mohamed Abdel Hameed Egypt
L. Jani Anbarasi India
Zhe Huang United States
Sherin M. Youssef Egypt
Xiaohui Luo China
Hang Zhang United States
Yangjie Cao China
Muhammad A. Rushdi relative to Hiroharu Kawanaka Japan Hiroharu Kawanaka's profile →
Citations per field
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Citations per year

Countries citing papers authored by Muhammad A. Rushdi

Since Specialization
Citations

This map shows the geographic impact of Muhammad A. Rushdi'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 A. Rushdi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Muhammad A. Rushdi more than expected).

Fields of papers citing papers by Muhammad A. Rushdi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Muhammad A. Rushdi. 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 A. Rushdi. The network helps show where Muhammad A. Rushdi may publish in the future.

Co-authors

The 25 scholars most cited alongside Muhammad A. Rushdi, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Muhammad A. Rushdi Line = papers co-authored together Muhammad A. Rushdi links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 46 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201975
2 201972
3 201737
4 201625
5 201823
6 201820
7 202018
8 202117
9 201816
10 202214
11 201814
12 201914
13 201314
14 202110
15 202310
16 20129
17 20238
18 20197
19 20216
20 20175

About Muhammad A. Rushdi

Muhammad A. Rushdi is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Media Technology, Radiology, Nuclear Medicine and Imaging and Molecular Biology, having authored 46 papers that have together received 473 indexed citations. Recurring topics across this work include Image and Signal Denoising Methods (7 papers), AI in cancer detection (6 papers), Chaos-based Image/Signal Encryption (6 papers), Advanced Image Processing Techniques (5 papers), Image Processing Techniques and Applications (4 papers), Medical Image Segmentation Techniques (3 papers), Functional Brain Connectivity Studies (3 papers) and Radiomics and Machine Learning in Medical Imaging (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (194 citations), Industrial and Manufacturing Engineering (65 citations), Media Technology (43 citations), Neurology (37 citations) and Artificial Intelligence (136 citations). Muhammad A. Rushdi has collaborated with scholars based in Egypt, United States and Saudi Arabia. Frequent co-authors include M. H. Annaby, Ayman El‐Baz, Mohammed Mohammed Mohammed Gomaa, Inas A. Yassine, Jeffrey Ho, Heba I. Elkhouly, Ahmed M. Mahmoud, Mohsen Ali, Yassin Abdelsamad and M. Rasmy. Their work appears in journals such as Biomedical Signal Processing and Control, Signal Processing Image Communication, Optik, IEEE Transactions on Semiconductor Manufacturing 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.

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