Mohammed S. Sayed

1.4k citations
102 papers · 887 · h-index 16

Impact in

Papers in

Mohammed S. Sayed

95 papers receiving 842 citations

Peers

Mohammed S. Sayed
Comparison fields: 5 of 110
  • Signal Processing 181
  • Computer Vision and Pattern Recognition 325
  • Industrial and Manufacturing Engineering 131
  • Health Informatics 11
  • Artificial Intelligence 183
Replace Vikas Singh with:
Vikas Singh India
Ghada M. El‐Banby Egypt
Takuro Sato Japan
Piotr Porwik Poland
Zhaohui Cai China
Yangjie Cao China
Jen‐Shiun Chiang Taiwan
Yikai Zhang China
Sonain Jamil South Korea
Tamer Rabie United Arab Emirates
Mohammed S. Sayed relative to Vikas Singh India Vikas Singh's profile →
Citations per field
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Citations per year

Countries citing papers authored by Mohammed S. Sayed

Since Specialization
Citations

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

Fields of papers citing papers by Mohammed S. Sayed

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mohammed S. Sayed. 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 Mohammed S. Sayed. The network helps show where Mohammed S. Sayed may publish in the future.

Co-authors

The 25 scholars most cited alongside Mohammed S. Sayed, 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 Mohammed S. Sayed Line = papers co-authored together Mohammed S. Sayed links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 102 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202396
2 202061
3 201645
4 202430
5 201828
6 202327
7 201827
8 202226
9 201425
10 201621
11 201420
12 201920
13 201919
14 201818
15 201317
16 201815
17 201615
18 201615
19 200314
20 201914

About Mohammed S. Sayed

Mohammed S. Sayed is a scholar working on Computer Vision and Pattern Recognition, Signal Processing, Artificial Intelligence, Biomedical Engineering and Electrical and Electronic Engineering, having authored 102 papers that have together received 887 indexed citations. Recurring topics across this work include Video Coding and Compression Technologies (41 papers), Advanced Data Compression Techniques (27 papers), Advanced Vision and Imaging (26 papers), Image and Video Quality Assessment (8 papers), AI in cancer detection (8 papers), Wireless Body Area Networks (7 papers), Image Processing Techniques and Applications (7 papers) and Industrial Vision Systems and Defect Detection (7 papers). The work is most often cited by research in Signal Processing (181 citations), Computer Vision and Pattern Recognition (325 citations), Industrial and Manufacturing Engineering (131 citations), Health Informatics (11 citations) and Artificial Intelligence (183 citations). Mohammed S. Sayed has collaborated with scholars based in Egypt, Canada and Japan. Frequent co-authors include Ahmed Shalaby, Wael Badawy, Mahmoud I. Abdalla, Rania A. Elsayed, Omar M. Saad, Mohsen Rashwan, Abdelhamied A. Ateya, Ahmed A. Abd El‐Latif, Mohamed Hammad and Paweł Pławiak. Their work appears in journals such as IEEE Access, Multimedia Tools and Applications, IEEE Transactions on Circuits and Systems for Video Technology, Engineering Applications of Artificial Intelligence and IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

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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