Mohammed S. Sayed

1.3k citations
101 papers · 857 · h-index 16

Impact in

Papers in

Mohammed S. Sayed

93 papers receiving 805 citations

Peers

Mohammed S. Sayed
Comparison fields: 5 of 115
  • Signal Processing 181
  • Computer Vision and Pattern Recognition 329
  • Industrial and Manufacturing Engineering 130
  • Health Informatics 12
  • Media Technology 50
Replace Vikas Singh with:
Vikas Singh India
Piotr Porwik Poland
Takuro Sato Japan
Zhaohui Cai China
John Glossner United States
Jen‐Shiun Chiang Taiwan
Tamer Rabie United Arab Emirates
Yikai Zhang China
Abdullah Alqahtani Saudi Arabia
Hongxu Yin United States
Mohammed S. Sayed relative to Vikas Singh India Vikas Singh's profile →
Citations per field
00.5×5.4×
Vikas Singh · 1×
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 101 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202381
2 202060
3 201645
4 202327
5 201827
6 202426
7 201826
8 202226
9 201425
10 201622
11 201920
12 201420
13 201919
14 201818
15 201317
16 201615
17 201815
18 201914
19 200314
20 201614

About Mohammed S. Sayed

Mohammed S. Sayed is a scholar working on Computer Vision and Pattern Recognition, Signal Processing, Biomedical Engineering, Artificial Intelligence and Electrical and Electronic Engineering, having authored 101 papers that have together received 857 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), Image Processing Techniques and Applications (7 papers), Industrial Vision Systems and Defect Detection (7 papers), Wireless Body Area Networks (7 papers) and AI in cancer detection (7 papers). The work is most often cited by research in Signal Processing (181 citations), Computer Vision and Pattern Recognition (329 citations), Industrial and Manufacturing Engineering (130 citations), Health Informatics (12 citations) and Media Technology (50 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, Mohsen Rashwan, Rania A. Elsayed, Omar M. Saad, Abdelhamied A. Ateya, Paweł Pławiak, Mohamed Hammad and Ahmed A. Abd El‐Latif. Their work appears in journals such as IEEE Access, IEEE Transactions on Circuits and Systems for Video Technology, Multimedia Tools and Applications, Future Internet and International Journal on Document Analysis and Recognition (IJDAR).

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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