Hamam Mokayed

733 citations
47 papers · 386 · h-index 11

Impact in

Papers in

Hamam Mokayed

39 papers receiving 373 citations

Peers

Hamam Mokayed
Comparison fields: 5 of 82
  • Computer Vision and Pattern Recognition 164
  • Media Technology 50
  • Artificial Intelligence 108
  • Computer Graphics and Computer-Aided Design 11
  • Industrial and Manufacturing Engineering 27
Replace Milan Tripathi with:
Milan Tripathi Nepal
Junliang Chen China
Yanan Liu China
Ying Lv China
Yanli Shao China
Yancong Lin China
Guangwei Zhang China
Oliver Giudice Italy
Jianfeng Zhang China
Hamam Mokayed relative to Milan Tripathi Nepal Milan Tripathi's profile →
Citations per field
00.5×4.8×
Milan Tripathi · 1×
Citations per year

Countries citing papers authored by Hamam Mokayed

Since Specialization
Citations

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

Fields of papers citing papers by Hamam Mokayed

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hamam Mokayed. 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 Hamam Mokayed. The network helps show where Hamam Mokayed may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 47 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202255
2 202233
3 202232
4 202328
5 202124
6 202322
7 202320
8 202319
9 202214
10 202212
11 202311
12 202310
13 20099
14
Fusion of multi-classifiers for online signature verification using fuzzy logic inference
20117
15 20227
16 20217
17 20227
18 20246
19 20235
20 20255

About Hamam Mokayed

Hamam Mokayed is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Biomedical Engineering, Media Technology and Radiology, Nuclear Medicine and Imaging, having authored 47 papers that have together received 386 indexed citations. Recurring topics across this work include Handwritten Text Recognition Techniques (10 papers), Advanced Neural Network Applications (7 papers), AI in cancer detection (7 papers), Video Surveillance and Tracking Methods (6 papers), Vehicle License Plate Recognition (5 papers), Radiomics and Machine Learning in Medical Imaging (5 papers), Advanced Image and Video Retrieval Techniques (4 papers) and EEG and Brain-Computer Interfaces (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (164 citations), Media Technology (50 citations), Artificial Intelligence (108 citations), Computer Graphics and Computer-Aided Design (11 citations) and Industrial and Manufacturing Engineering (27 citations). Hamam Mokayed has collaborated with scholars based in Sweden, Malaysia and India. Frequent co-authors include Marcus Liwicki, Mathias Seuret, Wun‐She Yap, Khin Wee Lai, Yan Chai Hum, Rajkumar Saini, Yee Kai Tee, Palaiahnakote Shivakumara, Umapada Pal and Jerker Delsing. Their work appears in journals such as Scientific Reports, Sensors, Pattern Recognition Letters, Scientific Data and International journal of innovative computing, information & control.

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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