Khaled Ammar

620 citations
15 papers · 236 · h-index 6

Impact in

Papers in

Khaled Ammar

14 papers receiving 232 citations

Peers

Khaled Ammar
Comparison fields: 5 of 26
  • Computer Vision and Pattern Recognition 194
  • Information Systems 117
  • Signal Processing 55
  • Computer Networks and Communications 97
  • Artificial Intelligence 113
Replace Christopher R. Aberger with:
Christopher R. Aberger United States
Norbert Martínez-Bazán Spain
Oskar van Rest United States
Kaiwei Li China
Fuad Jamour Saudi Arabia
Marcus Paradies Germany
Can Lu Hong Kong
Tim Hegeman Netherlands
Yinglong Xia United States
Andrew Lamb United States
Khaled Ammar relative to Christopher R. Aberger United States Christopher R. Aberger's profile →
Citations per field
00.5×2.7×
Christopher R. Aberger · 1×
Citations per year

Countries citing papers authored by Khaled Ammar

Since Specialization
Citations

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

Fields of papers citing papers by Khaled Ammar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 18 scholars most cited alongside Khaled Ammar, 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 Khaled Ammar Line = papers co-authored together Khaled Ammar links everyone, so they are left out of the graph.

All Works

15 of 15 papers shown
#Work
1 2014123
2 201847
3 201517
4 201815
5 20217
6 20166
7 20165
8 20154
9 20133
10 20113
11 20242
12 20192
13 20221
14 20151
15 19880

About Khaled Ammar

Khaled Ammar is a scholar working on Computer Vision and Pattern Recognition, Signal Processing, Artificial Intelligence, Computer Networks and Communications and Information Systems, having authored 15 papers that have together received 236 indexed citations. Recurring topics across this work include Data Management and Algorithms (6 papers), Graph Theory and Algorithms (6 papers), Advanced Graph Neural Networks (4 papers), Energy Efficient Wireless Sensor Networks (3 papers), Semantic Web and Ontologies (2 papers), Cloud Computing and Resource Management (2 papers), Distributed Sensor Networks and Detection Algorithms (2 papers) and Data Quality and Management (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (194 citations), Information Systems (117 citations), Signal Processing (55 citations), Computer Networks and Communications (97 citations) and Artificial Intelligence (113 citations). Khaled Ammar has collaborated with scholars based in Canada, Switzerland and Germany. Frequent co-authors include Tianqi Jin, M. TAMER ÖZSU, Khuzaima Daudjee, Semih Salihoğlu, Frank McSherry, Manas Joglekar, Essam Mansour, Mário A. Nascimento, Mohamed Hosny and Michael Terry. Their work appears in journals such as Proceedings of the VLDB Endowment, Integration and Zagazig University Medical Journal.

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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