Fakhri Karray

102 papers receiving 1.9k citations

Hit Papers

A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19) 2021 · 218 citations
218202120262022202450100150200

Peers

Fakhri Karray
Comparison fields: 5 of 140
  • Health Informatics 73
  • Automotive Engineering 269
  • Artificial Intelligence 537
  • Computer Vision and Pattern Recognition 321
  • Control and Systems Engineering 312
Replace Yilun Lin with:
Yilun Lin China
Chrisina Jayne United Kingdom
Tolga Soyata United States
Haoyi Xiong China
Jie Tang China
Kwok Tai Chui Hong Kong
Jun Jason Zhang United States
Raed Abu Zitar Jordan
Mostafa A. Elhosseini Egypt
Iyad Abu Doush Jordan
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Countries citing papers authored by Fakhri Karray

Since Specialization
Citations

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

Fields of papers citing papers by Fakhri Karray

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 112 papers — load more, or switch the sort, to bring in the rest.

#Work
1
A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)
Hit paper breakdown →
2021218
2 2022112
3 2021105
4 2023101
5 2015100
6 200294
7 200780
8 201777
9 199771
10 202267
11 200247
12 202344
13 201638
14 201937
15 201937
16 202137
17 201937
18 201136
19 202232
20 200831

About Fakhri Karray

Fakhri Karray is a scholar working on Artificial Intelligence, Control and Systems Engineering, Computer Vision and Pattern Recognition, Computer Networks and Communications and Electrical and Electronic Engineering, having authored 112 papers that have together received 1.9k indexed citations. Recurring topics across this work include Dynamics and Control of Mechanical Systems (13 papers), Adaptive Control of Nonlinear Systems (9 papers), Advanced Battery Technologies Research (8 papers), Robotic Path Planning Algorithms (8 papers), Smart Grid Energy Management (8 papers), IoT and Edge/Fog Computing (7 papers), Electric Vehicles and Infrastructure (7 papers) and Anomaly Detection Techniques and Applications (7 papers). The work is most often cited by research in Health Informatics (73 citations), Automotive Engineering (269 citations), Artificial Intelligence (537 citations), Computer Vision and Pattern Recognition (321 citations) and Control and Systems Engineering (312 citations). Fakhri Karray has collaborated with scholars based in Canada, United Arab Emirates and United States. Frequent co-authors include Md. Milon Islam, Moayad Aloqaily, Mostafa F. Shaaban, Ehab F. El‐Saadany, E. Akhavan-Rezai, Jia Zeng, Reda Alhajj, Mohamed S. Kamel, Otman Basir and Mohsen Guizani. Their work appears in journals such as IEEE Access, Acta Astronautica, IEEE Consumer Electronics Magazine, IEEE Internet of Things Journal and IEEE Network.

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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