G. Auda

601 citations
16 papers · 369 · h-index 10

Impact in

Papers in

    • Neural Networks and Applications 14
    • Fuzzy Logic and Control Systems 4
    • Machine Learning and ELM 4
    • Face and Expression Recognition 5
    • Handwritten Text Recognition Techniques 3
    • Image Retrieval and Classification Techniques 2
    • Digital Imaging for Blood Diseases 1

G. Auda

16 papers receiving 331 citations

Peers

G. Auda
Comparison fields: 5 of 87
  • Artificial Intelligence 201
  • Computer Vision and Pattern Recognition 99
  • Signal Processing 41
  • Media Technology 23
  • Environmental Engineering 35
Replace Muhammad Zubair Rehman with:
Muhammad Zubair Rehman Malaysia
Norbert Jankowski Poland
Kai-Bo Duan Singapore
Frauke Friedrichs Germany
Hongfang Zhou China
Minyoung Kim South Korea
Radwa Marzouk Saudi Arabia
Neil A. Bomberger United States
Jaber S. Alzahrani Saudi Arabia
G. Auda relative to Muhammad Zubair Rehman Malaysia Muhammad Zubair Rehman's profile →
Citations per field
00.5×2.6×
Muhammad Zubair Rehman · 1×
Citations per year

Countries citing papers authored by G. Auda

Since Specialization
Citations

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

Fields of papers citing papers by G. Auda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 7 scholars most cited alongside G. Auda, 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 G. Auda Line = papers co-authored together G. Auda links everyone, so they are left out of the graph.

All Works

16 of 16 papers shown
#Work
1 2002107
2 199980
3 199842
4 199731
5 200224
6 200215
7 199814
8 199913
9 20029
10 20029
11 20027
12
Cooperative modular neural network classifiers
19967
13 20035
14 20023
15 20022
16 20021

About G. Auda

G. Auda is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Control and Systems Engineering and Signal Processing, having authored 16 papers that have together received 369 indexed citations. Recurring topics across this work include Neural Networks and Applications (14 papers), Face and Expression Recognition (5 papers), Fuzzy Logic and Control Systems (4 papers), Machine Learning and ELM (4 papers), Handwritten Text Recognition Techniques (3 papers), Advanced Decision-Making Techniques (3 papers), Image Retrieval and Classification Techniques (2 papers) and Digital Imaging for Blood Diseases (1 paper). The work is most often cited by research in Artificial Intelligence (201 citations), Computer Vision and Pattern Recognition (99 citations), Signal Processing (41 citations), Media Technology (23 citations) and Environmental Engineering (35 citations). G. Auda has collaborated with scholars based in Canada, Kuwait and Egypt. Frequent co-authors include Mohamed S. Kamel, Nayer Wanas, Fakhri Karray, Hazem Raafat, Ahmed M. Darwish, Fakhreddine Karray and Ahmed El‐Rabbany. Their work appears in journals such as Pattern Recognition Letters, Journal of Intelligent & Robotic Systems, International Journal of Neural Systems, Journal of Navigation and Neurocomputing.

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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