Mark Ibrahim

625 citations
4 papers · 77 · h-index 2

Impact in

    • Explainable Artificial Intelligence (XAI)
    • Adversarial Robustness in Machine Learning
    • Machine Learning and Data Classification
    • Anomaly Detection Techniques and Applications
    • Topic Modeling
    • Machine Learning in Healthcare

Papers in

    • Explainable Artificial Intelligence (XAI) 2
    • Adversarial Robustness in Machine Learning 2
    • Machine Learning and Data Classification 1
    • Anomaly Detection Techniques and Applications 1
    • Multimodal Machine Learning Applications 1
    • Advanced Neural Network Applications 1

Mark Ibrahim

3 papers receiving 75 citations

Peers

Mark Ibrahim
Comparison fields: 5 of 47
  • Health Informatics 6
  • Artificial Intelligence 60
  • Computer Vision and Pattern Recognition 14
  • Information Systems and Management 4
  • Computer Science Applications 3
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Mark Ibrahim relative to Pouya Pezeshkpour United States Pouya Pezeshkpour's profile →
Citations per field
00.5×1.5×2.5×
Pouya Pezeshkpour · 1×
Citations per year

Countries citing papers authored by Mark Ibrahim

Since Specialization
Citations

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

Fields of papers citing papers by Mark Ibrahim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 10 scholars most cited alongside Mark Ibrahim, 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 Mark Ibrahim Line = papers co-authored together Mark Ibrahim links everyone, so they are left out of the graph.

All Works

4 of 4 papers shown

About Mark Ibrahim

Mark Ibrahim is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Physical Therapy, Sports Therapy and Rehabilitation, Developmental and Educational Psychology and Biomedical Engineering, having authored 4 papers that have together received 77 indexed citations. Recurring topics across this work include Explainable Artificial Intelligence (XAI) (2 papers), Adversarial Robustness in Machine Learning (2 papers), Machine Learning and Data Classification (1 paper), Children's Physical and Motor Development (1 paper), Mechanics and Biomechanics Studies (1 paper), Multimodal Machine Learning Applications (1 paper), Advanced Neural Network Applications (1 paper) and Anomaly Detection Techniques and Applications (1 paper). The work is most often cited by research in Health Informatics (6 citations), Artificial Intelligence (60 citations), Computer Vision and Pattern Recognition (14 citations), Information Systems and Management (4 citations) and Computer Science Applications (3 citations). Mark Ibrahim has collaborated with scholars based in United States. Frequent co-authors include John Paisley, Cristian Canton Ferrer, Caner Hazırbaş, Ivan Evtimov, Tal Hassner, Albert Gordo, Chenliang Xu, Zhiheng Li, Jiachen Sun and Z. Morley Mao.

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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