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Researcher · Deakin University · Australia

Moloud Abdar

Moloud Abdar publishes mostly in Artificial Intelligence, Computer Vision and Pattern Recognition and Health Information Management, on topics such as Artificial Intelligence in Healthcare, Imbalanced Data Classification Techniques and AI in cancer detection.

ORCID 0000-0002-3059-6357 OpenAlex
World rank
#37,530
of 7,710,434 ranked researchers
Rank in Australia
#1,097
of 140,210
Works
118
Citations
9.5k
Citations per work
80.8
h-index
41
i10-index 72Top 2% in Artificial Intelligence
Moloud Abdar

What does Moloud Abdar research?

Moloud Abdar's work is mostly in Computer Science (48%), Medicine (7%), Health Professions (6%).

Physical Sciences: 57 works · 48.2% of their workComputer Science: 57 works · 48.2% of their workArtificial Intelligence: 38 works · 32.3% of their workComputer Vision and Pattern Recognition: 14 works · 11.9% of their workInformation Systems: 5 works · 4.0% of their workEverything else: 45 works · 38.3% of their workHealth Sciences: 16 works · 13.5% of their workMedicine: 9 works · 7.4% of their workRadiology, Nuclear Medicine and Imaging: 5 works · 4.0% of their workCardiology and Cardiovascular Medicine: 4 works · 3.4% of their workHealth Professions: 7 works · 6.1% of their workHealth Information Management: 7 works · 6.1% of their workTHEIR WORKMoloud Abdar118works
Physical Sciences48%Health Sciences14%

Their six largest subfields, inside their fields and domains; the grey slice is everything else they publish. The interactive view splits every one of their works down to single topics.

The fields and subfields, with their numbers
    1. Computer Science5748.2%
  2. Everything else4538.3%
    1. Medicine97.4%
    2. Health Professions76.1%
  1. 1Artificial Intelligence32% of their works
  2. 2Computer Vision and Pattern Recognition12% of their works
  3. 3Health Information Management6% of their works
  4. 4Radiology, Nuclear Medicine and Imaging4% of their works
  5. 5Information Systems4% of their works
  6. 6Cardiology and Cardiovascular Medicine3% of their works

Research topics

Which keywords describe Moloud Abdar's research?

The keywords of their largest research topics: Machine Learning, Deep Learning, Data Mining, Classification, Big Data Analytics, Healthcare, Convolutional Neural Networks and Imbalanced Data.

Size is their works in the topics tagged with each word, from their 8 largest topics. Each links to the topic it comes from most. The words come from the keyword list OpenAlex gives each topic, not from the text of the papers, so a word can appear that none of the papers uses.

All 22 words, with their numbers
  1. Machine Learning383 topics
  2. Deep Learning354 topics
  3. Data Mining292 topics
  4. Classification262 topics
  5. Big Data Analytics201 topic
  6. Healthcare201 topic
  7. Convolutional Neural Networks182 topics
  8. Imbalanced Data181 topic
  9. SMOTE181 topic
  10. Neural Networks172 topics
  11. Anomaly Detection91 topic
  12. Association Rules91 topic
  13. COVID-1991 topic
  14. Frequent Patterns91 topic
  15. Histopathology Images91 topic
  16. Medical Image Analysis91 topic
  17. Medical Imaging91 topic
  18. Unsupervised91 topic
  19. Hyperparameter Optimization81 topic
  20. Image Captioning81 topic
  21. Noisy Labels81 topic
  22. Visual Question Answering81 topic

Where does Moloud Abdar work?

Moloud Abdar's main affiliation in the publication record is Deakin University, Australia.

How many publications and citations does Moloud Abdar have?

OpenAlex counts 118 works and 9,535 citations for Moloud Abdar, an h-index of 41, ranking #37,530 of 7,710,434 researchers worldwide on the quality score.

Ranked on field-normalised citation excellence (50%), output (30%) and citations (20%). Counts come from OpenAlex author records, which occasionally merge different people who share a name or split one person into several.

Papers, co-authors, who cited this work and researchers on the nearest topics have tabs of their own, above. Is this your page? Claim it with your ORCID iD, or request a correction or removal.

Discussion

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