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Researcher · University of Johannesburg · South Africa

Ibomoiye Domor Mienye

Ibomoiye Domor Mienye publishes mostly in Artificial Intelligence, Health Information Management and Computer Vision and Pattern Recognition, on topics such as Imbalanced Data Classification Techniques, Machine Learning in Healthcare and Artificial Intelligence in Healthcare.

ORCID 0000-0001-9353-8894 OpenAlex
World rank
#46,078
of 7,710,434 ranked researchers
Rank in South Africa
#102
of 29,826
Works
61
Citations
5.8k
Citations per work
95.7
h-index
30
i10-index 39Top 4.8% in Artificial Intelligence
Ibomoiye Domor Mienye

What does Ibomoiye Domor Mienye research?

Ibomoiye Domor Mienye's work is mostly in Computer Science (45%), Medicine (9%), Health Professions (6%).

Physical Sciences: 27 works · 44.9% of their workComputer Science: 27 works · 44.9% of their workArtificial Intelligence: 22 works · 35.3% of their workComputer Vision and Pattern Recognition: 3 works · 4.8% of their workInformation Systems: 3 works · 4.8% of their workEverything else: 24 works · 40.1% of their workHealth Sciences: 9 works · 15.0% of their workMedicine: 5 works · 9.0% of their workHealth Informatics: 3 works · 4.8% of their workRadiology, Nuclear Medicine and Imaging: 3 works · 4.2% of their workHealth Professions: 4 works · 6.0% of their workHealth Information Management: 4 works · 6.0% of their workTHEIR WORKIbomoiye DomorMienye61works
Physical Sciences45%Health Sciences15%

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 Science2744.9%
  2. Everything else2440.1%
    1. Medicine59.0%
    2. Health Professions46.0%
  1. 1Artificial Intelligence35% of their works
  2. 2Health Information Management6% of their works
  3. 3Computer Vision and Pattern Recognition5% of their works
  4. 4Information Systems5% of their works
  5. 5Health Informatics5% of their works
  6. 6Radiology, Nuclear Medicine and Imaging4% of their works

Research topics

Which keywords describe Ibomoiye Domor Mienye's research?

The keywords of their largest research topics: Healthcare, Machine Learning, Deep Learning, Precision Medicine, Artificial Intelligence, Big Data, Classification 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 23 words, with their numbers
  1. Healthcare293 topics
  2. Machine Learning243 topics
  3. Deep Learning213 topics
  4. Precision Medicine192 topics
  5. Artificial Intelligence142 topics
  6. Big Data142 topics
  7. Classification121 topic
  8. Imbalanced Data121 topic
  9. Electronic Health Records111 topic
  10. Predictive Modeling111 topic
  11. Surveillance112 topics
  12. Big Data Analytics101 topic
  13. Medical Diagnosis101 topic
  14. Clinical Decision Support81 topic
  15. Medical Imaging81 topic
  16. Ethics61 topic
  17. Fairness61 topic
  18. ALPR51 topic
  19. Anomaly Detection51 topic
  20. Automatic License Plate Recognition51 topic
  21. Interpretable Models51 topic
  22. Machine Learning Interpretability51 topic
  23. Unsupervised51 topic

Where does Ibomoiye Domor Mienye work?

Ibomoiye Domor Mienye's main affiliation in the publication record is University of Johannesburg, South Africa.

How many publications and citations does Ibomoiye Domor Mienye have?

OpenAlex counts 61 works and 5,837 citations for Ibomoiye Domor Mienye, an h-index of 30, ranking #46,078 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.

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