Science Explorer Back to mapMap
Discussion
Researcher · Velammal Educational Trust · India

S. Gunasundari

S. Gunasundari publishes mostly in Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging, on topics such as Metaheuristic Optimization Algorithms Research, Radiomics and Machine Learning in Medical Imaging and AI in cancer detection.

OpenAlex
World rank
#3,766,910
of 7,710,434 ranked researchers
Rank in India
#87,038
of 280,097
Works
22
Citations
238
Citations per work
10.8
h-index
8
i10-index 7Top 56% in Artificial Intelligence
S. Gunasundari

What does S. Gunasundari research?

S. Gunasundari's work is mostly in Computer Science (45%), Medicine (13%), Engineering (5%).

Physical Sciences: 11 works · 49.3% of their workComputer Science: 10 works · 44.5% of their workArtificial Intelligence: 6 works · 27.0% of their workComputer Vision and Pattern Recognition: 3 works · 12.7% of their workInformation Systems: 1 works · 4.8% of their workEngineering: 1 works · 4.8% of their workElectrical and Electronic Engineering: 1 works · 4.8% of their workEverything else: 8 works · 38.0% of their workHealth Sciences: 3 works · 12.7% of their workMedicine: 3 works · 12.7% of their workRadiology, Nuclear Medicine and Imaging: 2 works · 9.5% of their workPulmonary and Respiratory Medicine: 1 works · 3.2% of their workTHEIR WORKS. Gunasundari22works
Physical Sciences49%Health Sciences13%

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 Science1044.5%
    2. Engineering14.8%
  2. Everything else838.0%
    1. Medicine312.7%
  1. 1Artificial Intelligence27% of their works
  2. 2Computer Vision and Pattern Recognition13% of their works
  3. 3Radiology, Nuclear Medicine and Imaging10% of their works
  4. 4Information Systems5% of their works
  5. 5Electrical and Electronic Engineering5% of their works
  6. 6Pulmonary and Respiratory Medicine3% of their works

Research topics

Which keywords describe S. Gunasundari's research?

The keywords of their largest research topics: Deep Learning, Machine Learning, Convolutional Neural Networks, Feature Extraction, Medical Imaging, Differential Evolution, Particle Swarm Optimization and Quantitative Analysis.

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.

All 21 words, with their numbers
  1. Deep Learning145 topics
  2. Machine Learning114 topics
  3. Convolutional Neural Networks62 topics
  4. Feature Extraction62 topics
  5. Medical Imaging62 topics
  6. Differential Evolution41 topic
  7. Particle Swarm Optimization41 topic
  8. Quantitative Analysis41 topic
  9. Radiomics41 topic
  10. Action Recognition31 topic
  11. Anomaly Detection31 topic
  12. Histopathology Images31 topic
  13. Medical Image Analysis31 topic
  14. Object Tracking31 topic
  15. Pose Estimation31 topic
  16. Unsupervised31 topic
  17. Visual Tracking31 topic
  18. Brain Tumor21 topic
  19. Differential Privacy21 topic
  20. Federated Learning21 topic
  21. MRI21 topic

Where does S. Gunasundari work?

S. Gunasundari's main affiliation in the publication record is Velammal Educational Trust, India.

How many publications and citations does S. Gunasundari have?

OpenAlex counts 22 works and 238 citations for S. Gunasundari, an h-index of 8, ranking #3,766,910 of 7,710,434 researchers worldwide on the quality score.

Ranked on field-normalised 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

Ask a question or share a note about S. Gunasundari. Posts are plain text and can be reported.