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Umme Rabab Syed
ResearcherPublications, citations & collaboration network
Umme Rabab Syed is a researcher indexed in ScholarIQ from OpenAlex & ORCID. ScholarIQ records 11 works, 6 citations, an h-index of 1 and an i10-index of 0.
11
Works
6
Citations
1
h-index
0
i10-index
IDs:OpenAlex
How has Umme Rabab Syed's publication output changed over time?
ScholarIQpublication output · 2014–2025
Output grew800% over the shown period — from 1 works in 2014 to 9 in 2025.
1
9
20142025
What are the most-cited papers on Umme Rabab Syed?
ScholarIQmost cited works
EfficientNetB0-Based Lightweight Model for Accurate and Real-Time American Sign Language Recognition
Charan Kumar Manjunatha, Haider Ali Khan, Raja Hashim Ali, Umme Rabab Syed
20253 Citations
Population Growth: an asset or a curse? : Role played by economic freedom and education in turning population growth into an asset
Umme Rabab Syed
DiVA (Södertörn University). 20141 CitationsOPEN ACCESS
Bias-Mitigation and Multi-Task Deep Learning for Facial Attribute Prediction Across Demographics
Boxuan Chen, Aiman Darakhshan, Rashid Ali, Umme Rabab Syed
20251 Citations
High-Accuracy Plant Disease Detection: A ResNet18 Transfer Learning Approach for Multi-Class Classification
Serik Sailau, M. Owais Mohsin, Raja Hashim Ali, Umme Rabab Syed
20251 Citations
Parameter-Efficient Fine-Tuning of Compact Language Models for Professional LinkedIn Post Generation
Lovepreet Kaur, M. Owais Mohsin, Rashid Ali, Umme Rabab Syed
20250 Citations
Related on ScholarIQ
Ghulam Ishaq Khan Institute of Engineering Sciences and Technology
Institution
EfficientNetB0-Based Lightweight Model for Accurate and Real-Time American Sign Language Recognition
Paper
Population Growth: an asset or a curse? : Role played by economic freedom and education in turning population growth into an asset
Paper
Bias-Mitigation and Multi-Task Deep Learning for Facial Attribute Prediction Across Demographics
Paper
High-Accuracy Plant Disease Detection: A ResNet18 Transfer Learning Approach for Multi-Class Classification
Paper
Parameter-Efficient Fine-Tuning of Compact Language Models for Professional LinkedIn Post Generation
Paper