Yassine Daadaa Source Confirmed

OverviewAI-generated summary

Yassine Daadaa's research focuses on the application of deep learning and artificial intelligence techniques to address challenges in healthcare and human-computer interaction. His work includes developing lightweight vision transformer models for classifying skin lesions and optimizing EfficientNet architectures for identifying hypertensive and diabetic retinopathy. Daadaa has also investigated biometric identification systems using electrocardiogram (ECG) signals and photoplethysmography (PPG) signals, employing convolutional neural networks and attention-based layers. Further research explores the recognition of cardiac health through ECG signals using residual-dense convolutional neural networks and the use of EEG data for deep learning-based stress and anxiety detection. He has also contributed to developing web search result exploration mechanisms for blind users and integrating modalities into context-aware eLearning systems.

Metrics

• h-index: 8
• Publications: 22
• Citations: 172