Yassine Daadaa

OverviewAI-generated summary

Yassine Daadaa's research focuses on the application of advanced computational techniques, particularly deep learning and neural networks, to analyze complex biological and medical data. His work includes developing models for classifying medical conditions from imaging and signal data. Recent publications explore the use of lightweight separable vision transformers for skin lesion classification and optimized EfficientNet architectures for detecting hypertensive and diabetic retinopathy. Daadaa has also investigated novel approaches for biometric authentication using photoplethysmography (PPG) signals and hybrid Convolutional Neural Network (CNN) architectures. Further research includes developing CNN architectures for cardiac health recognition based on electrocardiography (ECG) signals and exploring deep learning for stress and anxiety detection through electroencephalography (EEG) data. He also contributes to accessibility research with a mechanism for blind users to explore web search results.

Metrics

• h-index: 8
• Publications: 22
• Citations: 176