M. Moattar

A review on machine learning and deep learning approaches for depressive disorder diagnosis based on EEG and ERP signals

A Multimodal Deep Learning Based Framework for Early and Accurate Diagnosis of Depression Using Electroencephalography and Event‐Related Potential Signals

This is the first study to integrate EEG and ERP data in a multimodal fashion using three feature sets, achieving a high accuracy of 91% with GRU‐based classification and indicates that the combination of multimodal features and deep learning can help improve the accuracy of depression diagnosis and develop effective a...

Sentiment Analysis of Imbalanced Dataset Through Data Augmentation and Generative Annotation Using DistilBERT and Low‐Rank Fine‐Tuning

Experimental results on the Twitter US Airline Sentiment dataset demonstrate that the proposed framework achieves strong classification performance while maintaining low training complexity, highlighting the effectiveness of combining Large Language Model (LLM) based data augmentation with parameter‐efficient transform...