104 citations · 111 across the 10 of their papers we have counts for
10 papers
NeuroStrata: An Electroencephalographic Connectivity-Aware Deep Representation Learning Framework for Dynamic Brain Network Analysis of Mental Stress
Sayantan Acharya, Hamzeh Asgharnezhad, Abbas Khosravi +3
This study introduces NeuroStrata, a connectivity-aware deep representation learning framework for EEG-based mental stress analysis using Time-Varying Partial Directed Coherence (T…
UCTECG-Net: Uncertainty-aware Convolution Transformer ECG Network for Arrhythmia Detection
Hamzeh Asgharnezhad, Pegah Tabarisaadi, Abbas Khosravi +2
Deep learning has improved automated electrocardiogram (ECG) classification, but limited insight into prediction reliability hinders its use in safety-critical settings. This paper…
Rewiring Human Brain Networks via Lightweight Dynamic Connectivity Framework: An EEG-Based Stress Validation
Sayantan Acharya, Abbas Khosravi, Douglas Creighton +2
In recent years, Electroencephalographic analysis has gained prominence in stress research when combined with AI and Machine Learning models for validation. In this study, a lightw…
Exploring the Relationships Between Physiological Signals During Automated Fatigue Detection
Kourosh Kakhi, Abbas Khosravi, Roohallah Alizadehsani +1
Fatigue detection using physiological signals is critical in domains such as transportation, healthcare, and performance monitoring. While most studies focus on single modalities,…
A Quad-Step Approach to Uncertainty-Aware Deep Learning for Skin Cancer Classification
Hamzeh Asgharnezhad, Pegah Tabarisaadi, Abbas Khosravi +2
Accurate skin cancer diagnosis is vital for early treatment and improved patient outcomes. Deep learning (DL) models have shown promise in automating skin cancer classification, ye…
Fatigue Monitoring Using Wearables and AI: Trends, Challenges, and Future Opportunities
Kourosh Kakhi, Senthil Kumar Jagatheesaperumal, Abbas Khosravi +2
Monitoring fatigue is essential for improving safety, particularly for people who work long shifts or in high-demand workplaces. The development of wearable technologies, such as f…
Machine Learning Models for the Identification of Cardiovascular Diseases Using UK Biobank Data
Sheikh Mohammed Shariful Islam, Moloud Abrar, Teketo Tegegne +10
Machine learning models have the potential to identify cardiovascular diseases (CVDs) early and accurately in primary healthcare settings, which is crucial for delivering timely tr…
Leveraging Optimal Transport for Enhanced Offline Reinforcement Learning in Surgical Robotic Environments
Maryam Zare, Parham M. Kebria, Abbas Khosravi
Most Reinforcement Learning (RL) methods are traditionally studied in an active learning setting, where agents directly interact with their environments, observe action outcomes, a…
Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review
Mahboobeh Jafari, Afshin Shoeibi, Marjane Khodatars +11
In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. CVDs appear with minor symptoms and progressively get worse. The majori…
UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detection
Moloud Abdar, Soorena Salari, Sina Qahremani +7
The COVID-19 (Coronavirus disease 2019) pandemic has become a major global threat to human health and well-being. Thus, the development of computer-aided detection (CAD) systems th…