A Correlation-Driven Mapping For Deep Learning application in detecting artifacts within the EEG
Objective: When developing approaches for automatic preprocessing of electroencephalogram (EEG) signals in non-isolated demanding environment such as intensive care unit (ICU) or […]
A Multi-Stream Feature Fusion Approach for Traffic Prediction
Accurate and timely traffic flow prediction is crucial for intelligent transportation systems (ITS). Recent advances in graph-based neural networks have achieved promising […]
Gender Identification from Arabic Speech Using Machine Learning
Speech recognition is becoming increasingly used in real-world applications. One of the interesting applications is automatic gender recognition which aims to recognize […]
A Deep Multiscale Spatiotemporal Network for Assessing Depression from Facial Dynamics
Recently deep learning models have been successfully employed in video-based affective computing applications. One key application is automatic depression recognition from facial […]
A Deep Multiscale Spatiotemporal Network for Assessing Depression from Facial Dynamics
Recently deep learning models have been successfully employed in video-based affective computing applications. One key application is automatic depression recognition from facial […]
MyoKey
The seamless textual input in Augmented Reality (AR) is very challenging and essential for enabling user-friendly AR applications. Existing approaches such as […]
Deep learning assisted CSI estimation for joint URLLC and eMBB resource allocation
Multiple-input multiple-output (MIMO) is a key for the fifth generation (5G) and beyond wireless communication systems owing to higher spectrum efficiency, spatial […]
Understanding Smartwatch Battery Utilization in the Wild
Smartwatch battery limitations are one of the biggest hurdles to their acceptability in the consumer market. To our knowledge, despite promising studies […]
Deep Learning Meets Cognitive Radio
Learning the channel occupancy patterns to reuse the underutilised spectrum frequencies without interfering with the incumbent is a promising approach to overcome […]
Low Complexity Autoencoder based End-to-End Learning of Coded Communications Systems
End-to-end learning of a communications system using the deep learning-based autoencoder concept has drawn interest in recent research due to its simplicity, […]