SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach
Electroencephalogram (EEG) is a common base signal used to monitor brain activities and
diagnose sleep disorders. Manual sleep stage scoring is a time-consuming task for sleep …
diagnose sleep disorders. Manual sleep stage scoring is a time-consuming task for sleep …
Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach
Electrocardiogram (ECG) signal is a common and powerful tool to study heart function and
diagnose several abnormal arrhythmias. While there have been remarkable improvements …
diagnose several abnormal arrhythmias. While there have been remarkable improvements …
HAN-ECG: An interpretable atrial fibrillation detection model using hierarchical attention networks
Atrial fibrillation (AF) is one of the most prevalent cardiac arrhythmias that affects the lives of
many people around the world and is associated with a five-fold increased risk of stroke and …
many people around the world and is associated with a five-fold increased risk of stroke and …
Application of deep reinforcement learning for extremely rare failure prediction in aircraft maintenance
The use of aircraft operational logs to predict potential failure that may lead to disruption
poses many challenges and has yet to be fully explored. Given that aircraft are high-integrity …
poses many challenges and has yet to be fully explored. Given that aircraft are high-integrity …
Distributed cooperative spectrum sharing in UAV networks using multi-agent reinforcement learning
A Shamsoshoara, M Khaledi, F Afghah… - 2019 16th IEEE …, 2019 - ieeexplore.ieee.org
In this paper, we develop a distributed mechanism for spectrum sharing among a network of
unmanned aerial vehicles (UAV) and licensed terrestrial networks. This method can provide …
unmanned aerial vehicles (UAV) and licensed terrestrial networks. This method can provide …
An autonomous spectrum management scheme for unmanned aerial vehicle networks in disaster relief operations
This paper studies the problem of spectrum shortage in an unmanned aerial vehicle (UAV)
network during critical missions such as wildfire monitoring, search and rescue, and disaster …
network during critical missions such as wildfire monitoring, search and rescue, and disaster …
ECGNET: Learning where to attend for detection of atrial fibrillation with deep visual attention
The complexity of the patterns associated with atrial fibrillation (AF) and the high level of
noise affecting these patterns have significantly limited the application of current signal …
noise affecting these patterns have significantly limited the application of current signal …
Use of a quantum genetic algorithm for coalition formation in large-scale UAV networks
Task allocation among a network of heterogeneous resource-constrained Unmanned Aerial
Vehicles (UAVs) in an unknown and remote environment is still a challenging problem …
Vehicles (UAVs) in an unknown and remote environment is still a challenging problem …
A survey of temporal credit assignment in deep reinforcement learning
The Credit Assignment Problem (CAP) refers to the longstanding challenge of
Reinforcement Learning (RL) agents to associate actions with their long-term …
Reinforcement Learning (RL) agents to associate actions with their long-term …
Single-modal and multi-modal false arrhythmia alarm reduction using attention-based convolutional and recurrent neural networks
This study proposes a deep learning model that effectively suppresses the false alarms in
the intensive care units (ICUs) without ignoring the true alarms using single-and multi-modal …
the intensive care units (ICUs) without ignoring the true alarms using single-and multi-modal …