HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System
Processes, 2021 Nowadays, network attacks are the most crucial problem of modern society. All networks, from small to large, are vulnerable to network threats. An intrusion detection (ID) system is critical for mitigating and identifying malicious threats in networks ...
Muhammad Ashfaq Khan
exaly +3 more sources
A New Spiking Convolutional Recurrent Neural Network (SCRNN) With Applications to Event-Based Hand Gesture Recognition [PDF]
Front Neurosci, 2020 The combination of neuromorphic visual sensors and spiking neural network offers a high efficient bio-inspired solution to real-world applications.
Y. Xing +2 more
semanticscholar +4 more sources
EEG-based emotion recognition using 4D convolutional recurrent neural network [PDF]
Cognitive Neurodynamics, 2020 Fangyao Shen +2 more
exaly +3 more sources
Convolutional Recurrent Neural Networks forHyperspectral Data Classification [PDF]
Remote Sensing, 2017 Deep neural networks, such as convolutional neural networks (CNN) and stacked autoencoders, have recently been successfully used to extract deep features for hyperspectral data classification. Recurrent neural networks (RNN) are another type of neural networks, which are widely used for sequence analysis because they are constructed to extract ...
Hao Wu, Saurabh Prasad
exaly +3 more sources
Convolutional Recurrent Neural Network for Dynamic Functional MRI Analysis and Brain Disease Identification [PDF]
Front Neurosci, 2022 Dynamic functional connectivity (dFC) networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) help us understand fundamental dynamic characteristics of human brains, thereby providing an efficient solution for automated ...
K. Lin +5 more
semanticscholar +2 more sources
A convolutional-recurrent neural network approach to resting-state EEG classification in Parkinson’s disease [PDF]
J Neurosci Methods, 2021 Background: Parkinsons disease (PD) is expected to become more common, particularly with an aging population. Diagnosis and monitoring of the disease typically rely on the laborious examination of physical symptoms by medical experts, which is ...
Soojin Lee +4 more
semanticscholar +2 more sources
A convolutional recurrent neural network with attention framework for speech separation in monaural recordings [PDF]
Sci Rep, 2021 Most speech separation studies in monaural channel use only a single type of network, and the separation effect is typically not satisfactory, posing difficulties for high quality speech separation.
Chao Sun +7 more
semanticscholar +2 more sources
Automated detection of mouse scratching behaviour using convolutional recurrent neural network [PDF]
Sci Rep, 2021 Scratching is one of the most important behaviours in experimental animals because it can reflect itching and/or psychological stress. Here, we aimed to establish a novel method to detect scratching using deep neural network.
Koji Kobayashi +5 more
semanticscholar +2 more sources
ECG Signal Classification for the Detection of Cardiac Arrhythmias Using a Convolutional Recurrent Neural Network [PDF]
Physiol Meas, 2018 Objective: The electrocardiogram (ECG) provides an effective, non-invasive approach for clinical diagnosis in patients with cardiac diseases such as atrial fibrillation (AF). AF is the most common cardiac rhythm disturbance and affects ~2% of the general
Zhaohan Xiong +5 more
semanticscholar +2 more sources
Automated Cough Analysis with Convolutional Recurrent Neural Network [PDF]
BioengineeringChronic cough is associated with several respiratory diseases and is a significant burden on physical, social, and psychological health. Non-invasive, real-time, continuous, and quantitative monitoring tools are highly desired to assess cough severity, the effectiveness of treatment, and monitor disease progression in clinical practice and research ...
Yiping Wang +8 more
openaire +4 more sources