Artificial intelligence techniques for automated diagnosis of neurological disorders
Background: Authors have been advocating the research ideology that a computer-aided
diagnosis (CAD) system trained using lots of patient data and physiological signals and …
diagnosis (CAD) system trained using lots of patient data and physiological signals and …
Efficient training of physics‐informed neural networks via importance sampling
MA Nabian, RJ Gladstone… - Computer‐Aided Civil and …, 2021 - Wiley Online Library
Physics‐informed neural networks (PINNs) are a class of deep neural networks that are
trained, using automatic differentiation, to compute the response of systems governed by …
trained, using automatic differentiation, to compute the response of systems governed by …
Concrete crack detection using context‐aware deep semantic segmentation network
Computer‐vision and deep‐learning techniques are being increasingly applied to inspect,
monitor, and assess infrastructure conditions including detection of cracks. Traditional vision …
monitor, and assess infrastructure conditions including detection of cracks. Traditional vision …
A sensitivity and robustness analysis of GPR and ANN for high-performance concrete compressive strength prediction using a Monte Carlo simulation
DV Dao, H Adeli, HB Ly, LM Le, VM Le, TT Le… - Sustainability, 2020 - mdpi.com
This study aims to analyze the sensitivity and robustness of two Artificial Intelligence (AI)
techniques, namely Gaussian Process Regression (GPR) with five different kernels …
techniques, namely Gaussian Process Regression (GPR) with five different kernels …
A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals
An important subfield of brain–computer interface is the classification of motor imagery (MI)
signals where a presumed action, for example, imagining the hands' motions, is mentally …
signals where a presumed action, for example, imagining the hands' motions, is mentally …
Zernike‐moment measurement of thin‐crack width in images enabled by dual‐scale deep learning
FT Ni, J Zhang, ZQ Chen - Computer‐Aided Civil and …, 2019 - Wiley Online Library
Although crack inspection is a routine practice in civil infrastructure management (especially
for highway bridge structures), it is time‐consuming and safety‐concerning to trained …
for highway bridge structures), it is time‐consuming and safety‐concerning to trained …
Human gait recognition based on frame-by-frame gait energy images and convolutional long short-term memory
Human gait recognition is one of the most promising biometric technologies, especially for
unobtrusive video surveillance and human identification from a distance. Aiming at …
unobtrusive video surveillance and human identification from a distance. Aiming at …
Spatiotemporal gated graph attention network for urban traffic flow prediction based on license plate recognition data
The accurate forecasting of traffic states is an essential application of intelligent
transportation system. Due to the periodic signal control at intersections, the traffic flow in an …
transportation system. Due to the periodic signal control at intersections, the traffic flow in an …
A graph deep learning method for short‐term traffic forecasting on large road networks
Short‐term traffic flow prediction on a large‐scale road network is challenging due to the
complex spatial–temporal dependencies, the directed network topology, and the high …
complex spatial–temporal dependencies, the directed network topology, and the high …
Dynamic urban traffic rerouting with fog‐cloud reinforcement learning
Dynamic rerouting has been touted as a solution for urban traffic congestion. However, its
implementation is stymied by the complexity of urban traffic. To address this, recent studies …
implementation is stymied by the complexity of urban traffic. To address this, recent studies …