An experimental review on deep learning architectures for time series forecasting
In recent years, deep learning techniques have outperformed traditional models in many
machine learning tasks. Deep neural networks have successfully been applied to address …
machine learning tasks. Deep neural networks have successfully been applied to address …
Machine learning techniques for diagnosis of alzheimer disease, mild cognitive disorder, and other types of dementia
Alzheimer's disease (AD) is one of the most common form of dementia which mostly affects
elderly people. AD identification in early stages is a difficult task in medical practice and …
elderly people. AD identification in early stages is a difficult task in medical practice and …
Self-supervised learning for electroencephalography
Decades of research have shown machine learning superiority in discovering highly
nonlinear patterns embedded in electroencephalography (EEG) records compared with …
nonlinear patterns embedded in electroencephalography (EEG) records compared with …
Automatic detection method of tunnel lining multi‐defects via an enhanced You Only Look Once network
Z Zhou, J Zhang, C Gong - Computer‐Aided Civil and …, 2022 - Wiley Online Library
Aiming to solve the challenges of low detection accuracy, poor anti‐interference ability, and
slow detection speed in the traditional tunnel lining defect detection methods, a novel deep …
slow detection speed in the traditional tunnel lining defect detection methods, a novel deep …
Efficient machine learning models for prediction of concrete strengths
In this study, an efficient implementation of machine learning models to predict compressive
and tensile strengths of high-performance concrete (HPC) is presented. Four predictive …
and tensile strengths of high-performance concrete (HPC) is presented. Four predictive …
Graph neural network and reinforcement learning for multi‐agent cooperative control of connected autonomous vehicles
A connected autonomous vehicle (CAV) network can be defined as a set of connected
vehicles including CAVs that operate on a specific spatial scope that may be a road network …
vehicles including CAVs that operate on a specific spatial scope that may be a road network …
Deep learning‐based crack damage detection using convolutional neural networks
A number of image processing techniques (IPTs) have been implemented for detecting civil
infrastructure defects to partially replace human‐conducted onsite inspections. These IPTs …
infrastructure defects to partially replace human‐conducted onsite inspections. These IPTs …
Automated EEG-based screening of depression using deep convolutional neural network
In recent years, advanced neurocomputing and machine learning techniques have been
used for Electroencephalogram (EEG)-based diagnosis of various neurological disorders. In …
used for Electroencephalogram (EEG)-based diagnosis of various neurological disorders. In …
Tiny‐Crack‐Net: A multiscale feature fusion network with attention mechanisms for segmentation of tiny cracks
Convolutional neural networks (CNNs) have gained growing interest in recent years for their
advantages in detecting cracks on concrete bridge components. Class imbalance is a …
advantages in detecting cracks on concrete bridge components. Class imbalance is a …
A lightweight encoder–decoder network for automatic pavement crack detection
Cracks are the most common damage type on the pavement surface. Usually, pavement
cracks, especially small cracks, are difficult to be accurately identified due to background …
cracks, especially small cracks, are difficult to be accurately identified due to background …