Machine learning algorithms are increasingly used in various remote sensing applications
due to their ability to identify nonlinear correlations. Ensemble algorithms have been …

In recent years, deep learning techniques have outperformed traditional models in many
machine learning tasks. Deep neural networks have successfully been applied to address …

Decades of research have shown machine learning superiority in discovering highly
nonlinear patterns embedded in electroencephalography (EEG) records compared with …

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 …

In the field of tunnel lining crack identification, the semantic segmentation algorithms based
on convolution neural network (CNN) are extensively used. Owing to the inherent locality of …

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 …

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 …

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 …

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 …

Ground‐penetrating radar (GPR) is widely used to determine the location of buried pipes
without excavation, and machine learning has been researched to automatically identify the …