Deep learning, which is a subfield of machine learning, has opened a new era for the
development of neural networks. The auto-encoder is a key component of deep structure …

This article reviews recent developments in the applications of machine learning, data-
driven modeling, transfer learning, and autonomous experimentation for the discovery …

As technology evolves, more components are integrated into printed circuit boards (PCBs)
and the PCB layout increases. Because small defects on signal trace can cause significant …

Change detection based on remote sensing (RS) images has a wide range of applications
in many fields. However, many existing approaches for detecting changes in RS images with …

Semantic segmentation methods based on deep neural networks have achieved great
success in recent years. However, training such deep neural networks relies heavily on a …

Medical image processing is one of the most important topics in the Internet of Medical
Things (IoMT). Recently, deep learning methods have carried out state-of-the-art …

Abstract Background and Objective: Glomeruli identification, ie, detection and
characterization, is a key procedure in many nephropathology studies. In this paper …

Automated surface inspection is an important task in many manufacturing industries and
often requires machine learning driven solutions. Supervised approaches, however, can be …

Seismic trace interpolation is an important technique because irregular or insufficient
sampling data along the spatial direction may lead to inevitable errors in multiple …

Extracting 3D information from aerial images is an important and still challenging topic in
photogrammetry and remote sensing. Height estimation from only a single aerial image is an …