This study investigates the application of deep learning-based image segmentation using
2D optical imaging for the microstructural characterisation of composite materials with …

Effectively training deep learning models relies heavily on large datasets, as insufficient
instances can hinder model generalization. A simple yet effective way to address this is by …

The use of X-ray computed tomography (XCT) has seen significant growth over a broad
range of disciplines including biology, earth science, engineering, and many more. It is now …

In this study, we present a protocol to visualize the architecture of tracer fibers in paper using
X-ray tomography. We prepared tracer fibers by depositing iron oxide nanoparticles on the …

Long-term time-series forecasting is critical in nu-merous domains, including economics,
climate modeling, and energy management. Traditional deep learning models often struggle …

This study introduces a new framework that utilizes artificial neural networks (ANN) to
analyze data and forecast the tribological properties of Al8090/TiB2/C composites. For …