Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …

Image denoising faces significant challenges, arising from the sources of noise. Specifically,
Gaussian, impulse, salt, pepper, and speckle noise are complicated sources of noise in …

The last decade witnessed increasingly rapid progress in self‐driving vehicle technology,
mainly backed up by advances in the area of deep learning and artificial intelligence (AI) …

Recent advancements in perception for autonomous driving are driven by deep learning. In
order to achieve robust and accurate scene understanding, autonomous vehicles are …

Tool condition monitoring and machine tool diagnostics are performed using advanced
sensors and computational intelligence to predict and avoid adverse conditions for cutting …

Detecting objects remains one of computer vision and image understanding applications'
most fundamental and challenging aspects. Significant advances in object detection have …

Abstract Machine learning (ML) plays the core function to intellectualize the transportation
systems. Recent years have witnessed the advent and prevalence of deep learning which …

For many decades, experimental solid mechanics has played a crucial role in characterizing
and understanding the mechanical properties of natural and novel artificial materials …

Connected and autonomous vehicles (CAVs) will form the backbone of future next-
generation intelligent transportation systems (ITS) providing travel comfort, road safety …

Semantic segmentation has made striking progress due to the success of deep
convolutional neural networks. Considering the demands of autonomous driving, real-time …