Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

In recent years, the use of machine learning (ML) in computational chemistry has enabled
numerous advances previously out of reach due to the computational complexity of …

Anomaly detection, aka outlier detection or novelty detection, has been a lasting yet active
research area in various research communities for several decades. There are still some …

Due to limited resources and environmental pollution, monitoring the geological
environment has become essential for many countries' sustainable development. As various …

The advancements in machine learning (ML) opened a new opportunity to bring intelligence
to the low-end Internet-of-Things (IoT) nodes, such as microcontrollers. Conventional ML …

Modern vehicles, including connected vehicles and autonomous vehicles, nowadays
involve many electronic control units connected through intravehicle networks (IVNs) to …

Hyperspectral images (HSIs) provide detailed spectral information through hundreds of
(narrow) spectral channels (also known as dimensionality or bands), which can be used to …

The recent developments in the modern data collection tools, techniques, and storage
capabilities are leading towards huge volume of data. The dimensions of data indicate the …

Nanophotonics has garnered intensive attention due to its unique capabilities in molding the
flow of light in the subwavelength regime. Metasurfaces (MSs) and photonic integrated …

Domain adaptation has become a prominent problem setting in machine learning and
related fields. This review asks the question: How can a classifier learn from a source …