The ongoing miniaturization of spectrometers creates a perfect synergy with the common
advantages of near-infrared (NIR) spectroscopy, which together provide particularly …

Deep learning (DL) is emerging as a new tool to model spectral data acquired in analytical
experiments. Although applications are flourishing, there is also much interest currently …

Recently, combining the synergistic properties of two-dimensional (2D) and zero-
dimensional (0D) materials has attracted significant attention for energy applications. Here …

Kinematic motion detection aims to determine a person's actions based on activity data.
Human kinematic motion detection has many valuable applications in health care, such as …

This research proposes a novel Hybrid Success History Intelligent Optimizer with Gaussian
Transformation (SHIOGT) for solving different complexity level optimization problems and for …

Food adulteration is a serious food safety issue, and it is visually difficult to detect metanil
yellow adulteration in chickpea flour. The objective of this study was to develop a non …

Rainfall nowcasting has become increasingly important as we move into an era where more
and more storms are occurring in many countries as a result of climate change. Developing …

In recent years, spectral analysis methods have developed rapidly. A key feature is the use
of chemometric methods to process spectral data for performing qualitative and quantitative …

Abstract Convolutional Neural Networks (CNNs) have proven to be a valuable Deep
Learning (DL) algorithm to model near-infrared spectral data in Chemometrics. However …

Preventive identification of mechanical parts failures has always played a crucial role in
machine maintenance. Over time, as the processing cycles are repeated, the machinery in …