The rise and fall of artificial neural networks is well documented in the scientific literature of
both computer science and computational chemistry. Yet almost two decades later, we are …

The nature of manufacturing systems faces ever more complex, dynamic and at times even
chaotic behaviors. In order to being able to satisfy the demand for high-quality products in an …

Coupling machine learning algorithms with spatial analytical techniques for landslide
susceptibility modeling is a worth considering issue. So, the current research intend to …

Metabolomics aims to provide a global snapshot of all small-molecule metabolites in cells
and biological fluids, free of observational biases inherent to more focused studies of …

The need for accurate predictions of water quality in rivers has encouraged researchers to
develop new methods and to improve the predictive ability of conventional models. In recent …

This review focuses on recent and potential advances in chemometric methods in relation to
data processing in metabolomics, especially for data generated from mass spectrometric …

This paper proposes a novel approach for driving chemometric analyses from spectroscopic
data and based on a convolutional neural network (CNN) architecture. For such purpose …

We show here that machine learning is a powerful new tool for predicting the elastic
response of zeolites. We built our machine learning approach relying on geometric features …

Metabolomics is the comprehensive study of small molecule metabolites in biological
systems. By assaying and analyzing thousands of metabolites in biological samples, it …

Model-population analysis (MPA) was recently proposed as a general framework for
designing new types of chemometrics and bioinformatics algorithms, and it has found …