Deep learning regression for quantitative LIBS analysis

TL;DR: In this article , the authors proposed a new residual learning framework coupled with multiscale convolutional layers, whose advantages are discussed from the perspective of gradient descent during backpropagation and information theory.

TL;DR: Recent advances in machine learning methodologies for LIBS quantitative analysis focus on improving the accuracy and reliability of quantitative results by addressing challenges such as experimental conditions, sample surface state, and matrix effect.

TL;DR: This study employs LIBS to characterize Ni-Zn ferrite films prepared by magnetron sputtering, analyzing element distribution, film thickness, and optical properties, demonstrating LIBS' capability in analyzing element content changes in binary doped thin films.

TL;DR: Researchers develop and optimize an automated aluminium scrap sorting facility using computational modelling, achieving efficient sorting and reducing downcycling, with model predictions validated through actual facility operation and improved environmental and economic benefits.

TL;DR: In this article, the adaptive estimates of lower-order moments are used for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimate of lowerorder moments.

TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.

TL;DR: This paper shows that rectifying neurons are an even better model of biological neurons and yield equal or better performance than hyperbolic tangent networks in spite of the hard non-linearity and non-dierentiabil ity.