Machine learning automates material analysis and design using X-ray spectroscopy data

Understanding the properties of different materials is an important step in material design. X-ray absorption spectroscopy ...
EurekAlert!

A new AI-powered method to automate material analysis and design

X-ray absorption spectroscopy (XAS) provides valuable information about a material’s properties and electronic states.
Wastewater Digest

Innovative approaches to sludge management: The role of AI, machine learning, and emerging technologies

Explore how artificial intelligence and digital innovations are transforming sludge dewatering in wastewater systems, ...
Phys.org

Nanopore signals and machine learning unlock new molecular analysis tool

Understanding molecular diversity is fundamental to biomedical research and diagnostics, but existing analytical tools struggle to distinguish subtle variations in the structure or composition among ...
Scientific Research Publishing

Bankruptcy Prediction in the Polish Banking Industry Using Principal Component Analysis and BP Neural Network ()

Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation, College of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin, China. With the ...
C&EN

Automated Machine-Learning-Driven Analysis of Microplastics by TGA-FTIR for Enhanced Identification and Quantification

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Microplastics persist as ubiquitous environmental contaminants, and efficient methods ...
Frontiers

Integrating machine learning and multi-omics analysis to reveal nucleotide metabolism-related immune genes and their functional validation in ischemic stroke

Background: Ischemic stroke (IS) is a major global cause of death and disability, linked to nucleotide metabolism imbalances. This study aimed to identify nucleotide metabolism-related genes ...
C&EN

Open-Source Visual Programming Software for Introducing Principal Component Analysis to the Analytical Curriculum

Principal component analysis (PCA) is one of the most common exploratory data analysis techniques with applications in outlier detection, dimensionality reduction, graphical clustering, and ...
Science Daily

'Democratizing chemical analysis':Chemists use machine learning and robotics to identify chemical compositions from images

Chemists have created a machine learning tool that can identify the chemical composition of dried salt solutions from an image with 99% accuracy. By using robotics to prepare thousands of samples and ...
IEEE

A Method of Kernel Principal Component Analysis and Machine Learning Algorithms for Fault Diagnosis of Power Transformers

Abstract: As one of the most important equipment in the power system, it is of great significance to conduct fault diagnosis research on transformers. Aiming at the problem of difficult selection of ...
IEEE

Investigating Principal Component Analysis Impact on the Performance of Machine Learning Classifiers: A Health Informatics Application

Abstract: Principal Components Analysis (PCA) is a popular dimensionality reduction and feature selection technique. It works by swiping non-informative features while retaining those that are still ...