Analytics Insight

Machine Learning, Stanford University

Stanford University’s Machine Learning (XCS229) is a 100% online, instructor-led course offered by the Stanford School of ...
BMJ

Development of an interpretable machine learning model for frailty risk prediction in older adult care institutions: a mixed-methods, cross-sectional study in China

Objective To develop and validate an interpretable machine learning (ML)-based frailty risk prediction model that combines real-time health data with validated scale assessments for enhanced ...
IEEE

Quo Vadis, Quantum Machine Learning?: Quantum kernel methods meet Earth observation

Abstract: The analysis of satellite images has attracted significant research interest due to its numerous applications and unparalleled scalability in Earth observation (EO). Although artificial ...
Frontiers

Mapping stability and instability hotspots in Jiangsu’s vegetation: an explainable machine learning approach to climatic and anthropogenic drivers

Understanding vegetation stability is essential for evaluating ecosystem resilience and informing adaptive land management under changing climatic conditions. This study investigated the ...
C&EN

A New Pulsed Neutron-Gamma Density Logging Method Based on Gamma-Ray Spectra and Machine Learning

However, NGD faces several challenges associated with gamma-ray generation and attenuation complexities. Unlike GGD, which utilizes 0.662 MeV monoenergetic γ rays from a 137 Cs source, NGD employs ...
PNAS

Machine learning in chemistry

The Perspective by Tiwary et al. (8) offers a comprehensive overview of generative AI methods in computational chemistry. Approaches that generate new outputs (e.g., inferring phase transitions) by ...
TechRepublic

20+ Machine Learning Methods in Groundbreaking Periodic Table From MIT, Google, Microsoft

20+ Machine Learning Methods in Groundbreaking Periodic Table From MIT, Google, Microsoft Your email has been sent A new “periodic table for machine learning” is reshaping how researchers explore AI, ...
Scientific Research Publishing

Application of Machine Learning Methods in Parameter Prediction of Deep Lacustrine Oil Shale Reservoirs—Taking the Triassic Chang 7 in the Longdong Area as an Example ()

1 School of Earth Science and Engineering, Xi’an Shiyou University, Xi’an, China. 2 Key Laboratory of Petroleum Geology and Reservoir, Xi’an Shiyou University, Xi’an, China. In the course of oil 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 ...