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 ...
Scientific Research Publishing

Dietterich, T.G. (2000) Ensemble Methods in Machine Learning. In: Kittler, J. and Roli, F., Eds., International Workshop on Multiple Classifier Systems, Springer, 1-15.

ABSTRACT: This paper proposes a hybrid machine learning framework for early diabetes prediction tailored to Sierra Leone, where locally representative datasets are scarce. The framework integrates ...
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 ...
chromatographyonline

A Novel Machine Learning Method for Predicting Retention Time of Small Molecule Pharmaceutical Compounds Across Reversed-phase HPLC Columns

Traditional QSRR models are limited to single-column predictions, hindering adaptability across diverse LC setups in pharmaceutical settings. The new ML-based approach predicts retention times using ...
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 ...
IEEE

Transfer Kernel Extreme Learning Machine Based on Bidirectional Cross Domain Approximation

Abstract: Kernel extreme learning machine (KELM) not only inherits the high learning efficiency of the traditional extreme learning machine (ELM), but also mitigates the instability caused by random ...
techxplore

Machine learning methods are best suited to catch liars, according to science of deception detection

Scientists have revealed that Convolutional Neural Networks (CNNs), a type of deep learning algorithm, demonstrate superior performance compared to conventional non-machine learning approaches when ...