Machine learning offers faster, more reliable analysis of Fermi surfaces in search of spintronic materials

The search for next-generation electronic materials often starts with studying the Fermi surface, which serves as a map of a ...
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 ...
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 ...
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 ...
GitHub

Doubly-Robust-Kernel-Proxy-Variable-Algorithm

This repository provides code for a doubly robust algorithm for Proxy Causal Learning (PCL) using kernel methods, avoiding density ratio estimation. All code is written in Python 3 using the JAX ...
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 ...
Journal of Medical Internet Research

Using Machine Learning Methods to Predict Early Treatment Outcomes for Multidrug-Resistant or Rifampicin-Resistant Tuberculosis to Enhance Patient Cure Rates: Development and ...

Objective: This study compared a conventional logistic regression model with machine learning (ML) models using demographic and clinical data to predict outcomes at 2 and 6 months of treatment for MDR ...
Live Science

Scientists use quantum machine learning to create semiconductors for the first time – and it could transform how chips are made

Researchers have found a way to make the chip design and manufacturing process much easier — by tapping into a hybrid blend of artificial intelligence and quantum computing. When you purchase through ...
IEEE

Series Arc Fault Identification Based on a Novel Newton-Optimized Sparse Kernel Support Vector Machine

Abstract: Existing machine learning-based methods for series arc fault (SAF) identification still suffer from slow training speed when dealing with large-scale SAF datasets. For this reason, we ...