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 ...
IEEE

Hybrid Classical–Quantum Kernel Learning for Scalable and Secure Link State Prediction in Software-Defined Networks

Abstract: Software-Defined Networks (SDNs) with their centralized control system and enhanced programmability requires a sophisticated approach to predict link states in complex network topologies.
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 ...
IEEE

Robust Beamforming for Space–Air–Ground Integrated Network (SAGIN) With CSI Uncertainty Using Multiple Kernel Learning

Abstract: This paper focuses on the robust beamforming for space-air-ground integrated networks (SAGIN) under uncertain channel state information (CSI). In SAGIN, uncertain CSI undermines the precise ...
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 ...
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 ...
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 ...