#machine learning

topicmachine learning

88 papers · 1 filter

astro-ph.GA2026

Identifying backsplash galaxies using machine learning

Roan Haggar, Elizaveta Sazonova, Cameron R. Morgan +5

The paper presents a machine‑learning model trained on The Three Hundred cluster simulations that can identify backsplash galaxies in observations, achieving about 70% purity/compl…

cs.SE2026

A Low-Cost Human-in-the-Loop Investigation of Toxicity on GitHub at Scale

Rahat Rizvi Rahman, Mia Mohammad Imran, Kostadin Damevski

The paper introduces a human-in-the-loop workflow that combines a small local LLM with a lightweight Random Forest validator to efficiently label toxicity in over 124,000 GitHub is…

cond-mat.soft2026

Physics-Guided Interpretable Machine Learning Framework for Anomalous Transport in Crowded Media with Tunable Flexibility

Zakiya Shireen, Sujin B. Babu

The paper introduces a physics‑guided interpretable machine‑learning framework that combines Brownian Cluster Dynamics simulations with surrogate models and SHAP analysis to quanti…

eess.SP2026

Multi-Dimensional Entropy for Vibration Measurement Data Quality Assessment and Erroneous Signal Identification in Wind Turbines

Xiao-Ming Yuan, Zishun Wang, Donghui Zhao +2

The paper introduces a Multi-Dimensional Entropy (MDE) metric that evaluates the quality of vibration measurements in wind turbines by analyzing time‑domain, spectral, and frequenc…

cs.CV2026

A systematic evaluation of machine learning classifiers for event-by-event background rejection in LAFOV PET scanners

Konrad Klimaszewski, Michał Obara, Mateusz Bala +5

The paper evaluates machine‑learning classifiers (XGBoost, AdaBoost, neural networks) for classifying individual PET coincidence events to reduce background in large‑field‑of‑view…

cs.AI2026

A Density-Matrix Framework for Electronic-Structure Analysis of Functional-Group and Salt Effects in Lithium-Metal Electrolytes

Mingkang Liu, Huize Yu, Yanbin Gao +3

The paper introduces EMolStudio, an AI-driven density‑matrix framework that predicts electronic‑structure properties of lithium‑metal electrolyte molecules and their explicit Li⁺ s…