When natural disasters or extreme weather events hit, delivering aid quickly and efficiently to those affected is crucial. Humanitarian relief efforts commonly rely on the combination of trucks and ...
A machine-learning model developed by Weill Cornell Medicine investigators may provide clinicians with an early warning of a ...
A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...
As the core equipment in industrial production, rotating machinery bearings play a critical role. However, traditional feature extraction algorithms for vibration signals are susceptible to noise ...
A machine learning algorithm used gene expression profiles of patients with gout to predict flares. The PyTorch neural network performed best, with an area under the curve of 65%. The PyTorch model ...
Abstract: Extreme learning machine (ELM) is an effective and efficient neural model for universal approximation. However, its practical performance can degrade due to weight noise, node faults, and ...
Abstract: This research intends to create a novel approach for solving fractional differential equations (FDEs) of both linear and nonlinear types utilizing the fractional shifted Legendre neural ...
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SAVANA uses a machine learning algorithm to identify cancer-specific structural variations and copy number aberrations in long-read DNA sequencing data. The complex structure of cancer genomes means ...
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