Single-cell imaging and machine learning reveal hidden coordination in algae's response to light stress

Researchers from several Parisian institutions have worked together to develop a non-destructive approach to study how ...
Scientific Research Publishing

A Multimodal Machine-Learning Framework for Predicting Lithium-Associated Renal Dysfunction from Synthetic Biomarker Trajectories ()

Long-term lithium therapy remains the most effective maintenance treatment for bipolar disorder, yet it poses a significant ...
Beebom

AI vs Machine Learning: What is the Difference?

AI is the broad goal of creating intelligent systems, no matter what technique is used. In comparison, Machine Learning is a specific technique to train intelligent systems by teaching models to learn ...
Frontiers

Improved multi-scale divergence entropy combined with extreme learning machine classifier for rotating machinery fault recognition

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 ...
Medscape

Machine Learning Algorithm Predicts Gout Flares Using RNA

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 ...
EurekAlert!

Neuromorphic devices and machine learning combine to make brain-like devices possible

As modern manufacturing increasingly relies on artificial intelligence (AI), automation, and real-time data processing, the need for faster and more energy-efficient computing systems has never been ...
IEEE

Robust Fault-Aware Extreme Learning Machine Based on Maximum Correntropy

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 ...
IEEE

An Extreme Learning Machine Technique for the Numerical Solution of Fractional Differential Equations

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 ...