Results 211 to 220 of about 218,805 (280)

Linear predictive coding electroencephalography algorithms predict mortality in Parkinson's disease. [PDF]

Clin Park Relat Disord
Jamshidi S, Espinoza AI, Heinzman JT, May P, Uc EY, Narayanan NS, Dasgupta S.   +6 more
europepmc   +1 more source

Visualizing Hyperparameter Performance Dependencies

, 2022
openaire   +2 more sources

RAMS: Residual‐Based Adversarial‐Gradient Moving Sample Method for Scientific Machine Learning in Solving Partial Differential Equations

Advanced Intelligent Discovery, EarlyView.
We propose a residual‐based adversarial‐gradient moving sample (RAMS) method for scientific machine learning that treats samples as trainable variables and updates them to maximize the physics residual, thereby effectively concentrating samples in inadequately learned regions.
Weihang Ouyang, Min Zhu, Wei Xiong, Si‐Wei Liu, Lu Lu   +4 more
wiley   +1 more source

Experimental and ML-assisted optimization of injection timing and EGR in a diesel engine fueled with palmyra biodiesel. [PDF]

RSC Adv
Nair JN, Rao TS, Sreekara Reddy MBS, Raju VD, Venu H, Hadi AA, Smerat A, Khan TMY, Shaik AS, Khan MA.   +9 more
europepmc   +1 more source

A Multimodal Intelligent System for Human Digital Twin Simulation with Continuous Kinematic Data Tracking, Biometric Prognosis, and Cognitive State Feedback in Industrial Environments

Advanced Intelligent Discovery, EarlyView.
This article implements a unified human digital twin framework that integrates cutting edge actuation, sensing, simulation, and bidirectional feedback capability. The approach includes integrating multimodal sensing, AI, and biomechanical simulation into one compact system.
Tajbeed Ahmed Chowdhury, Sebastian Gratz‐Kelly, Eric Wagner, Paul Motzki, Martina Lehser   +4 more
wiley   +1 more source

Advanced machine learning techniques for predicting dump slope stability in Indian opencast coal mines. [PDF]

Sci Rep
Sahoo AK, Tripathy DP, Jayanthu S.
europepmc   +1 more source

Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics

Advanced Intelligent Discovery, EarlyView.
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong, Zijian Huang, Xuanwu Wang, Zhenghui Feng, Wei Liu, Bingding Huang   +5 more
wiley   +1 more source

Exploring the Boundaries of Modern Quantum Annealers with RNA Structure Prediction. [PDF]

J Phys Chem B
Andrews B, Abraham J, Fox D, Radja A.
europepmc   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source