Results 201 to 210 of about 336,714 (315)

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source

Critical evaluation of the theory and practice of feed-forward neural networks for genomic prediction. [PDF]

G3 (Bethesda)
Kusmec A, Negus KL, Yu J.
europepmc   +1 more source

Detailed model diagnostics (sample size, R², RMSE, parameter standard errors)

ruijing zhang
openalex   +1 more source

Machine Learning Driven Inverse Design of Broadband Acoustic Superscattering

Advanced Intelligent Discovery, EarlyView.
Multilayer acoustic superscatterers are designed using machine learning to achieve broadband superscattering and strong sound insulation. By incorporating a weighted mean absolute error into the loss function, the forward and inverse neural networks accurately map structural parameters to spectral responses.
Lijuan Fan, Xiangliang Zhang, Ying Wu
wiley   +1 more source

Forecasting crude oil futures price with energy uncertainty: Evidence from machine learning methods. [PDF]

PLoS One
Wei X.
europepmc   +1 more source

Testing RMSE for the MovieLens 100K dataset (m = 9).

Tomoya Yanagi (21156455), Shunnosuke Ikeda (21156458), Noriyoshi Sukegawa (19418916), Yuichi Takano (3335556)   +3 more
openalex   +1 more source

Evaluation of a fitted Weibull distribution using the PD method: R² (a), RMSE (b), PDE (c), and their relationship to mean wind speed (d)

, 2013
Yuyu Zhou, J Smith Steven
openalex   +1 more source

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

values that yield the minimum RMSE values of one-step ahead predictions.

Burak Alakent (463512), Erkan Isikli (21526601), Cigdem Kadaifci (21526604), Tonguc S. Taspinar (21526607)   +3 more
openalex   +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