Results 241 to 250 of about 3,284,626 (292)

Natural Gas Purification Plants Based on Interpretive Structural Models and Bayesian Networks. [PDF]

ACS Omega
Gong J, Li J, Tan L, Zhang L, Wang Y, Zhao J.   +5 more
europepmc   +1 more source

Multicenter Validation of Machine Learning Models for Predicting Myopia Progression Across School‐Based Screening and Optical Intervention Settings in Chinese Children and Adolescents

Advanced Intelligent Discovery, EarlyView.
Machine‐learning models predict myopia trajectories from 12 routine ocular variables in 40 445 children across 10 hospitals. Externally validated across school‐based screening and optical‐intervention settings, the best models achieve AUROC 0.97 for myopia status and R2 0.83 for spherical equivalent, enabling personalized risk stratification to curb ...
Yitong Zhang, Mingxuan Liu, Yifei Chen, Ying Wu, Lijun Zhang, Mengyao Guo, Yulan Yan, Runmei Wei, Zihan Cui, Peng Luo, Fang Chen, Bo Yang, Youcang Han, Xiaoyang Liu, Qiyuan Tian   +14 more
wiley   +1 more source

Particle Swarm Optimization as a Noise‐Tolerant Strategy for High‐Entropy Alloy Research

Advanced Intelligent Discovery, EarlyView.
A customized particle swarm optimization algorithm is benchmarked against batch Bayesian optimization across four high‐entropy alloy oxygen‐reduction landscapes. While both methods locate the global optimum comparably under noise‐free conditions, Bayesian optimization’s surrogate model tends to overfit noisy data.
Ahmad Tirmidzi, Matthias Arenz, Vladislav A. Mints   +2 more
wiley   +1 more source

Modeling chronic pain interconnections using Bayesian networks: insights from the Qatar Biobank study. [PDF]

Front Pain Res (Lausanne)
Al-Khinji AAMA, Malouche D.
europepmc   +1 more source

Physics‐Guided Modeling of Temperature‐Dependent Electrical Conductivity of Functional Oxides

Advanced Intelligent Discovery, EarlyView.
This complete workflow presents an automated, data‐driven screening of functional oxides based on their predicted electrical conductivity. A curated dataset is fed into TriCondNet, a charge‐transport, physics‐guided neural network that classifies an input composition's conduction mechanism and then simulates its conductivity–temperature profile.
Elias Assaad, Jae Evans, Troy D. Manning, Matthew J. Rosseinsky, Kerys Trathen, Bei Peng, Matthew S. Dyer   +6 more
wiley   +1 more source

Bond Energy Descriptors Enable Machine Learning With Limited Data: Design of Capping Agents for Thermoplastic Polyurethane Recycling

Advanced Intelligent Discovery, EarlyView.
Data scarcity is a critical bottleneck for machine learning in applied chemistry. We show that physics‐informed descriptors enable a simple linear model to predict the deblocking temperatures of capped diisocyanates to within 11 °C from just 19 measurements, demonstrating that embedding physical insight into descriptors, rather than relying on ...
Remsha Rafiq, Barbaro Zulueta, Hannah Zucco, Ramakrishna Suresh, Jason E. Shoemaker, Michael Call, Daylan Sheppard, Glenn Cormack, John A. Keith, Götz Veser   +9 more
wiley   +1 more source

Adaptive Non-Stationary Fuzzy Time Series Forecasting with Bayesian Networks. [PDF]

Sensors (Basel)
Wang B, Liu X.
europepmc   +1 more source

SuperResNET: Model‐Free Single‐Molecule Network Analysis Software Achieves Molecular Resolution of Nup96

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
SuperResNET is a powerful integrated software that reconstructs network architecture and molecular distribution of subcellular structures from single molecule localization microscopy datasets. SuperResNET segments the nuclear pore complex and corners, extracts size, shape, and network features of all segmented nuclear pores and uses modularity analysis
Yahongyang Lydia Li, Ismail M. Khater, Christian Hallgrimson, Ben Cardoen, Timothy H. Wong, Ghassan Hamarneh, Ivan R. Nabi   +6 more
wiley   +1 more source

Predicting Atrial Fibrillation Relapse Using Bayesian Networks: Explainable AI Approach. [PDF]

JMIR Cardio
Alves JM, Matos D, Martins T, Cavaco D, Carmo P, Galvão P, Costa FM, Morgado F, Ferreira AM, Freitas P, Dias CC, Rodrigues PP, Adragão P.   +12 more
europepmc   +1 more source

Exploring stroke risk factors in different genders using Bayesian networks: a cross-sectional study involving a population of 134,382. [PDF]

BMC Public Health
Linghu L, Huang Y, Qiu L, Wang X, Zhang J, Ma L, Li C, Wang L.   +7 more
europepmc   +1 more source