Results 171 to 180 of about 35,702 (279)

Moisture content prediction of cigar leaves air-curing process based on stacking ensemble learning model. [PDF]

Front Plant Sci
Xing Z, Shi Y, Zhang K, Ding S, Shi X.
europepmc   +1 more source

Machine learning model identifies tibial anatomical variables as potential risk factors for anterior cruciate ligament injury

Knee Surgery, Sports Traumatology, Arthroscopy, EarlyView.
Abstract Purpose The tibial slope is a well‐known risk factor for anterior cruciate ligament (ACL) injury. As machine learning continues to progress, it has become an increasingly explored tool for clinical screening and risk factor analysis. This study aims to develop and validate a prognostic machine learning model to predict the outcome of ACL ...
Cheng‐Hao Kao, Javad Hashemi, James Slauterbeck, Naveen Chandrashekar   +3 more
wiley   +1 more source

Malicious Traffic Detection Method for Power Monitoring Systems Based on Multi-Model Fusion Stacking Ensemble Learning. [PDF]

Sensors (Basel)
Zhang H, Liang Y, Li Y, Wang S, Gong H, Zhai J, Zhang H.   +6 more
europepmc   +1 more source

Structure‐Aware Machine Learning for Polymers: A Hierarchical Graph Network for Predicting Properties From Statistical Ensembles

Macromolecular Rapid Communications, EarlyView.
This work presents a structure‐aware graph convolutional network that models polymers as statistical ensembles to predict macroscopic properties. By combining topologically realistic graphs generated via kinetic Monte Carlo simulations with explicit molar mass distributions, the framework achieves high accuracy in classifying architectures and ...
Julian Kimmig, Yannik Köster, Timo Koswig, Punith Raviswamy, Subhash V. S. Ganti, Stefan Zechel, Christopher Kuenneth, Ulrich S. Schubert   +7 more
wiley   +1 more source

Challenges and Opportunities in Machine Learning for Light‐Emitting Polymers

Macromolecular Rapid Communications, EarlyView.
The performance of light‐emitting polymers emerges from coupled effects of chemical diversity, morphology, and exciton dynamics across multiple length scales. This Perspective reviews recent design strategies and experimental challenges, and discusses how machine learning can unify descriptors, data, and modeling approaches to efficiently navigate ...
Tian Tian, Yinyin Bao
wiley   +1 more source

New Opportunities For the Integration of Artificial Intelligence With Materials Science: From Large Language Models to Embodied Large Models

Materials Genome Engineering Advances, EarlyView.
This review first introduces the diversified applications of large language models in materials discovery. Subsequently, the evolution of autonomous experimentation platforms empowered by large language models is analyzed. Finally, four key future research interests are proposed to develop embodied large models for driving autonomous experimentation ...
Zhen Song, Yishuai Cai, Yadong Yu, Pan Xiang, Minglong Li, Zhe Liu, Mengyan Dai   +6 more
wiley   +1 more source

Enhancing pH prediction accuracy in Al₂O₃ gated ISFET using XGBoost regressor and stacking ensemble learning. [PDF]

Sci Rep
Panda A, Datar R, Deshpande S, Bacher G.
europepmc   +1 more source

Designing High‐Entropy Alloys With Low Stacking Fault Energy Through Interpretable Machine Learning

Materials Genome Engineering Advances, EarlyView.
In this study, we developed an interpretable machine learning (ML) ensemble framework and, by integrating the VEC criterion with the proposed machine learning scoring parameter in the alloy composition screening process, successfully designed multiple CoCrFeNiMn‐based HEAs with TWIP/TRIP effects and without the BCC phase.
Shuai Nie, Yixuan He, Haoxiang Liu, Xudong Liu, Haifeng Wang, Ziqing He, Menghao Yang   +6 more
wiley   +1 more source

Improved Prediction of Hourly PM_2.5 Concentrations with a Long Short-Term Memory Optimized by Stacking Ensemble Learning and Ant Colony Optimization. [PDF]

Toxics
Liu Z, Hong X.
europepmc   +1 more source

Solute Segregation in Polycrystalline Aluminum From Hybrid Monte Carlo and Molecular Dynamics Simulations With a Unified Neuroevolution Potential

Materials Genome Engineering Advances, EarlyView.
We develop an efficient GPU implementation of the hybrid Monte Carlo and molecular dynamics method in the GPUMD package and use it, in combination with the neuroevolution potential, to simulate the segregation of 15 solutes in polycrystalline Al, revealing distinct segregation patterns and the mechanisms of solute strengthening and embrittlement ...
Keke Song, Jiahui Liu, Yuanxu Zhu, Shunda Chen, Zheyong Fan, Yanjing Su, Ping Qian   +6 more
wiley   +1 more source