Results 111 to 120 of about 27,097 (268)
Clinical Anatomy, EarlyView.ABSTRACT The aim of this study is to perform high accuracy sex prediction from clavicle images using proposed hybrid deep learning models and traditional deep learning models. The clavicle of 807 female and 805 male individuals obtained from Computed Tomography were segmented in 3D format and saved in jpeg format as superior–inferior and right–left ...
Yusuf Secgin +8 more
wiley +1 more source
Laminar Burning Velocity of Ammonia‐Hydrogen Mixtures Predicted by Machine Learning
Chemie Ingenieur Technik, EarlyView.Ammonia–hydrogen blends aid defossilization, but slow combustion kinetics require complex modeling. Although chemical kinetic modeling is computationally expensive, machine learning surrogates offer fast predictions. This study trained GPR, DT, and ANN models on 1445 experimental points.
Georg Klepp +2 more
wiley +1 more source
The Canadian Journal of Chemical Engineering, EarlyView.This study shows that integrating multiple machine learning models with optimization and decision‐making improves chemical process design, and that a consensus‐based strategy across models provides more robust and reliable operating recommendations than any single model, especially under limited or noisy data conditions.
Farough Agin +2 more
wiley +1 more source
Deep Underground Science and Engineering, EarlyView.The fused data extracted from the distributed monitoring system as the data basis, combined with dynamic geological data, are imported into a deep learning model. As the geological conditions of mining and excavation change, the risk of water inrush at the working face is retrieved in real time.
Yongjie Li +4 more
wiley +1 more source
A direct normal irradiation forecasting model based on artificial neural networks
Revue des Énergies Renouvelables, 2016 We investigate the forecasting of the hourly Direct Normal Irradiation (DNI) using Artificial Neural Networks (ANN). The data used are hourly satellite data for the region of Ouarzazate in the South West Mediterranean basin region.
I. Belhaj +3 more
doaj
Deep Underground Science and Engineering, EarlyView.Probabilistic natural gradient boosting and Gaussian process regression models accurately predict rate‐dependent rock strength across lithologies. Static strength and strain rate dominate, while geometric factors have minimal influence, enabling interpretable and uncertainty‐aware predictions for dynamic geomechanical applications. Abstract The dynamic
Hadi Fathipour‐Azar
wiley +1 more source
Deep Underground Science and Engineering, EarlyView.This study establishes a multi‐factor coupling framework for predicting breakdown pressure in laminated shale by integrating experimental hydraulic fracturing tests, physics‐informed neural networks (PINNs), and Sobol sensitivity analysis. It reveals how differential stress, the bedding dip angle, and the injection rate interact to influence fracture ...
Tao Wang +6 more
wiley +1 more source
Generative Models in Inorganic Crystals Discovery and Inverse Design
ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.Generative inverse‐design samples from the vast inorganic crystal design space by starting from target properties such as band gap, stability, and ion transport. This Review examines the representations, generative models, and validation workflows needed to translate candidate structures into stable, potentially synthesizable materials for applications
Tao Li +5 more
wiley +1 more source
ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.A transparent and flexible ITO/ZnO/TiO2/MXene optoelectronic memristor is developed via low‐temperature solution processing, exhibiting intrinsic self‐rectification and analog synaptic plasticity. The device enables wavelength‐selective photosynaptic response and multimodal learning behaviors, demonstrating strong potential for neuromorphic ...
Shoaib Anwer +5 more
wiley +1 more source