Results 181 to 190 of about 3,700 (260)

Symbolic Regression‐Guided Feature Engineering for Predicting Magnetization in Cu‐Based Alloys Under Data‐Scarce Conditions

Materials Genome Engineering Advances, EarlyView.
A symbolic regression approach (SISSO) with physics‐informed feature engineering achieves high‐accuracy prediction of magnetic properties in Cu‐based alloys under data‐scarce conditions. The framework offers an interpretable and transferable strategy for accelerated alloy design.
Buyang Ma, Jiejie Li, Qiuju Zhang, Fenghua Chen, Bo Li, Ziqi Tian, Liang Chen   +6 more
wiley   +1 more source

Development and evaluation of clinical pregnancy prediction models for intrauterine insemination using three machine learning algorithms. [PDF]

Front Endocrinol (Lausanne)
Fu Y, Li D, Xia X, Li C, Wang J, Zhang C.   +5 more
europepmc   +1 more source

DeepRelaxo: Fast Mono‐Exponential Magnitude Brain R2* Mapping With Reduced Echoes Using Self‐Supervised Deep Learning

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose We introduce DeepRelaxo, a fast and generalizable deep learning method for estimating brain R2* maps from multi‐echo gradient echo (ME‐GRE) acquisitions with arbitrary echo configurations, including shortened echo trains for accelerated scans.
Samiha Prima, Zhuang Xiong, Alan H. Wilman, Hongfu Sun   +3 more
wiley   +1 more source

Advancing Normal Tissue Complication Probability Modeling with Supervised Contrastive Learning for Predicting Osteoradionecrosis. [PDF]

Proc Symp Appl Comput
Anyimadu EA, Zhang X, Fuller CD, Marai GE, Canahuate G.   +4 more
europepmc   +1 more source

Physics‐Guided Neural Network for Quantitative Parameter Mapping Using Balanced Steady State Free Precession MRI

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose To propose a new method using a physics‐guided neural network for quantitative parameter mapping in balanced steady‐state free precession (bSSFP) imaging. Theory and Methods We trained physics‐guided neural networks with a multilayer perceptron using simulated bSSFP signals generated from tissue parameters (T1$$ {T}_1 $$, T2$$ {T}_2 $$,
Hye‐Ryeong Choi, Huan Minh Luu, Sung‐Hong Park   +2 more
wiley   +1 more source

Development of an artificial intelligence prediction model for moderate-to-severe COPD exacerbations using continuous multiple unobtrusive sensors: protocol of a multicentre prospective observational study. [PDF]

BMJ Open Respir Res
Vásquez-Andrade R, Alcaraz-Serrano V, Buekers J, Bufano P, Laurino M, Celi A, Watz H, Gea J, Carbonaro N, Tognetti A, Garcia-Aymerich J.   +10 more
europepmc   +1 more source

Experimental Comparative Analysis of Hole‐Making Strategies and Cutting Parameters on Flexural Properties and Induced Delamination in S2 Glass and Basalt Fiber‐Reinforced Polymers

Polymer Composites, EarlyView.
Hole‐making strategy and machining parameters influence delamination and hole quality in S2‐glass and basalt FRP composites, which subsequently affet flexural performance. Improved hole integrity leads to enhanced structural performance during three‐point bending.
Sara Saeed Abdulrahman Eltahir, Cagatay Yilmaz, Sinan Kesriklioglu   +2 more
wiley   +1 more source

Artificial intelligence-based framework for early detection of heart disease using enhanced multilayer perceptron. [PDF]

Front Artif Intell
Abdullah M.
europepmc   +1 more source

Polar‐low track prediction using machine‐learning methods

Quarterly Journal of the Royal Meteorological Society, EarlyView.
Machine‐learning models are developed to produce reliable and efficient forecasts of polar‐low (PL) trajectories 12 hours ahead. A temporal model (RLSTM) benefiting from the rolling‐forecast strategy, improves overall prediction accuracy and is suitable for quick experimentation, while a spatiotemporal model (PL‐UNet), incorporating both historical and
Ziying Yang, Rune Grand Graversen, Filippo Maria Bianchi, Jiping Liu, Ruibo Lei   +4 more
wiley   +1 more source

Explainable tabular deep learning models for antenatal cesarean delivery prediction in multiparous women. [PDF]

BMC Pregnancy Childbirth
Yalçın E, Tanyıldız H, Aslan S, Demir SC, Sucu M, Uzay Fİ, Aykut S, Yalçın EM, Biçer A.   +8 more
europepmc   +1 more source