Results 191 to 200 of about 296,242 (293)

Electroweak bootstrap

Physical Review D, 1983
openaire   +2 more sources

Identification of Exhaled Volatile Organic Compounds Biomarkers for Lung Cancer Under Data‐Limited Conditions Using Data Augmentation and Multi‐View Feature Selection

Advanced Intelligent Discovery, EarlyView.
This work introduces a novel framework for identifying non‐small cell lung cancer biomarkers from hundreds of volatile organic compounds in breath, analyzed via gas chromatography‐mass spectrometry. This method integrates generative data augmentation and multi‐view feature selection, providing a stable and accurate solution for biomarker discovery in ...
Guancheng Ren, Bohao Liu, Zhiyuan Qu, Xue Jiang, Heng Zhao, Danyao Qu, Ruizhi Ning, Chen Su, Taoping Liu, Guangjian Zhang, Weiwei Wu   +10 more
wiley   +1 more source

A practice-oriented guide to statistical inference in linear modeling for non-normal or heteroskedastic error distributions. [PDF]

Behav Res Methods
Rajh-Weber H, Huber SE, Arendasy M.
europepmc   +1 more source

Wild bootstrap of the mean in the infinite variance case [PDF]

, 2011
Giuseppe Cavaliere, Iliyan Georgiev, Robert Taylor   +2 more
openalex   +1 more source

A Data‐Centric Approach to Quantifying the Forward and Inverse Relationship Between Laser Powder Bed Fusion Process Parameters and as‐Built Surface Roughness of IN718 Parts

Advanced Intelligent Systems, EarlyView.
This study introduces the first inverse machine learning model to predict laser powder bed fusion process parameters for targeted surface roughness of Inconel 718 parts. Unlike prior approaches, it incorporates spatial surface characteristics for improved accuracy.
Samsul Mahmood, Bart Raeymaekers
wiley   +1 more source

A new automatic forecasting method based on explainable deep dendritic artificial neural network. [PDF]

Sci Rep
Bas E, Egrioglu E.
europepmc   +1 more source

Predicting Postresection Colorectal Liver Metastases Recurrence Using Advanced Graph Neural Networks with Explainability and Causal Inference

Advanced Intelligent Systems, EarlyView.
This study introduces a framework that combines graph neural networks with causal inference to forecast recurrence and uncover the clinical and pathological factors driving it. It further provides interpretability, validates risk factors via counterfactual and interventional analyses, and offers evidence‐based insights for treatment planning ...
Jubair Ahmed, Md. Abdur Rahman, Mohaimenul Azam Khan Raiaan, Sami Azam   +3 more
wiley   +1 more source

Correction: Which method is optimal for estimating variance components and their variability in generalizability theory? Evidence from a set of unified rules for bootstrap method. [PDF]

PLoS One
Li G.
europepmc   +1 more source

Cross‐Modal Characterization of Thin‐Film MoS2 Using Generative Models

Advanced Intelligent Systems, EarlyView.
Cross‐modal learning is evaluated using atomic force microscopy (AFM), Raman spectroscopy, and photoluminescence spectroscopy (PL) through unsupervised learning, regression, and autoencoder models. Autoencoder models are used to generate spectroscopy data from the microscopy images.
Isaiah A. Moses, Chen Chen, Joan M. Redwing, Wesley F. Reinhart   +3 more
wiley   +1 more source

T-shaped alignments integrating HIV-1 near full-length genome and partial pol sequences can improve phylogenetic inference of transmission clusters. [PDF]

PLoS Comput Biol
Guang A, Dunn CW, Novitsky V, Howison M, Kantor R.   +4 more
europepmc   +1 more source