Results 111 to 120 of about 331,090 (323)
SIAM Journal on Imaging Sciences, 2013 We describe a method for analyzing the shape variability of images, called geometric PCA. Our approach is based on the use of deformation operators to model the geometric variability of images around a reference mean pattern. This leads to a new algorithm for estimating shape variability.
Bigot, Jérémie +2 more
openaire +8 more sources
A review of artificial intelligence in brachytherapy
Journal of Applied Clinical Medical Physics, EarlyView.Abstract Artificial intelligence (AI) has the potential to revolutionize brachytherapy's clinical workflow. This review comprehensively examines the application of AI, focusing on machine learning and deep learning, in various aspects of brachytherapy.
Jingchu Chen +4 more
wiley +1 more source
Torus Principal Component Analysis with an Application to RNA Structures [PDF]
arXiv, 2015 There are several cutting edge applications needing PCA methods for data on tori and we propose a novel torus-PCA method with important properties that can be generally applied. There are two existing general methods: tangent space PCA and geodesic PCA.
arxiv
Arthritis Care &Research, EarlyView.Objective Walk With Ease (WWE) is a six‐week arthritis‐appropriate evidence‐based physical activity program traditionally offered in a face‐to‐face format. Because many populations encounter participation barriers to in‐person programs, WWE was modified for telephone delivery (WWE‐T).
Christine A. Pellegrini +5 more
wiley +1 more source
Electronic Journal of Plant Breeding, 2016 Fifty one accessions of foxtail millet (Setaria italica (L.) P. Beauv.), constituting a national elite germplasm collection were evaluated for morphological diversity based on nine quantitative traits viz., plant height, number of basal tillers, days to ...
S. Geethanjali and, M. Jegadeeswaran
doaj +1 more source
Regularised PCA to denoise and visualise data [PDF]
arXiv, 2013 Principal component analysis (PCA) is a well-established method commonly used to explore and visualise data. A classical PCA model is the fixed effect model where data are generated as a fixed structure of low rank corrupted by noise. Under this model, PCA does not provide the best recovery of the underlying signal in terms of mean squared error ...
arxiv
Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials
Advanced Engineering Materials, EarlyView.This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani +4 more
wiley +1 more source
Nomograph development for water erosion quantification in Wadi Cheliff’s catchment, Northern Algeria
Eurasian Journal of Soil ScienceWater erosion study is regarded as one of the most important axes in scientific researches. The erosive effect of water on the surface layers can have major consequences on soil loss and land degradation.
Ilhem Bouaichi, Bénina Touaibia
doaj +1 more source
Comparison of PCA with ICA from data distribution perspective [PDF]
arXiv, 2017 We performed an empirical comparison of ICA and PCA algorithms by applying them on two simulated noisy time series with varying distribution parameters and level of noise. In general, ICA shows better results than PCA because it takes into account higher moments of data distribution.
arxiv
Advanced Functional Materials, EarlyView.Ultrathin, flexible neural probes are developed with an innovative, biomimetic design incorporating brain tissue‐compatible materials. The material system employs biomolecule‐based encapsulation agents to mitigate inflammatory responses, as demonstrated through comprehensive in vitro and in vivo studies.
Jeonghwa Jeong +7 more
wiley +1 more source