Results 121 to 130 of about 52,512 (299)
Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion
Advanced Intelligent Discovery, EarlyView.This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia +1 more
wiley +1 more source
Bootstrap-quantile ridge estimator for linear regression with applications
PLoS ONEBootstrap is a simple, yet powerful method of estimation based on the concept of random sampling with replacement. The ridge regression using a biasing parameter has become a viable alternative to the ordinary least square regression model for the ...
Irum Sajjad Dar, Sohail Chand
doaj +2 more sources
Scientific ReportsThe Poisson-Inverse Gaussian regression model is a widely used method for analyzing count data, particularly in over-dispersion. However, the reliability of parameter estimates obtained through maximum likelihood estimation in this model can be ...
Fatimah A. Almulhim +5 more
doaj +1 more source
Advanced Intelligent Discovery, EarlyView.This work investigates the optimal initial data size for surrogate‐based active learning in functional material optimization. Using factorization machine (FM)‐based quadratic unconstrained binary optimization (QUBO) surrogates and averaged piecewise linear regression, we show that adequate initial data accelerates convergence, enhances efficiency, and ...
Seongmin Kim, In‐Saeng Suh
wiley +1 more source
机车电传动, 2023 Wheel tread wear is an important parameter to evaluate the operational safety of locomotives, yet timely and accurate monitoring is often lacking at wheel operation and maintenance sites.
FANG Xin +4 more
doaj
Advanced Intelligent Discovery, EarlyView.Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova +4 more
wiley +1 more source
(Non) Linear Regression Modeling [PDF]
We will study causal relationships of a known form between random variables. Given a model, we distinguish one or more dependent (endogenous) variables Y = (Y1, . . .
Čížek, Pavel
core
Harnessing Machine Learning to Understand and Design Disordered Solids
Advanced Intelligent Discovery, EarlyView.This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source
Weighted Kernel Ridge Regression to Improve Genomic Prediction
AgricultureNonparametric models have recently been receiving increased attention due to their effectiveness in genomic prediction for complex traits. However, regular nonparametric models cannot effectively differentiate the relative importance of various SNPs ...
Chenguang Diao +6 more
doaj +1 more source
Modified Ridge Parameters for Seemingly Unrelated Regression Model
In this paper, we modify a number of new biased estimators of seemingly unrelated regression (SUR) parameters which are developed by Alkhamisi and Shukur (2008), AS, when the explanatory variables are affected by multicollinearity.
Shukur , Ghazi +2 more
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