Results 151 to 160 of about 24,298 (249)

Probabilistic prediction of rate‐dependent rock strength using natural gradient boosting and Gaussian process regression

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

Explainable machine learning for early diagnosis of esophageal cancer: A feature-enriched Light Gradient Boosting Machine framework with Shapley Additive Explanations and Local Interpretable Model-Agnostic Explanations interpretations. [PDF]

J Int Med Res
Ridwan AM, Mohi Uddin KM.
europepmc   +1 more source

Explainable hybrid stacking ensemble method for hard rock pillar stability prediction and engineering applications

Deep Underground Science and Engineering, EarlyView.
This research proposes an interpretable hybrid stacking ensemble framework, optimized by the Sparrow Search Algorithm, to enhance hard rock pillar stability prediction. By integrating six machine learning models—k‐nearest neighbors, support vector machines, random forests, Gradient Boosting Decision Tree, eXtreme Gradient Boosting, and Light Gradient ...
Ning Wang, Guoyan Zhao, Meng Wang, Taraka Pattela   +3 more
wiley   +1 more source

Understanding Arteriosclerotic Heart Disease Patients Using Electronic Health Records: A Machine Learning and Shapley Additive exPlanations Approach. [PDF]

Healthc Inform Res, 2023
Miranda E, Adiarto S, Bhatti FM, Zakiyyah AY, Aryuni M, Bernando C.   +5 more
europepmc   +1 more source

Regeneration failure, fire, topography, and climate interact to drive temperate wet forest landscapes into fire traps

Ecography, EarlyView.
Where early successional forests are more flammable than old‐growth forests, forested landscapes are vulnerable to shifting into ‘fire traps' through positive feedbacks, where fire leads to more fire. These feedbacks are amplified by increased flammability driven by climate change, the presence of non‐native flammable plant species, and slowed ...
George L. W. Perry, Craig E. Simpkins, Kiri Reihana, James M. R. Brock, Peter J. Bellingham   +4 more
wiley   +1 more source

Predicting Resistance and Survival of HCC Patients Post-HAIC: Based on Shapley Additive exPlanations and Machine Learning. [PDF]

J Hepatocell Carcinoma
Yao F, Miao J, Quan B, Li J, Tang B, Lu S, Yin X.   +6 more
europepmc   +1 more source

Toward a Paradigm Shift in Low‐Temperature SCR Catalyst Design: Defect Engineering and Data‐Driven Integration

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
This work systematically reviews the key factors influencing the performance of low‐temperature NH3‐SCR. The mechanism and challenges of defect engineering strategies, such as oxygen vacancies, heteroatom doping, crystal facet exposure, and surface reconstruction, in controlling both activity and selectivity were analyzed.
Rongrong Kan, Pinjing He, Fan Lü, Hua Zhang   +3 more
wiley   +1 more source

Interpretable ensemble learning model with shapley additive explanations for predicting anxiety symptoms risk in Chinese older adults with body shape index abnormality. [PDF]

PLoS One
Wang K.
europepmc   +1 more source

Electronic Structure Modulation in Dopant‐Controlled Single‐Atom Graphene Catalysts for Efficient Hydrogen Evolution: A Machine Learning and First‐Principles Study

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
First‐principles DFT calculations and machine learning analysis show that heteroatom doping of graphene (G) significantly enhances the stabilization of transition‐metal single atoms by strengthening metal–support interactions and increasing charge transfer. N‐doped G exhibits higher adsorption energies, lower d‐band centers, and shorter TM–G bonds than
Sajjad Ali, Pir Muhammad Ismail, Muhammad Humayun, Mohamed Bououdina   +3 more
wiley   +1 more source

Machine learning‐based prediction of elevated N terminal pro brain natriuretic peptide among US general population

ESC Heart Failure, Volume 12, Issue 2, Page 859-868, April 2025.
Abstract Aims Natriuretic peptide‐based pre‐heart failure screening has been proposed in recent guidelines. However, an effective strategy to identify screening targets from the general population, more than half of which are at risk for heart failure or pre‐heart failure, has not been well established.
Yuichiro Mori, Shingo Fukuma, Kyohei Yamaji, Atsushi Mizuno, Naoki Kondo, Kosuke Inoue   +5 more
wiley   +1 more source