Results 11 to 20 of about 31,305 (267)

Explainable AI for survival analysis: a median-SHAP approach

CoRR
With the adoption of machine learning into routine clinical practice comes the need for Explainable AI methods tailored to medical applications. Shapley values have sparked wide interest for locally explaining models. Here, we demonstrate their interpretation strongly depends on both the summary statistic and the estimator for it, which in turn define ...
Lucile Ter-Minassian, Sahra Ghalebikesabi, Karla Diaz-Ordaz, Chris C. Holmes   +3 more
openaire   +2 more sources

Explainable Ensemble Learning for Maternal Health Risk in Low-Resource Settings

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Maternal health remains a global challenge, particularly in low-resource settings where accurate and timely risk prediction is essential to reducing maternal mortality. This study proposes an explainable machine learning framework for predicting maternal
Lilik Widyawati, Neny Sulistianingsih
doaj   +1 more source

A SHAP-based controversy analysis through communities on Twitter

World Wide Web
Abstract Controversy encompasses content that draws diverse perspectives, along with positive and negative feedback on a specific event, resulting in the formation of distinct user communities. Research on controversy can be broadly categorized into two domains: controversy detection/quantification, which aims to measure controversy on a topic,
Samy Benslimane, Thomas Papastergiou, Jérôme Azé, Sandra Bringay, Maximilien Servajean, Caroline Mollevi   +5 more
openaire   +1 more source

Association between green space coverage and dyslipidemia in adults: Combined with interpretable machine learning SHAP methods

环境与职业医学
BackgroundResearch on the association between dyslipidemia and green space coverage remains limited, and existing methods too rely on traditional fixed models to fully reveal the complex and nonlinear relationships and their interactions in large ...
Shiqi HUANG, Kehan ZHONG, Peizheng LI, Lu MA   +3 more
doaj   +1 more source

Effect of organic macerals on hydrocarbon generation ability of oil shale and coal and establishment of a contribution model – constraints from machine learning [PDF]

Oil Shale
To quantify the impact of organic macerals on hydrocarbon generation in oil shale and coal, this study analyzed 403 datasets. The results show distinct differences in maceral composition between the two materials. Using a random forest model with aquatic
Duoxiao Sun, Feng Ma, Hongbiao Wang, Hongliang Dang, Yanwei Bi, Pingchang Sun   +5 more
doaj   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source

Functionalizing Micro‐to‐Mesoscopic Electrode Architectures for Regulating Electron Transfer Behaviors in Electrocatalysis

Advanced Functional Materials, EarlyView.
A systematic review is conducted to assess the influence of electrode architecture across micro‐ to mesoscopic length scales on electron‐transfer pathways in electrocatalysis. We discuss the structure‐activity relationships in electrocatalytic applications, including resource recovery and environmental remediation, and provide cost‐effective, efficient
Manshu Zhao, Jiaojiao Zhang, Shideng Yuan, Xinmeng Yu, Keming Wan, Xiaoguang Duan, Zhining Wang   +6 more
wiley   +1 more source

What Do Machine Learning Models Reveal About Housing Unaffordability in Europe? Implications for Housing Policy

Slovak Journal of Civil Engineering
The paper aims to investigate the main factors affecting stress from the housing costs of household in Europe by applying machine learning methods to a set of structural, demographic, market and policy indicators.
Zajacova Janka, Stehlikova Beata, Zubkova Maria   +2 more
doaj   +1 more source

Beyond Presumptions: Toward Mechanistic Clarity in Metal‐Free Carbon Catalysts for Electrochemical H2O2 Production via Data Science

Advanced Materials, EarlyView.
Metal‐free carbon catalysts enable the sustainable synthesis of hydrogen peroxide via two‐electron oxygen reduction; however, active site complexity continues to hinder reliable interpretation. This review critiques correlation‐based approaches and highlights the importance of orthogonal experimental designs, standardized catalyst passports ...
Dayu Zhu, Hsi‐wen Wu, Xiao Wang, Jie Zhang   +3 more
wiley   +1 more source

Factors Associated with Family Members in Prison: An Imbalanced Machine Learning Approach

Anuario de Psicología Jurídica
This study explores the family environment of individuals deprived of liberty in Argentina through an imbalanced machine learning approach. Based on a national representative dataset of 17,139 individuals from various urban regions, machine learning ...
Mariana Politti, Luis A. Calvo Pascual, Gisela Delfino, Solange Rodríguez Espínola   +3 more
doaj   +1 more source