Results 131 to 140 of about 16,703 (255)

Prediction of Properties of Polymer Composite Formulations Using Ensemble Models With Feature Generation

Journal of Applied Polymer Science, Volume 143, Issue 25, July 5, 2026.
This study develops an interpretable machine‐learning framework to predict multiple properties of polymer composites based on composition and processing variables. By combining ensemble models with composition‐based feature generation and SHAP‐based interpretation, the approach reveals composition‐property relationships and supports efficient multi ...
Dong Ryeol Shin, Sung Kwang Lee
wiley   +1 more source

PEMETAAN DAN KLASIFIKASI WILAYAH RAWAN GEMPA DI INDONESIA DENGAN METODE K-MEANS DAN LIGHTGBM

Penelitian ini bertujuan untuk memetakan dan mengklasifikasikan wilayah rawan gempa di Indonesia dengan memanfaatkan algoritma K-Means dan LightGBM. Data historis gempa diperoleh dari Badan Meteorologi, Klimatologi, dan Geofisika (BMKG) sebagai dasar ...
Yudhi Selvina
core   +1 more source

Application of Machine Learning in Low‐Carbon Economy: A Comprehensive Review of Predicting Cycle Life of Lithium/Sodium‐Ion Batteries

Carbon Neutralization, Volume 5, Issue 4, July 2026.
This review provides a comprehensive and up‐to‐date overview of applications of machine learning in predicting lithium/sodium‐ion batteries' cycle life. ABSTRACT The cycle life of rechargeable batteries, such as lithium‐ion and sodium‐ion systems, is a critical performance metric that determines their suitability for various applications in the context
Bo Zhang, Xiao‐Min Zou, Xin Wen, Jia‐Lu Wu, Jing‐Jing Pan, Xin Tan, Jamie L. Cross   +6 more
wiley   +1 more source

Identification of mine water source based on TPE-LightGBM

Scientific Reports
AbstractMine water inrush is a serious threat to mine safety production. It is very important to identify water inrush source types quickly to prevent and control water damage. In this study, the aqueous chemical components Na+ + K+, Ca2+, Mg2+, Cl−, SO42− and HCO3− of different aquifers in Pingdingshan coalfield were selected as the characteristic ...
Man Wang, Jianguo Zhang, Han Li, Bo Zhang, Zhenwei Yang   +4 more
openaire   +3 more sources

Predicting the Complex, Multilevel Ecology of US County‐Level Diabetes Prevalence: A Cross‐Sectional, Artificial Intelligence Analysis

Diabetes/Metabolism Research and Reviews, Volume 42, Issue 5, July 2026.
ABSTRACT Background Diabetes and its risk factors are embedded in a complex multilevel ecology. Upstream factors (i.e., ‘forcing factors’ that refer to fundamental population‐based social, economic, and political structures shaping health outcomes long before disease develops) and downstream risk factors (i.e., the individual‐level characteristics and ...
Nicolaas P. Pronk, Shuaijie Wang, Richard Bergenstal, Anders Carlson, Colin Woodard, Ross Arena   +5 more
wiley   +1 more source

Random Integrated Subdata Ensemble Method for Key Variable Selection in Rare Event Setting

Journal of Forecasting, Volume 45, Issue 4, Page 1878-1889, July 2026.
ABSTRACT We propose a general variable selection procedure to identify key input variables by applying elastic net regression to representative subdata in place of the full sample to select variables. We combine the lists of selected variables from each subdata through ensemble techniques, using the frequency of selecting the variable across different ...
Ching‐Chi Yang, Justin Deng, Lih‐Yuan Deng, Henry Horng‐Shing Lu   +3 more
wiley   +1 more source

Hypertension Detection Using Explainable Stacked Ensemble Machine Learning From Clinical and Physiological Data: A Comprehensive Study

Health Science Reports, Volume 9, Issue 7, July 2026.
ABSTRACT Background and Aims Hypertension or high blood pressure is a life threatening common cardiovascular disease (CVD) all over the world. In the era of information technology, communication, and artificial intelligence, early prediction of hypertension using various techniques can be advantageous to alert patients.
Shah Muhammad Azmat Ullah, A. B. M. Aowlad Hossain, Md. Ebtidaul Karim   +2 more
wiley   +1 more source

An Explainable Machine Learning Model for Early Prediction of Incident Myocardial Injury in Patients With Severe Fever With Thrombocytopenia Syndrome

Journal of Medical Virology, Volume 98, Issue 7, July 2026.
ABSTRACT This study aimed to develop an explainable machine learning–based model to enable early prediction of incident myocardial injury during hospitalization among patients with severe fever with thrombocytopenia syndrome (SFTS). This multicenter retrospective cohort study included and analyzed clinical data from 1,088 patients with SFTS who were ...
Xiang Li, Xiaotong Yu, Wei Zhou, Sujuan Zhang, Zibo Fan, Yuanni Liu, Yi Shen, Zhenghua Zhao, Jianping Duan, Ling Lin, Zhihai Chen, Wei Zhang   +11 more
wiley   +1 more source

Prediksi Penyakit Diabetes menggunakan Teknik Imputasi Missforest dan Klasifikasi LightGBM

AbstrakDiabetes adalah salah satu penyakit kronis dengan grafik prevalensinya meningkat secara global. Penyakit ini disebabkan oleh gangguan metabolisme tubuh yang memengaruhi kadar gula darah, dan jika tidak ditangani sejak dini dapat menimbulkan ...
FERDIANSYAH, ALDOVA, KASYIDI, FATAN, UMBARA, FAJRI RAKHMAT   +2 more
core   +1 more source

Detection of Honey Adulteration Using Thermorheological and Spectroscopic Analyses: Independent Evaluation With Linear Latent‐Variable and Gradient Boosting Models

Journal of Food Science, Volume 91, Issue 7, July 2026.
ABSTRACT Honey adulteration is a prevalent economic fraud that demands robust and reliable detection methods. In this study, a proof‐of‐concept was developed on a comparative study of oscillatory thermorheology (20°C–80°C) and spectroscopic techniques, including UV‐Vis and fluorescence spectroscopy, for the detection of adulteration with glucose ...
Bilge Basturk Berk, Berkay Berk, Cagri Cavdaroglu, Neslihan Bozdogan, Sebnem Tavman, Seher Kumcuoglu, Sevcan Unluturk   +6 more
wiley   +1 more source