Results 111 to 120 of about 4,344 (219)

Interpretable Machine Learning for Forecasting Renewable Energy Production: A Cross‐Country Analysis

Engineering Reports, Volume 8, Issue 10, October 2026.
Our study introduces a hybrid machine learning framework that integrates real‐time analytics and explainable AI to forecast energy crises and renewable adoption with superior accuracy. By outperforming traditional statistical models, the research identifies key policy and infrastructure drivers, offering actionable pathways for resilient global energy ...
Sejuti Najneen, Niloy Ahmed Showrav, Azmaain Noor Miazi, Saikat Barua, Mahdy Rahman Chowdhury   +4 more
wiley   +1 more source

Optimized Breast Cancer Classification Using PCA-LASSO Feature Selection and Ensemble Learning Strategies With Optuna Optimization

IEEE Access
Breast cancer is one of the most prevalent and life-threatening diseases among women worldwide, making early and accurate detection crucial for effective treatment.
Prabhat Kumar Sahu, Taiyaba Fatma
doaj   +1 more source

Fuzzy‐Gated Selective Machine Learning for CTI‐Based Network Connection Management: A Pareto Evaluation

International Journal of Network Management, Volume 36, Issue 6, November/December 2026.
ABSTRACT Modern network operations require timely IP risk decisions without executing complete machine learning pipelines for every observed address. This work proposes an interpretable and conservative fuzzy‐gated selective inference architecture for multisource cyber threat intelligence (CTI)‐based network connection management.
Francisco V. J. Nobre, Ivo A. Pimenta, Janaína R. Santos, Davi O. Alves, Henrique V. Oliveira, Nubio Vidal, Rafael L. Gomes   +6 more
wiley   +1 more source

ESpma: A method for assessing biological/non‐biological interfaces using point‐clouds‐based structural features and protein language models

Protein Science, Volume 35, Issue 10, October 2026.
Abstract Distinguishing biological protein–protein interfaces from non‐biological contacts remains an important task in structural biology, particularly as protein complex structures continue to accumulate. Recent advances in protein language models (pLMs) have expanded their use across diverse protein prediction tasks, including sequence‐based protein–
Sarah Nozawa, Kentaro Tomii, Yoshinori Fukasawa   +2 more
wiley   +1 more source

PERAMALAN KUNJUNGAN WISATAWAN MANCANEGARA MENGGUNAKAN MODEL SARIMA-XGBOOST DENGAN OPTIMASI OPTUNA

The Indonesian tourism sector has experienced significant changes following the COVID-19 pandemic, highlighting the need for accurate forecasting systems to support data-driven policy decisions.
Malva, Maisie Yunita
core  

Neural Surrogate HMC: On Using Neural Likelihoods for Hamiltonian Monte Carlo in Simulation‐Based Inference

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 5, October 2026.
Abstract Bayesian inference methods such as Markov Chain Monte Carlo (MCMC) typically require repeated computations of the likelihood function, but in some scenarios, this is infeasible and alternative methods are needed. Simulation‐based inference methods address this problem by using machine learning to amortize computations.
L. M. Wolniewicz, P. Sadowski, C. Corti
wiley   +1 more source

Prediction of soil conditioner dosages for shield tunneling in sandy soil based on machine learning

Case Studies in Construction Materials
Inadequate soil conditioning during Earth Pressure Balance shield (EPBS) tunneling in sandy strata frequently causes operational issues. This study developed a data-driven framework integrating 15 operational and geological parameters from Shenyang Metro
Keqi Liu, Xueyi Liang, Wen Zhao, Xiumei Cao, Hezheng Pei   +4 more
doaj   +1 more source

Modified Prophet+Optuna Prediction Method for Sales Estimations

International Journal of Advanced Computer Science and Applications, 2022
Kohei Arai, Ikuya Fujikawa, Yusuke Nakagawa, Tatsuya Momozaki, Sayuri Ogawa   +4 more
openaire   +1 more source

A National Scale Hybrid Machine Learning Model for Enhanced Groundwater Depth Estimation

Water Resources Research, Volume 62, Issue 10, October 2026.
Abstract Groundwater is one of the most valuable natural resources, yet it is increasingly under pressure from climate change and human activities. Shallow groundwater depth (GWD) is a key indicator for evaluating groundwater availability and flood hazards, yet the measurements are often too sparse to capture its variability both in time and space.
Jun Liu, Raphael J. M. Schneider, Lars Troldborg, Yueling Ma, Reed Maxwell, Julian Koch   +5 more
wiley   +1 more source

A Methodological Study on Fetal Health Classification Using Optimized LightGBM with SMOTE and Optuna-Based Hyperparameter Optimization

Objective: Fetal health classification is of great clinical importance as it allows the early detection and management of fetal health problems during pregnancy.This study aims to enhance fetal health classification by integrating Light Gradient Boosting
Aslan, Serpil, Tanyildiz, Hayriye, Yalcin, Emre   +2 more
core   +1 more source