Results 71 to 80 of about 32,654 (268)

Further Detail Concerning the Deep Learning Model for Mortality After Total Gastrectomy

Annals of Gastroenterological Surgery, EarlyView.
Kentaro Goto, Haruku Fujita, Ryohei Onishi, Nobuaki Hoshino, Koya Hida   +4 more
wiley   +1 more source

Dynamic geo‐hydrogeological monitoring‐driven situational awareness for real‐time floor water inrush risk prediction in deep mining

Deep Underground Science and Engineering, EarlyView.
The fused data extracted from the distributed monitoring system as the data basis, combined with dynamic geological data, are imported into a deep learning model. As the geological conditions of mining and excavation change, the risk of water inrush at the working face is retrieved in real time.
Yongjie Li, Huiyong Yin, Fangying Dong, Tao Wu, Chao Zhang   +4 more
wiley   +1 more source

Prediction of Pipeline Defect Depth and Classification Based on CatBoost

Energy Science &Engineering, EarlyView.
Obtaining detection data using in‐line pipeline inspection, the synthetic minority oversampling technique (SMOTE) is applied to expand the sample set, thereby increasing the number of minority‐class samples. This approach effectively improves minority‐class detection and enhances pipeline safety assessment. ABSTRACT Magnetic flux leakage detection is a
Cong Chen, Rui Li, Guanwei Jia, Dongcun Qin   +3 more
wiley   +1 more source

An Innovative Approach for Forecasting Hydroelectricity Generation by Benchmarking Tree-Based Machine Learning Models

Applied Sciences
Hydroelectricity, one of the oldest and most potent forms of renewable energy, not only provides low-cost electricity for the grid but also preserves nature through flood control and irrigation support.
Bektaş Aykut Atalay, Kasım Zor
doaj   +1 more source

Ensemble Deep Learning–Based Wind Power Forecasting With Self‐Adaptive Osprey Optimization Algorithm

Energy Science &Engineering, EarlyView.
Design of Self‐Adaptive Osprey (SAO) algorithm: The novel SAO algorithm is designed by integrating the exploration capability of the conventional Osprey algorithm by including the self‐adaptiveness for enhancing the convergence rate. Ensemble Deep Learning for wind power forecasting: The wind forecasting is employed using the proposed Ensemble learning
Johncy Bai Johnson, Sivarani Thankamony Saradha Devi, Kumar Chandrasekaran, Sourav Barua   +3 more
wiley   +1 more source

PREDICTION INTERVALS IN MACHINE LEARNING: RESIDUAL BOOTSTRAP AND QUANTILE REGRESSION FOR CASH FLOW ANALYSIS

Barekeng
Time series forecasting often faces challenges in producing reliable predictions due to inherent uncertainty in dynamic systems. While point predictions are commonly used, they may not adequately capture this uncertainty, especially in financial systems ...
Wa Ode Rahmalia Safitri, Farit Mochamad Afendi, Budi Susetyo   +2 more
doaj   +1 more source

Interpretable Tree‐Based Models for Predicting Short‐Term Rockburst Risk Considering Multiple Factors

Energy Science &Engineering, EarlyView.
Interpretable tree‐based models integrate microseismic, geological, and mining indicators to predict short‐term rockburst risk. SHAP analysis reveals the dominant role of energy‐related features and clarifies nonlinear factor interactions, enabling transparent and reliable early‐warning in deep coal mines.
Shuai Chen, Linming Dou, Atao Li, Wu Cai, Lei Zhang   +4 more
wiley   +1 more source

Short-term load forecasting based on multi-frequency sequence feature analysis and multi-point modified FEDformer

Frontiers in Energy Research
Given the complexity and dynamic nature of short-term load sequence data, coupled with prevalent errors in traditional forecasting methods, this study introduces a novel approach for short-term load forecasting.
Kaiyuan Hou, Xiaotian Zhang, Junjie Yang, Jiyun Hu, Guangzhi Yao, Jiannan Zhang   +5 more
doaj   +1 more source

Ground subsidence risk assessment method using PS-InSAR and LightGBM: a case study of Shanghai metro network

International Journal of Digital Earth
Ground subsidence is a common geological hazard in urban areas that endangers the safety of infrastructure, such as subways. In this study, the ground subsidence risk assessment method considering both ground subsidence intensity and susceptibility is ...
Long Chai, Xiongyao Xie, Cheng Wang, Genji Tang, Zhe Song   +4 more
semanticscholar   +1 more source

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

Journal of Forecasting, EarlyView.
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