Results 71 to 80 of about 13,208 (247)

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

Early prediction of acute kidney injury in traumatic and non‐traumatic rhabdomyolysis using an interpretable machine learning model: A multicenter study with external validation

Journal of Intelligent Medicine, EarlyView.
Abstract Acute kidney injury (AKI) is a common and severe complication of rhabdomyolysis (RM), and early risk stratification remains challenging because of its multifactorial and heterogeneous nature. We developed and externally validated an interpretable machine learning (ML) model for early prediction of AKI in RM across traumatic and non‐traumatic ...
Chunli Liu, Jie Shi, Fengjuan Wang, Liang Ma, Huangang Hu, Li Zhang, Jie Song, Heng Jin, Dingwei Yang, Xiaoqin Guo, Haojun Fan, Qi Lv   +11 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

Intelligent diagnosis‐related group classification for 134,189 patients in eastern China using machine learning

Journal of Intelligent Medicine, EarlyView.
Intelligent DRG Classification with ELGWO‐LightGBM. Abstract Diagnosis‐related group (DRG) classification is crucial for healthcare cost management and resource allocation, but traditional manual classification by physicians is inefficient and error‐prone, especially for large‐scale medical data.
Yanxi Zhang, Yihan Cheng, Yuhui Ruan, Kun Wang   +3 more
wiley   +1 more source

Wind Speed Multi-Mode Ensemble Forecasting for Wind Farms Based on Machine Learning

南方能源建设
[Introduction] With the extensive construction of wind farms, the combination of researches on different machine learning algorithms and meteorological forecasting modes has received widespread attention.
Sheng GAO, Peihua XU, Zhenghong CHEN
doaj   +1 more source

AEM Water Electrolyzer Performance Prediction and Multi-Objective Optimization Using a LightGBM-Based Surrogate Model [PDF]

EPJ Web of Conferences
This study proposes a LightGBM-based surrogate model for fast performance prediction and multi-objective optimization of an anion exchange membrane (AEM) water electrolyzer. A dataset of 5000 samples was generated from an electrochemical simulation model
Denden C., Hajji B., Rabhi A., Bosche J., Tourneur J.   +4 more
doaj   +1 more source

High‐Fidelity Synthetic Raman Spectra Generation for Sinter Basicity Prediction Using β$$ \beta $$‐Variational Autoencoders

Journal of Raman Spectroscopy, EarlyView.
A β$$ \beta $$‐variational autoencoder with β$$ \beta $$ = 0.1 generates high‐fidelity synthetic Raman spectra of industrial sinter with a 16‐fold improvement in spectral fidelity over SMOTE‐based augmentation (KL divergence: 0.0075 vs. 0.121), enabling reliable basicity prediction (R2 = 0.83) from limited labeled datasets.
Marjorie Ariele Pereira, Daniel Cruz Cavalieri, Adilson Ribeiro Prado, Cassius Zanetti Resende, Erica Simões Rodrigues   +4 more
wiley   +1 more source

A Multi-Class Heart Disease and Stroke Risk Classification Framework for Combined Assessment: A Machine Learning Approach [PDF]

E3S Web of Conferences
This study introduces a combined multi-class machine learning system in predicting both heart disease and stroke risks. This was achieved by creating a harmonized dataset by merging two independent cardiovascular cohorts via conditionbased feature ...
Rakin Abrar, Ahamed Md. Istiak, Hassin Farhan, Kushal Kashfi Shormita, Hasan Shouvo Nazmul, Alom Md. Samiul   +5 more
doaj   +1 more source

Heterogeneity and Controlling Mechanisms of Soil Organic Carbon Stocks Across Erosion Zones in China Under Future Climate Scenarios

Land Degradation &Development, EarlyView.
ABSTRACT Soil organic carbon (SOC), as a key indicator of global carbon cycling and climate change response, plays a critical role in regional ecological security and agricultural sustainability. This study employs three machine learning models including the LightGBM, XGBoost, and random forest to simulate the spatial distribution of topsoil SOC stocks
Xinxin Jin, Xintong Liu, Qianlai Zhuang, Zijiao Yang, Zicheng Wang, Chen Li, Shuai Wang, Yang Wang   +7 more
wiley   +1 more source

Proteomics of Nitrotyrosine: Integrating Mass Spectrometry and Immunodetection in Redox‐Driven Pathology

Mass Spectrometry Reviews, EarlyView.
ABSTRACT Nitrooxidative stress, driven by excess reactive nitrogen species like peroxynitrite, contributes to the pathogenesis of many chronic diseases. Among its molecular footprints, 3‐nitrotyrosine (3NT) has emerged as a biologically relevant marker of protein nitration.
Brîndușa Alina Petre
wiley   +1 more source