Results 81 to 90 of about 16,703 (255)

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

An Optimized LightGBM Model for Fraud Detection

Journal of Physics: Conference Series, 2020
Abstract The rapid development of e-commerce and the growing popularity of credit cards have made online transactions smooth and convenient. However, large numbers of online transactions are also the targets of online credit card fraud, which aggregate to enormous losses annually. In response to this trend, many machine learning and deep
openaire   +1 more source

Machine Learning‐Based Estimation of Reference Evapotranspiration and Crop Coefficients for Wheat Under Diverse Climatic Conditions

Irrigation and Drainage, EarlyView.
ABSTRACT Accurate estimation of reference evapotranspiration (ET0) and crop coefficients (Kc) is critical for irrigation planning, particularly in data‐limited regions where agriculture dominates freshwater consumption. Although machine learning (ML) methods have been widely applied to ET0 and Kc estimation, most studies address these parameters ...
Ilker Angin, Hasan Er, Mete Yaganoglu, Muhammed Sungur Demir, Abdullah Muratoglu   +4 more
wiley   +1 more source

Feature importance for the RF and LightGBM models.

Feature importance for the RF and LightGBM models.
Jan Andrysek (8945657), Mohammad Pourmahmood Aghababa (18290996)   +1 more
core   +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

A Comprehensive Study to Compare Different Compound Representations for Predicting Carcinogenicity In Vivo

Journal of Applied Toxicology, EarlyView.
ABSTRACT Carcinogenicity evaluation is a critical component of chemical risk assessment, yet traditional in vivo testing remains time consuming, costly, and ethically challenging. Computational approaches based on machine learning offer promising alternatives, but the relative contributions of different molecular representation strategies for ...
Iuri Barbosa Pereira, Rogerio Salvini, Eloisa Dutra Caldas   +2 more
wiley   +1 more source

List of hyperparameters to optimize in the lightGBM models.

List of hyperparameters to optimize in the lightGBM models.
John Uelmen (8878430), Liudmila Sergeevna Mainzer (4061737), Shubham Rawlani (17743363), Weihao Ge (6080207), Joshua Allen (2405632), Guangya Wan (17743360), Rebecca Lee Smith (8878433)   +6 more
core   +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

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

Top-20 variable importance of LightGBM.

Background and objectiveAcute Kidney Injury (AKI) is a common and severe complication in patients diagnosed with sepsis. It is associated with higher mortality rates, prolonged hospital stays, increased utilization of medical resources, and financial ...
Huirui Han (18349492), Song Chen (69646), Yanfen Li (3239550), Wei Liu (20030), Jianshan Shi (18349489)   +4 more
core   +1 more source