Results 121 to 130 of about 33,486 (254)

Ultra-short-term wind power prediction method combining financial technology feature engineering and XGBoost algorithm. [PDF]

Heliyon, 2023
Guan S, Wang Y, Liu L, Gao J, Xu Z, Kan S.   +5 more
europepmc   +1 more source

Interpretable Short‐Term Electric Load Forecasting

Advanced Intelligent Systems, EarlyView.
A temporal fusion transformer is implemented to generate day‐ahead forecasts of the hourly electrical load of a departmentbuilding at an Italian university. A forecasting performance improvement of more than 25% compared with established benchmark models and a provision of inherent robust interpretability insights reveal the potential of this model for
Alessandro Nicola, Giorgia Ghione, Vincenzo Randazzo, Vincenzo Franzitta, Marco Badami, Eros Pasero, Giansalvo Cirrincione   +6 more
wiley   +1 more source

Impact of COVID-19 on mental health in China: analysis based on sentiment knowledge enhanced pre-training and XGBoost algorithm. [PDF]

Front Public Health, 2023
Huang R, Wang X.
europepmc   +1 more source

Integrating Reinforcement Learning With Explainable Artificial Intelligence for Real‐Time Clinical Decision Support in Dynamic Healthcare Environments

Advanced Intelligent Systems, EarlyView.
A hybrid Reinforcement Learning–Explainable AI framework integrates SHAP and LIME explanations directly into a Deep Q‐Network inference loop for real‐time ICU decision support. Trained on 18 142 mechanically ventilated stays from the eICU database, the system attains 93.0% decision accuracy, 20% fewer errors than RL alone, and a 91% clinician trust ...
Jannatul Ferdaus Disha, MD Faiaz Zaman Dehan, Kazi Md. Tanvir Anzum   +2 more
wiley   +1 more source

Clinical Data based XGBoost Algorithm for infection risk prediction of patients with decompensated cirrhosis: a 10-year (2012-2021) Multicenter Retrospective Case-control study. [PDF]

BMC Gastroenterol, 2023
Zheng J, Li J, Zhang Z, Yu Y, Tan J, Liu Y, Gong J, Wang T, Wu X, Guo Z.   +9 more
europepmc   +1 more source

Integrating machine learning, deep learning, and image analysis for seed species classification

Applications in Plant Sciences, EarlyView.
Abstract Premise The growing demand for wildflower seeds in ecological restoration requires reliable species identification, yet current market products often contain heterogeneous species. As seed identification is labor‐intensive and requires advanced botanical knowledge, we evaluated multiple segmentation and classification approaches to determine ...
Jonathan Ashworth, Eloise Barrett, Rebonto Haque, Ben Wildey, Anthony Hall, Felix Shaw, Jose J. De Vega   +6 more
wiley   +1 more source

UWB Indoor Localization Based on XGBoost NLOS Identification and DS-TWR Ranging [PDF]

Radioengineering
Indoor environments present significant challenges for ultra-wideband (UWB) localization due to ranging errors and non-line-of-sight (NLOS) propagation.
X. Yao Z. Xu, G. Liu
doaj  

Preliminary prediction of semen quality based on modifiable lifestyle factors by using the XGBoost algorithm. [PDF]

Front Med (Lausanne), 2022
Zhou M, Yao T, Li J, Hui H, Fan W, Guan Y, Zhang A, Xu B.   +7 more
europepmc   +1 more source

Direct Comparison Between Loss‐to‐Follow‐Up and Statistical Fragility Is Methodologically Inappropriate, and Fragility Reflects the P Value, Not Trial Robustness: A Simulation Analysis of 300,000 Randomized Controlled Trials

Arthroscopy, EarlyView.
Purpose To assess the relation between the fragility index (FI) and reverse fragility index (RFI) with the minimum number of patients needed to reverse statistical significance (e.g. henceforth termed the lost to follow‐up index (LTFI) and reverse LTFI (R‐LTFI), respectively) and apply machine learning to identify which trial parameters are most ...
Prushoth Vivekanantha, Helena Son, Jeffrey Kay, Satyavenkata Kotipalli, Marc Daniel Bouchard, Kim Madden, Nicole Simunovic, Olufemi R. Ayeni   +7 more
wiley   +1 more source

A Wireless Acoustic Emission Sensor System with ACMD-IGWO-XGBoost Algorithm for Living Tree Moisture Content Diagnosis. [PDF]

Plants (Basel), 2023
Yang Z, Wu Y, Liu Y.
europepmc   +1 more source