Results 211 to 220 of about 33,486 (254)

CALORIES BURNT PREDICTION USING XGBOOST ALGORITHM

International Research Journal of Modernization in Engineering Technology and Science
openaire   +1 more source

Artificial intelligence–driven decoupling structure–activity relationship for lithium‐ion batteries

InfoScience, EarlyView.
Artificial intelligence can efferently accelerate the high‐throughput screening of battery materials, the analysis of multiphase mechanisms, and the precise prediction of capacity and cycle life. This review systematically summarizes the applications of machine learning (ML) in decoupling the complex structure‐activity relationships of lithium‐ion ...
Tao Wang, Yuhang Meng, Tianhao Chen, Xiangfen Jiang, Yoshio Bando, Jinjue Zeng, Xuebin Wang   +6 more
wiley   +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

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

Optimizing Laboratory Autoverification in Complete Blood Count Testing Using Machine Learning: A Performance Evaluation Study

Journal of Clinical Laboratory Analysis, EarlyView.
This cross‐sectional study evaluated the feasibility of applying machine learning for complete blood count autoverification. Among 63,201 CBC results, the XGBoost model achieved higher sensitivity and predictive values compared with the traditional rule‐based system. ABSTRACT Background Autoverification improves laboratory efficiency by reducing manual
Sinsorn Srirujee, Peempol Chokchaipermpoonphol   +1 more
wiley   +1 more source

Comparison of machine learning methods for prediction of venous thromboembolism among hospitalized adults

Journal of Hospital Medicine, EarlyView.
Abstract Background Hospital‐acquired venous thromboembolism (HA‐VTE) is a significant cause of morbidity and mortality among hospitalized adults. Accurate prediction of HA‐VTE is crucial for timely intervention and prevention. While logistic regression is widely used for the development of clinical prediction models, there is ongoing interest in the ...
Yeji Ko, Benjamin French, Jonathan S. Schildcrout, Merna El‐Rifai, Adam Wright, Benjamin F. Tillman, Amanda S. Mixon, Henry J. Domenico   +7 more
wiley   +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

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

Common‐Mode Rejection Shifted‐Excitation Raman Difference Spectroscopy (CMR‐SERDS) Preserves Broad Structure Predictive of Soil Organic Carbon

Journal of Raman Spectroscopy, EarlyView.
Common‐mode rejection (CMR) is introduced as a physics‐motivated preprocessing method for shifted excitation Raman difference spectroscopy (SERDS) that removes the shared background of paired measurements while preserving the noncommon excitation‐dependent component. Applied to more than 900 North American soil samples, CMR improves soil organic carbon
Mahsa Zarei, Miayan Larose, Natalia V. Solomatova, Sadegh Shokatian, Edward Grant   +4 more
wiley   +1 more source

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Application of XGBoost algorithm in the optimization of pollutant concentration

Atmospheric Research, 2022
Xinyuan Zhou, Qingyong Li, Xingqin An
exaly   +2 more sources