Results 141 to 150 of about 95,729 (269)
Using Xgboost Models for Daily Rainfall Prediction
Anales de Geografía de la Universidad ComplutenseMachine learning models for predicting daily precipitation have gained traction in recent years. Understanding the benefits of using this technology in different regions is a relevant research topic. For this reason, this study aims to evaluate daily precipitation estimated forecasts from climate data between 1983 and 2019 in Itirapina, São Paulo ...
Rafael Grecco Sanches +6 more
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British Journal of Clinical Pharmacology, EarlyView.Abstract Aim Tacrolimus dosing in the early post‐kidney transplant period is challenging due to a narrow therapeutic index and substantial interindividual pharmacokinetic (PK) variability. This study aimed to develop and validate mechanism‐informed machine learning (ML) models to support individualized tacrolimus dosing during this critical period ...
Hui Yu +4 more
wiley +1 more source
Battery Energy, Volume 4, Issue 2, March 2025.Optimized ML framework for predicting RP and Dj phases in perovskite solar cells. ABSTRACT Two‐dimensional (2D) lead halide perovskites (LHPs) have captured a range of interest for the advancement of state‐of‐the‐art optoelectronic devices, highly efficient solar cells, next‐generation energy harvesting technologies owing to their hydrophobic nature ...
Basir Akbar, Kil To Chong, Hilal Tayara
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Biotechnology Progress, EarlyView.Abstract Artificial intelligence and automation are no longer just buzzwords in the biopharmaceutical industry. The manufacturing of a class of biologics, comprising monoclonal antibodies, cell therapies, and gene therapies, is far more complex than that of traditional small molecule drugs.
Shyam Panjwani, Hao Wei, John Mason
wiley +1 more source
Carbon and Hydrogen, EarlyView.A machine‐learning framework integrating multimodel prediction, feature selection, and SHAP interpretability is developed to uncover structure–performance relationships of Cu‐based CO2‐to‐methanol catalysts. The optimized XGBoost model and an online prediction platform enable accurate selectivity prediction and data‐driven catalyst design.
Conglong Su +11 more
wiley +1 more source
Zhendong Ceshi yu Zhenduan针对声音信号分析在诊断带式输送机托辊故障中的高维特征存在信息冗余、计算量大和诊断效果不理想等问题，笔者构建了声音信号特征精简策略，基于Circle混沌映射、Levy飞行策略和正弦因子改进了霜冰优化算法（rime optimization algorithm，简称RIME），记作CLSRIME。再结合极致梯度提升模型（extreme gradient boosting，简称XGBOOST），构建了CLSRIME‑XGBOOST带式输送机托辊轴承故障诊断方法。首先，利用梅尔倒谱系数（Mel⁃scale ...
doaj +1 more source
Tree Boosting With XGBoost - Why Does XGBoost Win "Every" Machine Learning Competition?
, 2016 Tree boosting has empirically proven to be a highly effective approach to predictive modeling. It has shown remarkable results for a vast array of problems. For many years, MART has been the tree boosting method of choice. More recently, a tree boosting method known as XGBoost has gained popularity by winning numerous machine learning competitions.
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Phishing URL Detection Using XGBoost
International Journal for Research in Applied Science and Engineering TechnologyAbstract: Phishing attacks are a major threat to cybersecurity, affecting individuals and organizations around the world. In this project we are developing a phishing site detection system using XGBoost, a widely used machine learning algorithm that is well-known for its effectiveness and precision in classification tasks.
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Computational and Machine‐Learning Studies of Ethylene Oligomerization
Carbon and Hydrogen, EarlyView.This review focuses on recent advances in computational and machine‐learning studies of ethylene oligomerization, highlighting mainstream catalyst systems based on Co, Ta, Ti, Zr, and Hf, with particular emphasis on Fe‐ and Cr‐based catalysts and their controlling factors governing reactivity and LAO distribution.
Zhixin Qin +3 more
wiley +1 more source
Drug Response Prediction Using XGBOOST
International Journal for Research in Applied Science and Engineering TechnologyAbstract: An essential issue in computational personalised medicine is the prediction of drug responses. There have been several proposals for approaches to this problem that rely on machine learning, particularly deep learning. Nevertheless, these approaches often portray the medications as strings, an implausible representation of molecules ...
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