Results 151 to 160 of about 150,441 (331)

Izslēgšanas metode XGBoost algoritmam

, 2019
Dropout method, DART algorithm, XGBoost algorithm and XGBoost hyperparameters (α, λ, γ, srinkage un subsample) related to model regularization were researched. Ways of graphically depicting the effect of dropouts and XGBoost hyperparameters on the contribution of individual decion trees on the output of the model and on the overfit of the model were ...
openaire   +1 more source

Extracting spatial effects from machine learning model using local interpretation method: An example of SHAP and XGBoost

Computers, Environment and Urban Systems, 2022
Ziqi Li
semanticscholar   +1 more source

Potential contribution of biomass gasification‐based technology in energy transition: a technical review coupled with bibliometric studies

Biofuels, Bioproducts and Biorefining, EarlyView.
Abstract Biomass gasification technology has been extensively researched around the world; however, there is a need to evaluate the current research landscape and evolutionary direction of research in the broader context of energy transition. A systematic bibliometric analysis of the Web of Science database was performed for articles that fall within ...
Olasunkanmi Opeoluwa Adeoye, Electo Eduardo Silva Lora, Rubenildo Vieira Andrade, Leonardo Peña Pupo, René Lesme Jaén, Osvaldo José Venturini   +5 more
wiley   +1 more source

Mechanism‐informed machine learning for individualized tacrolimus dose adjustment in the early post‐kidney transplant period

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, Zihan Qin, Logan S. Smith, Jeong M. Park, Hao‐Jie Zhu   +4 more
wiley   +1 more source

A Modular Mechanistic In Silico Model for In Vitro Transcription Process Yield and Product Quality Prediction

Biotechnology and Bioengineering, EarlyView.
ABSTRACT In vitro transcription (IVT) plays a critical role in the manufacture of mRNA vaccines and therapeutics. Optimizing mRNA yield and ensuring product quality, such as capping efficiency and integrity, are essential but mechanistically complex. This study presents a modular mechanistic model of the IVT process to advance scientific understanding ...
Keqi Wang, Keilung Choy, Eli Reiser, Jinxiang Pei, Hua Zheng, Aparajita Dasgupta, Fuqiang Cheng, Guogang Dong, Bhanu Chandra Mulukutla, Joshua Mannheimer, Carolyn Huang, Hooman Farsani, Wei Xie   +12 more
wiley   +1 more source

Double diffusive convection of NEPCM in an S-shaped porous cavity under magnetic field influence with localized heat and mass sources

Open Physics
This study presents a novel numerical investigation of double-diffusive convection in an S-shaped porous cavity filled with Nano-Encapsulated Phase Change Material (NEPCM), subjected to localized thermal and solutal sources under magnetic field influence.
Alhejaili Weaam, Aali Alotaibi Munirah, Aly Abdelraheem M.   +2 more
doaj   +1 more source

Analysis of Ruddlesden‐Popper and Dion‐Jacobson 2D Lead Halide Perovskites Through Integrated Experimental and Computational Analysis

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
wiley   +1 more source

Implementation of Machine Learning Models to Predict Functionality of Pea Flour From Its Composition

Cereal Chemistry, EarlyView.
ABSTRACT Background and Objectives The goal of this research was to examine the relationship between the composition and functionality of pea flour using the following machine learning algorithms: linear regression, partial least squares regression (PLSR), Gaussian process regression (GPR), support vector regression, gradient‐boosted decision trees ...
Colten N. Nickerson, Sarah Hogarth, Andrea K. Stone, Gene C. Arganosa, Pankaj Bhowmik, Tom D. Warkentin, Jordan Ubbens, Michael T. Nickerson   +7 more
wiley   +1 more source

Using Xgboost Models for Daily Rainfall Prediction

Anales de Geografía de la Universidad Complutense
Machine 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, Rodrigo Sanches Miani, Bruno César dos Santos, Rodrigo Martins Moreira, Gustavo Zen de Figueiredo Neves, Vandoir Bourscheidt, Pedro Augusto Toledo Rios   +6 more
openaire   +1 more source

Ensemble‐based soil liquefaction assessment: Leveraging CPT data for enhanced predictions

Civil Engineering Design, Volume 7, Issue 1, Page 23-35, March 2025.
Abstract This study focuses on predicting soil liquefaction, a critical phenomenon that can significantly impact the stability and safety of structures during seismic events. Accurate liquefaction assessment is vital for geotechnical engineering, as it informs the design and mitigation strategies needed to safeguard infrastructure and reduce the risk ...
Arsham Moayedi Far, Masoud Zare
wiley   +1 more source