Results 91 to 100 of about 72,345 (262)

Core loss prediction method for magnetic components based on machine learning

Nantong Daxue xuebao. Ziran kexue ban
Magnetic components play a key role in energy transfer, storage, and filtering, directly affecting the size,weight, loss, and cost of power converters. Therefore, accurate prediction of core loss is essential.
YAO Qida; PING Peng; ZHU Xinyi; ZHU Xinfan
doaj   +1 more source

Comparing XGBoost and ARCH-LM Models for IPO Valuation in the Iranian Capital Market [PDF]

International Journal of Management, Accounting and Economics
The main objective of this research is to conduct a rigorous comparative analysis of the performance of the eXtreme Gradient Boosting (XGBoost) algorithm against the traditional Generalized Autoregressive Conditional Heteroskedasticity (ARCH-LM) model in
Fatemeh Malmir, Farshid Kheirollahi, Hossein Yarahmadi, Farid Sefaty   +3 more
doaj   +1 more source

Accelerating Primary Screening of USP8 Inhibitors from Drug Repurposing Databases with Tree‐Based Machine Learning

Advanced Intelligent Discovery, EarlyView.
This study introduces a tree‐based machine learning approach to accelerate USP8 inhibitor discovery. The best‐performing model identified 100 high‐confidence repurposable compounds, half already approved or in clinical trials, and uncovered novel scaffolds not previously studied. These findings offer a solid foundation for rapid experimental follow‐up,
Yik Kwong Ng, Sharon Ai Er Lam, Wei Chun Lim, Hannah Hui Min Toh, Shi Jun Ang   +4 more
wiley   +1 more source

Loan Default Prediction Using Machine Learning Algorithms

Journal of Informatics and Web Engineering
Financial institutions constantly face at the risk of default by borrowers which can result in significant financial losses. It is essential to develop an appropriate predictive model for loan default to reduce these risks and minimise financial losses ...
Zhi Zheng Kang, Sin Yin Teh, Samuel Yong Guang Tan, Wei Chien Ng   +3 more
doaj   +1 more source

Dropout method for XGBoost algorithm

, 2019
Tika izpētīta izslēgšanas metode, DART algoritms, XGBoost algoritms un XGBoost algoritma hiperparametri (α, λ, γ, srinkage un subsample), kas saistīti ar modeļa regularizāciju un pārpielāgošanas samazināšanu. Tika izpētīti un attēloti izslēgšanas metodes
Freimanis, Andris
core  

Enhancing Sampling Performance in XGBoost by Ensemble Feature Engineering [PDF]

Feature engineering is crucial in enhancing model performance, yet effectively combining multiple feature transformations to maximize their benefits remains a key challenge. In this study, we propose an innovative approach that integrates various feature
Pan JS, Kong L, Suganthan PN, Snášel V, Ojha V   +4 more
core   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

Intraoperative complications in percutaneous coronary intervention for acute myocardial infarction: development of a risk prediction model

Shanghai Jiaotong Daxue xuebao. Yixue ban
Objective·Patients with acute myocardial infarction (AMI) undergoing percutaneous coronary intervention (PCI) are at risk of severe complications, such as hypotension and malignant arrhythmias, which directly affect procedural success and patient ...
RUAN Qingqing, SU Shuzhi, LI Yanting, REN Yuan, DAI Yong, QIAO Zengyong   +5 more
doaj   +1 more source

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source

A STUDY ON MACHINE LEARNING-BASED APPROACHES FOR EARLY DETECTION OF PARKINSON’S DISEASE

Tạp chí Khoa học
Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by the gradual loss of dopaminergic neurons in the brain, leading to both motor and non-motor symptoms.
Tran Thi Huong
doaj   +1 more source