Results 61 to 70 of about 447 (169)

tkzeng/ngboost: NGBoost for GeneBayes v1.0

<p>NGBoost modified for GeneBayes v1.0</p ...
mlko53, Eric Ma, Giulia A.D. Savorgnan, Tommy, Zain Patel, Christopher Smith, Anand Avati, Tony Zeng, Daiki Katsuragawa, Angel Droth, Lucca Zenóbio, cd, Carlos Aguilar, Jose Manuel Llorens, kenmatsu4, Zhuyi Xue, Alejandro Schuler, Zhirui (Jerry) Wang, Ross Johnstone, Sams, Joshua Adelman, Blake Atkinson, YutaYamazaki, yu9824, dguzelkokar, Daisy Ding, Sai Anil, Tony Duan, Ryan Wolbeck   +28 more
core   +1 more source

Parameter Estimation in Land Surface Models: Challenges and Opportunities With Data Assimilation and Machine Learning

Journal of Advances in Modeling Earth Systems, Volume 17, Issue 11, November 2025.
Abstract Accurately predicting terrestrial ecosystem responses to climate change over long‐timescales is crucial for addressing global challenges. This relies on mechanistic modeling of ecosystem processes through land surface models (LSMs). Despite their importance, LSMs face significant uncertainties due to poorly constrained parameters, especially ...
Nina Raoult, Natalie Douglas, Natasha MacBean, Jana Kolassa, Tristan Quaife, Andrew G. Roberts, Rosie Fisher, Istem Fer, Cédric Bacour, Katherine Dagon, Linnia Hawkins, Nuno Carvalhais, Elizabeth Cooper, Michael C. Dietze, Pierre Gentine, Thomas Kaminski, Daniel Kennedy, Hannah M. Liddy, David J. P. Moore, Philippe Peylin, Ewan Pinnington, Benjamin Sanderson, Marko Scholze, Christian Seiler, T. Luke Smallman, Noemi Vergopolan, Toni Viskari, Mathew Williams, John Zobitz   +28 more
wiley   +1 more source

Revealing Brain Tumor Using Cross-Validated NGBoost Classifier: NG Boost Classifier

, 2020
Brain is the most complicated and delicate anatomical structure in human body. Statistics proves that, among various brain ailments, brain tumor is most fatal and in many cases they become carcinogenic.
Bandyopadhyay, Samir, Dutta, Shawni
core   +1 more source

Applying explainable artificial intelligence and ensemble boosting based machine learning models in predicting the thermal conductivity of nano enhanced phase change materials

Next Materials
This study attempts the integration of explainable artificial intelligence and ensemble boosting based machine learning framework (XAI-EBBML-F) in predicting the thermal conductivity of nano enhanced phase change materials (NePCMs).
G. Manikandan, S. Senthil Kumar, M. Sridharan   +2 more
doaj   +1 more source

Predictive Approaches for 3D‐Printing: Methods and Approaches for Polymeric Materials

WIREs Computational Molecular Science, Volume 15, Issue 5, September/October 2025.
3D printing shows great promise, but suffers from a restricted range of materials. We show how computational methods, from physics‐based to data‐driven methods, can streamline the development of new polymer materials. Developing integrated approaches offers future opportunities in autonomous discovery and design, harnessing 3D printing's full potential.
Isabel Cooley, Weiling Wang, Vladimir Kozyrev, Ricky D. Wildman, Blair F. Johnston, Anna K. Croft   +5 more
wiley   +1 more source

Analysis of the Utilization of Machine Learning to Map Flood Susceptibility

Journal of Flood Risk Management, Volume 18, Issue 2, June 2025.
ABSTRACT This article provides an analysis of the utilization of Machine Learning (ML) models in Flood Susceptibility Mapping (FSM), based on selected publications from the past decade (2013–2023). Recognizing the challenge that some stages of ML modeling inherently rely on experience or trial‐and‐error approaches, this work aims at establishing a ...
Ali Pourzangbar, Peter Oberle, Andreas Kron, Mário J. Franca   +3 more
wiley   +1 more source

Explainable Machine Learning for Efficient Diabetes Prediction Using Hyperparameter Tuning, SHAP Analysis, Partial Dependency, and LIME

Engineering Reports, Volume 7, Issue 1, January 2025.
The significance of this study is centered around the creation of a precise and resilient diabetes classification framework that is inherently transparent. The results of the framework perform well consistently and it suggests that the framework may be more likely to generalize effectively to new, unseen data.
Md. Manowarul Islam, Habibur Rahman Rifat, Md. Shamim Bin Shahid, Arnisha Akhter, Md Ashraf Uddin, Khandaker Mohammad Mohi Uddin   +5 more
wiley   +1 more source

Friction torque prediction of tapered roller bearings under different assembly conditions based on XGBoost and SHAP

Advances in Mechanical Engineering
Friction torque serves as a critical metric for assessing the dynamic performance of tapered roller bearings (TRBs), influenced by factors such as input speed, temperature, preload force, radial load, and fit tolerance.
Guangjun Feng, Yunqing Zhang, Jinbao Zhang   +2 more
doaj   +1 more source

Uncertainty-aware estimation of compressive strength for fly ash concrete with enhanced probabilistic prediction performance by incorporating the associated mortar strength

Scientific Reports
Machine learning (ML) has been widely utilized to estimate the compressive strength (f c) of fly ash (FA) concrete due to its high efficiency and accuracy.
Ping Chen, Cheng Li, Ao Li, Yonghao Mo, Zhaoyu Huang, Miaomiao Xue, Guangyue Meng, Y. Sun   +7 more
doaj   +1 more source

Nano-Material Printing Ensuring Uniform Filament Quality in MXene Structures

Журнал нано- та електронної фізики
The developments of nano-material enable precise MXene fabrication, though pneumatic 3D printing still struggles with filament uniformity due to sensitivity in pressure, ink concentration, nozzle diameter and velocity. Traditional trial-and-error methods
Chaitanya P. Kale, P. William, Mohammed Almakki, Pravin B. Khatkale, Prashant M. Yawalkar, Ritesh Ravindra Agwan, Prashant V. Thokal   +6 more
doaj   +1 more source