Výsledky vyhľadávania - "Natural gradient boosting algorithm"

A comparative study of machine learning models for construction costs prediction with natural gradient boosting algorithm and SHAP analysis

Autori: Pobithra Das Abul Kashem Imrul Hasan a ďalší

Zdroj: Asian Journal of Civil Engineering. 25:3301-3316

NGBoost algorithm-based prediction of mechanical properties of a hot-rolled strip and its interpretability research with ANOVA values.

Autori: Wu, Hongyi Jin, Jinwen Li, Zhiwei

Zdroj: AIMS Mathematics; 2024, Vol. 9 Issue 11, p1-23, 23p

Predmety: STEEL strip, ROLLED steel, STRUCTURAL reliability, IMPACT (Mechanics), ENGINEERING design

Probabilistic Shear Strength Prediction for Deep Beams Based on Bayesian-Optimized Data-Driven Approach.

Autori: Liu, Mao-Yi Li, Zheng Zhang, Hang

Zdroj: Buildings (2075-5309); Oct2023, Vol. 13 Issue 10, p2471, 16p

Predmety: BOOSTING algorithms, MACHINE learning, CONCRETE beams, PROBABILITY density function, SHEAR strength, DATABASES, FORECASTING

Uncertainty quantification for Bayesian active learning in rupture life prediction of ferritic steels.

Autori: Mamun, Osman Taufique, M. F. N. Wenzlick, Madison a ďalší

Zdroj: Scientific Reports; 2/8/2022, Vol. 12 Issue 1, p1-9, 9p

Predmety: FERRITIC steel, ACTIVE learning, QUANTILE regression, BOOSTING algorithms, KRIGING, CHEMICAL processes, CREEP (Materials)

Probabilistic machine learning for enhanced chiller sequencing: A risk-based control strategy.

Autori: Chen, Zhe Zhang, Jing Xiao, Fu a ďalší

Zdroj: Energy & Built Environment; Oct2025, Vol. 6 Issue 5, p783-795, 13p

Predmety: ENERGY management, DEMAND forecasting, MACHINE learning, LOSS control, STABILITY of linear systems, STOCHASTIC learning models, MEASUREMENT uncertainty (Statistics)

Risk-Aware Optimal Dispatch of Resource Aggregators Integrating NGBoost-Based Probabilistic Renewable Forecasting and Bi-Level Building Flexibility Engagements

Autori: Hong Tang Zhe Chen Hangxin Li a ďalší

Zdroj: Engineering, Vol 53, Iss , Pp 76-89 (2025)

Predmety: Resource aggregators, Demand-side flexibility, NGBoost probabilistic forecasting, Interactive flexibility engagement, Chance-constrained programming, Engineering (General). Civil engineering (General), TA1-2040

Popis súboru: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2095809925001067; https://doaj.org/toc/2095-8099

Prístupová URL adresa: https://doaj.org/article/7dc1e52962924783b358449e0d67a31d

Predicting the Effects of Nano Additives and Elevated Temperatures on Concrete Compressive Strength Utilizing Machine Learning.

Autori: Dahish, Hany A. Alturki, Mansour

Zdroj: Buildings (2075-5309); Sep2025, Vol. 15 Issue 18, p3349, 25p

Predmety: COMPRESSIVE strength, MACHINE learning, TEMPERATURE effect, CONCRETE mixing, BOOSTING algorithms, RANDOM forest algorithms, NANOCOMPOSITE materials

NGBoost Classifier Using Deep Features for Pneumonia Chest X-Ray Classification.

Autori: Satish Chandra, Nagashree Raj, Shyla Mahanand, B. S.

Zdroj: Applied Sciences (2076-3417); Sep2025, Vol. 15 Issue 17, p9821, 14p

Predmety: PNEUMONIA, CHEST X rays, ARTIFICIAL neural networks, IMAGE recognition (Computer vision), MACHINE learning, CLASSIFICATION

Construction Quality Risk Assessment Models for Justifying Inherent Defects Insurance: Quantified Evidence from Big Data in Court Cases.

Autori: Liu, Tianhong Chong, Heap-Yih Wei, Xinyan a ďalší

Zdroj: Journal of Management in Engineering; Jul2025, Vol. 41 Issue 5, p1-12, 12p

Predmety: ARTIFICIAL neural networks, LEGAL judgments, KNOWLEDGE graphs, SUPPORT vector machines, LEGAL costs

From Empirical Measurements to AI Fusion—A Holistic Review of SOH Estimation Techniques for Lithium-Ion Batteries in Electric and Hybrid Vehicles.

Autori: Shan, Runzhe Wang, Yaxuan Guo, Shilong a ďalší

Zdroj: Energies (19961073); Jul2025, Vol. 18 Issue 13, p3542, 42p

Predmety: LITHIUM-ion batteries, ELECTRIC vehicles, HYBRID electric vehicles, ARTIFICIAL intelligence

Bankruptcy prediction using machine learning and Shapley additive explanations.

Autori: Nguyen, Hoang Hiep Viviani, Jean-Laurent Ben Jabeur, Sami

Zdroj: Review of Quantitative Finance & Accounting; Jul2025, Vol. 65 Issue 1, p107-148, 42p

Predmety: MACHINE learning, CREDIT risk management, ARTIFICIAL intelligence, MATHEMATICAL statistics, RANDOM forest algorithms

Predicting existing tunnel deformation from adjacent foundation pit construction using hybrid machine learning.

Autori: Wu, Xianguo Feng, Zongbao Liu, Jun a ďalší

Zdroj: Automation in Construction. Sep2024, Vol. 165, pN.PAG-N.PAG. 1p.

Predmety: *BUILDING foundations, *TUNNEL design & construction, *BOOSTING algorithms, *RANDOM forest algorithms, *NATURAL selection

Probabilistic machine learning-based phytoplankton abundance using hyperspectral remote sensing

Autori: Do Hyuck Kwon Jung Min Ahn Jong Cheol Pyo a ďalší

Zdroj: GIScience & Remote Sensing, Vol 62, Iss 1 (2025)

Predmety: Hyperspectral remote sensing, algal bloom, deep learning, probabilistic modeling, Mathematical geography. Cartography, GA1-1776, Environmental sciences, GE1-350

Popis súboru: electronic resource

Relation: https://doaj.org/toc/1548-1603; https://doaj.org/toc/1943-7226

Prístupová URL adresa: https://doaj.org/article/5375b58989a24805b39fad5e343f569c

Geospatial Analysis and Machine Learning Framework for Urban Heat Island Intensity Prediction: Natural Gradient Boosting and Deep Neural Network Regressors with Multisource Remote Sensing Data.

Autori: Hoang, Nhat-Duc Nguyen, Quoc-Lam

Zdroj: Sustainability (2071-1050); May2025, Vol. 17 Issue 10, p4287, 26p

Concrete compressive strength classification using hybrid machine learning models and interactive GUI.

Autori: Alsaadawi, Mostafa M. Elshaarawy, Mohamed Kamel Hamed, Abdelrahman Kamal

Zdroj: Innovative Infrastructure Solutions; 4/28/2025, Vol. 10 Issue 5, p1-29, 29p

Taming Data‐Driven Probability Distributions.

Autori: Baruník, Jozef Hanus, Luboš

Zdroj: Journal of Forecasting; Mar2025, Vol. 44 Issue 2, p676-691, 16p

Predmety: ECONOMIC forecasting, RATE of return on stocks, TIME series analysis, BUSINESS forecasting, MACHINE learning

Improved forecasting of the compressive strength of ultra‐high‐performance concrete (UHPC) via the CatBoost model optimized with different algorithms.

Autori: Katlav, Metin Ergen, Faruk

Zdroj: Structural Concrete; Feb2025, Vol. 26 Issue 1, p212-235, 24p

Predmety: OPTIMIZATION algorithms, PARTICLE swarm optimization, STANDARD deviations, GRAPHICAL user interfaces, COMPRESSIVE strength

Application of tuned random forests model on cement paste including fly ash and MgO expansive additive.

Autori: Liu, Dongxia

Zdroj: Multiscale & Multidisciplinary Modeling, Experiments & Design; Jan2025, Vol. 8 Issue 1, p1-18, 18p

NGBoost algorithm-based prediction of mechanical properties of a hot-rolled strip and its interpretability research with ANOVA values

Autori: Hongyi Wu Jinwen Jin Zhiwei Li

Zdroj: AIMS Mathematics, Vol 9, Iss 11, Pp 33000-33022 (2024)

Predmety: hot-rolled strip steel, mechanical properties, ngboost algorithm, anova value, Mathematics, QA1-939

Popis súboru: electronic resource

Relation: https://doaj.org/toc/2473-6988

Prístupová URL adresa: https://doaj.org/article/77afb86c37ab4234a934d65dd565355b

Trustworthy machine learning-enhanced 3D concrete printing: Predicting bond strength and designing reinforcement embedment length.

Autori: Ma, Xin-Rui Wang, Xian-Lin Chen, Shi-Zhi

Zdroj: Automation in Construction. Dec2024:Part A, Vol. 168, pN.PAG-N.PAG. 1p.

Predmety: *CONCRETE construction, *BOND strengths, *REINFORCED concrete, *STRUCTURAL engineering, *THREE-dimensional printing