Results 11 to 20 of about 4,489 (189)
Intelligence modeling of solubility of raloxifene and density of solvent for green supercritical processing of medicines for enhanced solubility [PDF]
Scientific ReportsIn this study, a dataset for solubility of raloxifene and CO2 density was analyzed using different regression models to reveal the correlation between inputs and drug solubility via supercritical processing.
Hashem O. Alsaab, Yusuf S. Althobaiti
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Gaussian Process Regression Model for Damage Localization in Plates Based on Modal Data [PDF]
Journal of Rehabilitation in Civil Engineering, 2022 The applications of plate like structures in different fields of engineering are increasing. In this paper, a new damage detection method investigated based on Gaussian process regression model (GPR).
Seyed Sina Kourehli
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Application of Gaussian Process Regression (GPR) in Gas Hydrate Mitigation
Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, 2021 The production of oil and natural gas contributes to a significant amount of revenue generation in Malaysia thereby strengthening the country’s economy. The flow assurance industry is faced with impediments during smooth operation of the transmission pipeline in which gas hydrate formation is the most important.
null Sachin Dev Suresh +4 more
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Yantu gongcheng xuebao, 2023 In order to establish an optimal model for estimating the uniaxial compressive strength (UCS) of rocks as well as its reasonable estimation, a fully Bayesian Gaussian process regression method (fB-GPR) is proposed by combining the Gaussian process ...
SONG Chao , ZHAO Tengyuan , XU Ling
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Soft sensor based on Gaussian process regression and its application in erythromycin fermentation process [PDF]
Chemical Industry and Chemical Engineering Quarterly, 2016 Erythromycin fermentation process is a typical microbial fermentation process. Soft sensors can be used to estimate biomass of Erythromycin fermentation process for their relative low cost, simple development, and ability to predict difficult-to-
Mei Congli +5 more
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Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression
Engineering Applications of Computational Fluid Mechanics, 2021 Utilizing new approaches to accurately predict groundwater level (GWL) in arid regions is of vital importance. In this study, support vector regression (SVR), Gaussian process regression (GPR), and their combination with wavelet transformation (named ...
Shahab S. Band +7 more
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Modeling of Cutting Force in the Turning of AISI 4340 Using Gaussian Process Regression Algorithm
Applied Sciences, 2021 Machining process data can be utilized to predict cutting force and optimize process parameters. Cutting force is an essential parameter that has a significant impact on the metal turning process.
Mahdi S. Alajmi, Abdullah M. Almeshal
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Algorithms, 2021 Probabilistic solar power forecasting has been critical in Southern Africa because of major shortages of power due to climatic changes and other factors over the past decade. This paper discusses Gaussian process regression (GPR) coupled with core vector
Edina Chandiwana +2 more
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IEEE Access, 2019 Gaussian process regression (GPR) is frequently used for uncertain measurement and prediction of nonstationary time series in the Internet of Things data, nevertheless, the generalization and regression efficacy of GPR are directly impacted by its ...
Lanlan Kang +5 more
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Performance evaluation of friction stir welding using machine learning approaches
MethodsX, 2018 The aim of the present study is to evaluate the potential of sophisticated machine learning methodologies, i.e. Gaussian process (GPR) regression, support vector machining (SVM), and multi-linear regression (MLR) for ultimate tensile strength (UTS) of ...
Shubham Verma +2 more
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