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Dynamic response surface methodology using Lasso regression for organic pharmaceutical synthesis

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2,   Pages 221-236 doi: 10.1007/s11705-021-2061-y

Abstract: time-resolved data to model the dynamic behavior, dynamic response surface methodology (DRSM), a data-driven modelingTwo approaches can be adopted in the estimation of the model parameters: stepwise regression, used inseveral of previous publications, and Lasso regression, which is newly incorporated in this paper forTherefore, DRSM with Lasso regression can provide faster and more accurate data-driven models for a variety

Keywords： data-driven modeling     pharmaceutical organic synthesis     Lasso regression     dynamic response surface methodology    

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Modeling of unconfined compressive strength of soil-RAP blend stabilized with Portland cement using multivariateadaptive regression spline

Ali Reza GHANIZADEH, Morteza RAHROVAN

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 4,   Pages 787-799 doi: 10.1007/s11709-019-0516-8

Abstract: compressive strength (UCS) of soil-RAP blend stabilized with Portland cement based on multivariate adaptive regression

Keywords： soil-reclaimed asphalt pavement blend     Portland cement     unconfined compressive strength     multivariate adaptive regression    

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PyLUR: Efficient software for land use regression modeling the spatial distribution of air pollutants

Xuying Ma, Ian Longley, Jennifer Salmond, Jay Gao

Frontiers of Environmental Science & Engineering 2020, Volume 14, Issue 3, doi: 10.1007/s11783-020-1221-5

Abstract: GDAL/OGR libraries are used to do spatial analysis in the modeling and prediction.Land use regression (LUR) models have been widely used in air pollution modeling.This regression-based approach estimates the ambient pollutant concentrations at un-sampled points ofself-developed software comprises four modules: a potential predictor variable generation module, a regressionmodeling module, a model validation module, and a prediction and mapping module.

Keywords： LUR     Air pollution modelling     GIS spatial analysis     GDAL/OGR Python     Pollutant concentration mapping    

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Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regression

Tanvi SINGH, Mahesh PAL, V. K. ARORA

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 3,   Pages 674-685 doi: 10.1007/s11709-018-0505-3

Abstract: M5 model tree, random forest regression (RF) and neural network (NN) based modelling approaches wereResults suggest improved performance by RF regression for both pile groups.Model developed using RF regression approach with smooth pile group data was found to be in good agreement

Keywords： batter piles     oblique load test     neural network     M5 model tree     random forest regression     ANOVA    

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Modeling the methyldiethanolamine-piperazine scrubbing system for CO

Stefania Moioli,Laura A. Pellegrini

Frontiers of Chemical Science and Engineering 2016, Volume 10, Issue 1,   Pages 162-175 doi: 10.1007/s11705-016-1555-5

Abstract: ASPEN Plus has been used for thermodynamic modeling.

Keywords： vapor-liquid equilibrium     methyldietanolamine     piperazine     regression     Electrolyte-NRTL    

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Multiple regression models for energy consumption of office buildings in different climates in China

Siyu ZHOU, Neng ZHU

Frontiers in Energy 2013, Volume 7, Issue 1,   Pages 103-110 doi: 10.1007/s11708-012-0220-z

Abstract: Then on the basis of the simulated results, the multiple regression models were developed respectivelyAccording to the analysis of regression coefficients, the appropriate building envelope design schemesAt last, the regression model evaluations consisting of the simulation evaluations and the actual caseevaluations were performed to verify the feasibility and accuracy of the regression models.It is believed that the regression models developed in this paper can be used to estimate the energy

Keywords： regression model     energy consumption     building envelope     office building     different climates    

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Interaction behavior and load sharing pattern of piled raft using nonlinear regression and LM algorithm-based

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 5,   Pages 1181-1198 doi: 10.1007/s11709-021-0744-6

Abstract: The obtained results are then checked statistically with nonlinear multiple regression (NMR) and artificialneural network (ANN) modeling, and some prediction models are proposed.

Keywords： interaction     load sharing ratio     piled raft     nonlinear regression     artificial neural network    

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Multivariable regression model for Fox depth correction factor

Ravi Kant MITTAL, Sanket RAWAT, Piyush BANSAL

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 1,   Pages 103-109 doi: 10.1007/s11709-018-0474-6

Abstract: Therefore, this paper presents a non-linear regression model for the analysis of effect of embedment

Keywords： settlement     embedment     Fox depth correction factor     regression     multivariable    

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of driver-response relationships: identifying factors using a novel framework integrating quantile regression

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 6, doi: 10.1007/s11783-023-1676-2

Abstract:

● A novel framework integrating quantile regression with machine learning

Keywords： Driver-response     Upper boundary of relationship     Interpretable machine learning     Quantile regression    

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Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach

Pijush Samui, Jagan J

Frontiers of Structural and Civil Engineering 2013, Volume 7, Issue 2,   Pages 133-136 doi: 10.1007/s11709-013-0202-1

Abstract: This article examines the capability of Gaussian process regression (GPR) for prediction of effective

Keywords： unsaturated soil     effective stress parameter     Gaussian process regression (GPR)     artificial neural network    

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Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive

Faezehossadat KHADEMI,Mahmoud AKBARI,Sayed Mohammadmehdi JAMAL,Mehdi NIKOO

Frontiers of Structural and Civil Engineering 2017, Volume 11, Issue 1,   Pages 90-99 doi: 10.1007/s11709-016-0363-9

Abstract: In this paper, considering the experimental results, three different models of multiple linear regressionevaluate the compressive strength of concrete with different mix designs, however, multiple linear regression

Keywords： concrete     28 days compressive strength     multiple linear regression     artificial neural network     ANFIS     sensitivity    

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Prediction of cutting forces in machining of unidirectional glass fiber reinforced plastics composite

Surinder Kumar GILL, Meenu GUPTA, P. S. SATSANGI

Frontiers of Mechanical Engineering 2013, Volume 8, Issue 2,   Pages 187-200 doi: 10.1007/s11465-013-0262-x

Abstract: Based on statistical analysis, multiple regression model for cutting forces is derived with satisfactory

Keywords： reinforced plastics (UD-GFRP) composites     machining     cutting forces (tangential     feed and radial force)     ANOVA     regressionmodeling     carbide tool (K10)    

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SPT based determination of undrained shear strength: Regression models and machine learning

Walid Khalid MBARAK, Esma Nur CINICIOGLU, Ozer CINICIOGLU

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 1,   Pages 185-198 doi: 10.1007/s11709-019-0591-x

Abstract: With this study, along with the conventional methods of simple and multiple linear regression models,

Keywords： undrained shear strength     linear regression     random forest     gradient boosting     machine learning     standard    

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Presentation of regression analysis, GP and GMDH models to predict the pedestrian density in various

Iraj BARGEGOL; Seyed Mohsen HOSSEINIAN; Vahid NAJAFI MOGHADDAM GILANI; Mohammad NIKOOKAR; Alireza OROUEI

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 2,   Pages 250-265 doi: 10.1007/s11709-021-0785-x

Abstract: Regression analysis was then applied to determine the relationship between SMS, flow rate, andBy the use of regression analysis, the mathematical relationships between variables in all facilities

Keywords： pedestrian density     regression analysis     GP model     GMDH model    

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Identifying factors that influence soil heavy metals by using categorical regression analysis: A case

Jun Yang, Jingyun Wang, Pengwei Qiao, Yuanming Zheng, Junxing Yang, Tongbin Chen, Mei Lei, Xiaoming Wan, Xiaoyong Zhou

Frontiers of Environmental Science & Engineering 2020, Volume 14, Issue 3, doi: 10.1007/s11783-019-1216-2

Abstract: In this study, a categorical regression was used to identify the factors that influence soil heavy metalsinfluence of different factors on the soil heavy metal contents in Beijing was analyzed using a categorical regressionA categorical regression represents a suitable method for identifying the factors that influence soil

Keywords： Soil     Heavy metal     Influencing factor     Categorical regression     Identification method    

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Title Author Date Type Operation

Dynamic response surface methodology using Lasso regression for organic pharmaceutical synthesis

Journal Article

Modeling of unconfined compressive strength of soil-RAP blend stabilized with Portland cement using multivariateadaptive regression spline

Ali Reza GHANIZADEH, Morteza RAHROVAN

Journal Article

PyLUR: Efficient software for land use regression modeling the spatial distribution of air pollutants

Xuying Ma, Ian Longley, Jennifer Salmond, Jay Gao

Journal Article

Modeling oblique load carrying capacity of batter pile groups using neural network, random forest regression

Tanvi SINGH, Mahesh PAL, V. K. ARORA

Journal Article

Modeling the methyldiethanolamine-piperazine scrubbing system for CO

Stefania Moioli,Laura A. Pellegrini

Journal Article

Multiple regression models for energy consumption of office buildings in different climates in China

Siyu ZHOU, Neng ZHU

Journal Article

Interaction behavior and load sharing pattern of piled raft using nonlinear regression and LM algorithm-based

Journal Article

Multivariable regression model for Fox depth correction factor

Ravi Kant MITTAL, Sanket RAWAT, Piyush BANSAL

Journal Article

of driver-response relationships: identifying factors using a novel framework integrating quantile regression

Journal Article

Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach

Pijush Samui, Jagan J

Journal Article

Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive

Faezehossadat KHADEMI,Mahmoud AKBARI,Sayed Mohammadmehdi JAMAL,Mehdi NIKOO

Journal Article

Prediction of cutting forces in machining of unidirectional glass fiber reinforced plastics composite

Surinder Kumar GILL, Meenu GUPTA, P. S. SATSANGI

Journal Article

SPT based determination of undrained shear strength: Regression models and machine learning

Walid Khalid MBARAK, Esma Nur CINICIOGLU, Ozer CINICIOGLU

Journal Article

Presentation of regression analysis, GP and GMDH models to predict the pedestrian density in various

Iraj BARGEGOL; Seyed Mohsen HOSSEINIAN; Vahid NAJAFI MOGHADDAM GILANI; Mohammad NIKOOKAR; Alireza OROUEI

Journal Article

Identifying factors that influence soil heavy metals by using categorical regression analysis: A case

Jun Yang, Jingyun Wang, Pengwei Qiao, Yuanming Zheng, Junxing Yang, Tongbin Chen, Mei Lei, Xiaoming Wan, Xiaoyong Zhou

Journal Article