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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: Two 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 forHere, we show that both approaches yield similarly accurate models, while the computational time of LassoTherefore, 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    

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    

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 artificial

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

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    

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    

compressive strength of soil-RAP blend stabilized with Portland cement using multivariate adaptive regression

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    

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    

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    

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    

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    

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    

presence of different random noises and uncertainty: Implementation of generalized Gaussian process regression

Nasser L. AZAD,Ahmad MOZAFFARI

Frontiers of Mechanical Engineering 2015, Volume 10, Issue 4,   Pages 405-412 doi: 10.1007/s11465-015-0354-x

Abstract: the modeling and analysis of automotive engines’ behavior during coldstart operations by using regressionThis encourages automotive engineers to take advantage of knowledge-based modeling tools and regressionThen, by using a Gaussian process regression machine (GPRM), a reliable model is used for the sake of

Keywords: automotive engine     calibration     coldstart operation     Gaussian process regression machine (GPRM)     uncertainty    

Expanding the Scope of Multivariate Regression Approaches in Cross-Omics Research Article

Xiaoxi Hu, Yue Ma, Yakun Xu, Peiyao Zhao, Jun Wang

Engineering 2021, Volume 7, Issue 12,   Pages 1725-1731 doi: 10.1016/j.eng.2020.05.028

Abstract: amount of high-dimensional data and thus have increased the demand for proper and efficient multivariate regressionTherefore, in this article, we introduce the reduced rank regression method and its extensions, sparsereduced rank regression and subspace assisted regression with row sparsity, which hold potential tomeet the above demands and thus improve the interpretability of regression models.extensions provide valuable guidelines for future omics research, especially with respect to multivariate regression

Keywords: Multivariate regression methods     Reduced rank regression     Sparsity     Dimensionality reduction     Variable    

Interactive image segmentation with a regression based ensemble learning paradigm Article

Jin ZHANG, Zhao-hui TANG, Wei-hua GUI, Qing CHEN, Jin-ping LIU

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 7,   Pages 1002-1020 doi: 10.1631/FITEE.1601401

Abstract: This paper presents a novel interactive image segmentation method via a regression-based ensemble modelFirst, two spline regressors with a complementary nature are constructed based on multivariate adaptive regressionsplines (MARS) and smooth thin plate spline regression (TPSR).Next, a support vector regression (SVR) based decision fusion model is adopted to integrate the results

Keywords: Interactive image segmentation     Multivariate adaptive regression splines (MARS)     Ensemble learning     Thin-platespline regression (TPSR)     Semi-supervised learning     Support vector regression (SVR)    

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    

Title Author Date Type Operation

Dynamic response surface methodology using Lasso regression for organic pharmaceutical synthesis

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

compressive strength of soil-RAP blend stabilized with Portland cement using multivariate adaptive regression

Ali Reza GHANIZADEH, Morteza RAHROVAN

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

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

presence of different random noises and uncertainty: Implementation of generalized Gaussian process regression

Nasser L. AZAD,Ahmad MOZAFFARI

Journal Article

Expanding the Scope of Multivariate Regression Approaches in Cross-Omics Research

Xiaoxi Hu, Yue Ma, Yakun Xu, Peiyao Zhao, Jun Wang

Journal Article

Interactive image segmentation with a regression based ensemble learning paradigm

Jin ZHANG, Zhao-hui TANG, Wei-hua GUI, Qing CHEN, Jin-ping LIU

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