Log in

Relevant bibliographies by topics / Multilinear regression (MLR)

Contents

Journal articles

Academic literature on the topic 'Multilinear regression (MLR)'

Author: Grafiati

Published: 4 June 2021

Last updated: 3 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Multilinear regression (MLR).'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Multilinear regression (MLR)"

1

Kilo, Jafar La, and Akram La Kilo. "Kajian HKSA Antimalaria Senyawa Turunan Quinolon-4(1H)-imines Menggunakan Metode MLR-ANN." Jambura Journal of Chemistry 1, no. 1 (2019): 21–26. http://dx.doi.org/10.34312/jambchem.v1i1.2104.

Full text

Abstract:

Quantitatif Structure-Activity Relationship (QSAR) study of 22 antimalarial compounds of Quinolon-4(1H)-imines derivatives has been done using multilinear regression (MLR) and artificial neural network (ANN) methods. The best QSAR model was obtained from ANN analysis indicated by its higher correlation coefficient (r2) compared to MLR method, i.e. 0.931 with most influential descriptors is qC1, qC5, qC11, qN14 and log P.Keywords: Quinolon-4(1H)-imines, Antimalarial, QSAR, MLR-ANNTelah dilakukan kajian analisis Hubungan Kuantitatif Struktur Aktivitas (HKSA) terhadap 22 senyawa antimalaria turun

APA, Harvard, Vancouver, ISO, and other styles

2

Chen, Wei-Bo, and Wen-Cheng Liu. "Water Quality Modeling in Reservoirs Using Multivariate Linear Regression and Two Neural Network Models." Advances in Artificial Neural Systems 2015 (June 9, 2015): 1–12. http://dx.doi.org/10.1155/2015/521721.

Full text

Abstract:

In this study, two artificial neural network models (i.e., a radial basis function neural network, RBFN, and an adaptive neurofuzzy inference system approach, ANFIS) and a multilinear regression (MLR) model were developed to simulate the DO, TP, Chl a, and SD in the Mingder Reservoir of central Taiwan. The input variables of the neural network and the MLR models were determined using linear regression. The performances were evaluated using the RBFN, ANFIS, and MLR models based on statistical errors, including the mean absolute error, the root mean square error, and the correlation coefficient,

APA, Harvard, Vancouver, ISO, and other styles

3

Lin, Jie, and Chris W. Brown. "Near-IR Fiber-Optic Temperature Sensor." Applied Spectroscopy 47, no. 1 (1993): 62–68. http://dx.doi.org/10.1366/0003702934048424.

Full text

Abstract:

A fiber-optic temperature sensor based on the perturbations of near-IR water bands has been developed. These fiber-optic sensors are very simple and readily fabricated. Models for expressing temperature can be developed by linear regression (LR) of the absorbance at one selected wavenumber, by multilinear regression (MLR) of the absorbances at several selected wavenumbers, or by principal component regression (PCR) using entire spectra. The standard errors of prediction for temperature are 0.53 to 1.64°C for the LR model, 0.22 to 0.85°C for the MLR model, and 0.16 to 0.32°C for the PCR model o

APA, Harvard, Vancouver, ISO, and other styles

4

de la Maza, Gabriel, Nicole Williams, Esteban Sáez, Kyle Rollins, and Christian Ledezma. "Liquefaction-Induced Lateral Spread in Lo Rojas, Coronel, Chile: Field Study and Numerical Modeling." Earthquake Spectra 33, no. 1 (2017): 219–40. http://dx.doi.org/10.1193/012015eqs012m.

Full text

Abstract:

This paper describes a detailed field survey conducted at Lo Rojas fishermen port in Coronel, where extensive liquefaction-induced lateral spread was reported for the 2010, Mw 8.8 Maule earthquake. The survey includes SPT and SCPT soundings, as well as the use of surface-based geophysical techniques. The data was used to evaluate a multilinear regression (MLR) lateral-spread expression and to develop a detailed hydro-mechanical finite element model. Results of the MLR equation were over-conservative and proved to be very sensitive to the distance from the site to the energy source. On the othe

APA, Harvard, Vancouver, ISO, and other styles

5

Bui, Moayedi, Gör, Jaafari, and Foong. "Predicting Slope Stability Failure through Machine Learning Paradigms." ISPRS International Journal of Geo-Information 8, no. 9 (2019): 395. http://dx.doi.org/10.3390/ijgi8090395.

Full text

Abstract:

In this study, we employed various machine learning-based techniques in predicting factor of safety against slope failures. Different regression methods namely, multi-layer perceptron (MLP), Gaussian process regression (GPR), multiple linear regression (MLR), simple linear regression (SLR), support vector regression (SVR) were used. Traditional methods of slope analysis (e.g., first established in the first half of the twentieth century) used widely as engineering design tools. Offering more progressive design tools, such as machine learning-based predictive algorithms, they draw the attention

APA, Harvard, Vancouver, ISO, and other styles

6

Roberts, Keith J., Brian A. Colle, Nickitas Georgas, and Stephan B. Munch. "A Regression-Based Approach for Cool-Season Storm Surge Predictions along the New York–New Jersey Coast." Journal of Applied Meteorology and Climatology 54, no. 8 (2015): 1773–91. http://dx.doi.org/10.1175/jamc-d-14-0314.1.

Full text

Abstract:

AbstractA multilinear regression (MLR) approach is developed to predict 3-hourly storm surge during the cool-season months (1 October–31 March 31) between 1979 and 2012 using two different atmospheric reanalysis datasets and water-level observations at three stations along the New York–New Jersey coast (Atlantic City, New Jersey; the Battery in New York City; and Montauk Point, New York). The predictors of the MLR are specified to represent prolonged surface wind stress and a surface sea level pressure minimum for a boxed region near each station. The regression underpredicts relatively large

APA, Harvard, Vancouver, ISO, and other styles

7

Bueso-Bordils, Jose I., Pedro A. Aleman-López, Sara Costa-Piles, et al. "Obtaining Microbiological and Pharmacokinetic Highly Predictive Equations." Current Topics in Medicinal Chemistry 18, no. 11 (2018): 908–16. http://dx.doi.org/10.2174/1568026618666180712092326.

Full text

Abstract:

In this paper, a Multilinear Regression (MLR) analysis has been carried out in order to accurately predict physicochemical properties and biological activities of a group of antibacterial quinolones by means of a set of structural descriptors called topological indices. The aim of this work is to develop prediction equations for these properties after collecting the maximum number of data from the literature on antibacterial quinolones. The five regression functions selected by presenting the best combination of various statistical parameters, subsequently validated by means of internal valida

APA, Harvard, Vancouver, ISO, and other styles

8

Katritzky, Alan R., Yueying Ren, Svetoslav H. Slavov, and Mati Karelson. "A comparative QSAR study of SVM and PPR in the correlation of lithium cation basicities." Collection of Czechoslovak Chemical Communications 74, no. 1 (2009): 217–41. http://dx.doi.org/10.1135/cccc2008191.

Full text

Abstract:

Correlation of gas-phase lithium cation basicities (LCB) of 259 diverse compounds extends the published datasets utilizing multilinear, support vector machine (SVM) and projection pursuit regression (PPR) modeling. The best multiple linear regression (BMLR) method implemented in CODESSA was used to: (i) build multiparameter linear QSPR models and (ii) select set of descriptors for further treatment by the SVM and PPR. The external predictivity and the performance of each of the above methods was estimated and compared to those of the other techniques. The PPR method produced results superior t

APA, Harvard, Vancouver, ISO, and other styles

9

Gupta, Ishank, Deepak Devegowda, Vikram Jayaram, Chandra Rai, and Carl Sondergeld. "Machine learning regressors and their metrics to predict synthetic sonic and mechanical properties." Interpretation 7, no. 3 (2019): SF41—SF55. http://dx.doi.org/10.1190/int-2018-0255.1.

Full text

Abstract:

Planning and optimizing completion design for hydraulic fracturing require a quantifiable understanding of the spatial distribution of the brittleness of the rock and other geomechanical properties. Eventually, the goal is to maximize the stimulated reservoir volume with minimal cost overhead. The compressional and shear velocities ([Formula: see text] and [Formula: see text], respectively) can also be used to calculate Young’s modulus, Poisson’s ratio, and other mechanical properties. In the field, sonic logs are not commonly acquired and operators often resort to regression to predict synthe

APA, Harvard, Vancouver, ISO, and other styles

10

Lestari, Evie Kama, Agus Dwi Ananto, Maulida Septiyana, and Saprizal Hadisaputra. "QSAR treatment of meisoindigo derivatives as a potentbreast anticancer agent." Acta Chimica Asiana 2, no. 2 (2019): 114. http://dx.doi.org/10.29303/aca.v2i2.12.

Full text

Abstract:

A quantitative structure-activity relationship (QSAR) analysis of meisoindigo derivatives as a breast anticancer has been carried out. This study aimed to obtain the best QSAR model in order to design new meisoindigo based compounds with best anticancer activity. The semiempirical PM3 method was used for descriptor calculation. The best QSAR model was built using multilinear regression (MLR) with enter method. It was found that there were 19 new meisoindigo derivativeswith better predictive a potent anticancer agent. The best compound was (E)-2-(1-((3-ethylisoxazol-5-yl)methyl)-2-oxoindolin-3-

APA, Harvard, Vancouver, ISO, and other styles

More sources

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!