Conecte-se

Bibliografias temáticas / Multilinear regression (MLR)

Índice

Artigos de revistas

Literatura científica selecionada sobre o tema "Multilinear regression (MLR)"

Autor: Grafiati

Publicado: 4 de junho de 2021

Última modificação: 3 de fevereiro de 2022

Crie uma referência precisa em APA, MLA, Chicago, Harvard, e outros estilos

Selecione um tipo de fonte:

Consulte a lista de atuais artigos, livros, teses, anais de congressos e outras fontes científicas relevantes para o tema "Multilinear regression (MLR)".

Ao lado de cada fonte na lista de referências, há um botão "Adicionar à bibliografia". Clique e geraremos automaticamente a citação bibliográfica do trabalho escolhido no estilo de citação de que você precisa: APA, MLA, Harvard, Chicago, Vancouver, etc.

Você também pode baixar o texto completo da publicação científica em formato .pdf e ler o resumo do trabalho online se estiver presente nos metadados.

Artigos de revistas sobre o assunto "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.

Texto completo da fonte

Resumo:

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

Estilos ABNT, Harvard, Vancouver, APA, etc.

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.

Texto completo da fonte

Resumo:

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,

Estilos ABNT, Harvard, Vancouver, APA, etc.

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.

Texto completo da fonte

Resumo:

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

Estilos ABNT, Harvard, Vancouver, APA, etc.

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.

Texto completo da fonte

Resumo:

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

Estilos ABNT, Harvard, Vancouver, APA, etc.

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.

Texto completo da fonte

Resumo:

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

Estilos ABNT, Harvard, Vancouver, APA, etc.

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.

Texto completo da fonte

Resumo:

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

Estilos ABNT, Harvard, Vancouver, APA, etc.

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.

Texto completo da fonte

Resumo:

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

Estilos ABNT, Harvard, Vancouver, APA, etc.

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.

Texto completo da fonte

Resumo:

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

Estilos ABNT, Harvard, Vancouver, APA, etc.

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.

Texto completo da fonte

Resumo:

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

Estilos ABNT, Harvard, Vancouver, APA, etc.

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.

Texto completo da fonte

Resumo:

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-

Estilos ABNT, Harvard, Vancouver, APA, etc.

Mais fontes

Oferecemos descontos em todos os planos premium para autores cujas obras estão incluídas em seleções literárias temáticas. Contate-nos para obter um código promocional único!