• Title, Summary, Keyword: MLR

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A DFT and QSAR Study of Several Sulfonamide Derivatives in Gas and Solvent

  • Abadi, Robabeh Sayyadi kord;Alizadehdakhel, Asghar;Paskiabei, Soghra Tajadodi
    • Journal of the Korean Chemical Society
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    • v.60 no.4
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    • pp.225-234
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    • 2016
  • The activity of 34 sulfonamide derivatives has been estimated by means of multiple linear regression (MLR), artificial neural network (ANN), simulated annealing (SA) and genetic algorithm (GA) techniques. These models were also utilized to select the most efficient subsets of descriptors in a cross-validation procedure for non-linear -log (IC50) prediction. The results obtained using GA-ANN were compared with MLR-MLR, MLR-ANN, SA-ANN and GA-ANN approaches. A high predictive ability was observed for the MLR-MLR, MLR-ANN, SA-ANN and MLR-GA models, with root mean sum square errors (RMSE) of 0.3958, 0.1006, 0.0359, 0.0326 and 0.0282 in gas phase and 0.2871, 0.0475, 0.0268, 0.0376 and 0.0097 in solvent, respectively (N=34). The results obtained using the GA-ANN method indicated that the activity of derivatives of sulfonamides depends on different parameters including DP03, BID, AAC, RDF035v, JGI9, TIE, R7e+, BELM6 descriptors in gas phase and Mor 32u, ESpm03d, RDF070v, ATS8m, MATS2e and R4p, L1u and R3m in solvent. In conclusion, the comparison of the quality of the ANN with different MLR models showed that ANN has a better predictive ability.

SELECTION OF WAELENGTH REGION FOR PLS BRIX CALIBRATION OF MANGO BY MLR METHOD

  • Sarawong, Sirinnapa;Sornsrivichai, Jinda;Kawano, Sumio
    • Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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    • pp.1625-1625
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    • 2001
  • The calibration equations for Brix value determination of intact mango were developed using the NIR spectra in a short wavelength region from 700 to 1100 nm. Multiple linear regression (MLR) and partial least square regression (PLS) was used for the calibration. It was found that the best wavelength region for PLS calibration from 900 to 1000 nm was similar to the wavelength region selected by MLR from 906 nm to 996 nm. Both MLR and selected region PLS provided sufficiently accurate prediction equations for Brix determination of intact mango. For MLR, the prediction results were SEP = 0.45 Brix and Bias = -0.04 Brix while PLS prediction results were SEP : 0.46 Brix and Bias = -0.2 Brix. It was concluded that MLR and PLS would have similar abilities in making calibration equation for Brix determination of intact mango if the appropriate wavelengths or wavelength region were selected. The appropriate wavelength region for PLS regression could be assumed by using the wavelength region selected by MLR in place of random selection, The relationship between calibration results of MLR and PLS regression is discussed.

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Multivariate Statistical Analysis and Prediction for the Flash Points of Binary Systems Using Physical Properties of Pure Substances (순수 성분의 물성 자료를 이용한 2성분계 혼합물의 인화점에 대한 다변량 통계 분석 및 예측)

  • Lee, Bom-Sock;Kim, Sung-Young
    • Journal of the Korean Institute of Gas
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    • v.11 no.3
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    • pp.13-18
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    • 2007
  • The multivariate statistical analysis, using the multiple linear regression(MLR), have been applied to analyze and predict the flash points of binary systems. Prediction for the flash points of flammable substances is important for the examination of the fire and explosion hazards in the chemical process design. In this paper, the flash points are predicted by MLR based on the physical properties of pure substances and the experimental flash points data. The results of regression and prediction by MLR are compared with the values calculated by Raoult's law and Van Laar equation.

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MLR-tree : Spatial Indexing Method for Window Query of Multi-Level Geographic Data (MLR 트리 : 다중 레벨 지리정보 데이터의 윈도우 질의를 위한 공간 인덱싱 기법)

  • 권준희;윤용익
    • Journal of KIISE:Databases
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    • v.30 no.5
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    • pp.521-531
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    • 2003
  • Multi-level geographic data can be mainpulated by a window query such as a zoom operation. In order to handle multi-level geographic data efficiently, a spatial indexing method supporting a window query is needed. However, the conventional spatial indexing methods are not efficient to access multi-level geographic data quickly. To solve it, other a few spatial indexing methods for multi-level geographic data are known. However these methods do not support all types of multi-level geographic data. This paper presents a new efficient spatial indexing method, the MLR-tree for window query of multi-level geographic data. The MLR-tree offers both high search performance and no data redundancy. Experiments show them. Moreover, the MLR-tree supports all types of multi-level geographic data.

Prediction of Acute Toxicity to Fathead Minnow by Local Model Based QSAR and Global QSAR Approaches

  • In, Young-Yong;Lee, Sung-Kwang;Kim, Pil-Je;No, Kyoung-Tai
    • Bulletin of the Korean Chemical Society
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    • v.33 no.2
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    • pp.613-619
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    • 2012
  • We applied several machine learning methods for developing QSAR models for prediction of acute toxicity to fathead minnow. The multiple linear regression (MLR) and artificial neural network (ANN) method were applied to predict 96 h $LC_{50}$ (median lethal concentration) of 555 chemical compounds. Molecular descriptors based on 2D chemical structure were calculated by PreADMET program. The recursive partitioning (RP) model was used for grouping of mode of actions as reactive or narcosis, followed by MLR method of chemicals within the same mode of action. The MLR, ANN, and two RP-MLR models possessed correlation coefficients ($R^2$) as 0.553, 0.618, 0.632, and 0.605 on test set, respectively. The consensus model of ANN and two RP-MLR models was used as the best model on training set and showed good predictivity ($R^2$=0.663) on the test set.

Strength prediction of rotary brace damper using MLR and MARS

  • Mansouri, I.;Safa, M.;Ibrahim, Z.;Kisi, O.;Tahir, M.M.;Baharom, S.;Azimi, M.
    • Structural Engineering and Mechanics
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    • v.60 no.3
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    • pp.471-488
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    • 2016
  • This study predicts the strength of rotary brace damper by analyzing a new set of probabilistic models using the usual method of multiple linear regressions (MLR) and advanced machine-learning methods of multivariate adaptive regression splines (MARS), Rotary brace damper can be easily assembled with high energy-dissipation capability. To investigate the behavior of this damper in structures, a steel frame is modeled with this device subjected to monotonic and cyclic loading. Several response parameters are considered, and the performance of damper in reducing each response is evaluated. MLR and MARS methods were used to predict the strength of this damper. Displacement was determined to be the most effective parameter of damper strength, whereas the thickness did not exhibit any effect. Adding thickness parameter as inputs to MARS and MLR models did not increase the accuracies of the models in predicting the strength of this damper. The MARS model with a root mean square error (RMSE) of 0.127 and mean absolute error (MAE) of 0.090 performed better than the MLR model with an RMSE of 0.221 and MAE of 0.181.

Determination of Research Octane Number using NIR Spectral Data and Ridge Regression

  • Jeong, Ho Il;Lee, Hye Seon;Jeon, Ji Hyeok
    • Bulletin of the Korean Chemical Society
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    • v.22 no.1
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    • pp.37-42
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    • 2001
  • Ridge regression is compared with multiple linear regression (MLR) for determination of Research Octane Number (RON) when the baseline and signal-to-noise ratio are varied. MLR analysis of near-infrared (NIR) spectroscopic data usually encounters a collinearity problem, which adversely affects long-term prediction performance. The collinearity problem can be eliminated or greatly improved by using ridge regression, which is a biased estimation method. To evaluate the robustness of each calibration, the calibration models developed by both calibration methods were used to predict RONs of gasoline spectra in which the baseline and signal-to-noise ratio were varied. The prediction results of a ridge calibration model showed more stable prediction performance as compared to that of MLR, especially when the spectral baselines were varied. . In conclusion, ridge regression is shown to be a viable method for calibration of RON with the NIR data when only a few wavelengths are available such as hand-carry device using a few diodes.

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A Correction of East Asian Summer Precipitation Simulated by PNU/CME CGCM Using Multiple Linear Regression (다중 선형 회귀를 이용한 PNU/CME CGCM의 동아시아 여름철 강수예측 보정 연구)

  • Hwang, Yoon-Jeong;Ahn, Joong-Bae
    • Journal of the Korean earth science society
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    • v.28 no.2
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    • pp.214-226
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    • 2007
  • Because precipitation is influenced by various atmospheric variables, it is highly nonlinear. Although precipitation predicted by a dynamic model can be corrected by using a nonlinear Artificial Neural Network, this approach has limits such as choices of the initial weight, local minima and the number of neurons, etc. In the present paper, we correct simulated precipitation by using a multiple linear regression (MLR) method, which is simple and widely used. First of all, Ensemble hindcast is conducted by the PNU/CME Coupled General Circulation Model (CGCM) (Park and Ahn, 2004) for the period from April to August in 1979-2005. MLR is applied to precipitation simulated by PNU/CME CGCM for the months of June (lead 2), July (lead 3), August (lead 4) and seasonal mean JJA (from June to August) of the Northeast Asian region including the Korean Peninsula $(110^{\circ}-145^{\circ}E,\;25-55^{\circ}N)$. We build the MLR model using a linear relationship between observed precipitation and the hindcasted results from the PNU/CME CGCM. The predictor variables selected from CGCM are precipitation, 500 hPa vertical velocity, 200 hPa divergence, surface air temperature and others. After performing a leave-oneout cross validation, the results are compared with the PNU/CME CGCM's. The results including Heidke skill scores demonstrate that the MLR corrected results have better forecasts than the direct CGCM result for rainfall.

Applicability evaluation of aerodynamic approaches for evaporation estimation using pan evaporation data (증발접시 증발량자료를 이용한 공기동력학적 증발량 산정 방법의 적용성 평가)

  • Rim, Chang-Soo
    • Journal of Korea Water Resources Association
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    • v.50 no.11
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    • pp.781-793
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    • 2017
  • In this study, applicabilities of aerodynamic approaches for the estimation of pan evaporation were evaluated on 56 study stations in South Korea. To accomplish this study purpose, previous researchers' evaporation estimation equations based on aerodynamic approaches were grouped into seven generalized evaporation models. Furthermore, four multiple linear regression (MLR) models were developed and tested. The independent variables of MLR models are meteorological variables such as wind speed, vapor pressure deficit, air temperature, and atmospheric pressure. These meteorological variables are required for the application of aerodynamic approaches. In order to consider the effect of autocorrelation, MLR models were developed after differencing variables. The applicability of MLR models with differenced variables was compared with that of MLR models with undifferenced variables and the comparison results showed no significant difference between the two methods. The study results have indicated that there is strong correlation between estimated pan evaporation (using aerodynamic models and MLR models) and measured pan evaporation. However, pan evaporation are overestimated during August, September, October, November, and December. Most of meteorological variables that are used for MLR models show statistical significance in the estimation of pan evaporation. Vapor pressure deficit was turned out to be the most significant meteorological variable. The second most significant variable was air temperature; wind speed was the third most significant variable, followed by atmospheric pressure.

Soil Fertility Evaluation by Application of Geographic Information System for Tobacco Fields (지리정보시스템을 활용한 연초재배 토양의 비옥도 평가)

  • 석영선;홍순달;안정호
    • Journal of the Korean Society of Tobacco Science
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    • v.21 no.1
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    • pp.36-48
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    • 1999
  • Field test was conducted in Chungbuk province to evaluate the soil fertility using landscape and soil attributes by application of geographic information system(GIS) in 48 tobacco fields during 2 years(1996 ; 23 fields, 1997 ; 25 fields). The soil fertility factors and fertilizer effects were estimated by twenty five independent variables including 13 chemical properties and 12 GIS databases. Twenty five independent variables were classified by two groups, 15 quantitative indexes and 10 qualitative indexes and were analyzed by multiple linear regression (MLR) of SAS, REG and GLM models. The estimation model for evaluation of soil fertility and fertilizer effect was made by giving the estimate coefficient for each quantitative index and for each group of qualitative index significantly selected by MLR. Estimation for soil fertility factors and fertilizer effects by independent variables was better by MLR than single regression showing gradually improvement by adding chemical properties, quantitative indexes and qualitative indexes of GIS. Consequently, it is assumed that this approach by MLR with quantitative and qualitative indexes was available as an evaluation model of soil fertility and recommendation of optimum fertilization for tobacco field.

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