Multiple linear regression (MLR) model
<p>Prediction of Left Ventricular Mass Index Using Electrocardiography in Essential Hypertension – A Multiple Linear Regression Model</p>
... predictive model for LV mass using ECG are very ...their regression model, the correlation improved signi fi ...a multiple linear regression model to determine LV mass ...
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A hybrid of multiple linear regression clustering model with support vector machine for colorectal cancer tumor size prediction
... hybrid model of Multiple Linear Regression Clustering (MLRC) combined with Support Vector Machine (SVM) to predict tumor size of colorectal cancer ...models: Multiple Linear ...
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Comparative qsar analysis of histone deacetylase 6 (hdac6) inhibitors as anti cancer agents
... using Multiple Linear Regression (MLR), Principal Component Regression (PCR) besides Partial east Squares regression (PLS) methods, in addition to a 3D QSAR model which ...
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On the use of fuzzy c regression truncated models for health indicator in intensive care unit
... The models/technique discussed are multiple linear regression MLR model, analytical hierarchy process AHP technique, fuzzy c-means FCM model, fuzzy c-regression models FCRM, fuzzy linear[r] ...
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Comparison between multiple linear regression and fuzzy C regression models towards scale of health in ICU
... The multiple linear regression (MLR) model is well-known in analyzing linear ...the MLR and FCRM models were done to find the better model by using the mean square ...
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A new hybrid of fuzzy c means method and fuzzy linear regression model in predicting manufacturing income
... as multiple linear regression (MLR) and fuzzy linear regression (FLR) as proposed by Tanaka and Ni and to improve the FCM method and FLR model proposed by Zolfaghari to ...
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Evaluation of Three Evaporation Estimation Techniques In A Semi-Arid Region (Omar El Mukhtar Reservoir Sluge, Libya- As a case Study)
... generalized regression neural network (GRNN), fuzzy genetic (FG), least square support vector machine (LSSVM), multivariate adaptive regression spline (MARS), adaptive neuro-fuzzy inference systems with ...
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Prediction of Indonesian Inflation Rate Using Regression Model Based on Genetic Algorithms
... the regression method ...Indonesia. Multiple linear regression (MLR) is used in this study [13], aiming to find a pattern that is closest to the characteristics of historical inflation ...
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Comparative Analysis of Multiple Linear Regression and Artificial Neural Network for Predicting Friction and Wear of Automotive Brake Pads Produced from Palm Kernel Shell
... of multiple linear regression (MLR) and artificial neural network (ANN) for prediction of wear rate and coefficient of friction brake pad produced from palm kernel shell was carried ...Two ...
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On the use of fuzzy C-regression truncated models for health indicator in intensive care unit
... the MLR model can be adapted in FCRM models such as least median squares (LMS), least trimmed squares (LTS), deepest regression and least quartile difference (LQD) ...better model in modelling ...
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A Three-Step Neural Network Artificial Intelligence Modeling Approach for Time, Productivity and Costs Prediction: A Case Study in Italian Forestry
... A multiple linear regression model (MLR) and artificial neural network (ANN) have been carried out to predict gross time, productivity and costs estimation in a series of qualitative ...
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Time series support vector regression models with missing data treatments for water level prediction
... appropriate model and dominant variables for predicting water level in Galas ...the model, the study used four different methods which are Multiple Linear Regression (MLR), ...
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Dielectric microwave sensors with multivariate calibration
... seven model parameters are calculated from the mea- sured dielectric spectra using a non-linear curve fitting proce- ...the model parameters the samples were divided randomly into a calibration (2/3) ...
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Evaluation of multiple variate selection methods from a biological perspective: a nutrigenomics case study
... builds multiple linear regression models based on small subsets of ...the model size (number of variates) as well as identifying the best ...minimum model size considered was 2 ...
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Multiple linear regression and neural network for electric load forecasting
... load flow studies and likelihood analysis to calculate the requirement of generators, to determine line flows and to ensure that the system continues to operate reliably (Taylor and McSharry, 2007). In addition, it is ...
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Download Download PDF
... appropriate model was chosen for the study. The Multiple Linear Regression analysis method was eventually chosen for its ability to accommodate several variables and its high level of ...
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5633.pdf
... (n=41). Model performance was gauged using root mean squared error (RMSE), adjusted R 2 , percent type I errors, percent type II errors, and percent correct (100 – percent ...when model results recommend ...
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Evolutionary optimization of classifiers and features for single-trial EEG Discrimination
... single-trial limb laterality discrimination, and that the optimal EEG channels differ much between subjects. It should be noted that this study has focused on compar- ing classifiers rather than maximizing prediction ...
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IDENTIFICATION OF NEW HIV 1 PROTEASE INHIBITORS BY MULTIPLE LINEAR REGRESSION (MLR) AND PHYSICO CHEMICAL DESCRIPTORS
... Representative compounds from designed inhibitors were also tested against a panel of three to four drug-resistant protease varients. In QSAR, we seek to uncover correlations of biological activity with molecular ...
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Methodology to Predict NPA in Indian Banking System
... doing MLR analysis we do a step- wise regression which gives us the model with only the significant independent variable and also it keeps in check any issue which arises due to ...stepwise ...
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