In this empirical study, socioeconomic factors that can easily be extracted from families have been used to build a "home electricity usage prediction" model based on two variables, family monthly income and size. Each of these was evaluated individually. Two machine learning models were built using those as features. Models are “Linear regression” “Random Forest” algorithms. This study reveale...

todays, dynamic models are supposed as the most important tools in erosion and sediment phenomenadue to their complexities and existence of many affecting factors. towards, the present study wasconducted in the kojour watershed for daily sediment modeling using daily rainfall, discharge andsediment during 2007 to 2010. the modeling process was carried out all data and the monthly andseasonally ...

Existing research on mixtures of regression models are limited to directly observed predictors. The estimation of mixtures of regression for measurement error data imposes challenges for statisticians. For linear regression models with measurement error data, the naive ordinary least squares method, which directly substitutes the observed surrogates for the unobserved error-prone variables, yie...

The topic of this paper is formant frequency prediction using multiple linear regression. This technique provides robust formant frequency estimates but with limited precision. We apply this approach to predict formant frequencies of one male speaker comparing three different spectral representations. Mel scaled filterbanks are shown to perform slightly better than linear prediction based cepst...

musa is one of the novel techniques in csa, which lays its foundation on linear goal programming, developed for overcoming prior csa models’ weaknesses such as coping with ordinal nature of data and low fitness. employing a simple questionnaire, musa develops the interval scale and the level of satisfaction as well as determining its determinants in addition to several fruitful indices. this pa...

Recently Learning –Based Approach for Super– resolution (SR) has been used witch generate favorable result. In this paper image super –resolution based on the multiple kernel regression is presented. This approach’s core is to learn the map between the space of high resolution image patches and the space of blurred high – resolution image patches.That are the interpolation result generated from...

A comparative workflow, including linear and non-linear QSAR models, was carried out to evaluate the predictive accuracy of models and predict the inhibition activity of a series of aryl-substituted isobenzofuran-1(3H)-ones. The data set consisted of 34 compounds was classified into the training and test sets, randomly. Molecular descriptors were selected using the genetic algorithm (GA) as a f...