International electricity trade as a strategic commodity plays prominent role in the foreign market of countries. Electricity export forecasting leads to better production planning, supply security, blackouts reduc-tion, and obligations fulfillment. This paper aimed provide model for forecasting. In this regard, consumption different consumer sectors, gas consumption, population, GDP, electric-...

A new variable selection wrapper method named the Monte Carlo variable selection (MCVS) method was developed utilizing the framework of the Monte Carlo cross-validation (MCCV) approach. The MCVS method reports the variable selection results in the most conventional and common measure of statistical hypothesis testing, the P-values, thus allowing for a clear and simple statistical interpretation...

In the present work, the support vector machine (SVM) and Adaboost-SVM have been used to develop a classification model as a potential screening mechanism for a novel series of 5-HT(1A) selective ligands. Each compound is represented by calculated structural descriptors that encode topological features. The particle swarm optimization (PSO) and the stepwise multiple linear regression (Stepwise-...

Quantitative relationships between molecular structure and azolo-adamantanes derivatives were discovered by different chemometric tools including factor analysis based multiple linear regressions (FA-MLR), principle component regression analysis (PCRA), and genetic algorithm-partial least squares GA-PLS. The FA-MLR describes the effect of geometrical and quantum indices on enzyme inhibition act...

abstract reference evapotranspiration (eto) is an essential parameter required for proper management of agricultural crop irrigation. eto is influenced by many different hydrological variables and as a result is a very complex procces. eto is usually estimated by empirical or process-orinented models (mathematical relationships) from historical weather data. the need for accurate estimates of e...

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...

In this work, quantitative structure-retention relationship (QSRR) approaches were applied for modeling and prediction of the retention index of 282 amino acids (AAs) and carboxylic acids (CAs). Descriptors that were used to encode structural features of molecules in a data set were calculated by using the Dragon software. The genetic algorithm (GA) and stepwise multiple linear regression (MLR)...

The hydrophobicity of peptide is an important factor that affects the dissolution behavior of proteins and peptides, also affect the physical and chemical properties. In this study, each amino acid side chain was characterized using three structure parameters (heuristic molecular lipophilicity potential, HMLP). The HMLP parameters, total surface area(S), lipophilic indices (L), and hydrophilic ...

The main objective of the research is to compare the accurateness of Artificial Neural Networks (ANN) and Multiple Linear Regressions (MLR) model for Specific Fuel Consumption for pyrolysis oil blended with diesel used in a single cylinder diesel engine. In this study, parameters i. e. Injection Timing, Injection Pressure, Compression Ratio, and Load are taken. Artificial Neural Networks (ANN) ...