nowadays, software cost estimation (sce) with machine learning techniques are more performance than other traditional techniques which were based on algorithmic techniques. in this paper, we present a new hybrid model of multi-layer perceptron (mlp) artificial neural network (ann) and ant colony optimization (aco) algorithm for high accuracy in sce called multilayer perceptron ant colony optimi...

An artificial neural network (ANN) model was employed to predict the soil cohesion and soil internal friction angle. The soil samples were collected from different cultivated sites in seven regions in Saudi Arabia. Direct shear box method was used to determine soil cohesion and soil internal friction angle. The input factors to ANN model were soil dry density, soil moisture content and soil tex...

In this paper an ANN model is developed to forecast the monthly inflation of Bangladesh as a function of its own previous value. The model selects a feed-forward backpropagation ANN with two inputs, one hidden layer with five hidden neurons and one output as the optimum network. The model is tested with actual time series data of inflation in case of Bangladesh and forecast evaluation criteria....

This work develops a rule curve-based conjunctive use management model for optimizing the operating rules for a lake–groundwater system with off-stream storage lakes. The proposed procedure is a simulation-optimization approach that embeds an Artificial Neural Network (ANN) instead of a groundwater numerical model into a genetic algorithm (GA). The direct physical exchange between lake water wi...

This paper deals with modeling of plasma cutting process using artificial neural network (ANN). The objective was to develop an ANN model in order to predict the ten-point height of irregularities (Rz) in terms of three input parameters, cutting current, plate thickness and cutting speed. To this aim, a feed-forward backpropagation ANN was developed and trained using the experimental data. Afte...

abstract spatial prediction of soil organic carbon is a crucial proxy to manage and conserve natural resources, monitoring co2 and preventing soil erosion strategies within the landscape, regional, and global scale. the objectives of this study was to evaluate capability of artificial neural network and multivariate linear regression models in order to predict soil organic carbon using terrain ...

BACKGROUND Iturin A is a potential lipopeptide antibiotic produced by Bacillus subtilis. Optimization of iturin A yield by adding various concentrations of asparagine (Asn), glutamic acid (Glu) and proline (Pro) during the fed-batch fermentation process was studied using an artificial neural network-genetic algorithm (ANN-GA) and uniform design (UD). Here, ANN-GA based on the UD data was used f...

Short-term water demand modeling plays a key role in urban water resources planning and management. The importance of demand prediction is even greater in countries like Iran with frequent periods of drought. Short-term water demand estimation is useful for planning and management of water and wastewater facilities such as pump scheduling, control of reservoirs and tanks volume, pressure manage...

objective(s): this study aims to evaluate and predict the thermal conductivity of iron oxide nanofluid at different temperatures and volume fractions by artificial neural network (ann) and correlation using experimental data. methods: two-layer perceptron feedforward artificial neural network and backpropagation levenberg-marquardt (bp-lm) training algorithm are used to predict the thermal cond...

pearl millet has tolerance to harsh growing conditions such as drought. it is at least equivalent to maize and generally superior to sorghum in protein content and metabolizable energy levels. thus it is of importance for poultry feeding. amino acid (aa) determination is expensive and time consuming. therefore nutritionists have prompted a search for alternatives to estimate aa levels. traditio...