in this study greenhouse tomato production was investigated from energy consumption and greenhouse gas (ghg) emission point of views. moreover, artificial neural networks (anns) and adaptive neuro-fuzzy inference systems (anfis) were employed to model energy consumption for greenhouse tomato production. total energy input and output were calculated as 1316.14 and 281.1 gj/ha. among the all ener...

predictive quantitative structure–activity relationship was performed on the novel 4-oxo-1,4-dihydroquinoline and 4-oxo-4h-pyrido[1,2-a]pyrimidine derivatives to explore relationship between the structure of synthesized compounds and their anti-hiv-1 activities. in this way, the suitable set of the molecular descriptors was calculated and the important descriptors using the variable selections ...

Within the framework of the OPTIVIP project, an optic nerve based visual prosthesis is developed in order to restore partial vision to the blind. One of the main challenges is to understand, decode and model the physiological process linking the stimulating parameters to the visual sensations produced in the visual field of a blind volunteer. We propose to use adaptive neural techniques. Two pr...

Background: Forecasting of air pollutants has become a popular topic of environmental research today. For this purpose, the artificial neural network (AAN) technique is widely used as a reliable method for forecasting air pollutants in urban areas. On the other hand, the evolutionary polynomial regression (EPR) model has recently been used as a forecasting tool in some environmental issues. In ...

In the process of learning we may presume the neural networks are simplified models of the biological neurons system. The Artificial Neural Network ( ANN ) is an information processing system which is inspired by brain learning system. It is assumed that brain is composed of a large number of highly interconnected processing elements working in groups to solve specific problems. Various network...

This paper presents a new model for predicting the compressive strength of steel-confined concrete on circular concrete filled steel tube (CCFST) stub columns under axial loading condition based on Artificial Neural Networks (ANNs) by using a large wide of experimental investigations. The input parameters were selected based on past studies such as outer diameter of column, compressive strength...

Artificial neural networks (ANNs) are computational models, loosely inspired by biological neural networks, consisting of interconnected groups of artificial neurons which process information using a connectionist approach. ANNs are widely applied to problems like pattern recognition, classification, and time series analysis. The success of an ANN application usually requires a high number of e...

We may presume the neural networks are simplified models of the biological neurons system. The Artificial Neural Network (ANN) is an information processing system which is inspired by brain learning system. It is assumed that brain is composed of a large number of highly interconnected processing elements working in groups to solve specific problems. Various networks and algorithms have been pr...

Logistic regression occupies a central position in the field of credit scoring as it is relatively well understood and an explicit formula can be derived on which credit decisions may be based. Although artificial neural networks may be more powerful than logistic regression, it is not widely used in credit scoring because it is a black box with respect to interpretation and the absence of reas...

natural fire inflicting irreparable damage to rangelands and forest areas is cause changes in landscape ecology. the purpose of this research is comparison of artificial neural network (ann) and line regression (lr) models to predict of forest and rangelands fires to this end, the data consist fire burned area and fire were used weather data over a period of 7 years (2006-2012(.the result indic...