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drought forecasting in khash city by using neural network model hossein negaresh associate professor of geography and environmental planningfaculty, university of sistan & baluchestan mohsen armesh holding master degree in climatology in environmental planning extended abstract 1- introduction drought is condition of lack of rainfall and increase in temperature occurring in any climatic condit...

due to its critical impact and significant destructive nature during and after seismic events, soil liquefaction and liquefactioninduced lateral ground spreading have been increasingly important topics in the geotechnical earthquake engineering field during the past four decades. the aim of this research is to develop an empirical model for the assessment of liquefaction-induced lateral ground ...

every woman is at risk of ovarian cancer; about 90 percent of women who develop ovarian cancer are above 40 years of age, with the high number of ovarian cancers occurring at the age of 60 years and above. early and correct diagnosis of ovarian cancer can allow proper treatment and as a result reduce the mortality rate. in this paper, we proposed a hybrid of synthetic minority over-sampling tec...

this paper deals with volume fraction optimization of functionally graded (fg) beams resting on elastic foundation for maximizing the first natural frequency. the two-constituent functionally graded beam consists of ceramic and metal. these constituents are graded through the thickness of beam according to a generalized power-law distribution. one of the advantages of using generalized power- l...

abstract prediction of input flow into water resources is regarded as one of the most important issues in optimum planning and management in producing electro-water energy and optimum allocation of water into different consumption sources. different parameters affect on input discharge into dams. climate variables including temperature and rainfall have the most effect on input runoff rate to w...

the descriptors computed by hyperchem® software were employed to represent the solubility of 40 drug molecules in supercritical carbon dioxide using an artificial neural network with the architecture of 15-4-1. the accuracy of the proposed method was evaluated by computing average of absolute error (ae) of calculated and experimental logarithm of solubilities. the ae (±sd) of data sets was 0.4 ...

objective(s): a fast and reliable evaluation of the binding energy from a single conformation of a molecular complex is an important practical task. artificial neural networks (anns) are strong tools for predicting nonlinear functions which are used in this paper to predict binding energy. we proposed a structure that obtains binding energy using physicochemical molecular descriptions of the se...

in this study, we focused on the gait of parkinson’s disease (pd) and presented a gray box model for it. we tried to present a model for basal ganglia structure in order to generate stride time interval signal in model output for healthy and pd states. because of feedback role of dopamine neurotransmitter in basal ganglia, this part is modelled by “elman network”, which is a neural network stru...

Production of highly viscous tar sand bitumen using Steam Assisted Gravity Drainage (SAGD) with a pair of horizontal wells has advantages over conventional steam flooding. This paper explores the use of Artificial Neural Networks (ANNs) as an alternative to the traditional SAGD simulation approach. Feed forward, multi-layered neural network meta-models are trained through the Back-...