In Australia, when stormwater systems were first introduced over 100 years ago, they were constructed independently of the sewer systems, and they are normally the responsibility of the third level of government, i.e., local government or city councils. Because of the increasing age of these stormwater systems and their worsening performance, there are serious concerns in a significant number o...

Transition Initiation Sites (TIS) prediction is a challenging problem in computational biology. In the literature TIS is predicted using various machine learning techniques such as Neural Network (NN), Support Vector Machine, etc. We have applied Principal Component Analysis (PCA) to remove highly correlated features which improves the performance in terms of time and accuracy. In this paper we...

A feedback Group Method of Data Handling (GMDH)-type neural network algorithm is proposed, and is applied to nonlinear system identification and medical image analysis of liver cancer. In this feedback GMDH-type neural network algorithm, the optimum neural network architecture is automatically selected from three types of neural network architectures, such as sigmoid function neural network, ra...

In this research we concentrate on developing a system dynamics (SD) model for county cycle economy research. We analyze the causality of the county cycle economy system, set up a set of formulations based on the use of group method of data handling (GMDH), then build a system dynamics model to simulate the evolving process of this county system. Through this model and simulation, we get a seri...

Neural networks with active neurons which selforganize their structure can use inductive sorting-out GMDH algorithms for their neurons. New threshold type GMDH algorithm with polynomial complexity is developed to decrease computing time in case of large input data sample.

A Probabilistic Neural Network (PNN) is defined as an implementation of statistical algorithm called Kernel discriminate analysis in which the operations are organized into multilayered feed forward network with four layers: input layer, pattern layer, summation layer and output layer. A PNN is predominantly a classifier since it can map any input pattern to a number of classifications. Among t...

Objective (s): Artificial Neural Networks (ANN) are widely used for predicting systems’ behavior. GMDH is a type of ANNs which has remarkable ability in pattern recognition. The aim the current study is proposing a model to predict dynamic viscosity of silver/water nanofluid which can be used as antimicrobial fluid in several medical purposes.Materials and Methods: In order to have precise mode...

in the present study, computational fluid dynamics (cfd) techniques and artificial neural networks (ann) are used to predict the pressure drop value (δp ) of al2o3-water nanofluid in flat tubes. δp  is predicted taking into account five input variables: tube flattening (h), inlet volumetric flow rate (qi  ), wall heat flux (qnw  ), nanoparticle volume fraction (φ) and nanoparticle diameter (dp ...

In this paper, we introduce the architecture of Genetic Algorithm (GA) based Feed-forward Polynomial Neural Networks (PNNs) and discuss a comprehensive design methodology. A conventional PNN consists of Polynomial Neurons, or nodes, located in several layers through a network growth process. In order to generate structurally optimized PNNs, a GA-based design procedure for each layer of the PNN ...