A quantitative procedure was developed to predict the composition of ternary ground spice mixtures using an electronic nose. Basil, cinnamon, and garlic were mixed in different compositions and presented to an enose. Nineteen training mixtures were used to build predictive models. Model performance was tested using 5 other mixtures. Three neural network structures—multilayer perceptron (MLP), M...

Landslide is one of the natural disasters that occur in Malaysia. Topographic factors such as elevation, slope angle, slope aspect, general curvature, plan curvature, and profile curvature are considered as the main causes of landslides. In order to determine the dominant topographic factors in landslide mapping analysis, a study was conducted and presented in this paper. There are three main s...

Background: The goal of this study is to illustrate an optimum variable selection method using established Multiple Linear Regression (MLR) models and validate the Multilayer Perceptron Neural Network (MLP) models. Initially, all selected variables will be passed through bootstrap methodology, they were screened for significant relationships. Objective: work analyze construct a model factor lin...

Accurately predicting passenger flow at rail stations is an effective way to reduce operation and maintenance costs, improve the quality of travel while meeting future demand. The improvement data acquisition capability allows fine-grained large-scale built environment be extracted. Therefore, this paper focuses on investigating relationship between around station discusses whether can applied ...

A character recognition system using soft computing techniques is presented in this paper. We define fuzzy sets on the Hough transform of each character pattern pixel and synthesize additional fuzzy sets by t-norms. The heights of these t-norms form an n-dimensional feature vector for the character. A 3n-dimensional vector is then generated from the n-dimensional feature vector by defining thre...

This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), ...

Rainfall is very important parameter in hydrological model. Many techniques and models have been developed for rainfall time series prediction. In this study an artificial neural network (ANN) based model was developed for rainfall time series forecasting. Proposed model used Multilayer perceptron (MLP) network with back propagation algorithm for training. Discharge and rainfall data are took a...

We propose a biologically motivated brain-inspired single neuron perceptron (SNP) with universal approximation and XOR computation properties. This computational model extends the input pattern and is based on the excitatory and inhibitory learning rules inspired from neural connections in the human brain's nervous system. The resulting architecture of SNP can be trained by supervised excitator...

This thesis investigates feedforward neural networks in the context of classi cation tasks with respect to the detection of patterns that do not belong to the same categories of patterns used to train the network. This refers to the problem of the detection and/or rejection of spurious or novel patterns. In particular, the multilayer perceptron network (MLP) trained with the backpropagation alg...