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With growing economic and political influence in the world, the important role played by Oceania in population issues should not be neglected. So it is very important and urgent to find an effective way to make a proper population projections. Nonetheless, the traditional methods focused on fertility and mortality may lead to the lack of all-sidedness in projection results. We realize that the historical...
The goal of this study is to develop an accurate artificial neural network (ANN)-based model to predict significant quality of refined palm oil which is Free Fatty Acid (FFA) content. The variables; FFA content, Iodine Value (IV), moisture content, bleaching earth and citric acid dosage as well as the pressure and temperature of the deodorizer is used to build the ANN prediction model. A feed forward...
A Deep Neural Network (DNN) using the same activation function for all hidden neurons has an optimization limitation due to its single mathematical functionality. To solve it, a new DNN with different activation functions is designed to globally optimize both parameters (weights and biases) and function selections. In addition, a novel Genetic Deep Neural Network (GDNN) with different activation functions...
In this paper, a neural model intended to efficiently determine the number of moving electromagnetic sources of stochastic radiation in the monitoring space sector is presented. Neural model is based on a probabilistic neural network. As an illustration, one-dimensional case is considered in which the noisy sources are moving only in the azimuth plane.
This paper proposes a radial basis function (RBF) network trained using ridge extreme learning machine to predict the future trend from the past stock index values. Here the task of predicting future stock trend i.e. the up and down movements of stock price index values is cast as a classification problem. Recently extreme learning machine (ELM) is used as an efficient learning algorithm for single...
Currently, microalgae cultivation is one of the most promising alternative solutions to alleviate the value of CO2 concentration. Microalgae growth rate is convinced to be the indicator to measure the effectiveness in capturing CO2. In this paper, the microalgal growth behavior by means of various pH concentrations is observed. From the observation data, the growth behavior is modeled by regression...
Speaker recognition has made great progress under the laboratory environment, but in real life the performance of speaker recognition system is affected by various factors including environmental noise. This paper studies the performance of speaker recognition system in noisy environment and presents Speaker recognition system using modified Mel-Frequency Cepstral Coefficients (MFCC) technique based...
Speaker recognition has made great progress under the laboratory environment, but in real life the performance of speaker recognition system is affected by various factors including environmental noise. This paper studies the performance of speaker recognition system in noisy environment and presents Speaker recognition system using Mel-Frequency Cepstral Coefficients (MFCC) technique based on different...
Recently, an efficient learning algorithm called extreme learning machine (ELM) has been proposed for training of single hidden layer feed forward neural networks (SLFNs). ELM has shown good generalization performances for many real applications with an extremely fast learning speed. This study proposes a computational efficient functional link artificial neural network (CEFLANN) trained with ELM...
Mortality rate increases all over the world on a daily basis. The reason for this could be largely adduced to the increase in the number of patients with cardiovascular diseases. To worsen the case, many physicians have been known for misdiagnosis of patients reporting heart related ailment. In this paper, an intelligent system has been design which will help in effective diagnosis of the patient...
The comparison of two classifiers, the Extreme Learning Machine (ELM) and the Support Vector Machine (SVM) is considered for performance, resources used (neurons or support vector kernels) and computational complexity (speed). Both implementations are of similar type (C++ compiled as Octave .mex files) to have a better evaluation of speed and computational complexity. Our results indicate that ELM...
Anorexia nervosa is a quiet dangerous disease that detected in an early stage may save the patient life and flushes his veins with “passion-red blood” pumping happiness and hope with every heartbeat. A study based on back propagation neural network giving machines the ability to mimic the human function to detect a “haunted person by anorexia “ from a person obsessed by “looking “wow” as a model under...
An information adaptive test system of student's knowledge was proposed. It is based on the three-criterion decision-making model of transferring between the test difficulties levels using neural network. Knowledge check results in the groups of students studying in the distance learning system Moodle were compared with knowledge check results groups of students who were trained using the improved...
In this paper, we propose the Multi-Layer Perceptron (MLP) technique for Neighbor Selection in Peer-to-Peer (P2P) Computing to reduce the communication overhead. The selection of Neighbor is one of the challenging areas in P2P Computing. Root Mean Square Error and Testing time are two Parameters considered for neighbor selection in P2P network. The objective of the proposed technique is to minimize...
In this study, seismic attributes have been used to estimate well logs in one of the Iranian petroleum reservoirs. Three static methods have been evaluated: the linear model, the multilayer perceptron (MLP) and the radial basis function (RBF). For linear case, the selection of appropriate attributes was determined by forward selection and for nonlinear one, the selection was based on the genetic algorithm...
Epilepsy is common disorder of the brain that affects the cerebral cortex. EEG is one of the main tools for diagnosis neuron activities and brain disorders. EEG signals store important information that is useful for diagnosis neural diseases. In this work, implementation of epilepsy classifier using neural network was employed. Identification of three classes EEG signals: seizure free, pre-seizure...
As one of the most important factors influencing the development potential of the country, China's population attracts considerable attention. Some official organizations regularly publish the predictions of China's population every calendar year. However, most of the predictions use the standard cohort-component method, which does not allow for all relevant impact factors and may lose sight of some...
This paper presents an approach for automatic diagnosis of Autism Spectrum Disorder (ASD) among males using functional Magnetic Resonance Imaging (fMRI). fMRI has the capability to identify any abnormal neural interactions that may be responsible for behavioral symptoms observed in ASD patients. In this paper, the regional homogeneity of the voxels in the 116 regions of the automated anatomical labeling...
Typical navigation systems do not use object recognition as part of their autonomous driving systems. People often use hand-crafted features (e.g. lanes, traffic lights, intersections) based on programmers' knowledge about the environment. Those agents are usually brittle during real-world tests. However, landmarks, as a type of object, need to be recognized for an autonomous navigation system to...
Science learned models based on limited data are usually fragile, researchers suggest the adoption of virtual samples to improve the prediction model. In this study, nonparametric statistical tool, Kolmogorov-Smirnov test, is introduced to examine the distribution of virtual samples without any assumption about the underlying population. The examination procedure would help control the quality of...
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