Application of Artificial Neural Networks in Estimating Participation in Elections

Khaze, Seyyed Reza; Màsdàrí, Mohàmmàd; Hojjatkhah, Sohrab

doi:10.5121/ijitmc.2013.1303

Cited by 9 publications

(2 citation statements)

References 13 publications

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“…This function is very necessary because the activation function is used as a feed-forward for both layers. The activation function used is sigmoid [27]. If a logistic activation function is to be applied where xi is the input value (predictor variable), the input value is converted to (0, 1).…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Implementation multiple linear regresion in neural network predict gold price

Yanto

Sanjaya

Yulasmi

et al. 2021

IJEECS

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<p>The movement of gold prices in the previous period was crucial for investors. However, fluctuations in gold price movements always occur. The problem in this study is how to apply multiple linear regression (MRL) in predicting artificial neural networks (ANN) of gold prices. MRL is mathematical calculation technique used to measure the correlation between variables. The results of the MRL analysis ensure that the network pattern that is formed can provide precise and accurate prediction results. In addition, this study aims to develop a predictive pattern model that already exists. The results of the correlation test obtained by MRL provide a correlation of 62% so that the test results are said to have a significant effect on gold price movements. Then the prediction results generated using an ANN has a mean squared error (MSE) value of 0.004264%. The benefits obtained in this study provide an overview of the gold price prediction pattern model by conducting learning and approaches in testing the accuracy of the use of predictor variables.</p>

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Implementation multiple linear regresion in neural network predict gold price

Yanto

Sanjaya

Yulasmi

et al. 2021

IJEECS

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“…input/output pairs) is capable of developing a precise nonlinear mapping, using a learning algorithm by adjusting the network weights. It has been proved that a two-layer MLP can effectively approximate any nonlinear mapping [8].MLP training uses majorly Back-propagation (BP) algorithm [7], where a steepest descent gradient approach and a chain-rule are adopted for back-propagated error correction from the output layer [1]. Significant efforts have been put into improving the speed of convergence, generalization performance, and the discriminative ability of MLP.…”

Section: Multilayer Perceptron [Mlp]mentioning

confidence: 99%

Network Learning and Training of a Cascaded Link-Based Feed Forward Neural Network (CLBFFNN) in an Intelligent Trimodal Biometric System

E¹,

J²

2018

IJAIA

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Presently, considering the technological advancement of our modern world, we are in dire need for a system that can learn new concepts and give decisions on its own. Hence the Artificial Neural Network is all that is required in the contemporary situation. In this paper, CLBFFNN is presented as a special and intelligent form of artificial neural networks that has the capability to adapt to training and learning of new ideas and be able to give decisions in a trimodal biometric system involving fingerprints, face and iris biometric data. It gives an overview of neural networks.

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