Traffic Image Analysis Based on Stacked Denoising Autoencoder Neural Network

This study aims to explore major neural network models - Stacked Denoising Autoencoder (SDAE), Deep Belief Network (DBN), Backpropagation - that have recently garnered attention and propose the most suitable and reliable artificial neural network model for real-time road traffic information collecti...

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Published in:Journal of Innovation Information Technology and Application Vol. 5; no. 2; pp. 183 - 192
Main Author: Kim, Daehyon
Format: Journal Article
Language:English
Published: Pusat Penelitian dan Pengabdian Masyarakat (P3M), Politeknik Negeri Cilacap 29.12.2023
Subjects:
autoencoding
backpropagation
deep belief network
neural network
stacked denoising
ISSN:2716-0858, 2715-9248
Online Access:Get full text
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Abstract This study aims to explore major neural network models - Stacked Denoising Autoencoder (SDAE), Deep Belief Network (DBN), Backpropagation - that have recently garnered attention and propose the most suitable and reliable artificial neural network model for real-time road traffic information collection. In this study, to enhance the reliability of experimental results, numerous experiments were conducted under identical conditions (such as parameter values and network configuration) by setting different initial values for the weight vector. The results of the experiments were statistically validated to draw conclusions. The research results showed that the SDAE model exhibited the most superior performance, while the accuracy of the DBN was somewhat lower compared to the SDAE model. On the other hand, the Backpropagation model demonstrated a relatively low predictive accuracy compared to both models, particularly showing a significant influence of the initial values
AbstractList This study aims to explore major neural network models - Stacked Denoising Autoencoder (SDAE), Deep Belief Network (DBN), Backpropagation - that have recently garnered attention and propose the most suitable and reliable artificial neural network model for real-time road traffic information collection. In this study, to enhance the reliability of experimental results, numerous experiments were conducted under identical conditions (such as parameter values and network configuration) by setting different initial values for the weight vector. The results of the experiments were statistically validated to draw conclusions. The research results showed that the SDAE model exhibited the most superior performance, while the accuracy of the DBN was somewhat lower compared to the SDAE model. On the other hand, the Backpropagation model demonstrated a relatively low predictive accuracy compared to both models, particularly showing a significant influence of the initial values
Author Kim, Daehyon
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SubjectTerms autoencoding
backpropagation
deep belief network
neural network
stacked denoising
Title Traffic Image Analysis Based on Stacked Denoising Autoencoder Neural Network
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