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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) International Journal of Advances in Intelligent Informatics Indonesian Journal of Electrical Engineering and Computer Science
Cong-Danh Huynh
Thu Dau Mot University
Computer Science & IT

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Deep learning application for real-time prediction of COVID-19 outbreak with susceptible-infected-recovered-deceased model Hoang-Sy Nguyen; Thu Ngan Phan Thi; Cong-Danh Huynh
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 1: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i1.pp567-576

Abstract

Due to the complex nature of a pandemic such as COVID-19, forecasting how it would behave is difficult, but it is indeed of utmost necessity. Furthermore, adapting predictive models to different data sets obtained from different countries and areas is necessary, as it can provide a wider view of the global pandemic situation and more information on how models can be improved. Therefore, we combine here the long-short-term memory (LSTM) model and the traditional susceptible-infected-recovered-deceased (SIRD) model for the COVID-19 prediction task in Ho Chi Minh City, Vietnam. In particular, LSTM shows its strength in processing and making accurate numerical predictions on a large set of historical input. Following the SIRD model, the whole population is divided into 4 states (S), (I), (R), and (D), and the changes from one state to another are governed by a parameter set. By assessing the numerical output and the corresponding parameter set, we could reveal more insights about the root causes of the changes. The predictive model updates every 10 days to produce an output that is closest to reality. In general, such a combination delivers transparent, accurate, and up-to-date predictions for human experts, which is important for research on COVID-19.
Deep reinforcement learning autoencoder with RA-GAN and GAN Hoang-Sy Nguyen; Cong-Danh Huynh
International Journal of Advances in Intelligent Informatics Vol 8, No 3 (2022): November 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v8i3.896

Abstract

Deep learning utilization to optimize block-structured communication systems has attracted tremendous attention from researchers. Nevertheless, owing to the extensive data transmission between the transmitter and the receiver, communication, in this case, is hard to establish and maintain effectively. As a solution for this, we first investigate typical end-to-end learning for a communication system, Generative Adversarial Network (GAN). Then, two problems associated with GAN-based systems, the gradient vanishing and overfitting, are reviewed. Subsequently, a residual aided GAN (RA-GAN) is proposed as means to overcome these problems. In the proposed learning scheme, the residual learning and the regularization method are used to mitigate the gradient vanishing and over-fitting problems. In the proposed learning scheme, the residual learning and the regularization method are used to mitigate the gradient vanishing and over-fitting problems. Finally, the numerical results performed in MATLAB for simulation and Codelabs for training have proven that the RA-GAN scheme has near-optimal performance and outperforms the conventional GAN scheme. Throughout this case study, readers can understand the issues that would occur when deep learning is applied to a communication system and possible approaches to address them.
Digital transformation for shipping container terminals using automated container code recognition Hoang-Sy Nguyen; Cong-Danh Huynh; Nhat-Quan Bui
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 3: June 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i3.24137

Abstract

Due to the sweeping waves of global industry development, the number of containers passing through terminal ports increases every day. Therefore, it is essential to automate the identification process for the container codes to replace the manual identification for more efficient logistics and safer workplace. This paper aims to design and evaluate the performance of such a system. Specifically, automated container codes recognition (ACCR) has been implemented. This is a novel container tracking model based on image processing algorithms and machine learning (ML) algorithms to be applied in ports. There are three steps in this system: character detection, character isolation, and character recognition. The first step is to identify an area with 10 digits and 26 capitals. After detecting the text area, the second step is to separate the characters. Each character is recognized in the last step by the classification method. In particular, features are extracted with the histogram of oriented gradients (HOG) algorithm and support vector machines (SVMs) for training and prediction. The trained ML model is then used to classify characters and digits according to what it has learned. In general, the digital technologies in logistics and container management in ports will benefit from the proposed algorithms.
Co-Authors Hoang-Sy Nguyen Hoang-Sy Nguyen Nhat-Quan Bui Thu Ngan Phan Thi