Hue University Journal of Science: Techniques and Technology
https://jos.hueuni.edu.vn/index.php/hujos-tt
<p><strong>ISSN (Print) 2588-1175 </strong></p> <p><strong>ISSN (Online) 2615-9732</strong></p> <p><strong>Editor in chief: </strong>Do Thi Xuan Dung</p> <p><strong>Chair Editor: </strong>Vo Viet Minh Nhat</p> <p><strong>Managing Editor: </strong>Tran Xuan Mau</p> <p><strong>Technical Editor: </strong>Duong Duc Hung</p> <p><strong>Phone:</strong> 02343845658 | <strong>Email: </strong>ddhung@hueuni.edu.vn</p>Đại học Huếen-USHue University Journal of Science: Techniques and Technology2588-1175A Low Power – Long Range IoT Development Board based on LoRa Technology
https://jos.hueuni.edu.vn/index.php/hujos-tt/article/view/6795
<p>LoRa is an advanced technology investigated and applied widely to the IoT field because of its power efficiency and wireless connection. Therefore, a development kit supporting LoRa technology is a vital device that helps engineers to develop a faster and more stable IoT – LoRa-based system. The development kit not only requires a small size to be easily integrated into other systems but also has a low power consumption to adapt to the requirement of IoT devices. In this paper, we propose a development kit for an IoT platform using LoRa technology. The power consumption and Received Signal Strength Indication (RSSI) of this kit are addressed. The development kit works well, as anticipated.</p>Phan Hai-PhongVan-Kiem DuongThi-Kieu TranViet-Dung VoHuu-Hanh Hoang
Copyright (c) 2022
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2022-12-312022-12-311312B51410.26459/hueunijtt.v131i2B.6795Modeling and forecasting the spread of COVID-19 pandemic: The case of Vietnam
https://jos.hueuni.edu.vn/index.php/hujos-tt/article/view/6644
<p>Examining the evolution of the COVID-19 pandemic in many countries since the beginning of the pandemic outbreak in early 2020, we found a common pattern of daily infections with a skewed distribution with different peaks. This trend was observed in Vietnam. Based on those observations, we adapted the skewed distribution function Logistic Growth (Skewed Logistic Growth – SLG) to develop our model for forecasting COVID-19 infections. In the case of Vietnam, we focused on the fourth outbreak – the largest and most complicated pandemic in the country to date. The results depict a clear pattern following closely with the actual development of three infection waves during the fourth outbreak. This confirms that the model can be used to forecast the spread of COVID-19 in the coming time, as the pandemic situation will be more complicated due to the appearance of new variants (i.e., Delta, Omicron, etc.) along with critical adjustments in the government pandemic control and prevention strategies. The model forecasted that the fourth outbreak would peak between the end of December 2021 and the end of January 2022, with about 16,000 new cases per day. The forecasting results are useful for the government and relevant agencies to proactively design timely and effective solutions for prevention. It further proposes various directions for future research to enrich the methodological aspects and empirical evidence of the research domain.</p>Xuan Thanh MaiVan Xuan Mai
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2022-12-312022-12-311312B153210.26459/hueunijtt.v131i2B.6644Feature’s importance assessment for activation probability measure in topic’s diffusion prediction
https://jos.hueuni.edu.vn/index.php/hujos-tt/article/view/6877
<p>In this study, we aim to estimate the sigma coefficient in the activation probability calculation for a topic’s diffusion prediction problem. In our previous studies, we proposed an aggregated activation probability combination of the metapath and text information, in which sigma is the characteristic coefficient of interest’s similarity based on textual content. σ is a parameter that controls the rates of the influence of active probability based on the metapath and interest similarity on aggregated activation probability. In a previous study, we supposed the equal importance between the metapath and textual information, when σ = 0.5. However, for different datasets, this coefficient differs, depending on the meaning of the meta-path and the textual information. In this study, we continue to investigate the importance of the sigma coefficient for the effectiveness of the topic’s diffusion prediction problem on the bibliographic network. We propose to utilize the two most common methods for feature selection: the ANOVA test and mutual information to obtain the significance of two features MP (metapath) and the IS (textual information). The experimental results show that the use of the feature selection methods to estimate the sigma coefficient is reliable and improves the predictive performance of the topic’s diffusion compared with the standard assignment of 0.5.</p>Thi Kim Thoa HoQuang Vu Bui
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http://creativecommons.org/licenses/by/4.0
2022-12-312022-12-311312B334710.26459/hueunijtt.v131i2B.6877Topic diffusion prediction on bibliographic network: effect of topic modeling on activation probability measure
https://jos.hueuni.edu.vn/index.php/hujos-tt/article/view/6749
<p>In this research, we propose using topic modeling to estimate activation probability for predicting topic diffusion on the bibliographic networks. We utilize the supervised method to predict the propagation of a specific topic. We propose a new method to calculate activation probability for an active node and an inactive node based on the meta-path and textual information using topic modeling. Firstly, based on textual information, topic modeling is suggested to measure activation probability, namely the textual information. Secondly, combining the meta-path and textual information, we propose a new method to estimate activation probability, namely the aggregated activation probability, in which the textual information is measured by topic modeling. We conduct experiments on dissimilar topics of the bibliographic network datasets. Experimental results demonstrate that topic modeling improves the accuracy of diffusion prediction compared with term frequency–inverse document frequency.</p>Thi Kim Thoa HoQuang Vu Bui
Copyright (c) 2022
http://creativecommons.org/licenses/by/4.0
2022-12-312022-12-311312B496310.26459/hueunijtt.v131i2B.6749