• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.1-6
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• 2021

Currently, virus infection such as Corona 19 has become commonplace, and interest in unmanned systems is increasing in the field for non-face-to-face ICT services. In this paper, the function and performance of remotely successfully controlling a store through video and audio using an IP PBX with a broadcasting function was verified through a test. And the fully unmanned system is not gaining credibility due to various technical problems, however the central control system is a very efficient and reliable system because the controller can directly control the customer while monitoring the access and the inside of the store through the video and audio. In the future, we plan to study a completely unmanned remote control system using A.I technology.

• International journal of advanced smart convergence
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• v.8 no.1
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• pp.1-8
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• 2019

Coronaviruses are known respiratory pathogens. In the past, most human coronaviruses were thought to cause mild symptoms such as cold. However recently, as seen in the Severe Acute Respiratory Syndrome (SARS) and the Middle East Respiratory Syndrome (MERS), infectious diseases with severe pulmonary disease and respiratory symptoms are caused by coronaviruses, making research on coronaviruses become important. Considering previous studies, we constructed 'HCoV-IMDB (Human Corona Virus Immune Database)' to systematically provide genetic information on human coronavirus and host immune information, which can be used to analyze the interaction between human coronavirus and host immune proteins. The 'HCoV-IMDB' constructed in the study can be used to search for genetic information on human coronavirus and host immune protein and to download data. A BLAST search specific to the human coronavirus, one of the database functions, can be used to infer genetic information and evolutionary relationship about the query sequence.

• The Korean Journal of Emergency Medical Services
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• v.25 no.1
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• pp.85-103
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• 2021

Purpose: This study measured the mental health levels of 119 paramedics, and identified the association of mental health levels with safety environments, personal protective equipment, and coronavirus risk awareness. Methods: A total of 119 out of 428 from Daegu and Gyeongbuk took part in this study. The statistical analysis methods were the t-test, ANOVA, Pearson's correlation analysis and multiple regression analysis. Results: In a multiple regression analysis, females (β=-.137, p=<.001) showed a higher relevance to negative mental health than males. The moderate satisfied (β=-.088, p=.014) and dissatisfied (β=-.147, p=.006) showed a higher relevance to negative mental health than higher satisfied. Moderate stress perception (β=-.199, p=<.001) and higher stress perception (β=-.414, p=<.001) showed a higher relevance to negative mental health than lower stress perception. Corona-virus risk awareness (β=-.050, p=.045) was related to negative mental health and safety environment (β=.136, p=<.001). Personal protective equipment (β=.147, p=<.001) were related to positive mental health. Conclusion: Conclusively, it is necessary to develop and implement high-quality intervention programs using significantly influencing variables to impact the mental health of 119 paramedics.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.132-138
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• 2020

Worldwide, the number of corona virus disease 2019 (COVID-19) confirmed cases is rapidly increasing. Although vaccines and treatments for COVID-19 are being developed, the disease is unlikely to disappear completely. By attaching a smart sensor to the respirator worn by medical staff, Internet of Things (IoT) technology and artificial intelligence (AI) technology can be used to automatically detect the medical staff's infection symptoms. In the case of medical staff showing symptoms of the disease, appropriate medical treatment can be provided to protect the staff from the greater risk. In this study, we design and develop a system that detects cough, a typical symptom of respiratory infectious diseases, by applying IoT technology and artificial technology to respiratory protection. Because the cough sound is distorted within the respirator, it is difficult to guarantee accuracy in the AI model learned from the general cough sound. Therefore, coughing and non-coughing sounds were recorded using a sensor attached to a respirator, and AI models were trained and performance evaluated with this data. Mel-spectrogram conversion method was used to efficiently classify sound data, and the developed cough recognition system had a sensitivity of 95.12% and a specificity of 100%, and an overall accuracy of 97.94%.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.93-102
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• 2021

The world is facing an unprecedented economic, social and political crisis with the spread of COVID-19. The Corona Virus (COVID-19) and its global spread have resulted in declaring a pandemic by the World Health Organization. The deadly pandemic of 21st century has spread its wings across the globe with an exponential increase in the number of cases in many countries. The developing and underdeveloped countries are struggling hard to counter the rapidly growing and widespread challenge of COVID-19 because it has greatly influenced the global economies whereby the underdeveloped countries are more affected by its devastating impacts, especially the life of the low-income population. Information and Communication Technology (ICT) were particularly useful in spreading key emergency information and helping to maintain extensive social distancing. Updated information and testing results were published on national and local government websites. Mobile devices were used to support early testing and contact tracing. The government provided free smartphone apps that flagged infection hotspots with text alerts on testing and local cases. The purpose of this research work is to provide an in depth overview of emerging technologies and recent ICT developments to combat COVID-19 Pandemic. Finally, the author highlights open challenges in order to give future research directions.

• The Journal of Information Systems
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• v.29 no.4
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• pp.123-135
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• 2020

Purpose The purpose of this study is to investigate the development process and the effectiveness of the EISS (epidemiological investigation support system), which prevents the spread of infectious diseases like a novel corona virus disease, COVID-19. Design/methodology/approach This study identified the existing epidemiological support system for MERS through prior research and studied the case of the development of a newly developed epidemiological support system based on cloud computing infrastructure for COVID-19 through inter-ministerial collaboration in 2020. Findings The outbreak of COVID-19 drove the Korean Government began the development of the EISS with private companies. This system played a significant role in flattening the spread of infection during several waves in which the number of confirmed cases increased rapidly in Korea, However, we need to be careful in handling confirmed patients' private data affecting their privacy.

• Journal of radiological science and technology
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• v.44 no.6
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• pp.679-688
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• 2021

This study is a questionnaire about the lesson environment that radiation major students prefer in a non-face-to-face live online lesson environment for a total of 133 students, 65 second graders and 68 third graders who are enrolled in the department of radiology at a university located in the Seoul metropolitan area. And checked the satisfactory level by grade. The questionnaire consists of three categories: 1st real-time non-face-to-face lectures, 2nd professor lectures, and 3rd corona lectures. A total of 14 questions, with multiple choice and descriptive response methods. As an evaluation method, in the case of a multiple-choice question, the average was calculated using a 5-point Likert scale. As a result of conducting the independent sample T-test of the SPSS program, the response by grade was P > 0.05, and no significant result was shown by the contents of the questionnaire survey of the second grade. As for the lecture method of the department of radiology after the end of Covid-19 virus, it is better to promote face-to-face lessons in radiation training subjects and non-face-to-face real-time education in subjects centered on radiation theory.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.225-233
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• 2022

The recent outbreak of corona virus (COVID-19) infectious disease had made its forecasting critical cornerstones in most scientific studies. This study adopts a machine learning based time series model - Auto Regressive Integrated Moving Average (ARIMA) model to forecast COVID-19 confirmed cases in Botswana over 60 days period. Findings of the study show that COVID-19 confirmed cases in Botswana are steadily rising in a steep upward trend with random fluctuations. This trend can also be described effectively using an additive model when scrutinized in Seasonal Trend Decomposition method by Loess. In selecting the best fit ARIMA model, a Grid Search Algorithm was developed with python language and was used to optimize an Akaike Information Criterion (AIC) metric. The best fit ARIMA model was determined at ARIMA (5, 1, 1), which depicted the least AIC score of 3885.091. Results of the study proved that ARIMA model can be useful in generating reliable and volatile forecasts that can used to guide on understanding of the future spread of infectious diseases or pandemics. Most significantly, findings of the study are expected to raise social awareness to disease monitoring institutions and government regulatory bodies where it can be used to support strategic health decisions and initiate policy improvement for better management of the COVID-19 pandemic.

• Journal of Korean Academy of Dental Administration
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• v.9 no.1
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• pp.44-50
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• 2021

Owing to the Corona virus (COVID-19) crisis, virtual education has been expanded. Accordingly, this study was conducted to determine the direction of oral health education by examining participants' satisfaction with virtual education and educational media preferences. This study collected data from a Naver Form online survey targeting 290 university students across the country, from May 10 to 31, 2021. The collected data were analyzed using IBM SPSS 20.0. According to the data, satisfaction with virtual classes was 3.36 points in 5-point Likert scale, satisfaction factors were reduced commuting time and money expenditure, and the highest dissatisfaction factor was a decrease in lecture concentration. The media platform that most interested students pursing oral health education was YouTube. The oral health education that participants wished to receive through virtual education included how to prevent tooth decay, how to prevent gum disease, and how to brush teeth. In conclusion, it is necessary to develop various media like Zoom, YouTube, and virtual reality programs so that students feel motivated to utilize oral health education and improve oral health.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.2018-2043
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• 2022

Nowadays, COVID-19 infections are influencing our daily lives which have spread globally. The major symptoms' of COVID-19 are dry cough, sore throat, and fever which in turn to critical complications like multi organs failure, acute respiratory distress syndrome, etc. Therefore, to hinder the spread of COVID-19, a Computerized Doughty Predictor Framework (CDPF) is developed to yield benefits in monitoring the progression of disease from Chest CT images which will reduce the mortality rates significantly. The proposed framework CDPF employs Convolutional Neural Network (CNN) as a feature extractor to extract the features from CT images. Subsequently, the extracted features are fed into the Adaptive Dragonfly Algorithm (ADA) to extract the most significant features which will smoothly drive the diagnosing of the COVID and Non-COVID cases with the support of Doughty Learners (DL). This paper uses the publicly available SARS-CoV-2 and Github COVID CT dataset which contains 2482 and 812 CT images with two class labels COVID⁺ and COVI^-. The performance of CDPF is evaluated against existing state of art approaches, which shows the superiority of CDPF with the diagnosis accuracy of about 99.76%.