• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.125-134
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• 2024

The novel coronavirus 2019 is called COVID-19 has outspread swiftly worldwide. An early diagnosis is more important to control its quick spread. Medical imaging mechanics, chest calculated tomography or chest X-ray, are playing a vital character in the identification and testing of COVID-19 in this present epidemic. Chest X-ray is cost effective method for Covid-19 detection however the manual process of x-ray analysis is time consuming given that the number of infected individuals keep growing rapidly. For this reason, it is very important to develop an automated COVID-19 detection process to control this pandemic. In this study, we address the task of automatic detection of Covid-19 by using a popular deep learning model namely the VGG19 model. We used 1300 healthy and 1300 confirmed COVID-19 chest X-ray images in this experiment. We performed three experiments by freezing different blocks and layers of VGG19 and finally, we used a machine learning classifier SVM for detecting COVID-19. In every experiment, we used a five-fold cross-validation method to train and validated the model and finally achieved 98.1% overall classification accuracy. Experimental results show that our proposed method using the deep learning-based VGG19 model can be used as a tool to aid radiologists and play a crucial role in the timely diagnosis of Covid-19.

• Journal of Preventive Medicine and Public Health
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• v.53 no.3
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• pp.151-157
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• 2020

Objectives: The outbreak of coronavirus disease 2019 (COVID-19) is one of the main public health challenges currently facing the world. Because of its high transmissibility, COVID-19 has already caused extensive morbidity and mortality in many countries throughout the world. An accurate estimation of the basic reproduction number (R₀) of COVID-19 would be beneficial for prevention programs. In light of discrepancies in original research on this issue, this systematic review and meta-analysis aimed to estimate the pooled R₀ for COVID-19 in the current outbreak. Methods: International databases (including Google Scholar, Science Direct, PubMed, and Scopus) were searched to identify studies conducted regarding the R₀ of COVID-19. Articles were searched using the following keywords: "COVID-19" and "basic reproduction number" or "R₀." The heterogeneity among studies was assessed using the I² index, the Cochran Q test, and T². A random-effects model was used to estimate R₀ in this study. Results: The mean reported R₀ in the identified articles was 3.38±1.40, with a range of 1.90 to 6.49. According to the results of the random-effects model, the pooled R₀ for COVID-19 was estimated as 3.32 (95% confidence interval, 2.81 to 3.82). According to the results of the meta-regression analysis, the type of model used to estimate R₀ did not have a significant effect on heterogeneity among studies (p=0.81). Conclusions: Considering the estimated R₀ for COVID-19, reducing the number of contacts within the population is a necessary step to control the epidemic. The estimated overall R₀ was higher than the World Health Organization estimate.

• The Journal of Bigdata
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• v.3 no.1
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• pp.55-62
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• 2018

In this study, we have developed a model for predicting the number of patients (flu, pneumonia, and outbreak) using Big Data, which has been mainly performed overseas. Existing patient number system by government adopt procedures that collects the actual number and percentage of patients from several big hospital. However, prediction model in this study was developed combing a real-time collection of disease-related words and various other climate data provided in real time. Also, prediction number of patients were counted by machine learning algorithm method. The advantage of this model is that if the epidemic spreads rapidly, the propagation rate can be grasped in real time. Also, we used a variety types of data to complement the failures in Google Flu Trends.

• Journal of the Korea Society of Computer and Information
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• v.19 no.11
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• pp.149-157
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• 2014

Diabetes is a chronic disease that requires continuous medical care and patient-self management education to prevent acute complications and reduce the risk of long-term complications. The worldwide prevalence and incidence of diabetes mellitus are reached epidemic proportions in most populations. Early detection of diabetes could help to prevent its onset by taking appropriate preventive measures and managing lifestyle. The major objective of this research is to develop an automated decision support system for detection of diabetes using mixture of experts model. The performance of the classification algorithms was compared on the Pima Indians diabetes dataset. The result of this study demonstrated that the mixture of expert model achieved diagnostic accuracies were higher than the other automated diagnostic systems.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.4
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• pp.78-95
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• 2022

The result of previous studies and epidemiological invstigations for infectious diseases epidemic in livestock have shown that trips made by livestock-related vehicles are the main cause of the spread of these epidemics. In this study, the OD traffic volume of livestock freight vehicle during the week in each zone was calculated using livestock facility visit history data and digital tachograph data. Based on this, a model for predicting the spread of infectious diseases in livestock was developed. This model was trained using zonal records of foot-and-mouth disease in Gyeonggi-do for one week in January and February 2015 and in positive, it was succesful in predicting the outcome in all out of a total 13 actual infected samples for test.

• Journal of Korean Public Health Nursing
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• v.32 no.2
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• pp.194-207
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• 2018

Purpose: The purpose of this study was to identify the trends regarding diet and exercise intervention studies for Korean obese children between 2010 and 2017. Methods: This study was synthesized and reviewed systematically by meta-analysis. In addition, a total of thirty nine studies were investigated. The random effect model was used for meta-analysis. Results: Most studies used single interventions and that their theoretical frameworks still required improvement. In addition, on-line education programs still need to increase their number over that of off-line ones. Regarding the dependent variables for understandings the influences obesity may have on Korean children, most studies took advantage of biological indicators. In terms of the effects of obesity management programs, multiple interventions have gained a competitive edge over single ones for Korean obese children's diet and exercise. In a similar vein, healthy eating habits and adequate physical activities would have more positive effects on Korean children' obesity management programs. Conclusion: Further various studies will be needed for the early detection and prevention of obese children through varied interventions and qualitative improvement of studies.

• Journal of Nutrition and Health
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• v.46 no.6
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• pp.540-551
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• 2013

Vitamin D deficiency (VDD) is becoming an epidemic and thereby a global health problem. Further, VDD adversely affects calcium metabolism and skeletal health, and is associated with increased risk of several diseases, e.g., autoimmune diseases, several types of cancers, type 2 diabetes mellitus, cardiovascular diseases, infectious diseases, asthma, psoriatic arthritis, and etc. To evaluate the prevalence of VDD in Korea, and then to evaluate the association of several factors with serum 25(OH)D level, the author analyzed the data of 14,456 individuals who were 10 years of age and over from the Fifth Korea National Health and Nutrition Examination Survey 1 & 2 (KNHANES V-1 & 2) conducted by the Korean Centers for Disease Control & Prevention. As a result, among Koreans (age $${\geq_-}$$ 10years), 65.9% of males and 77.7% of females were below optimum blood serum 25(OH)D (20 ng/mL). VDD is more severe in female than in male at all age groups. In addition, the younger generations had less 25(OH)D level than older generations in Korea. The analysis by complex sample general linear model (CSGLM) suggested that blood 25(OH)D concentration was related with gender (p < .001), residence (p = .030), occupation (p < .001), anemia (p < .001) and physical activity (p < .001). In conclusion, VDD is pandemic and it is more severe in younger generations in Korea. Further, from the results by CSGLM, serum 25(OH)D status is closely related with the life style of Koreans.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.171-179
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• 2021

In the international markets, financial variables can be volatile and may affect each other, especially in the crisis times. COVID-19, which began in China in 2019 and spread to many countries of the world, created a crisis not only in the global health system but also in the international financial markets and economy. The purpose of this study is to analyze the contagious effect of the COVID-19 pandemic on the volatility of selected financial variables such as Bitcoin, gold, oil price, and exchange rates and the connections between the volatilities of these variables during the pandemic. For this aim, we use the ARMA-EGARCH model to measure the impact of volatility and shocks. In other words, it is aimed to measure whether the impact of the shock on the financial variables of the contagiousness of the epidemic is also transmitted to the markets. The data was collected from secondary and daily data from September 2th 2019 to December 20th, 2020. It can be said that the findings obtained have statistically significant effects on the conditional variability of the variables. Therefore, there are findings that the shocks in the market are contaminated with each other.

• International Journal of Advanced Culture Technology
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• v.10 no.3
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• pp.272-280
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• 2022

During last few years, pandemic of COVID-19 has been a global issue. Under the COVID-19, global events have been restricted or canceled to secure public hygiene and safety. Since one of the largest global events is Olympic Games, we selected recent Olympic Games as our case of analysis. Tokyo Olympic Games (TOG) was held in 2021, but it encountered a millennium disaster, the pandemic of COVID-19. In such a special period, it is of great significance to explore the emotional tendency of global views before and TOG via artificial intelligence. This paper vastly collects the TOG comment data of mainstream websites in South Korea, China, and the United States by implementing crawler program for sentiment analysis (SA). And we use a variety of sentiment analysis models to compare the accuracy of the experimental results, to obtain more reliable SA results. In addition, in the prediction results, to reduce the distortion of opinion by a minority, we introduce an algorithm called "Removing Biased Minority Opinions (RBMO)" and provide how to apply this method to the interpretation domain. Through our method, more authoritative SA results were obtained, which in turn provided a basis for predicting the sentiment tendency of countries around the world in TOG during the COVID-19 epidemic.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.149-158
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• 2022

The pandemic of Covid-19 (Coronavirus Disease 19) has devastated the world, affected millions of people, and disrupted the world economy. The cause of the Covid19 epidemic has been identified as a new variant known as Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV2). It motives irritation of a small air sac referred to as the alveoli. The alveoli make up most of the tissue in the lungs and fill the sac with mucus. Most human beings with Covid19 usually do no longer improve pneumonia. However, chest x-rays of seriously unwell sufferers can be a useful device for medical doctors in diagnosing Covid19-both CT and X-ray exhibit usual patterns of frosted glass (GGO) and consolidation. The introduction of deep getting to know and brand new imaging helps radiologists and medical practitioners discover these unnatural patterns and pick out Covid19-infected chest x-rays. This venture makes use of a new deep studying structure proposed to diagnose Covid19 by the use of chest X-rays. The suggested model in this work aims to predict and forecast the patients at risk and identify the primary COVID-19 risk variables