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Background/Objectives: This study identifies the key factors that enhance medical service by analyzing keywords of 761 papers registered in the Research Information Sharing Service(RISS) until 2019 in South Korea. Methods/Statistical analysis: Web crawling was performed based on Python to collect academic papers whose title or keywords include "medical service" among papers registered in RISS. Also, text mining, social network analysis, centrality analysis, and topic modeling were applied as big data analysis methods, and R 4.0.2, UCINET 6.7, and NetDraw 2.172 were used as software for analysis. Findings: Among the 3,175 keywords obtained as a result of text mining, keywords such as "Medical service", "Customer satisfaction", "Medical service quality", "Reuse intention", "Healthcare service", "Medical service value", "Medical tourism", "Emergency medical service", "Medical service use", "Loyalty", "Reliability", and "Telemedicine" were obtained at a high frequency of occurrence. A social network was derived based on 40 core keywords obtained from text mining, and there was a strong connection between "Customer satisfaction - Medical service quality", "Medical service - Customer satisfaction", "Customer satisfaction - Reuse intention", "Medical value quality - Reuse intention", "Customer satisfaction - Medical service value". Five topics such as "Reuse and recommendation intention", "Telemedicine & u-Healthcare", "Medical service accessibility", "Satisfaction management", and "Medical tourism" were obtained as critical topics representing the obtained keywords. The topic "Reuse and recommendation intention" with the highest proportion among the five topics indicates that there is a high quality of the relationship between the consumer and service provider when the consumers are delighted and confident in the service provider's service. Improvements/Applications: Based on the big data analysis method, the key factors of medical service improvement and the relationship between factors were presented. It can be used as fundamental data in attempts to enhance medical services. [ABSTRACT FROM AUTHOR]

During the Ming Dynasty, China and Korea exchanged frequently and recorded a large amount of written information, which is of great value for understanding the culture of that time. The large amount of data makes it difficult to conduct quantitative analysis by researchers, which makes the analysis limited. This paper carries out a research on the cognition and analysis method of Chinese and Korean envoys to foreign cultures in Ming Dynasty based on big data analysis technology. Based on the literature research, this paper determines the ontology model establishment method to efficiently detect the written records of Chinese and Korean envoys in Ming Dynasty. The established ontology model and the improved clustering analysis method can improve the efficiency of data detection, reduce the error of data detection, and provide data basis for the research of this paper. According to the technology of big data analysis, this paper analyzes the focus and status class of Chinese and Korean envoys in Ming Dynasty and analyzes cognition of the Chinese and Korean envoys for the foreign culture. The results show that the envoys of the Chinese and Korean pay different attention to the foreign culture due to their different cognition of the foreign culture, compared with Ming Dynasty envoys, Korean envoys paid 15.3 percent less attention to geography, 19.7 percent more to history, 11.7 percent more to people, and 16 percent less to customs. This reflects the two envoys' different perceptions of the foreign culture. And the status class of the envoys exacerbates this difference. In the early Ming Dynasty, the creative diversity of Ming envoys was far lower than that of Korean envoys. As time went by, the creative diversity of Ming envoys increased. The results provide support for further understanding of Chinese and Korean culture and their relationship in Ming Dynasty. [ABSTRACT FROM AUTHOR]

The purpose of this paper was to evaluate the usability of Korean logistics in life (LIL) startups mobile apps that aim to provide more convenience and a better quality of life. Determining an app’s usability requires a different set of evaluation criteria from that of desktop sites or full sites due to the inherent characteristics of the mobile environment. This paper derived 10 evaluation criteria from a literature review, calculated the weights of each criterion, and performed a usability test for 14 Korean LIL apps involving 27 subjects. To highlight areas of improvement, a comparison was done using a top ranked app on Google Play which had the best score in terms of usability (in 10 items test) among the best Korean LIL app. The average usability score of the LIL apps represented an overall lack of high usability. The evaluation criteria of the apps showed higher scores in the error-free, prediction, and consistency variables categories. In contrast, this was not the case for effectiveness, usefulness, and interaction, which were relatively low. Usability and interest had a slightly negative relationship when implementing big data analytics. From the quadrants analysis, it was found that for most of the evaluation criteria, continuous improvement was needed. As a result of logistics 4.0, the number of LIL apps is growing rapidly. This paper is the first to define the concept of LIL and develop an evaluation model for usability, which was then used to identify the ease of use, learnability, effectiveness, and efficiency of representative LIL apps in Korea. [ABSTRACT FROM AUTHOR]

South Korea is at an important juncture in its history to decide whether to continue its investment to become a first-mover of artificial intelligence (A.I.) platform technology or stay as a fast follower. This paper compares South Korea's A.I. platform capacity to that of the United States, China and the European Union by reviewing publicly opened documents and reports on AI platform strategies and policies using the three elements of the national innovation capacity: common innovation infrastructure, cluster-specific conditions, and quality of linkages. This paper found three major areas the South Korean government can focus on in the A.I. platform industry. First, South Korea needs to increase its investment in the A.I. field and expand its public-private collaboration activities. Second, unlike the U.S. and the U.K., South Korea lacks data protection policies. Third, South Korea needs to build a high-performance system and environment to experiment with artificial intelligence technology and big data. [ABSTRACT FROM AUTHOR]

Objective: To determine age-related risk factors for chronic rhinosinusitis (CRS) with asthma. Methods: Data were obtained from a national survey of non-hospitalized civilians conducted by the Korean Center for Disease Control and Prevention. CRS diagnosis was based on the guidelines of the European Position Paper on Rhinosinusitis and Nasal Polyps 2020. Asthma was judged based on whether the patient had been diagnosed with asthma in the past. Of the 45,811 survey participants, 26,335 were included in the cross-sectional study. Participants included in the study were divided into the control, CRS, and CRS with asthma groups. Age-related risk factors were analyzed in patients aged < 60 or > 60 years. Univariate logistic analyses were performed to evaluate the relationship between groups. Risk factors included age, sex, household income, residence, education level, occupation, and body mass index (BMI). Results: Education level (Odds Ratio [OR]: 0.342, P =.0003), BMI (OR: 1.09, P =.0082), and total IgE (TIgE) levels (OR: 5.582, P =.003) were significantly different between the control and the CRS with asthma group. Education level (OR: 0.478, P =.0016) and TIgE levels (OR: 4, P =.0218) were significantly different between the CRS and CRS with asthma groups under 60 years of age. BMI (OR: 1.087, P =.0443; OR: 1.104, P =.0224) showed a significant difference between all three groups with age > 60 years. Conclusion: Progression to CRS with asthma is influenced by education level, occupation, and TIgE levels in patients under 60 years of age. BMI was the only influencing factor associated with the progression to CRS with asthma in those aged > 60 years. [ABSTRACT FROM AUTHOR]

Purpose: Despite a growing interest in business analytics (BA) from the business and academic communities, it is still unknown what truly motivates and hinders the adoption of BA. To have a clear picture of what will lead to the successful implementation of BA, this paper identifies contextual variables (e.g. user characteristics, organizational readiness and technology infrastructure/expertise) that significantly influence the BA adoption decision. Design/methodology/approach: This paper conducted a series of classification, discriminant and logistics regressions analyses to analyze the differences in mail survey responses between adopters and nonadopters of BA and then determine what either motivate or inhibit the BA adoption. Findings: Through a series of hypothesis testing, we discovered that large firms with a greater number of information technology (IT) staff and budget tended to adopt BA more than their smaller counterparts. Also, we found that BA skeptics, who did not fully recognize BA benefit potentials, were more concerned about BA implementation costs and experienced the greater organization resistance to BA adoption than the others did. Therefore, they were less likely to adopt BA. Originality/value: In the era of knowledge-based economy, the firm's ability to derive actionable insights from big data can be a game changer. Such ability can be developed and nurtured by utilizing BA which is designed to help business executives and policymakers make well-thought and informed decisions. This paper is one of the first attempts to develop practical guidelines for the successful implementation of BA based on the exploratory study of BA practices among the Korean firms. [ABSTRACT FROM AUTHOR]

Korea has focused its efforts on reducing fine dust particles in Seoul over the past day, with the focus on improving the quality of fine dust products, such as a complete reduction in public transportation, closing down power plants, and exchanging fine dust with China. Despite these efforts, however, the nation's fine dust concentration has not significantly decreased, leaving people feeling uneasy and dissatisfied. Therefore, this paper utilizes a multiple regression analysis conducted by using R, a tool for analyzing big data, to identify variables that affect the level of public transport satisfaction, and to analyze multiple regression analysis at each level. [ABSTRACT FROM AUTHOR]

In recent years, the major technology of smart ships in the shipbuilding and marine sectors focuses on data platforms, telecommunications, and cyber security issues, which can only be mounted on ships if they meet technical specifications and performance requirements defined by international standards, not problems solved through technological development. Norway and Japan are taking the lead in standard development in this field, and the Korea is also actively participating to reflect the opinions of the domestic industry in the international community. However, related academic research has not been carried out systematically. Therefore, this study attempts to analyze the current status and development of standards in data-related fields among subcommittees of Ships and Marine Technology (SC), which are covered by Technical Committee 8 (TC 8) of the International Organization for Standardization (ISO). Especially, the tasks for the relevant subjects will be performed by appealing to the international society through participation in various international conferences attended by the subcommittee of SC 6 'Navigation and Ship Operation', SC 11 'Intermodal and Short Sea Shipping' or a work group of WG 10 'Smart Shipping' under TC 8, considering the inputs from local experts and Korean Agency for Technology and Standards. In addition, this study examines the current status and problems of data platforms in the shipbuilding and marine sectors, and examines the strategies of Korea and the time and cost benefits that follow them. To this end, this study implements literature review and empirical analysis. This paper collected and analyzed Big Data from January 1, 2020 to December 31, 2020, using the keywords 'smart ship', 'autonomous ship', 'marine big data', 'data integration platform', and 'ship Big Data'. While the domestic shipbuilders were developing their own smart ship data integration platforms, they agreed that they would be vulnerable to global smart ship data integration platform initiatives and standard data development. If the data integration platform is developed or improved through unprecedented collaboration in the competitive systems of the shipbuilding industry, it is would improve the competitiveness of the Korean shipbuilding industry by applying the shipbuilders' Captive volume. Investment costs can also be reduced by leading international standardization and improving platforms for shipyards through joint responses from international standards (ISO). [ABSTRACT FROM AUTHOR]

The COVID-19 Epidemiological Investigation Support System (EISS) is a digital epidemiological tool, which utilizes location data from cellular base stations, credit card transactions records, and QR codes. It is a mass surveillance system that uses big data to track the entire infected population, featuring an extensive, automated, and speedy processing of data on personal location and the linkage of multiple databases from various governmental agencies. Based on interviews with people who have developed Korean digital epidemiology systems, this paper explores the technical, infrastructural, social, and institutional factors that have shaped Korean digital epidemiology since the 2014 avian flu crisis and examines the essential conditions of big data for digital epidemiology. The main findings are as follows: The feasibility of EISS goes beyond the matter of privacy; it is closely connected to technological infrastructures such as a high density of cellular base stations and private cloud systems; people's behavior such as a high rate of smartphone and credit card usage; and new forms of governance and institutions for speedy data processing. Multiple database linkage would develop EISS into a big data surveillance system that enables the prediction of risk-prone groups in a more preemptive manner. [ABSTRACT FROM AUTHOR]

This study uses the API of Upbit, one of Korea's cryptocurrency exchanges, to predict continuous time series for a limited period and cryptocurrencies using LSTM, a machine learning technique. The trading (buying and selling) point algorithm presented in this study was used to conduct experimental research on efficient profit creation for cryptocurrency investment. Several related studies have shown the results of time series prediction for long-term forecasts, such as a week or several months. Still, they have not attempted to make an ultra-short-term prediction in units of one minute. This paper attempts such a 1 min prediction. This is an experiment to create efficient profits by setting efficient trading (buying and selling) points using machine learning techniques and repeating these operations by an algorithm. Applying it to cryptocurrency shows the possibility of time series prediction. [ABSTRACT FROM AUTHOR]

In predicting energy consumption, classic econometric and statistical models are used to forecast energy consumption. These models may have limitations in an increasingly fast-changing energy market, which requires big data analysis of energy consumption patterns and relevant variables using complex mathematical tools. In current literature, there are minimal comparison studies reviewing machine learning algorithms to predict energy consumption in Korea. To bridge this gap, this paper compared three different machine learning algorithms, namely the Random Forest (RF) model, XGBoost (XGB) model, and Long Short-Term Memory (LSTM) model. These algorithms were applied in Period 1 (prior to the onset of the COVID-19 pandemic) and Period 2 (after the onset of the COVID-19 pandemic). Period 1 was characterized by an upward trend in energy consumption, while Period 2 showed a reduction in energy consumption. LSTM performed best in its prediction power specifically in Period 1, and RF outperformed the other models in Period 2. Findings, therefore, suggested the applicability of machine learning to forecast energy consumption and also demonstrated that traditional econometric approaches may outperform machine learning when there is less unknown irregularity in the time series, but machine learning can work better with unexpected irregular time series data. [ABSTRACT FROM AUTHOR]

At present, we live in prosperity contrary to the past times. As income increases, people enjoy wealth, but more people tend to pursue their own inner happiness: travel. People go to other places or visit foreign countries for business or journey. This study aims to identify the best tour route for foreign tourists in South Korea. Based on the review analysis results, this paper also aims to put forward techniques and methodologies required in practical affairs when developing a travel site or a travel application. On this note, it collected tourists' reviews at the Tripadvisor official website and conducted text mining technique as well as network analysis using R and Tagxedo, which is a big data analytic tool. The analysis results displayed that there were differences in travel preference, and especially, individual travelers had difficulty traveling by public transportation and selecting travel destinations. Therefore, customized travel routes were suggested for convenient use among travelers. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to illustrate how big data analytics pushed the limits of individuals' accountability as South Korea tried to control and contain coronavirus disease 2019 (COVID-19). Design/methodology/approach: The authors draw upon Deleuzo-Guattarian framework elaborating how a surveillant assemblage was rhizomatically created and operated to monitor a segment of the population holding them accountable. Publicly available secondary data, such as press release from the government and media coverage, were used. Findings: A COVID-19 Smart Management System and a Self-Quarantine Safety Protection App constituted a surveillance assemblage operating in a "state-form". This comprises the central government departments, local councils, policing systems, providers of telecommunication and financial services, and independent groups of people. This assemblage pushed the limits of accountability as individuals who tested positive or might bear possible future risks of the infection and transmission were held accountable for their locations and health conditions. Practical implications: Policymakers may consider constructing this type of state-form for containing and controlling pandemics, such as COVID-19, while dealing with the issue of undermined privacy. Social implications: The mass may consider to what extent individuals' personal information should be protected and how to hold the governments accountable for the legitimate use of such information. Originality/value: While accountability studies have largely focussed on formal organisations, the authors illustrated how a broader context of a state-form, harnessing big data analytics, pushes the limits of accountability. [ABSTRACT FROM AUTHOR]

In this paper we demonstrate the threshold effects of infectious diseases on livestock prices. Daily retail prices of pork and chicken were used as structured data; news and SNS mentions of African Swine Fever (ASF) and Avian Influenza (AI) were used as unstructured data. Models were tested for the threshold effects of disease-related news and SNS frequencies, specifically those related to ASF and AI, on the retail prices of pork and chicken, respectively. The effects were found to exist, and the values of ASF-related news on pork prices were estimated to be −9 and 8, indicating that the threshold autoregressive (TAR) model can be divided into three regimes. The coefficients of the ASF-related SNS frequencies on pork prices were 1.1666, 0.2663 and −0.1035 for regimes 1, 2 and 3, respectively, suggesting that pork prices increased by 1.1666 Korean won in regime 1 when ASF-related SNS frequencies increased. To promote pork consumption by SNS posts, the required SNS frequencies were estimated to have impacts as great as one standard deviation in the pork price. These values were 247.057, 1309.158 and 2817.266 for regimes 1, 2 and 3, respectively. The impact response periods for pork prices were estimated to last 48, 6, and 8 days for regimes 1, 2 and 3, respectively. When the prediction accuracies of the TAR and autoregressive (AR) models with regard to pork prices were compared for the root mean square error, the prediction accuracy of the TAR model was found to be slightly better than that of the AR. When the threshold effect of AI-related news on chicken prices was tested, a linear relationship appeared without a threshold effect. These findings suggest that when infectious diseases such as ASF occur for the first time, the impact on livestock prices is significant, as indicated by the threshold effect and the long impact response period. Our findings also suggest that the impact on livestock prices is not remarkable when infectious diseases occur multiple times, as in the case of AI. To date, this study is the first to suggest the use of SNS to promote meat consumption. [ABSTRACT FROM AUTHOR]

The policy change in the Data 3 Act is one of the issues that should be noted at a time when non-face-to-face business strategies become important after COVID-19. The Data 3 Act was implemented in South Korea on August 5, 2020, calling 'Big Data 3 Act' and 'Data Economy 3 Act,' and so personal information that was not able to identify a particular individual could be utilized without the consent of the individual. With the implementation of the Data 3 Act, it is possible to establish a fair economic ecosystem by ensuring fair access to data and various uses. In this paper, the law on the protection of personal information, which is the core of the Data 3 Act, was compared around Korea, the European Union and the United States, and the implications were derived through network analysis of keywords. [ABSTRACT FROM AUTHOR]

South Korea has allowed parallel importation since 1995. Parallel importation causes competition among importers in the logistics process allowing, consumers to purchase foreign brand products at low prices. Most parallel importers base product pricing on subjective judgements. Fashion products in particular, have different sales rates depending on trends and seasons, so sales performance varies greatly depending on selling price timing and policy. The merchandiser (MD) set the price on parallel importation products by aggregating information on imported products and pricing goods. However, this customized process is very time consuming for the MD. This is because the logistics process of parallel importation's customs clearance procedures and repair works is complicated and takes a significant amount of time. In this paper, we propose an improved parallel importation logistics process based on big data, which automatically sets the price of parallel importation products. [ABSTRACT FROM AUTHOR]

Information communication technology (ICT)-based mine safety management systems are being introduced at numerous mining sites to track the location of equipment and workers in real time and monitor environmental changes. This paper presents the results of a case study in which the big data created by an ICT-based mine safety management system are used for simulating truck haulage operations. An underground limestone mine located in Danyang, South Korea was studied, and the data generated over three months, from October 1 to December 31, 2018, were analyzed. Truck tag packet data recognized by relays were extracted and analyzed to calculate the averages and standard deviations of the truck travel times of each mine segment. A discrete event simulation program that simulates truck haulage operations in the study area was developed. Haulage times, the number of haulage operations, production output, and truck delay times were predicted, and results were compared with the actual operation results that were obtained on January 2 and 9, 2019. The difference between the predicted and actual results for the total amount of loaded ore was 30 tons for January 2 and 0 tons for January 9. The mean absolute error between the predicted and observed truck travel times was 0.13 min for January 2 and 0.14 min for January 9. The truck travel times that were measured differently according to the data aggregation period were set as temporal factors, and truck haulage simulations were performed. The results showed that more reliable simulation results were obtained as data accumulation time increased. [ABSTRACT FROM AUTHOR]

Smartphones and PCs have become essential components of our daily life. People are expressing their opinions freely in SNS by using these devices. We are able to predict public opinions on specific subject by analyzing the related big data in SNS. In this paper, we have collected opinion data in SNS and analyzed reputation by text mining in order to make a prediction for the will of the people before 19th presidential election in South Korea. The result shows that our method makes more accurate estimate than other election polls. [ABSTRACT FROM AUTHOR]

In 2020 and 2021, humanity lived in fear due to the COVID-19 pandemic. However, with the development of artificial intelligence technology, mankind is attempting to tackle many challenges from currently unpredictable epidemics. Korean society has been exposed to various infectious diseases since the Korean War in 1950, and to overcome them, the six most serious cases in National Notifiable Infectious Diseases (NNIDs) category I were defined. Although most infectious diseases have been overcome, viral hepatitis A has been on the rise in Korean society since 2010. Therefore, in this paper, the prediction of viral hepatitis A, which is rapidly spreading in Korean society, was predicted by region using the deep learning technique and a publicly available dataset. For this study, we gathered information from five organizations based on the open data policy: Korea Centers for Disease Control and Prevention (KCDC), National Institute of Environmental Research (NIER), Korea Meteorological Agency (KMA), Public Open Data Portal, and Korea Environment Corporation (KECO). Patient information, water environment information, weather information, population information, and air pollution information were acquired and correlations were identified. Next, an epidemic outbreak prediction was performed using data preprocessing and 3D LSTM. The experimental results were compared with various machine learning methods through RMSE. In this paper, we attempted to predict regional epidemic outbreaks of hepatitis A by linking the open data environment with deep learning. It is expected that the experimental process and results will be used to present the importance and usefulness of establishing an open data environment. [ABSTRACT FROM AUTHOR]

This study quantifies the dynamic interrelationship between the KOSPI index return and search query data derived from the Naver DataLab. The empirical estimation using a bivariate GARCH model reveals that negative contemporaneous correlations between the stock return and the search frequency prevail during the sample period. Meanwhile, the search frequency has a negative association with the one-week- ahead stock return but not vice versa. In addition to identifying dynamic correlations, the paper also aims to serve as a test bed in which the existence of profitable trading strategies based on big data is explored. Specifically, the strategy interpreting the heightened investor attention as a negative signal for future returns appears to have been superior to the benchmark strategy in terms of the expected utility over wealth. This paper also demonstrates that the big data-based option trading strategy might be able to beat the market under certain conditions. These results highlight the possibility of big data as a potential source-which has been left largely untapped-for establishing profitable trading strategies as well as developing insights on stock market dynamics. [ABSTRACT FROM AUTHOR]

The purpose of this study is to present and develop a practical advertising model that minimizes transport advertisements, indiscriminate banners and flyers, and logo light installation problems with smart advertisements using projected beams. In order to develop an integrated smart advertisement that is convenient and inexpensive for companies operating at night, the scope of the research is determined as a practical advertisement that identifies the problems of existing bus and taxi smart advertisements and the needs of customers. The smart projected beam advertisement that the author claimed is a customized advertisement that solves the advertising problems of customers and local governments in Korea based on business hours, locational information, and big data. The projected beam outdoor advertising system developed by the author was designed to reduce environmental and resource waste, avoid unnecessary advertisement exposure for consumers, and obtain necessary information for businesses. Moreover, it was developed to reduce advertising costs for businesses. This is a very appropriate solution as an alternative to reduce the night time flyers and banners that local governments in Korea are struggling with. Consumers can avoid unnecessary advertisement exposure and obtain customized information, while advertisers who use the service can directly insert their sales information into advertisements in real-time using various media at a low cost. [ABSTRACT FROM AUTHOR]

Background and Objectives: Soft tissue sarcomas represent a heterogeneous group of malignant mesenchymal tissues. Despite their low prevalence, soft tissue sarcomas present clinical challenges for orthopedic surgeons owing to their aggressive nature, and perioperative wound infections. However, the low prevalence of soft tissue sarcomas has hindered the availability of large-scale studies. This study aimed to analyze wound infections after wide resection in patients with soft tissue sarcomas by employing big data analytics from the Hub of the Health Insurance Review and Assessment Service (HIRA). Materials and Methods: Patients who underwent wide excision of soft tissue sarcomas between 2010 and 2021 were included. Data were collected from the HIRA database of approximately 50 million individuals' information in the Republic of Korea. The data collected included demographic information, diagnoses, prescribed medications, and surgical procedures. Random forest has been used to analyze the major associated determinants. A total of 10,906 observations with complete data were divided into training and validation sets in an 80:20 ratio (8773 vs. 2193 cases). Random forest permutation importance was employed to identify the major predictors of infection and Shapley Additive Explanations (SHAP) values were derived to analyze the directions of associations with predictors. Results: A total of 10,969 patients who underwent wide excision of soft tissue sarcomas were included. Among the study population, 886 (8.08%) patients had post-operative infections requiring surgery. The overall transfusion rate for wide excision was 20.67% (2267 patients). Risk factors among the comorbidities of each patient with wound infection were analyzed and dependence plots of individual features were visualized. The transfusion dependence plot reveals a distinctive pattern, with SHAP values displaying a negative trend for individuals without blood transfusions and a positive trend for those who received blood transfusions, emphasizing the substantial impact of blood transfusions on the likelihood of wound infection. Conclusions: Using the machine learning random forest model and the SHAP values, the perioperative transfusion, male sex, old age, and low SES were important features of wound infection in soft-tissue sarcoma patients. [ABSTRACT FROM AUTHOR]

Background: Failed back surgery syndrome (FBSS) is a chronic condition that is characterized by persistent back pain following one or more spinal surgeries. Pharmacological interventions, such as the use of opioids and gabapentinoids, are frequently used in the treatment of FBSS. However, prolonged and excessive use of these medications can lead to dependence and adverse effects. This study investigates trends in opioid and gabapentinoid prescriptions among patients with FBSS in Korea from 2016 to 2020. Methods: Data from the Health Insurance and Review Agency were analyzed, and claims listing FBSS were selected for the study. Prescription patterns of opioids and gabapentinoids were classified based on the number of days prescribed per year. Results: Of the 390,095 patients diagnosed with FBSS, 41.6% of the patients were prescribed gabapentinoids, and 42.0% of them were prescribed opioids, while 10.6% of the patients were classified as long-term gabapentinoid users, 11.4% as long-term opioid users, and 7.4% of the patients were found to have long-term prescriptions for both drugs. The proportion of patients who received both gabapentinoid and opioid prescriptions increased annually. The doses of opioids prescribed have also increased along with the increase in the number of patients receiving opioid prescriptions. Conclusions: The prescription rates of opioids and gabapentinoids among patients with FBSS in Korea continue to increase steadily, posing potential risks of addiction and adverse effects. Further research is needed to better understand the actual status of addiction in patients with FBSS. [ABSTRACT FROM AUTHOR]

High-growth firms (HGFs), also known as gazelle firms, have attracted considerable attention due to their outstanding contributions to job creation and additional spillover benefits. Despite years of work, it is still challenging to predict high-growth firms, and many essential HGF characteristics are yet unknown. This study analyzes high-quality Korean firm data and predicts high-growth firms using big data and machine learning methods, such as LASSO, adaptive LASSO, and random forest analysis. Besides, we include variables related to business strategies and activities such as innovation, information systems like ERP, outsourcing, strategic alliances, and expansion to international markets to determine whether the addition of this information enhances HGF prediction. We discovered the significance of revenue growth, efficiency management, asset investment, and human resource management skills in increasing one's chances of becoming an HGF. The findings suggest that rather than being an erratic process or being purely driven by structural characteristics, firm growth is determined by the firm's endogenous strategic characteristics. Plain English Summary: With cutting-edge machine learning techniques including LASSO, adaptive LASSO, and random forest, we discovered the significance of revenue growth, efficiency management, asset investment, and human resource management skills in increasing one's chances of becoming an HGF. [ABSTRACT FROM AUTHOR]

As the number of researchers in South Korea has grown, there is increasing dissatisfaction with the selection process for national research and development (R&D) projects among unsuccessful applicants. In this study, we designed a system that can recommend the best possible R&D evaluators using big data that are collected from related systems, refined, and analyzed. Our big data recommendation system compares keywords extracted from applications and from the full-text of the achievements of the evaluator candidates. Weights for different keywords are scored using the term frequency–inverse document frequency algorithm. Comparing the keywords extracted from the achievement of the evaluator candidates', a project comparison module searches, scores, and ranks these achievements similarly to the project applications. The similarity scoring module calculates the overall similarity scores for different candidates based on the project comparison module scores. To assess the performance of the evaluator candidate recommendation system, 61 applications in three Review Board (RB) research fields (system fusion, organic biochemistry, and Korean literature) were recommended as the evaluator candidates by the recommendation system in the same manner as the RB's recommendation. Our tests reveal that the evaluator candidates recommended by the Korean Review Board and those recommended by our system for 61 applications in different areas, were the same. However, our system performed the recommendation in less time with no bias and fewer personnel. The system requiresrevisions to reflect qualitative indicators, such as journal reputation, before it can entirely replace the current evaluator recommendation process. [ABSTRACT FROM AUTHOR]

Skippable ads are known to provide better advertising experiences by giving viewers a sense of control with the ability to skip an ad after watching it for a few seconds. Despite the growing interest, few studies have investigated factors that influence skipping or viewership of pre-roll skippable ads. This study examines the determinants of pre-roll ad acceptance by using clickstream data of 2,078,090 users' ad and content viewing behaviors on a popular online video content platform in South Korea. We found that skippable ad acceptance is impacted by personal, situational, and contextual factors that influence the level of goal impediment in online video consumption contexts. We conclude with theoretical and practical implications of the findings. [ABSTRACT FROM AUTHOR]

Nowadays researchers and engineers are trying to build travel route recommendation systems to guide tourists around the globe. The tourism industry is on the rise and it has attracted researchers to provide such systems for comfortable and convenient traveling. Mobile internet growth is increasing rapidly. Mobile data usage and traffic growth has increased interest in building mobile applications for tourists. This research paper aims to provide design and implementation of a travel route recommendation system based on user preference. Real-time big data is collected from Wi-Fi routers installed at more than 149 unique locations in Jeju Island, South Korea. This dataset includes tourist movement patterns collected from thousands of mobile tourists in the year 2016–2017. Data collection and analysis is necessary for a country to make public policies and development of the global travel and tourism industry. In this research paper we propose an optimal travel route recommendation system by performing statistical analysis of tourist movement patterns. Route recommendation is based on user preferences. User preference can vary over time and differ from one user to another. We have taken three main factors into consideration to the recommend optimal route i.e., time, distance, and popularity of location. Beside these factors, we have also considered weather and traffic condition using a third-party application program interfaces (APIs). We have classified regions into six major categories. Popularity of location can vary from season to season. We used a Naïve Bayes classifier to find the probability of tourists going to visit next location. Third-party APIs are used to find the longitude and latitude of the location. The Haversine formula is used to calculate the distance between unique locations. On the basis of these factors, we recommend the optimal route for tourists. The proposed system is highly responsive to mobile users. The results of this system show that the recommended route is convenient and allows tourists to visit maximum number of famous locations as compared to previous data. [ABSTRACT FROM AUTHOR]

The damage caused by fire accidents is increasing worldwide. In particular, when a fire occurs, property damage directly affects the lives of citizens. Therefore, in this study, machine learning techniques were applied to analyze the prediction of the future amount of property damage from fire as well as the fire occurrence factors within a geographic area. To achieve this, three years of spatially distributed fire big data for Seoul, the capital of Korea, was used. For the predictive analysis of the amount of fire property damage, the results of analysis by applying machine learning techniques through k-fold cross-validation were calculated. As part of these results, when predicting and analyzing the amount of fire property damage using the random forest (RF) algorithm, an accuracy of 83% was calculated by comparing the predicted data with the actual data. On this basis, the importance of the fire risk factors was analyzed, and it was found that the main factor in the occurrence of fires is the condition of the facilities inside apartment houses. The findings of this study are expected to be used as an important guide for identifying property damage by fire and the factors determining the occurrence of fires in Korea, enabling the evaluation of their spatial distribution and the application of corrective measures to reduce possible damage by urban fires. [ABSTRACT FROM AUTHOR]

Ensuring food security has become of paramount importance due to the rising global population. In particular, the agriculture sector in South Korea faces several challenges such as an aging farming population and a decline in the labor force. These issues have led to the recognition of smart farms as a potential solution. In South Korea, the smart farm is divided into three generations. The first generation primarily concentrates on monitoring and controlling precise cultivation environments by leveraging information and communication technologies (ICT). This is aimed at enhancing convenience for farmers. Moving on to the second generation, it takes advantage of big data and artificial intelligence (AI) to achieve improved productivity. This is achieved through precise cultivation management and automated control of various farming processes. The most advanced level is the 3rd generation, which represents an intelligent robotic farm. In this stage, the entire farming process is autonomously managed without the need for human intervention. This is made possible through energy management systems and the use of robots for various farm operations. However, in the current Korean context, the adoption of smart farms is primarily limited to the first generation, resulting in the limited utilization of advanced technologies such as AI, big data, and cloud computing. Therefore, this research aims to develop the second generation of smart farms within the first generation smart farm environment. To accomplish this, data was collected from nine sensors spanning the period between 20 June to 30 September. Following that, we conducted kernel density estimation analysis, data analysis, and correlation heatmap analysis based on the collected data. Subsequently, we utilized LSTM, BI-LSTM, and GRU as base models to construct a stacking ensemble model. To assess the performance of the proposed model based on the analyzed results, we utilized LSTM, BI-LSTM, and GRU as the existing models. As a result, the stacking ensemble model outperformed LSTM, BI-LSTM, and GRU in all performance metrics for predicting one of the sensor data variables, air temperature. However, this study collected nine sensor data over a relatively short period of three months. Therefore, there is a limitation in terms of considering the long-term data collection and analysis that accounts for the unique seasonal characteristics of Korea. Additionally, the challenge of including various environmental factors influencing crops beyond the nine sensors and conducting experiments in diverse cultivation environments with different crops for model generalization remains. In the future, we plan to address these limitations by extending the data collection period, acquiring diverse additional sensor data, and conducting further research that considers various environmental variables. [ABSTRACT FROM AUTHOR]

Featured Application: Systematic review and meta-analysis deserves to be considered the gold standard of evidence-based medicine in the era of big data. In terms of medical health, we are currently living in the era of data science, which has brought tremendous change. Big data related to healthcare includes medical data, genome data, and lifelog data. Among medical data, public medical data is very important for actual research and medical policy reflection because it has data on a large number of patients and is representative. However, there are many difficulties in actually using such public health big data and designing a study, and conducting systematic review (SR) on the research topic can help a lot in the methodology. In this review, in addition to the importance of research using big data for the public interest, we will introduce important public medical big data in Korea and show how SR can be specifically applied in research using public medical big data. [ABSTRACT FROM AUTHOR]

Background: The data related to pain and catastrophic health expenditure (CHE) needed to be further explored. This study aims to understand the relationship between pain and CHE. Methods: Using cross-sectional analysis of 4-year data (2015-2018) from the Korea Health Panel, the prevalence of CHE and adjusted odds ratio (AOR) by pain type were confirmed. Results: Among participants (n = 46,597), the prevalence of pain and severe pain were 24.2% and 1.1%, respectively. The use of medical services in emergency rooms, hospitalizations, and outpatients increased in the order of pain-free, pain, and severe pain (P < 0.001). Prevalence of household CHE was 3.3% vs. 11.1% vs. 25.9%, (P < 0.001). The AOR of CHE was 1.5 (95% confidence interval [CI], 1.4-1.7) for pain and 3.1 (95% CI, 2.5-3.9) for severe pain. Household capacity to pay per year was lower and lower in the order of pain-free, pain, and severe pain ($25,094 vs. $17,965 vs. $14,056, P < 0.001). Also, the household out-of-pocket expenditure per year was higher and higher in the order of pain-free, pain, and severe pain ($1,649, $1,870, $2,331, P < 0.001). Conclusions: It can be inferred that pain is one of the mechanisms of poverty. Positivist healthcare policies for the prevention and management of pain should be sought. [ABSTRACT FROM AUTHOR]

Purpose We aimed to determine the trends in the use of radiotherapy (RT) and the expenses associated with it in South Korea. Materials and Methods The statistical data of the claims and reimbursement records provided on the Health and Insurance Review and Assessment Service website were utilized. This included information such as the number of patients, fractions, medical expenses according to treatment codes, in/outpatient, sex, age, and regions of hospitals. We analyzed data from 2016 to 2020. Results With a growing RT infrastructure and an increase in the number of radiation oncologists, the expenses for RT were 605.5 million USD in 2020, which had increased 1.5 times from 394.7 million USD in 2016. This growth was mainly because of the increased usage of advanced RT techniques. Furthermore, the proportion of intensity-modulated radiation therapy (IMRT) expenses in the total expenses increased by 1.6 times from 48.8% in 2016 to 76.9% in 2020. Advanced techniques were used more commonly in older individuals or children. However, the proportion of IMRT expenses increased mostly in young women. Additionally, geographical differences in RT use and expense were observed, although the gap in the IMRT fractions decreased among the regions. Conclusion Recent medical expenses associated with RT in Korea have increased in tandem with technological advances and changes in demographics. [ABSTRACT FROM AUTHOR]

Purpose This study aimed to elucidate the trends and characteristics of the cancer burden in Korea by cancer site, region, and income level. Materials and Methods Korean National Burden of Disease research methodology was applied to measure the cancer burden in Korea from 2008 to 2018. The cause of death and National Health Insurance claims data were obtained from Statistics Korea and the National Health Insurance Service, respectively. An incidence-based approach was applied to calculate the disability-adjusted life-years, which is a summary measure of population health. Results In the past decade, the cancer burden in Korea increased from 2,088 to 2,457 person-years per 100,000 population. Among the cancer burden, the years of life lost decreased, and the years lived with disabilities increased. Cancers of the trachea, bronchus, and lung had the highest disease burden, followed by those of the stomach, colon and rectum, liver, and breast. Conclusion The findings of this study can provide valuable quantitative data for prioritizing and evaluating cancer prevention strategies and implementing cancer policies. Estimating the difference in cancer burden according to region and income level within a country can yield useful data to understand the nature of the cancer burden and scale of the problem. In addition, the results of this study provide a better understanding of the causes of cancer patterns, thereby generating new hypotheses regarding its pathogenesis. [ABSTRACT FROM AUTHOR]

The Korean National Health Information Database (NHID) contains big data combining information obtained from the National Health Insurance Service and health examinations. Data are provided in the form of a cohort, and the NHID can be used to conduct longitudinal studies and research on rare diseases. Moreover, data on the cause and date of death are provided by Statistics Korea. Research and publications based on the NHID have increased explosively in the field of endocrine disorders. However, because the data were not collected for research purposes, studies using the NHID have limitations, particularly the need for the operational definition of diseases. In this review, we describe the characteristics of the Korean NHID, operational definitions of endocrine diseases used for research, and an overview of recent studies in endocrinology using the Korean NHID. [ABSTRACT FROM AUTHOR]

The Republic of Korea also suffered direct and indirect damages from the Fukushima nuclear accident in Japan and realized the significance of security due to the cyber-threat to the Republic of Korea Hydro and Nuclear Power Co., Ltd. With such matters in mind, this study sought to suggest a measure for improving security in the nuclear power plant. Based on overseas cyber-attack cases and attacking scenario on the control facility of the nuclear power plant, the study designed and proposed a nuclear power plant control network traffic analysis system that satisfies the security requirements and in-depth defense strategy. To enhance the security of the nuclear power plant, the study collected data such as internet provided to the control facilities, network traffic of intranet, and security equipment events and compared and verified them with machine learning analysis. After measuring the accuracy and time, the study proposed the most suitable analysis algorithm for the power plant in order to realize power plant security that facilitates real-time detection and response in the event of a cyber-attack. In this paper, we learned how to apply data for multiple servers and apply various security information as data in the security application using logs, and match with regard to application of character data such as file names. We improved by applying gender, and we converted to continuous data by resetting based on the risk of non-continuous data, and two optimization algorithms were applied to solve the problem of overfitting. Therefore, we think that there will be a contribution in the connection experiment of the data decision part and the optimization algorithm to learn the security data. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has greatly influenced the lifestyle and entertainment activities of the society that has significantly increased the growth rate of the gaming industry. While the studies on the game industry, one of the leading content industries, related to the pandemic has been done from various perspectives, little attention has been taken in regards to how the pandemic have impacted on the national digital game production and its industrial sustainability as a whole. Thus, this study was conducted to analyze the changes in the domestic game industry before and after the COVID-19 outbreak using the big data analysis of semantic network. This study aims to understand the growing trend in the gaming industry that can be helpful for the marketing and production of future games, as well as to guide the establishment of the public game policies in the game industry. The results showed that the COVID-19 pandemic positively decreased the public's worries and the government's restrictions towards gaming due to game addiction as a mental disease. However, its sudden change in the gamer's attitudes and the current gaming policies implied that for the sustainable development of the domestic game production, laws and regulations related to the game industry need to be reliable and planned on a long term basis since the industry is immensely large and is also related to several industries such as computing, programming, arts, and story contents. Accordingly, it is necessary to build an industrial ecology through which cluster complexes specializing in developing startups and small and medium-sized business can grow along with political support. [ABSTRACT FROM AUTHOR]

In the transition period to digital government, digital economy, and digital society to create new breakthroughs in economic growth and development, increase competitiveness and improve quality of life, databases, which have grown into big data, play an important part in determining success. This article analyzes the role of databases and big data in developing the digital economy from both macro and micro perspectives, at the same time collecting evidence on the experiences of China and South Korea in the build-up, management and operation of their digital economies based on databases and big data. From the evidence provided, we develop recommendations for Vietnam with regards to promoting digital transition and building the foundations for the digital economy. [ABSTRACT FROM AUTHOR]

In this study, big data analysis on Korea's "online tour", which emerged as an alternative to satisfy tourism needs after COVID-19, was conducted. After extracting keywords through text mining for 24,073 posts from the top three most frequently visited social media platforms, Naver, Daum, and Google, to gather tour information in Korea, frequency analysis and TF-IDF analysis were run. In addition, network analyses, such as centrality and convergence of iteration correlation (CONCOR) analyses, were performed. The results showed: First, the sense of presence via local live streaming is crucial. It is vital to prepare a suitable video environment where tourists can immerse themselves in the tour. Second, the interaction between travel agencies, local guides, and tourists is important because it can expand tourists' travel experiences. Third, the importance of online tour program content was revealed. It is necessary to increase the demand by designing various programs tailored to the audience. Fourth, new possibilities for local travel that had been neglected were uncovered. Fifth, the importance of online tourism production support was highlighted. The role of the government must be expanded to reinforce the digital capabilities of small- and medium-sized enterprises (SMEs) and to create jobs. Although the scope of this study is limited to Korea, it can definitely be used as a regional strategy. [ABSTRACT FROM AUTHOR]

The recent advent of the New Cold War and rapid changes in global situation are increasing the need for quick and accurate geopolitical risk measurement through quantitative analysis. This study intends to present a new military-security index, a method of measuring military-geopolitical risk using big data analysis. In this regard, South Korea, one of the countries with the highest level of geopolitical risk in the world, was analyzed and quantified by analyzing direct provocations and threats from neighboring countries. The data used include the results of quantifying provocation cases in neighboring countries according to time, frequency, and intensity, the results of analyzing news keywords related to military-security issues in neighboring countries, and real-time terrorism and cybersecurity-risk measurements. Based on this, a model that enables relatively accurate and timely analysis and prediction by indexing and time-series geopolitical risks is presented. [ABSTRACT FROM AUTHOR]

In recent years, patient transportation demand has increased rapidly worldwide, especially in the Republic of Korea. Patient transportation requires high accuracy, arrangements, and reasonable allocation in time to the nearest healthcare facilities to support healthcare efficiency. Based on the emergency medical services system process, this research proposes a complete approach for collecting and processing data, and building the operating system, a web-app architecture using ReactJS, NodeJS, and Python to optimize patient transportation based on pathologies, distances, and corresponding specialized hospitals. Our system was designed to crawl data from a public information website of Busan City. After that, it automatically aggregated and stored these data in MongoDB before processing and input into our system. The concept of big data analysis was also built in here. This crawled data were analyzed and applied the API shortest direction of Naver Cloud to identify recommended hospitals with the most intelligent route and least cost. We built a web app connected to a server to visualize the research results and recommend decision-making for the operator and dispatcher in each area. The experiment results showed that the proposed method could recommend the nearest healthcare facilities and routes based on pathologies, optimal distances and times, and travel costs in an actual application, thereby helping solve patient congestion problems, allocate appropriate medical resources, and support healthcare efficiency to solve significant social problems. [ABSTRACT FROM AUTHOR]

This study characterizes a destination network based on travel flow. Although the tourism literature has mostly discussed the static configurations of network structures, this study presents a dynamic destination network considering four seasons and daily periods. Given the advancement of connected technology, mobile sensor big data collected from international travelers visiting Korea were analyzed to explore movement behaviors across 250 cities in the country. Results demonstrate the dynamics of complex network systems in tourism destinations over time, such as seasons and diurnal variations. Its findings are crucial for developing vibrant destination management and contextual marketing. [ABSTRACT FROM AUTHOR]

COVID-19 is substantially reshaping the tourism and hospitality industries but studies on the changes in travel behaviour in response to the pandemic are limited. Using tourism big data, this research applies network science analytics to determine behavioural changes in travel mobility of domestic travellers who visited Jeju Island, Korea, from June 2019 to December 2020. The findings reveal significant reductions in the number of trips to a destination but also limited spatial connectivity and diversity in travel flow during the pandemic. A higher intensity of travel mobility to outdoor and coastal areas and shorter travel distances are evident during COVID-19. [ABSTRACT FROM AUTHOR]

Background/Aims: Biliary complications including biliary strictures and bile leaks are the most common complications that occur after liver transplantation (LT). Endoscopic treatment with endoscopic retrograde cholangiography (ERC) is considered the first-line treatment, and percutaneous transhepatic cholangiography (PTC) can serve as an alternative or rescue therapy. However, nationwide clinical data on the frequency of ERC and PTC following LT have not yet been investigated. Methods: Using the nationwide claims database, we investigated patients who underwent LT between 2012 and 2014 in Korea and followed them until 2015. We analyzed the prevalence and characteristics of patients and biliary procedures, including ERC and PTC implemented after LT. Results: A total of 3,481 patients underwent LT during the 3-year study period. Among them, 3.0% of patients underwent biliary intervention postoperatively during the same hospitalization period, and 21.4% of patients received biliary intervention later on after initially being discharged from the hospital following LT. A total of 16.9% and 12.1% of patients underwent ERC and PTC after LT, respectively. The median period from LT to the first biliary intervention was 7.8 months (interquartile range, 3.5 to 14.6 months), and these patients underwent an average of 3.2±2.8 biliary procedures during the follow-up period. Patients undergoing living donor LT were more than twice as likely to undergo biliary procedures as those undergoing deceased donor LT (25.5% vs 12.1%). Conclusions: Approximately one-fourth of patients in Korea who underwent LT subsequently underwent ERC or PTC. Compared with deceased donor LT patients, those undergoing living donor LT underwent more biliary interventions and were more difficult to treat. [ABSTRACT FROM AUTHOR]

Using a data set of Korean business groups in the period 1999–2006, just after the Asian Financial Crisis, this study shows how business groups’ financial leverage can lead group-affiliated firms to lose market share to industry rivals. This analysis reveals that the negative effect of group leverage is greater when an affiliated firm is financially weak. Additionally, high group leverage is more detrimental to firms operating in fast-growing industries, discouraging affiliated firms from investing while encouraging their rivals. The results suggest that groups’ financial positions encompass a substantial strategic dimension of group-affiliated firms. [ABSTRACT FROM PUBLISHER]

This study aims to predict the visit rate of game customers in order to efficiently deploy dealers to for table games in foreign-only casinos. The goal is to predict the number of customers visiting each casino game event by date and business hour, and ultimately, to predict the number of game tables necessary per game. Once the number of game tables has been decided upon, the number of game dealers going into the game will be decided, thus enabling efficient dealer operation. The number of games at the casino in question is nine, and the hours of operation are divided into three sections per day. The data used in the analysis are data on customer game history from January 2013 to December 2015 in certain areas of foreigner-only casinos in Korea, and external environment data such as holidays per country, exchange rate information, and weather conditions were utilized as well. In addition, the correlation between variables was analyzed and the key derived variables were utilized. As an analytical tool, SAS's Big Data analysis tool was used. The prediction shows that some games have a 10–16.2% prediction error based on MAPE. Further, by MAE, there are no more than five absolute errors. Further research could result in more accurate predictions if data such as customer's gaming preferences, environmental variables of casino businesses, and national situations were added to the analysis. [ABSTRACT FROM AUTHOR]

The growth of data production in the manufacturing industry causes the monitoring system to become an essential concept for decision-making and management. The recent powerful technologies, such as the Internet of Things (IoT), which is sensor-based, can process suitable ways to monitor the manufacturing process. The proposed system in this research is the integration of IoT, Machine Learning (ML), and for monitoring the manufacturing system. The environmental data are collected from IoT sensors, including temperature, humidity, gyroscope, and accelerometer. The data types generated from sensors are unstructured, massive, and real-time. Various big data techniques are applied to further process of the data. The hybrid prediction model used in this system uses the Random Forest classification technique to remove the sensor data outliers and donate fault detection through the manufacturing system. The proposed system was evaluated for automotive manufacturing in South Korea. The technique applied in this system is used to secure and improve the data trust to avoid real data changes with fake data and system transactions. The results section provides the effectiveness of the proposed system compared to other approaches. Moreover, the hybrid prediction model provides an acceptable fault prediction than other inputs. The expected process from the proposed method is to enhance decision-making and reduce the faults through the manufacturing process. [ABSTRACT FROM AUTHOR]

Since December 2019, the coronavirus disease (COVID-19) outbreak has caused many death cases and affected all sectors of human life. With gradual progression of time, COVID-19 was declared by the world health organization (WHO) as an outbreak, which has imposed a heavy burden on almost all countries, especially ones with weaker health systems and ones with slow responses. In the field of healthcare, deep learning has been implemented in many applications, e.g., diabetic retinopathy detection, lung nodule classification, fetal localization, and thyroid diagnosis. Numerous sources of medical images (e.g., X-ray, CT, and MRI) make deep learning a great technique to combat the COVID-19 outbreak. Motivated by this fact, a large number of research works have been proposed and developed for the initial months of 2020. In this paper, we first focus on summarizing the state-of-the-art research works related to deep learning applications for COVID-19 medical image processing. Then, we provide an overview of deep learning and its applications to healthcare found in the last decade. Next, three use cases in China, Korea, and Canada are also presented to show deep learning applications for COVID-19 medical image processing. Finally, we discuss several challenges and issues related to deep learning implementations for COVID-19 medical image processing, which are expected to drive further studies in controlling the outbreak and controlling the crisis, which results in smart healthy cities. [ABSTRACT FROM AUTHOR]

This study identified the meteorological variables that significantly impact the power generation of a solar power plant in Samcheonpo, Korea. To this end, multiple regression models were developed to estimate the power generation of the solar power plant with changing weather conditions. The meteorological data for the regression models were the daily data from January 2011 to December 2019. The dependent variable was the daily power generation of the solar power plant in kWh, and the independent variables were the insolation intensity during daylight hours (MJ/m2), daylight time (h), average relative humidity (%), minimum relative humidity (%), and quantity of evaporation (mm). A regression model for the entire data and 12 monthly regression models for the monthly data were constructed using R, a large data analysis software. The 12 monthly regression models estimated the solar power generation better than the entire regression model. The variables with the highest influence on solar power generation were the insolation intensity variables during daylight hours and daylight time. [ABSTRACT FROM AUTHOR]

Climate change increases the frequency and intensity of heatwaves, causing significant human and material losses every year. Big data, whose volumes are rapidly increasing, are expected to be used for preemptive responses. However, human cognitive abilities are limited, which can lead to ineffective decision making during disaster responses when artificial intelligence-based analysis models are not employed. Existing prediction models have limitations with regard to their validation, and most models focus only on heat-associated deaths. In this study, a random forest model was developed for the weekly prediction of heat-related damages on the basis of four years (2015–2018) of statistical, meteorological, and floating population data from South Korea. The model was evaluated through comparisons with other traditional regression models in terms of mean absolute error, root mean squared error, root mean squared logarithmic error, and coefficient of determination (R2). In a comparative analysis with observed values, the proposed model showed an R2 value of 0.804. The results show that the proposed model outperforms existing models. They also show that the floating population variable collected from mobile global positioning systems contributes more to predictions than the aggregate population variable. [ABSTRACT FROM AUTHOR]

Background: Big data reports related to diseases and health care for the Korean population have been published since the National Health Insurance Service (NHIS) and the Health Insurance Review & Assessment (HIRA) Service provided limited open access to their databases. Here, we reviewed the structure, content, and means of using data from the National Health Insurance (NHI) system for the benefit of Korean researchers and presented the latest publication trends in Korean healthcare data procured from the NHI and HIRA databases. Methods: Since 2013, researchers have been able to obtain nationwide population-based studies using the NHI and HIRA databases of the insured. We searched publications using the NHI and the HIRA databases between 2013 and 2019 retrieved from PubMed. Results: The NHI and HIRA databases provide nationwide population-based data. The total number of publications from 2014 to 2019 using NHI and HIRA databases is 2,541 and 655, respectively. A total of 5,465 endocrinology-related studies were performed during 2014 to 2019. Conclusion: The NHIS and HIRA databases have provided tools for guidelines to approach world-leading population-based epidemiology and disease research. [ABSTRACT FROM AUTHOR]