• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.1-7
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• 2021

Investor mood from social media is gaining increasing attention for leading a price movement in stock market. Based on the behavioral finance theory, this study argues that sentiment extracted from social media using big data technique can predict a real-time (short-run) price momentum in Chinese stock market. Collecting Sina Weibo posts that related to COVID-19 using keyword method, a daily influential weighted sentiment factors is extracted from the sizable raw data of over 2 millions of posts. We examine one supervised and 4 unsupervised sentiment analysis model, and use the best performed word-frequency and BiLSTM mdoel. The test result shows a similar movement between stock price change and sentiment factor. It indicates that public mood extracted from social media can in some extent represent the investors' sentiment and make a difference in stock market fluctuation when people are concentrating on a special events that can cause effect on the stock market.

• The Journal of the Korea Contents Association
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• v.21 no.3
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• pp.282-300
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• 2021

The purpose of this study was to explore what social media posts tell us about family life during the COVID-19 pandemic by examining the keywords and topics underlying posts on blogs and online forums. Our criteria for web crawling were (a) blog and forum posts on Naver and Daum, the top portal sites in Korea, (b) posts between February 23 and April 19, 2020, the period of the first heightened social distancing orders, and (c) inclusion of "COVID" and "family" or "COVID" and "home." We analyzed 351,734 posts using TF-IDF values and topic modeling based on latent Dirichlet allocation. We identified and named 22 topics including COVID-19 prevention, family infection, family health, dietary life and changes, religious life, stuck at home, postponed school year, family events, travel and vacations, concerns about family and friends, anxiety and stress, disaster and damage, COVID-19 warning text messages, family support policies, Shin-cheon-ji and Daegu. The results show that COVID-19 impacted various domains of family life including health, food, housing, religion, child care, education, rituals, and leisure as well as relationships and emotions.

• International Journal of Contents
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• v.17 no.1
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• pp.37-53
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• 2021

With the advent of the 5G era, smart phones and communications network technology have progressed, and mobile short video of people's life can be made, Of the new tools of communication, at present, China's social short video industry has shown rapid development, and the most representative of the short video app is Douyin (international version: Tiktok). Under the background of Uses and Gratifications Theory, this study discusse the relationship between Douyin users' preference degree, use motivation, use satisfaction and attention intention. This study divides the content of Douyin video into 10 categories, selects the form of an online questionnaire survey, uses SPSS software to conduct quantitative analysis of 202 questionnaires after screening, and finally draws the following conclusions: (1) The content preference degree of Douyin short video (the high group and low group) is different in users' use motivation, users' satisfaction degree and users' attention intention. ALL results are within the range of statistical significance.(2) Douyin users' video content preference degree has a positive impact on users' use motivation, users' satisfaction degree, and users' attention intention. (3) Douyin users' motivation has a positive impact on users' satisfaction and user' attention intention. (4) Douyin users' satisfaction degree has a positive impact on users' attention intention. Based on the research results, we suggest that Douyin platform pushes videos according to users' preferences. In addition, as the preference degree has an impact on users' motivation, satisfaction degree and attention intention of using the platform, it is important that the platform's focus should to pay attention to the preference degree of users. Collecting users' preferences at the early stage of users' entering the platform is a good way to learn from, and doing a good job of big data collection and management in the later operation.

• Electronics and Telecommunications Trends
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• v.36 no.1
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• pp.54-63
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• 2021

Since the recent COVID-19 pandemic, countries have been strengthening trade protection for their security, and the importance of securing strategic materials, such as food, is drawing attention. In addition to the cultural aspects, the global preference for food produced in Korea is increasing because of the Korean Wave. Thus, the Korean food industry can be developed into a high-value-added export food industry. Currently, Korea has a low self-sufficiency rate for foodstuffs apart from rice. Korea also suffers from problems arising from population decline, aging, rapid climate change, and various animal and plant diseases. It is necessary to develop technologies that can overcome the production structures highly dependent on the outside world of food and foster them into export-type system industries. The global agricultural industry-related technologies are actively being modified via data accumulation, e.g., environmental data, production information, and distribution and consumption information in climate and production facilities, and by actively expanding the introduction of the latest information and communication technologies such as big data and artificial intelligence. However, long-term research and investment should precede the field of living organisms. Compared to other industries, it is necessary to overcome poor production and labor environment investment efficiency in the food industry with respect to the production cost, equipment postmanagement, development tailored to the eye level of field workers, and service models suitable for production facilities of various sizes. This paper discusses the flow of domestic and international technologies that form the core issues of the site centered on the 4th Industrial Revolution in the field of agriculture, livestock, and fisheries. It also explains the environmental awareness production technologies centered on sustainable intelligence platforms that link climate change responses, optimization of energy costs, and mass production for unmanned production, distribution, and consumption using the unstructured data obtained based on detection and growth measurement data.

• International journal of advanced smart convergence
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• v.10 no.2
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• pp.145-150
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• 2021

Patients with Pressure injuries (PIs) may have pain and discomfort, which results in poorer patient outcomes and additional cost for treatment. This study was a part of larger research project that aimed at prediction modeling using a big data. The purpose of this study were to describe the characteristics of patients with PI in critical care; and to explore comorbidity and diagnostic and interventive procedures that have been done for patients in critical care. This is a secondary data analysis. Data were retrieved from a large clinical database, MIMIC-III Clinical database. The number of unique patients with PI was 2,286 in total. Approximately 60% were male and 68.4% were White. Among the patients, 9.9% were dead. In term of discharge disposition, 56.2% (33.9% Home, 22.3% Home Health Care) where as 32.3% were transferred to another institutions. The rest of them were hospice (0.8%), left against medical advice (0.7%), and others (0.2%). The top three most frequently co-existing kinds of diseases were Hypertension, not otherwise specified (NOS), congestive heart failure NOS, and Acute kidney failure NOS. The number of patients with PI who have one or more procedures was 2,169 (94.9%). The number of unique procedures was 981. The top three most frequent procedures were 'Venous catheterization, not elsewhere classified,' and 'Enteral infusion of concentrated nutritional substances.' Patient with a greater number of comorbid conditions were likely to have longer length of ICU stay (r=.452, p<.001). In addition, patient with a greater number of procedures that were performed during the admission were strongly tend to stay longer in hospital (r=.729, p<.001). Therefore, prospective studies focusing on comorbidity; and diagnostic and preventive procedures are needed in the prediction modeling of pressure injury development in ICU patients.

• Journal of Radiation Protection and Research
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• v.46 no.4
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• pp.184-193
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• 2021

Background: The radionuclides released by the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident 9 years ago are still being monitored by various research teams and the Japanese government. Comparison of different surveys' results could help evaluate the exposure doses and the mechanism of radiocesium behavior in the urban environment in the area. In this study, we clarified the relationship between land use and temporal changes in the ambient dose rates (air dose rates) using big data. Materials and Methods: We set a series of 1 × 1 km² meshes within the 80 km zone of the FDNPP to compare the different survey results. We then prepared an analysis dataset from all survey meshes to analyze the temporal change in the air dose rate. The selected meshes included data from all survey types (airborne, fixed point, backpack, and carborne) obtained through the all-time survey campaigns. Results and Discussion: The characteristics of each survey's results were then evaluated using this dataset, as they depended on the measurement object. The dataset analysis revealed that, for example, the results of the carborne survey were smaller than those of the other surveys because the field of view of the carborne survey was limited to paved roads. The location factor of different land uses was also evaluated considering the characteristics of the four survey methods. Nine years after the FDNPP accident, the location factor ranged from 0.26 to 0.49, while the half-life of the air dose rate ranged from 1.2 to 1.6. Conclusion: We found that the decreasing trend in the air dose rate of the FDNPP accident was similar to the results obtained after the Chernobyl accident. These parameters will be useful for the prediction of the future exposure dose at the post-accident.

• Journal of the Korea Convergence Society
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• v.13 no.4
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• pp.25-37
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• 2022

The government and private companies are endeavoring to help the digital healthcare industry grow. This includes easing regulations on the big data industry such as the amendment of the Data 3 Act. Despite these efforts, however, there have been constant demands for the amendment of laws related to the medical field and for securing medical data transmissions. In this paper, the Data 3 Act of Korea and the legal system related to healthcare are examined. Then the legal, institutional, and technical aspects of the strategies are compared to understand the issues and implications. Based on this, a legal and institutional strategy suitable for the digital healthcare industry in Korea is suggested. Additionally, a direction to improve social perception along with technical measures such as safe de-identification processing and data transmission are also proposed. This study hopes to contribute to the spread of various convergent industries along with the digital healthcare industry.

• Journal of Industrial Convergence
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• v.20 no.3
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• pp.41-46
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• 2022

This paper proposes a consumer-tailored solution to prevent the forgery and falsification of data by incorporating blockchain technology in the online and offline distribution of agricultural produce. The solution provides customized services to consumers based on an analysis of the data generated from the sales, distribution, and consumption of quality of the produce. It can also ensure the safety and credibility of the produce, and allow producers to identify consumption intent and the flow of distribution. Producers will be able to determine the flow of produce based on the data collected and thus tailor promotional efforts. This is expected to be the fourth industrial revolution in the agricultural produce distribution sector. Utilizing blockchain and big data technology to create integrated record management systems that combine multiple solutions will shape future technology trends. In addition, if eco-friendly certification is acknowledged as a valuable service and can be incorporated into the distribution process, this solution could become a one-stop distribution solution for agricultural produce.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2209-2224
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• 2022

The Internet of Things (IoT) is being used in a wide variety of fields due to the recent 4th industrial revolution. In particular, research is being conducted that combines IoT with the medical field such as telemedicine. Among them, the field of shock detection is a big issue in the medical field because the causes of shock are diverse, treatments are very complex, and require a high level of medical knowledge and experience. The transmission of infectious diseases is common when treating critically ill patients, especially patients with shock. Thus, to effectively care for shock patients, we propose an architecture that continuously monitors the patient's condition, and automatically recommends a drug injection treatment according to the patient's shock condition. The patient's hemodynamic information is continuously monitored, and the patient's shock generation information is recorded periodically. With the recorded patient information, the patient's condition is determined and automatically injected with necessary medication. The medical team can find out whether the patient's condition has improved by checking the recorded information through web applications. The study can help relieve the shortage of medical personnel and help prevent transmission of infectious disease in medical staff. We look forward to playing a role in helping medical staff by making recommendations for the diagnosis and treatment of complex and difficult shocks.

• Genomics & Informatics
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• v.19 no.4
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• pp.49.1-49.11
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• 2021

Nowadays, Genomic data constitutes one of the fastest growing datasets in the world. As of 2025, it is supposed to become the fourth largest source of Big Data, and thus mandating adequate high-performance computing (HPC) platform for processing. With the latest unprecedented and unpredictable mutations in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the research community is in crucial need for ICT tools to process SARS-CoV-2 RNA data, e.g., by classifying it (i.e., clustering) and thus assisting in tracking virus mutations and predict future ones. In this paper, we are presenting an HPC-based SARS-CoV-2 RNAs clustering tool. We are adopting a data science approach, from data collection, through analysis, to visualization. In the analysis step, we present how our clustering approach leverages on HPC and the longest common subsequence (LCS) algorithm. The approach uses the Hadoop MapReduce programming paradigm and adapts the LCS algorithm in order to efficiently compute the length of the LCS for each pair of SARS-CoV-2 RNA sequences. The latter are extracted from the U.S. National Center for Biotechnology Information (NCBI) Virus repository. The computed LCS lengths are used to measure the dissimilarities between RNA sequences in order to work out existing clusters. In addition to that, we present a comparative study of the LCS algorithm performance based on variable workloads and different numbers of Hadoop worker nodes.