• Parasites, Hosts and Diseases
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• v.61 no.1
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• pp.24-32
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• 2023

Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1), encoded by the polymorphic var multigene family, is a highly polymorphic antigen that plays a crucial role in the pathology of malaria. The contribution of the genetic diversity of var toward the immune escape of P. falciparum has not yet been fully elucidated. This study aimed to characterize the diversity of var repertoires by screening P. falciparum Duffy-binding-like α domain (PfDBLα) among field isolates from central Myanmar. Genetic analysis revealed that the D-H segments of var in Myanmar populations have an extensive polymorphic repertoire, with high numbers of unique sequence types in each individual. However, var genes from the global population, including Myanmar, shared close genetic lineages regardless of their geographic origins, indicating that they have not undergone rapid evolutionary changes.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.33 no.1
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• pp.139-166
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• 2022

Ethical Issues increase when people engage in smart technological systems such as 5G, IoT, Cloud computing services and AI applications. Range of this research is comparison of various literacy concepts and its ethical issues in considering of 5G features and UX. 86 research papers and reports which have been published within the recent 5 years (2017-2022), relating the research subject, are investigated and analyzed. Two results show that various literacies can be grouped into four areas and that some of common issues among those areas as well as unique issues of each area are identified. Based on the literature analysis, an Operational Definition of Ethics-Literacy is presented and the model of ethics-literacy curriculum supporting ethical behavior of 5G information professionals is developed and suggested.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.168-168
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• 2020

This study is to evaluate the drought-related bigdata characteristics published from South Korean by developing crawler. The 5 years (2013 ~ 2017) drought-related posted articles were collected from Korean internet search engine 'NAVER' which contains 13 main and 81 local daily newspapers. During the 5 years period, total 40,219 news articles including 'drought' word were found using crawler. To filter the homonyms liken drought to soccer goal drought in sports, money drought economics, and policy drought in politics often used in South Korea, the quality control was processed and 47.8 % articles were filtered. After, the 20,999 (52.2 %) drought news articles of this study were classified into four categories of water deficit (WD), water security and support (WSS), economic damage and impact (EDI), and environmental and sanitation impact (ESI) with 27, 15, 13, and 18 drought-related keywords in each category. The WD, WSS, EDI, and ESI occupied 41.4 %, 34.5 %, 14.8 %, and 9.3 % respectively. The drought articles were mostly posted in June 2015 and June 2017 with 22.7 % (15,097) and 15.9 % (10,619) respectively. The drought news articles were spatiotemporally compared with SPI (Standardized Precipitation Index) and RDI (Reservoir Drought Index) were calculated. They were classified into administration boundaries of 8 main cities and 9 provinces in South Korea because the drought response works based on local government unit. The space-time clustering between news articles (WD, WSS, EDI, and ESI) and indices (SPI and RDI) were tried how much they have correlation each other. The spatiotemporal clusters detection was applied using SaTScan software (Kulldorff, 2015). The retrospective and prospective cluster analyses were conducted for past and present time to understand how much they are intensive in clusters. The news articles of WD, WSS and EDI had strong clusters in provinces, and ESI in cities.

• Korean Journal of Artificial Intelligence
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• v.11 no.4
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• pp.9-13
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• 2023

In this study, we explored the potential of integrating interactive AI callbot technology into the medical consultation domain as part of a broader service development initiative. Aimed at enhancing patient satisfaction, the AI callbot was designed to efficiently address queries from hospitals' primary users, especially the elderly and those using phone services. By incorporating an AI-driven callbot into the hospital's customer service center, routine tasks such as appointment modifications and cancellations were efficiently managed by the AI Callbot Agent. On the other hand, tasks requiring more detailed attention or specialization were addressed by Human Agents, ensuring a balanced and collaborative approach. The deep learning model for voice recognition for this study was based on the Transformer model and fine-tuned to fit the medical field using a pre-trained model. Existing recording files were converted into learning data to perform SSL(self-supervised learning) Model was implemented. The ANN (Artificial neural network) neural network model was used to analyze voice signals and interpret them as text, and after actual application, the intent was enriched through reinforcement learning to continuously improve accuracy. In the case of TTS(Text To Speech), the Transformer model was applied to Text Analysis, Acoustic model, and Vocoder, and Google's Natural Language API was applied to recognize intent. As the research progresses, there are challenges to solve, such as interconnection issues between various EMR providers, problems with doctor's time slots, problems with two or more hospital appointments, and problems with patient use. However, there are specialized problems that are easy to make reservations. Implementation of the callbot service in hospitals appears to be applicable immediately.

• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.13-22
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• 2024

There is a need for and positive aspects of article-based advertising, but as exaggerated and disguised information is delivered due to some indiscriminate 'article-based advertisements', readers have difficulty distinguishing between general articles and article-based advertisements, leading to a lot of misinterpretation and confusion of information. is doing Since readers will continue to acquire new information and apply this information at the right time and place to bring a lot of value, it is judged to be even more important to distinguish between accurate general articles and article-like advertisements. Therefore, as differentiated information between general articles and article-like advertisements is needed, as part of this, for readers who have difficulty identifying accurate information due to such indiscriminate article-like advertisements in Internet newspapers, this paper introduces IT and AI technologies. We attempted to present a method that can be solved in terms of a system that incorporates, and this method was designed to extract articleable advertisements using a knowledge-based natural language processing method that finds and refines advertising keywords and deep learning technology.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.53-61
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• 2022

Recent years have drawn a great attention to generation MZ and Metaverse, due to 4th industrial revolution and the development of digital environment that blurs the boundary between reality and virtual reality. Generation MZ approaches the information very differently from the existing generations and uses distinguished communication methods. In terms of learning, they have different motivations, types, skills and build relationships differently. Meanwhile, Metaverse is drawing a great attention as a teaching method that fits traits of gen MZ. Thus, the current research aimed to investigate how to increase the use of Metaverse in Educational Technology. Specifically, this research examined the antecedents of popularity of Gather Town, a platform of Metaverse. Big data of news articles have been collected and analyzed using the Bigkinds system provided by Korea Press Foundation. The analysis revealed, first, a rapid increasing trend of media exposure of Gather Town since July 2021. This suggests a greater utilization of Gather Town in the field of education after the COVID-19 pandemic. Second, Word Association Analysis and Word Cloud Analysis showed high weights on education related words such as 'remote', 'university', and 'freshman', while words like 'Metaverse', 'Metaverse platform', 'Covid19', and 'Avatar' were also emphasized. Third, Network Analysis extracted 'COVID19', 'Avatar', 'University student', 'career', 'YouTube' as keywords. The findings also suggest potential value of Gather Town as an educational tool under COVID19 pandemic. Therefore, this research will contribute to the application and utilization of Gather Town in the field of education.

• The Journal of Bigdata
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• v.8 no.1
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• pp.111-129
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• 2023

With the diffusion of digital innovation, the adoption of innovative medical technologies based on artificial intelligence is increasing in the medical field. This is driving the launch and adoption of AI-based SaMD(Software as a Medical Device), but there is a lack of research on the factors that influence the adoption of SaMD by medical institutions. The purpose of this study is to identify key factors that influence medical institutions' decisions to adopt AI-based SaMDs, and to analyze the weights and priorities of these factors. For this purpose, we conducted Delphi surveys based on the results of literature studies on technology acceptance models in healthcare industry, medical AI and SaMD, and developed a research model by combining HOTE(Human, Organization, Technology and Environment) framework and HABIO(Holistic Approach {Business, Information, Organizational}) framework. Based on the research model with 5 main criteria and 22 sub-criteria, we conducted an AHP(Analytical Hierarchy Process) analysis among the experts from domestic medical institutions and SaMD providers to empirically analyze SaMD adoption factors. The results of this study showed that the priority of the main criteria for determining the adoption of AI-based SaMD was in the order of technical factors, economic factors, human factors, organizational factors, and environmental factors. The priority of sub-criteria was in the order of reliability, cost reduction, medical staff's acceptance, safety, top management's support, security, and licensing & regulatory levels. Specifically, technical factors such as reliability, safety, and security were found to be the most important factors for SaMD adoption. In addition, the comparisons and analyses of the weights and priorities of each group showed that the weights and priorities of SaMD adoption factors varied by type of institution, type of medical institution, and type of job in the medical institution.

• The Korean Journal of Archival Studies
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• no.70
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• pp.85-117
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• 2021

The purpose of this study is to investigate and analyze the current status of unit tasks, unit task operation, and record management problems used by local governments, and to present improvement measures using text-based big data technology based on the implications derived from the process. Local governments are in a serious state of record management operation due to errors in preservation period due to misclassification of unit tasks, inability to identify types of overcommon and institutional affairs, errors in unit tasks, errors in name, referenceable standards, and tools. However, the number of unit tasks is about 720,000, which cannot be effectively controlled due to excessive quantities, and thus strict and controllable tools and standards are needed. In order to solve these problems, this study developed a system that applies text-based analysis tools such as corpus and tokenization technology during big data analysis, and applied them to the names and construction terms constituting the record management standard. These unit task operation support tools are expected to contribute significantly to record management tasks as they can support standard operability such as uniform preservation period, identification of delegated office records, control of duplicate and similar unit task creation, and common tasks. Therefore, if the big data analysis methodology can be linked to BRM and RMS in the future, it is expected that the quality of the record management standard work will increase.

• Journal of the Korea Convergence Society
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• v.12 no.4
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• pp.207-216
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• 2021

Recently, there is increasing interest in attracting local tourist in the city to revitalize the local economy. For this purpose, customized tourism strategies based on the analysis of travel routes and consumption patterns are becoming important. However, existing studies either focused on limited mainstream tourist analysis or lacked analysis of tourists' behavior-based data perspectives. Therefore, this study aims to present a big data-based tourism strategy that provides customized information by analyzing the demand of individual travelers in details based on mobile service data and card expenditure data generated by the travelers in Yongin city. By tracing those data, this study visualized the tourists' itinerary and their expenditure patterns. The analysis of data from July 2017 to June 2018 shows that men tend to consume in various areas compared to women. It also shows consumption areas for people in their 30s and 40s are similar, whereas those in their 20s do not vary. Using the big data based on Geographic Information system, this study provides strategic insights to administrative personnel who are in charge of tour policy.

• Journal of Internet Computing and Services
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• v.16 no.3
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• pp.59-69
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• 2015

Many users visit websites every day to perform information retrieval, shopping, and community activities. On the other hand, there is intense competition among sites which attempt to profit from the Internet users. Thus, the owners or marketing officers of each site try to design a variety of marketing strategies including cooperation with other sites. Through such cooperation, a site can share customers' information, mileage points, and hyperlinks with other sites. To create effective cooperation, it is crucial to choose an appropriate partner site that may have many potential customers. Unfortunately, it is exceedingly difficult to identify such an appropriate partner among the vast number of sites. In this paper, therefore, we devise a new methodology for recommending appropriate partner sites to each site. For this purpose, we perform site clustering from the perspective of visitors' similarities, and then identify a group of sites that has a number of common customers. We then analyze the potential for the practical use of the proposed methodology through its application to approximately 140 million actual site browsing histories.