The Society of Convergence Knowledge Transactions ISSN:2287-8920(Print) 융복합지식학회논문지

Based on the experience of providing non-face-to-face care services using AI speakers and face-to-face services by ICT care managers to care for the older adults living alone, this study explored the future direction of digital technology and humanization-based services. Through this, the role of ICT care managers and the difficulties and achievements of the Happy Comminity AI speaker Project were identified. According to the results, ICT care managers provided face-to-face support for device use, linkage to resources, and emotional support. The AI speaker Project faced difficulties in the perspectives of the older adults and business operation. The outcomes of Happy Community AI Care Project were the continuous care, resolving information asymmetry, and changing users' positive emotions. Based on these results, it was suggested that the integration of digital technology and humanization-based services, consortium among operating organizations, and improvement of working conditions for ICT care managers are necessary to provide care services for the older adults living alone.

In this study, we wanted to see whether problem-oriented coping and emotion-oriented coping had a mediating effect on the process in which stress and coping methods of breast cancer patients affect the quality of life. For this purpose, we used survey data from 119 cancer survivors living in Chungcheong-do and gyunggi-do. A method to verify the mediating effects of problem-oriented coping and emotion-oriented coping was used in the relationship between the subject’s stress and quality of life. Baron and Kenny’s mediation effect analysis is, first, to conduct a regression analysis of the effect of the independent variable on the mediating variable, and second, to conduct a regression analysis of the effect of the independent variable on the dependent variable. As a result of the analysis, first, it was found that the subject’s problem-oriented coping style had an effect on quality of life (r=.253, p<.001). Second, it was found that the subject’s emotion-oriented coping style had an effect on quality of life (r=.275, p<.001). Third, in the relationship between stress and quality of life, it was found that problem-oriented coping and emotion-oriented coping had a mediating effect (problem-oriented mediation effect Z=1.93, ρ<0.005, emotion-oriented mediation effect Z=-0.299, ρ<0.001). Based on the results of the study, the stress and coping methods of breast cancer patients described nursing intervention methods to improve the quality of life.

Since the COVID-19 pandemic, unforeseen variables impacting the tourism landscape have increased, elevating the significance of long-term and medium-term future response strategies. Consequently, various digital technologies, such as tourism big data and travel IoT, and leisure sports AI, have been proposed to adapt to these changes, highlighting the importance of utilizing the metaverse. The metaverse offers a digitally fused environment combining virtuality and reality, expanding the potential for regional tourism promotion, economic stimulation, educational enhancement, and fostering digital talent. Therefore, based on a review of prior research, this paper aims to present strategies for leveraging metaverse technology to promote local tourism and urban regeneration. This paper introduces a virtual city map project harnessing the potential of the metaverse, including a VR surfing, paragliding, plogging, workation and so on. It allows the exploration of a region's unique history and culture through this platform, while integrating various immersive contents in city map content, providing an opportunity to experience innovative digital technologies. Through these initiatives, discussions have been made to reinforce local promotional efforts and activate regional economies.

As artificial intelligence (AI) services, such as Chat-GPT and autonomous driving, become increasingly integrated into our society, various tasks traditionally performed by humans are being replaced by AI. Legal professionals are not exempt from this trend. In countries like ours, where the number of legal practitioners is relatively low compared to the GDP, there is a growing need for active research on AI to enhance citizens' right to legal assistance. This is particularly crucial in areas such as the judicial system and legal representation, where the demand for AI is expected to rise. Especially in the context of criminal trials with low judicial credibility, the application of rigorous and mechanical AI-based adjudication could potentially have positive effects. However, there are numerous challenges that need to be overcome. Legal work is not merely repetitive or standardized; it requires an understanding of diverse perspectives within society. Legal professionals must be adept at interpreting abstract concepts such as legal sentiment and justice, in addition to finding and applying relevant legal provisions. This paper aims to discuss the potential applications of AI in the legal domain, focusing on the possibilities of AI replacing roles in criminal trials, such as judges, prosecutors, and defense attorneys. It will explore the feasibility of substitution and provide recommendations for relevant research

Recently smart agriculture is improving the agricultural competitiveness of productivity and product quality throughout all stages of the agricultural value chain (Value Chain) such as seeds, production, harvesting, distribution, and consumption by incorporating communication technologies (ICTs) such as the Internet of Things (IoT), artificial intelligence (AI), big data, and robots. However, abnormal climatic conditions such as extreme weather events, changes in temperature and precipitation patterns have a serious impact on agriculture, causing various agricultural problems. In this paper, we propose an AI-based efficient smart agricultural management technique to optimize autonomous sensor information so that it can actively respond to abnormal climate changes. The proposed technique employs an asymmetric processing method to prepare the data set for machine learning and AI models, ensuring balanced autonomous sensor information. The proposed technique ensures continuous learning of large-capacity real-time autonomous sensor information using the DNN model to cope with abnormal climatic conditions (water shortage, extreme temperature, seasonal change, humidity, etc.). In addition, the proposed technique plays the role of a distributed record storage system by grouping and processing real-time large-capacity autonomous sensor information into a blockchain so that the DNN service can be handled efficiently. The proposed technique may maintain and verify synchronization between autonomous sensor information by accumulating probability values at regular intervals for weights of autonomous sensor information.

The purpose of this study is to verify how a pastor's positive psychological capital and emotional labor influence emotional exhaustion and rapport. Based on data collected from 188 pastors, the results analyzed empirically through Structural Equation Modeling (SEM) are as follows. Firstly, it was found that a pastor's positive psychological capital has a significant positive impact on both deep acting and surface acting. Secondly, deep acting was not found to have a significant negative impact on emotional exhaustion, while surface acting was found to have a positive impact on emotional exhaustion. Thirdly, both deep acting and surface acting had significant positive effects on rapport. Fourthly, while both deep acting and surface acting mediated the relationship between positive psychological capital and emotional exhaustion as well as rapport, deep acting did not significantly mediate the relationship between positive psychological capital and emotional exhaustion. In conclusion, this study suggests the need to manage pastors' emotional labor and promote positive psychological capital and rapport.

Self-report depression screening tests are commonly used as an initial diagnosis method for depression. However, these tests are easily accessible on the internet, but there is no appropriate follow-up measure according to the test results, which leads to a counter-effect due to the lack of motivation for treatment. This study aims to provide reference information for the decision to visit a medical institution by providing visualized results of depression risk scale prediction and biosignal analysis. The system consists of a DAQ system for data acquisition, an artificial intelligence system for depression risk scale prediction, and a visualization system for biosignal analysis results and artificial intelligence prediction results. In particular, the DAQ system includes a wearable device- specific app based on a non-invasive biosignal acquisition scenario integrated with depression screening tests developed in this study. For the artificial intelligence system, we collected data from 29 male and female subjects aged 19 to 50 based on the integrated scenario and applied the artificial intelligence model trained and evaluated through the data.

As online grocery purchases continue to rise globally after pandemic, consumer reviews have gained significant influence over purchasing behavior. Analyzing user reviews in-depth and providing valuable information from the customer’s perspective has become crucial for improving the customer experience. This research project aims to analyze customer reviews using topic modeling to identify product group characteristics and purchase motives. A dataset of about 1.2 million reviews from Jan. 2021 to Dec. 2022, obtained from Korea’s leading food purchasing platform, was pre-processed using the MeCab Korean morpheme analysis algorithm. Researchers want to overcome the challenge of extracting such insights from general-purpose words like ‘good,’ ‘satisfaction,’ and ‘recommendation.’ Three types of topic modeling—BERTopic, LDA, and CTM—were applied to compare their performance on a test dataset. Finally CTM demonstrated the best performance and successfully extracted three relevant topics from the 79,616 valid review dataset: delivery status, product characteristics, and recipe-related topics. Analysis results offer academic and technical implications for improving customer experience based on providing valuable product and purchasing insights.

This study analyzed the causal relationship between emergency stress, physical fatigue, work-leisure balance, and organizational commitment through path analysis, considering that there are no prior studies dealing with emergency stress that can be experienced on a daily basis among various stress sources of social workers working in various social welfare facilities., using multi-group analysis to examine whether there are differences in pathways between groups by demographic factors such as gender, educational background, certificate level, number of years of service, and marital status of social workers. As a result of the overall sample analysis, it was found that the main route was 'emergency situation stress → physical fatigue → work-leisure balance → organizational commitment'. In a multi-group analysis, work-leisure balance further increases organizational commitment when academic background is relatively low, and physical fatigue negatively affects work-leisure balance as the number of years of service is shorter. This suggests that it is important to reduce physical fatigue and increase work-leisure balance through effective management of emergency stress in light of the social proposition of improving quality of life through work-life balance. Ultimately, as social workers are exposed to daily emergency stress, institutional efforts are constantly required to lower their emergency stress levels and alleviate physical fatigue to improve their quality of life.

Middle age is a period of significant changes and challenges in many aspects of life, and is a time when many changes occur physiologically, psychologically, and sociologically. At this time, women experience menopause, which is associated with a decline in menstrual function, and feel stress due to mental, sensory, and reproductive changes, concerns about gender identity, stress related to appearance, and changes among family members. Approximately 38-39% of middle-aged women in Korea experience menopausal symptoms. In order to improve these problems, we would like to see whether an aroma solution improves middle-aged women's symptoms through EEG (Electroencephalogram). The aroma oils used in the experiment were Bergamot oil and Clary sage oil, and colorless and odorless MCT (medium chain triglycerides) oil was also used as a carrier oil. To determine the aroma solution ratio, the test subject (50 years old, female) was given different ratios of Bergamot:Clary sage:MCT oil and the best ratio was determined through brain wave analysis. As a result of analyzing the average values of 5 out of 8 middle-aged women using the manufactured aroma solution, depression was improved in 5 of the test subjects. For most test subjects, α wave values showed an increasing trend. In addition, most test subjects showed a tendency for both δ-wave and θ-wave values to increase, confirming that sleep disorder symptoms were improved.

Recently, the construction of learning data for the development of medical image-based disease diagnosis and prediction models has been actively conducted. However, problems with data collection and management are emerging because the level of standardization is different for each medical institution. As standardization research is progressing to solve these problems, the use of HL7 FHIR is gradually expanding in accordance with the paradigm shift in digital health. This paper introduces a novel platform designed to facilitate both individual and collaborative research across multiple institutions, leveraging the medical information standard HL7 FHIR alongside the medical imaging standard Digital Imaging and Communications in Medicine (DICOM). Our platform offers comprehensive data monitoring capabilities, ensures the anonymization and standardized storage and management of patient information according to the OMOP-CDM (Observational Medical Outcomes Partnership Common Data Model), and provides clinical information via HL7 FHIR. By utilizing HL7 FHIR resources such as Patient, Observation, DiagnosticReport, and Bundle, we enable the efficient delivery and presentation of patient information and clinical reports on the StudyList. The platform is anticipated to serve as a crucial tool for validating medical imaging data collection and artificial intelligence models in multicenter clinical research.