• International journal of advanced smart convergence
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• v.13 no.3
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• pp.150-155
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• 2024

Genomic data plays a transformative role in medicine, biology, and forensic science, offering insights that drive advancements in clinical diagnosis, personalized medicine, and crime scene investigation. Despite its potential, the integration and analysis of diverse genomic datasets remain challenging due to compatibility issues and the specialized nature of existing tools. This paper presents the GenomeSync system, designed to overcome these limitations by utilizing the Hadoop framework for large-scale data handling and integration. GenomeSync enhances data accessibility and analysis through SQL-based search capabilities and machine learning techniques, facilitating the identification of genetic traits and the resolution of forensic cases. By pre-processing DNA profiles from crime scenes, the system calculates similarity scores to identify and aggregate related genomic data, enabling accurate prediction models and personalized treatment recommendations. GenomeSync offers greater flexibility and scalability, supporting complex analytical needs across industries. Its robust cloud-based infrastructure ensures data integrity and high performance, positioning GenomeSync as a crucial tool for reliable, data-driven decision-making in the genomic era.

• International Journal of Computer Science & Network Security
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• v.23 no.2
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• pp.133-139
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• 2023

As the old saying goes "a picture is worth a thousand words" data visualization is essential in almost every industry. Companies make Data-driven decisions and gain insights from visual data. However, there is a need to investigate the role of data visualization in human resource management. This review aims to highlight the power of data visualization in the field of human resources. In addition, visualize the latest trends in the research area of human resource and data visualization by conducting a quantitative method for analysis. The study adopted a literature review on recent publications from 2017 to 2022 to address research questions.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.162-168
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• 2023

As the old saying goes "a picture is worth a thousand words" data visualization is essential in almost every industry. Companies make Data-driven decisions and gain insights from visual data. However, there is a need to investigate the role of data visualization in human resource management. This review aims to highlight the power of data visualization in the field of human resources. In addition, visualize the latest trends in the research area of human resource and data visualization by conducting a bibliometric analysis. The study adopted a literature review on recent publications from 2017 to 2022 to address research questions.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.2
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• pp.67-77
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• 2020

Traditionally nuclear thermal hydraulic and nuclear safety has relied on numerical simulations to predict the system response of a nuclear power plant either under normal operation or accident condition. However, this approach may sometimes be rather time consuming particularly for design and optimization problems. To expedite the decision-making process data-driven models can be used to deduce the statistical relationships between inputs and outputs rather than solving physics-based models. Compared to the traditional approach, data driven models can provide a fast and cost-effective framework to predict the behavior of highly complex and non-linear systems where otherwise great computational efforts would be required. The objective of this work is to develop an AI algorithm to predict the peak fuel cladding temperature as a metric for the successful implementation of FLEX strategies under extended station black out. To achieve this, the model requires to be conditioned using pre-existing database created using the thermal-hydraulic analysis code, MARS-KS. In the development stage, the model hyper-parameters are tuned and optimized using the talos tool.

• Journal of Korea Entertainment Industry Association
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• v.15 no.2
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• pp.1-10
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• 2021

The purpose of this study is to explore a significant HPWS(High Performance Work System) model for the entertainment industry. HPWS is one of the most studied themes for managing human resources as well as a set of practices to elicit employees' commitment to an organization. Recently, the entertainment industry is growing rapidly, but it is difficult for entertainment firms to retain a stable profit unlike the manufacturing industry. This is because the performance of entertainment business tends to rely heavily on the capabilities and synergy of human resources. In order to suggest a systematic way to manage these, this research identified an effective HPWS model for entertainment business and provides a competitive advantage to entertainment firms, using ANP(Analytic Network Process). ANP is a multicriteria decision making technique that allows dependences and feedbacks among decision elements in the hierarchical or network structures in a holistic manner. The pairwise comparison data that prioritized the criteria of HPWS was collected from 28 team leaders in entertainment firms. According to our results, the most critical factor for HPWS in entertainment business is "employee involvement in decision-making." The sub-factors such as "open communication," "distributive decision-making," and "performance-driven reward" have a greater effect. These findings could provide implications for entertainment firms to determine which practices should be taken into account to accomplish HPWS.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1516-1529
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• 2016

While global manufacturing is becoming more competitive due to variety of customer demand, increase in production cost and uncertainty in resource availability, the future ability of manufacturing industries depends upon the implementation of Smart Factory. With the convergence of new information and communication technology, Smart Factory enables manufacturers to respond quickly to customer demand and minimize resource usage while maximizing productivity performance. This paper presents the development of a big data analytics platform architecture for Smart Factory. As this platform represents a conceptual software structure needed to implement data-driven decision-making mechanism in shop floors, it enables the creation and use of diagnosis, prediction and optimization models through the use of data analytics and big data. The completion of implementing the platform will help manufacturers: 1) acquire an advanced technology towards manufacturing intelligence, 2) implement a cost-effective analytics environment through the use of standardized data interfaces and open-source solutions, 3) obtain a technical reference for time-efficiently implementing an analytics modeling environment, and 4) eventually improve productivity performance in manufacturing systems. This paper also presents a technical architecture for big data infrastructure, which we are implementing, and a case study to demonstrate energy-predictive analytics in a machine tool system.

• Journal of Service Research and Studies
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• v.14 no.3
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• pp.188-205
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• 2024

The food truck business, which involves selling various types of food from mobile vehicles, has gained significant popularity in urban centers and at events. These food trucks have rapidly expanded due to their relatively low initial investment and high flexibility, attracting customers with unique menus and personalized services. However, as competition increases, the need to manage service quality to boost customer satisfaction and encourage repeat visits has become more critical. Despite this growing importance, there has been limited empirical research on the topic. This study aims to analyze customer experiences with food truck services to gain strategic insights for improving service quality. By applying structural topic modeling to customer review data, the study identified 50 key topics. The process included a comprehensive evaluation of model diagnostics and interpretability to determine the optimal number of topics, ultimately selecting the most relevant ones related to service experiences. The impact of these identified topics on overall customer satisfaction was empirically tested using regression analysis. The results showed that aspects such as "Food Taste," "Friendly Staff," and "Positive Emotion" had a positive influence on customer satisfaction, whereas "Delayed Service," "Negative Emotion," and "Beverage Service" had a negative impact. Based on this analysis, the study proposes concrete methods for food truck operators to systematically analyze customer feedback and use it to drive service improvements and innovation. This research highlights the importance of data-driven decision-making in small business environments like food trucks and contributes to expanding the application of topic modeling in the service industry.

• Korean Journal of Community Nutrition
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• v.26 no.3
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• pp.155-166
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• 2021

Objectives: Driven by a growth of single-person households and individualized lifestyles, solo dining in restaurants is an increasingly recognizable trend. However, a research gap exists in the comparison of solo and group diners' menu-decision making processes. Based on the self-control dilemma and the temporal construal theory as a theoretical framework, this study compared the ordering intentions of solo vs. group diners with healthy vs. indulgent (less healthy) entrées. The mediating role of consumption orientation and the moderating role of amount of menu nutrition information were further explored to understand the mechanism and a boundary condition. Methods: A scenario-based online survey was developed using a 2 (dining social context: solo vs. with others) × 3 (amount of menu nutrition information: no nutrition information vs. calories vs. calories/fat/sodium), between-subjects, experimental design. Consumers' level of nutrition involvement was controlled. A nationwide survey data (n = 224) were collected from a crowdsourcing platform in the U.S. Data were analyzed using multivariate analysis of covariance, independent t-test, univariate analysis of covariance, and moderated mediation analyses. Results: Findings reveal that solo (vs. group) diners have less (vs. more) intentions to order indulgent menu items due to a more utilitarian (vs. more hedonic) consumption orientation in restaurant dining. Findings also show that solo (vs. group) diners have more (vs. less) intentions to order healthy menu items when the restaurant menu presented nutrition information including calories, fat, and sodium. Conclusions: The findings contribute to the literature of foodservice management, healthy eating, and consumer behavior by revealing a mechanism and an external stimuli of solo vs. group diners' healthy menu-decision making process in restaurants. Furthermore, the findings provide restauranteurs and health professionals with insights into the positive and negative impacts of menu nutrition labelling on consumers' menu-decisions.

• Journal of Digital Contents Society
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• v.5 no.1
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• pp.54-60
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• 2004

This research employs the intelligent survey agent and online application from to help people to locate the information they would want from Internet quickly. Corporate sectors and indivisuals are in need of valuable and relevant information to swiftly adapt to the Internet-driven business or educational environment. Based on Internet GUI environment, this user-friendly online survey and online application form program uses the agent of increasing the interest and participation of users. The system helps along with the decision making process by quickening the information gathering process.

• The Journal of Korean Academy of Sensory Integration
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• v.17 no.3
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• pp.26-45
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• 2019

Objective : The purpose of this study is to develop educational goals, training content, and training methods for the intervention course of the Korean Academy of Sensory Integration (KASI) and to conduct competency-based intervention courses based on the competency model for sensory integration intervention. Methods : This study was conducted on work therapists who participated in the 2019 intervention course of KASI. In the first phase, educational needs were analyzed to set goals for the interventional course. In the second phase, a meeting of researchers drafted the intervention course education program and the methods of education, and the intervention course was conducted. In the third phase, the changes in educational satisfaction and performance level pre- and post-intervention course for each competency index were investigated. Results : The educational goals of "learning and applying the clinical reasoning process of sensory integration intervention" and "intervention by applying the principle of sensory integration intervention" were set after reflecting on the results of the analysis of the educational requirements. The length of the competency-based intervention course was 42 hours. The average education satisfaction level of participants in the arbitration process was 4.48±0.73, and the average education satisfaction level of the supervisor was 3.92±0.71. In both groups, the most satisfying curriculums were the data-driven decision-making process and the intervention goal-setting lecture. But the satisfaction level of was the lowest. Before and after the intervention course, there were significant changes in the performance of the two behavioral indicators of the analytic skills in the expertise competency cluster of the competency model. Conclusion : This study is meaningful in that it conducted a survey of educational needs, the development and implementation of an educational curriculum, and an education satisfaction survey through systematic courses necessary for education development.