• Journal of Korea Entertainment Industry Association
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• v.4 no.4
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• pp.72-76
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• 2010

Clinical nomogram is a graphical representation of numeric formula, constructed from clinical cases database of followed patients' treatment, which is used for medical predication. For a clinical nomogram to contribute patient care, it is required to accumulate as many as clinical cases and to extract medical prediction knowledge. It needs to be equipped with an effective method to build medical nomogram with high predication accuracy. It is desirable for medical nomogram to be accessible at patient care point. This paper proposes a medical nomogram service system architecture which takes into account the above-mentioned issues. The proposed system architecture includes a web-based database subsystem to maintain and keep track of clinical cases. On the periodic basis, a new clinical nomogram is reconstructed for the updated clinical database. For the convenient use of patient care practice environment, an app-based program is provided which makes prediction based on the most recent clinical nomogram constructed in the service system. The proposed method has been applied to a clinical nomogram service system development for recurrence and survival prediction in bladder cancer patients.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.280-283
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• 2022

This study aims to identify problems with existing e-learning content and non-face-to-face class methods, improve students' concentration, improve class achievement and educational effectiveness, and propose an artificial intelligence class system design using a web server. By using the function of face and eye tracking using OpenCV to identify attendance and concentration, and by inducing feedback through voice or message to questions asked by the instructor in the middle of class, learners relieve boredom caused by online classes and test by runner If the score is not reached, we propose an artificial intelligence education program system design that can bridge the academic gap and improve academic achievement by providing educational materials and videos for the wrong problem.

• Korea Trade Review
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• v.46 no.5
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• pp.65-81
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• 2021

The technological development in the era of the 4th industrial revolution is changing the paradigm of various industries. Various technologies such as big data, cloud, artificial intelligence, virtual reality, and the Internet of Things are used, creating synergy effects with existing industries, creating radical development and value creation. Among them, the logistics sector has been greatly influenced by quantitative data from the past and has been continuously accumulating and managing data, so it is highly likely to be linked with big data analysis and has a high utilization effect. The modern advanced technology has developed together with the data mining technology to discover hidden patterns and new correlations in such big data, and through this, meaningful results are being derived. Therefore, data mining occupies an important part in big data analysis, and this study tried to analyze data mining techniques that can contribute to the logistics field and common logistics using these data mining technologies. Therefore, by using the AHP technique, it was attempted to derive priorities for each type of efficient data mining for logisticalization, and R program and R Studio were used as tools to analyze this. Criteria of AHP method set association analysis, cluster analysis, decision tree method, artificial neural network method, web mining, and opinion mining. For the alternatives, common transport and delivery, common logistics center, common logistics information system, and common logistics partnership were set as factors.

• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.119-130
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• 2024

Universities are open systems that aim to prepare students to meet academic and industrial programs' expectations. It is important for universities to recognize these expectations and to make sure that they are achievable. To do so, graduates' progression tracking system is an essential tool for universities' development to ensure graduate students meet the market requirements. The purpose of this paper is to create automatic tracing system that captures information about students after graduation and creates annual report that represents the status of university students in term of employment or completing their study. It mainly assists graduates to find appropriate jobs that meet their desires or enabling them to complete their higher education by providing all these opportunities in one platform. The system main objective is to improve communication between graduate students, the university and companies. It also aims to identify the difficulties associated with graduate employability and changes are required to serve current students in term of creating new programs or activities. This helps universities to identify and address the existing curriculums and program's strengths and weaknesses and their adequacy, quality and competencies of a graduate in the labor market, which enhances the quality of higher education. we analyzed and implemented the tracing system using PHP language, which speeds up custom web application development and MySQL database, which guarantee data security, high performance, and other features. Graduate students found the proposed system usable and valuable.

• Journal of Practical Engineering Education
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• v.16 no.2
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• pp.185-193
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• 2024

With the advent of smartphones, people can access various information through the internet anytime and anywhere. Even in the vehicle environment, users can use the internet. Users interact with web and applications every day and get information. However, as the amount of data to be processed by the program increases, users inevitably receive a message to wait. User waiting is an inconvenient experience, but minimizing user waiting is the best way because there is time required for data processing. However, if the service processing time exceeds the expected time, users experience more severe boredom and pain. Therefore, various methods and researches are being conducted to alleviate the boredom of user waiting. The most commonly used method to alleviate user waiting boredom is loading. In this study, we investigated the effect of skeleton loading, the latest loading technique, on user waiting experience, and how attractive it is as a design technique in terms of UI compared to other loading techniques.

• Journal of Practical Engineering Education
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• v.16 no.1_spc
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• pp.33-39
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• 2024

This study examines the changes in learners' positive/negative perceptions of classroom experience and actual utilisation of AI chatbots in response to the recent changes in education trends caused by generative AI. AI chatbots were utilised in web programming classes for six classes of engineering students over two semesters. The learners' experience and usage were analysed from the beginning of the semester through surveys until the submission of midterm and final examination reports. The study's results indicate that the chatbot enhanced learning by providing Q/A feedback and solving practical problems. Additionally, the perception of the chatbot improved from midterm to the end of the course. The study also drew meaningful conclusions about the issue of community disconnection (personalisation) in the classroom and how to use it as educational software. This research is significant for the development of generative AI-based software.

• Imaging Science in Dentistry
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• v.54 no.1
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• pp.1-11
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• 2024

Purpose: This study was conducted to investigate the safety of dental imaging in pregnant women with respect to fetal health. Materials and Methods: Searches were conducted of the PubMed, Scopus, and Web of Science databases in May 2023. The inclusion criteria encompassed cross-sectional and longitudinal studies that focused on the analysis of diagnostic dental imaging in pregnant women, as well as studies utilizing phantoms to simulate imaging examinations. The exclusion criteria consisted of reviews, letters to the editor, book chapters, and abstracts from scientific conferences and seminars. Results: A total of 3,913 articles were identified. Based on a review of the titles and abstracts, 3,892 articles were excluded, leaving 21 articles remaining for full-text review. Of these, 18 were excluded, and 4 additional articles were included as cross-references. Ultimately, 7 articles underwent quantitative-qualitative analysis. Three retrospective studies were focused on pregnant women who underwent dental imaging procedures. The remaining 4 studies utilized female phantoms to simulate imaging examinations and represent the radiation doses absorbed by the uterus or thyroid. Conclusion: Few dental radiology studies have been conducted to determine the safe radiation threshold for pregnant women. Additionally, the reviewed articles did not provide numbers of dental examinations, by type, corresponding to this dose. Dental imaging examinations of pregnant women should not be restricted if clinically indicated. Ultimately, practitioners must be able to justify the examination and should adhere to the "as low as diagnostically acceptable, being indication-oriented and patient-specific" (ALADAIP) principle of radioprotection.

• Dementia and Neurocognitive Disorders
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• v.23 no.3
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• pp.127-135
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• 2024

Background and Purpose: To ensure data privacy, the development of defacing processes, which anonymize brain images by obscuring facial features, is crucial. However, the impact of these defacing methods on brain imaging analysis poses significant concern. This study aimed to evaluate the reliability of three different defacing methods in automated brain volumetry. Methods: Magnetic resonance imaging with three-dimensional T1 sequences was performed on ten patients diagnosed with subjective cognitive decline. Defacing was executed using mri_deface, BioImage Suite Web-based defacing, and Defacer. Brain volumes were measured employing the QBraVo program and FreeSurfer, assessing intraclass correlation coefficient (ICC) and the mean differences in brain volume measurements between the original and defaced images. Results: The mean age of the patients was 71.10±6.17 years, with 4 (40.0%) being male. The total intracranial volume, total brain volume, and ventricle volume exhibited high ICCs across the three defacing methods and 2 volumetry analyses. All regional brain volumes showed high ICCs with all three defacing methods. Despite variations among some brain regions, no significant mean differences in regional brain volume were observed between the original and defaced images across all regions. Conclusions: The three defacing algorithms evaluated did not significantly affect the results of image analysis for the entire brain or specific cerebral regions. These findings suggest that these algorithms can serve as robust methods for defacing in neuroimaging analysis, thereby supporting data anonymization without compromising the integrity of brain volume measurements.

• Journal of Web Engineering
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• v.13 no.7
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• pp.2440-2450
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• 2021

Homocysteine (Hcy) is well known to be increased in the metabolic syndrome (MetS) incidence. However, it remains unclear whether the relationship is causal or not. Recently, Mendelian Randomization (MR) has been popularly used to assess the causal influence. In this study, we adopted MR to investigate the causal influence of Hcy on MetS in adults using three independent cohorts. We considered one-sample MR and two-sample MR. We analyzed one-sample MR in 5902 individuals (2090 MetS cases and 3812 controls) from the KARE and two-sample MR from the HEXA (676 cases and 3017 controls) and CAVAS (1052 cases and 764 controls) datasets to evaluate whether genetically increased Hcy level influences the risk of MetS. In observation studies, the odds of MetS increased with higher Hcy concentrations (odds ratio (OR) 1.17, 95%CI 1.12-1.22, p < 0.01). One-sample MR was performed using two-stage least-squares regression, with an MTHFR C677T and weighted Hcy generic risk score as an instrument. Two-sample MR was performed with five genetic variants (rs12567136, rs1801133, rs2336377, rs1624230, and rs1836883) by GWAS data as the instrumental variables. For sensitivity analysis, weighted median and MR-Egger regression were used. Using one-sample MR, we found an increased risk of MetS (OR 2.07 per 1-SD Hcy increase). Two-sample MR supported that increased Hcy was significantly associated with increased MetS risk by using the inverse variance weighted (IVW) method (beta 0.723, SE 0.119, and p < 0.001), the weighted median regression method (beta 0.734, SE 0.097, and p < 0.001), and the MR-Egger method (beta 2.073, SE 0.843, and p = 0.014) in meta-analysis. The MR-Egger slope showed no evidence of pleiotropic effects (intercept -0.097, p = 0.107). In conclusion, this study represented the MR approach and elucidates the significant relationship between Hcy and the risk of MetS in the Korean population.

• Journal of Industrial Convergence
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• v.22 no.8
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• pp.105-113
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• 2024

The purpose of the study was to explain the effect of adolescents' problem-solving ability on sociality and to verify the mediating effect of self-esteem in the COVID-19 era. The study utilized the survey data using the web and mobile conducted by the Korea Youth Policy Institute, and selected 1,471 school-age adolescents as the subjects of the survey. For the analysis, the study performed various analysis methods including frequency analysis, descriptive statistics, correlation analysis, and mediating effect analysis of self-esteem using SPSS WIN 25.0 and PROCESS MACRO program. The results are as follows. First, it was shown that adolescents' problem-solving ability had a positive (+) effect on sociality. Second, there was a mediating effect of self-esteem in the relationship between adolescents' problem-solving ability and sociality. Based on these results, the practical engagement and various programs to improve adolescents' self-esteem and sociality were suggested.