• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.289-296
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• 2023

The purpose of this study aims to identify the key factors that contribute to successful online learning experiences for college students in the pre-course, during course, and post-course phases. A survey was conducted college students, and a total of 95 questionnaires were used for statistical analysis. The main findings revealed that in the pre-course phase, task value, academic self-efficacy, and control beliefs were significant factors. During course, interaction emerged as a crucial factor. Notably, students' satisfaction in the post-course phase is significantly influenced by academic self-efficacy and interaction. Understanding these factors will help inform the design and operation of effective college-level online courses to improve student experience and satisfaction.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.563-571
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• 2023

The purpose of the study is to understand the impact of nursing college students' awareness and self-esteem on nursing professionals. The research design of this study is a descriptive investigative study using convenient samples. The data collection collected structured questionnaires and Google's online survey methods for first- to fourth-year nursing college students at three universities in North Chungcheong Province. The collected data were analyzed using the SPSS window 25.0 program as frequency, percentage, mean and standard deviation, t-test and one-way ANOVA, and post-test as Scheffétest, Pearson correlation coefficient, and multiple regression. The study found that 21.7% (==-.181, p<.001), 2.8% major satisfaction, and 24.5% (β=.420, p<.001), so it is recommended to use it as basic data to establish a curriculum and teaching learning strategy to improve major satisfaction.

• Korean Journal of Veterinary Service
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• v.46 no.4
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• pp.293-302
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• 2023

This study provides a comprehensive analysis of the epidemiological trends and challenges in managing tuberculosis (TB) in livestock in Korea from 2013 to 2022. Tuberculosis, caused by the Mycobacterium tuberculosis complex, is a significant zoonotic disease affecting cattle, deer, and other domesticated animals. Despite the initiation of a test-and-slaughter eradication policy in 1964, TB has continued to persist in Korean livestock, particularly in cattle and deer. This study used data from the Korea Animal Health Integrated System and provincial animal health laboratories to analyze TB incidence in various livestock including different cattle breeds and deer species. The results from 2013 to 2022 showed a peak in TB cases in 2019 with a subsequent decline by 2022. The study highlighted a significant incidence of TB in Korean native cattle and the need for amore inclusive approach towards TB testing and control in different cattle breeds. Additionally, the study underscored the importance of addressing TB in other animals such as goats, wildlife, and companion animals for a holistic approach to TB eradication in Korea. The findings suggest that while the test-and-slaughter strategy has been historically effective, there is a need for adaptation to the current challenges, and learning from successful eradiation stories on other countries like Australia. A collaborative effort involving an expanded surveillance system, active private sector participation, and robust government support essential for the efficient eradication of TB in livestock in Korea.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3242-3265
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• 2023

Ensuring reliability of a machinery system involve the prediction of remaining useful life (RUL). In most RUL prediction approaches, noise is always considered for removal. Nevertheless, noise could be properly utilized to enhance the prediction capabilities. In this paper, we proposed a novel RUL prediction approach based on noise injection and a Kalman filter ensemble of modified bagging predictors. Firstly, we proposed a new method to insert Gaussian noises into both observation and feature spaces of an original training dataset, named GN-DAFC. Secondly, we developed a modified bagging method based on Kalman filter averaging, named KBAG. Then, we developed a new ensemble method which is a Kalman filter ensemble of KBAGs, named DKBAG. Finally, we proposed a novel RUL prediction approach GN-DAFC-DKBAG in which the optimal noise-injected training dataset was determined by a GN-DAFC-based searching strategy and then inputted to a DKBAG model. Our approach is validated on the NASA C-MAPSS dataset of aero-engines. Experimental results show that our approach achieves significantly better performance than a traditional Kalman filter ensemble of single learning models (KESLM) and the original DKBAG approaches. We also found that the optimal noise-injected data could improve the prediction performance of both KESLM and DKBAG. We further compare our approach with two advanced ensemble approaches, and the results indicate that the former also has better performance than the latters. Thus, our approach of combining optimal noise injection and DKBAG provides an effective solution for RUL estimation of machinery systems.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.101-106
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• 2023

Chronic illnesses are among the most common serious problems affecting human health. Early diagnosis of chronic diseases can assist to avoid or mitigate their consequences, potentially decreasing mortality rates. Using machine learning algorithms to identify risk factors is an exciting strategy. The issue with existing feature selection approaches is that each method provides a distinct set of properties that affect model correctness, and present methods cannot perform well on huge multidimensional datasets. We would like to introduce a novel model that contains a feature selection approach that selects optimal characteristics from big multidimensional data sets to provide reliable predictions of chronic illnesses without sacrificing data uniqueness.[1] To ensure the success of our proposed model, we employed balanced classes by employing hybrid balanced class sampling methods on the original dataset, as well as methods for data pre-processing and data transformation, to provide credible data for the training model. We ran and assessed our model on datasets with binary and multivalued classifications. We have used multiple datasets (Parkinson, arrythmia, breast cancer, kidney, diabetes). Suitable features are selected by using the Hybrid feature model consists of Lassocv, decision tree, random forest, gradient boosting,Adaboost, stochastic gradient descent and done voting of attributes which are common output from these methods.Accuracy of original dataset before applying framework is recorded and evaluated against reduced data set of attributes accuracy. The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy on multi valued class datasets than on binary class attributes.[1]

• Journal of Information Technology Applications and Management
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• v.31 no.1
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• pp.177-188
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• 2024

In this research, a cohort of two children, aged 7-8 years, was selected to participate in a specialized three-week training program aimed at enhancing their working memory. The program consisted of three sessions, each lasting approximately 30 minutes. The primary goal was to investigate the impact and developmental trajectory of working memory in school-aged children. Working memory plays a significant role in young children's learning and daily activities. To address the needs of this demographic, products should offer both educational and enjoyable activities that engage working memory. Digital educational tools, known for their flexibility, are suitable for both older individuals and young children. By updating software or modifying content, these tools can be effectively repurposed for young learners without extensive hardware changes, making them both cost-effective and practical. For example, memory training games initially designed for older adults can be adapted for young children by altering images, music, or storylines. Furthermore, incorporating elements familiar to children, like animals, toys, or fairy tales, can increase their engagement in these activities. Historically, working memory capabilities have been assessed predominantly through traditional intelligence tests. However, recent research questions the adequacy of these behavioral measures in accurately detecting changes in working memory. To bridge this gap, the current study utilized electroencephalography (EEG) as a more sophisticated and precise tool for monitoring potential changes in working memory after the training. The research findings were revealing. Participants showed marked improvement in their performance on n-back tasks, a standard measure for evaluating working memory. This improvement post-training strongly supports the effectiveness of the training program. The results indicate that such targeted and structured training programs can significantly enhance the working memory abilities of children in this age group, providing promising implications for educational strategies and cognitive development interventions.

• The Journal of Bigdata
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• v.8 no.2
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• pp.201-222
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• 2023

As the e-Commerce gets increased based on the platform, a lot of small and medium sized sellers have tried to develop the more effective strategies to maximize the profit. In order to increase the profitability, it is quite important to make the strategic decisions based on the range of promotion, discount rate and categories of products. This research aims to develop the business intelligence application which can help sellers of e-Commerce platform make better decisions. To decide whether or not to promote, it is needed to predict the level of increase in sales after promotion. I n this research, we have applied the various machine learning algorithm such as MLP(Multi Layer Perceptron), Gradient Boosting Regression, Random Forest, and Linear Regression. Because of the complexity of data structure and distinctive characteristics of product categories, Random Forest and MLP showed the best performance. It seems possible to apply the proposed approach in this research in support the small and medium sized sellers to react on the market changes and to make the reasonable decisions based on the data, not their own experience.

• Safety and Health at Work
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• v.15 no.2
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• pp.151-157
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• 2024

Background: Replacement drivers represent a significant portion of platform labor in the Republic of Korea, often facing night shifts and the demands of emotional labor. Research on replacement drivers is limited due to their widespread nature. This study examined the levels of presenteeism and absenteeism among replacement drivers in comparison to those of paid male workers in the Republic of Korea. Methods: This study collected data for replacement drivers and used data from the 6th Korean Working Conditions Survey for paid male workers over the age of 20 years. Propensity score matching was performed to balance the differences between paid workers and replacement drivers. Multivariable logistic regression was used to estimate the adjusted odds ratio (OR) and 95% confidence intervals for presenteeism and absenteeism by replacement drivers. Stratified analysis was conducted for age groups, educational levels, income levels, and working hours. The analysis was adjusted for variables including age, education, income, working hours, working days per week, and working duration. Results: Among the 1,417 participants, the prevalence of presenteeism and absenteeism among replacement drivers was 53.6% (n = 210) and 51.3% (n = 201), respectively. The association of presenteeism and absenteeism (adjusted OR [95% CI] = 8.42 [6.36-11.16] and 20.80 [95% CI = 14.60-29.62], respectively) with replacement drivers being significant, with a prominent association among the young age group, high educational, and medium income levels. Conclusion: The results demonstrated that replacement drivers were more significantly associated with presenteeism and absenteeism than paid workers. Further studies are necessary to establish a strategy to decrease the risk factors among replacement drivers.

• Journal of Korean Home Economics Education Association
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• v.35 no.4
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• pp.61-79
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• 2023

This study was approached as a method of reflective observation to build practical knowledge about home economics by reevaluating the experience of participating in the national development of the 2022 revised home economics curriculum through self-questioning, review, and reflection. The results are as follows: First, "all learners will still live in an unpredictable future society and choose the best actions to better themselves, their surroundings, and the world." Second, the value of home and education in the overall curriculum structure is "home economics education are life lessons that help future generations develop the competence necessary to lead a better life." Third, "in order to ensure the sustainability of all humanity and the world in the future society, the area of intentional learning that helps young people choose and lead better lives should be the core, and home economics education should focus on 'Good life Literacy'.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.93-112
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• 2015

Depending on the change in consumer's consumption pattern, existing retail shop has evolved in hypermarket or convenience store offering grocery and daily products mostly. Therefore, it is important to maintain the inventory levels and proper product configuration for effectively utilize the limited space in the retail store and increasing sales. Accordingly, this study proposed proper product configuration and inventory level strategy based on RFM(Recency, Frequency, Monetary) model and SOM(self-organizing map) for manage the retail shop effectively. RFM model is analytic model to analyze customer behaviors based on the past customer's buying activities. And it can differentiates important customers from large data by three variables. R represents recency, which refers to the last purchase of commodities. The latest consuming customer has bigger R. F represents frequency, which refers to the number of transactions in a particular period and M represents monetary, which refers to consumption money amount in a particular period. Thus, RFM method has been known to be a very effective model for customer segmentation. In this study, using a normalized value of the RFM variables, SOM cluster analysis was performed. SOM is regarded as one of the most distinguished artificial neural network models in the unsupervised learning tool space. It is a popular tool for clustering and visualization of high dimensional data in such a way that similar items are grouped spatially close to one another. In particular, it has been successfully applied in various technical fields for finding patterns. In our research, the procedure tries to find sales patterns by analyzing product sales records with Recency, Frequency and Monetary values. And to suggest a business strategy, we conduct the decision tree based on SOM results. To validate the proposed procedure in this study, we adopted the M-mart data collected between 2014.01.01~2014.12.31. Each product get the value of R, F, M, and they are clustered by 9 using SOM. And we also performed three tests using the weekday data, weekend data, whole data in order to analyze the sales pattern change. In order to propose the strategy of each cluster, we examine the criteria of product clustering. The clusters through the SOM can be explained by the characteristics of these clusters of decision trees. As a result, we can suggest the inventory management strategy of each 9 clusters through the suggested procedures of the study. The highest of all three value(R, F, M) cluster's products need to have high level of the inventory as well as to be disposed in a place where it can be increasing customer's path. In contrast, the lowest of all three value(R, F, M) cluster's products need to have low level of inventory as well as to be disposed in a place where visibility is low. The highest R value cluster's products is usually new releases products, and need to be placed on the front of the store. And, manager should decrease inventory levels gradually in the highest F value cluster's products purchased in the past. Because, we assume that cluster has lower R value and the M value than the average value of good. And it can be deduced that product are sold poorly in recent days and total sales also will be lower than the frequency. The procedure presented in this study is expected to contribute to raising the profitability of the retail store. The paper is organized as follows. The second chapter briefly reviews the literature related to this study. The third chapter suggests procedures for research proposals, and the fourth chapter applied suggested procedure using the actual product sales data. Finally, the fifth chapter described the conclusion of the study and further research.