• Journal of the Health Care and Life Science
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• v.11 no.1
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• pp.79-85
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• 2023

This study established a hypothesis that cooking students' learning motivation (ARCS) will have a positive (+) effect on learning attitudes, and verified the research model and hypothesis established through empirical analysis. In order to conduct an empirical study, this study conducted a survey of students at specialized high schools and culinary high schools in Korea, and based on a total of 402 samples, the hypothesis was verified through a reliability, suitability, and validity review of the research model. Frequency analysis, correlation analysis, and regression analysis were conducted with the SPSS/WIN statistical program 22.0 to verify the hypothesis of the study. In this study, the learning motivation (ARCS) theory analyzed the correlation between learning attitudes for students majoring in cooking to provide basic data on effective teaching and learning methods to improve learning outcomes of cooking education.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.5
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• pp.828-834
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• 1997

The knowledge of material stress-strain behavior is an essential requirement for design and analysis of deformation processes. Empirical stress-strain relationship and constitutive equations describing material behavior during deformation are being widely used, despite suffering some drawbacks in terms of ease of development, accuracy and speed. In the present study, back-propagation neural networks are used to model and predict the flow stresses of a HSLA steel under conditions of constant strain, strain rate and temperature. The performance of the network model is comparedto those of statistical models on rate equations. Well-trained network model provides fast and accurate results, making it superior to statistical models.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.1
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• pp.65-72
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• 2007

This paper aims to provide information to generate a statistical load model of an educational server by analyzing workload of an e-Learning sewer at Dankook University. The result of the analysis shows file size distribution, access frequency and transmission volume for each file type, access interval, changes in preference and clients access rate by networks. In particular, it had different results from previous studies about video file's size distribution and file distribution based on access frequency. This is because the characteristics of e-learning are influenced by using authoring tools for making into video file and by freeing the number of students who register for a course. The result in this paper can be used as a basic data for studies designed to improve e-learning system architecture and server performance.

• Journal of Information Processing Systems
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• v.1 no.1 s.1
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• pp.14-21
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• 2005

Even though mainly statistical methods have been used in anomaly network intrusion detection, to detect various attack types, machine learning based anomaly detection was introduced. Machine learning based anomaly detection started from research applying traditional learning algorithms of artificial intelligence to intrusion detection. However, detection rates of these methods are not satisfactory. Especially, high false positive and repeated alarms about the same attack are problems. The main reason for this is that one packet is used as a basic learning unit. Most attacks consist of more than one packet. In addition, an attack does not lead to a consecutive packet stream. Therefore, with grouping of related packets, a new approach of group-based learning and detection is needed. This type of approach is similar to that of multiple-instance problems in the artificial intelligence community, which cannot clearly classify one instance, but classification of a group is possible. We suggest group generation algorithm grouping related packets, and a learning algorithm based on a unit of such group. To verify the usefulness of the suggested algorithm, 1998 DARPA data was used and the results show that our approach is quite useful.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.219-228
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• 2021

The aim of this study was to investigate the learning styles with personalities of medical students which may affect the efficiency of teaching-learning system of clinical education to determine the association with the clinical performance examination achievement of the students. The learning styles and personality traits of 147 students of medical college were investigated. The obtained data were analyzed by statistical analysis including independent t-test and correlation analysis. The results of the analyses are as follows: there was significant difference in the participation model in the different genders; of the personality traits, there was significant difference in self-transcendence in the different genders, whereas there was significant difference in the persistence for past failure experiences; and there was significant association between the 6 sub-learning models(Independent vs. Dependent, Collaborative vs. Competitive, and Participant vs. Avoidant learning styles) and the personality traits(Novelty Seeking, Harm Avoidance, Reward Dependence, Persistence, Self-directedness, Cooperativeness and Self-transcendence). In addition, the participant type of students had higher scholastic achievements in clinical performance, and the students who scored high in self-transcendence and persistence also had higher clinical performance. In conclusion, the student's learning style and personalities affected the clinical scholastic performance. We believe that considering this current study, it would be possible to improve the quality of clinical education of medical teaching as well as helping medical students to choose career paths that are suitable for their personalities.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.4
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• pp.67-75
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• 2022

Recently, e-learning has been attracting significant attention due to COVID-19. However, while e-learning has many advantages, it has disadvantages as well. One of the main disadvantages of e-learning is that it is difficult for teachers to continuously and systematically monitor learners. Although services such as personalized e-learning are provided to compensate for the shortcoming, systematic monitoring of learners' concentration is insufficient. This study suggests a method to evaluate the learner's concentration by applying machine learning techniques. In this study, emotion and gaze data were extracted from 184 videos of 92 participants. First, the learners' concentration was labeled by experts. Then, statistical-based status indicators were preprocessed from the data. Random Forests (RF), Support Vector Machines (SVMs), Multilayer Perceptron (MLP), and an ensemble model have been used in the experiment. Long Short-Term Memory (LSTM) has also been used for comparison. As a result, it was possible to predict e-learners' concentration with an accuracy of 90.54%. This study is expected to improve learners' immersion by providing a customized educational curriculum according to the learner's concentration level.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.93-104
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• 2022

In forecasting realized volatility of the major US stock price indexes (S&P 500, Russell 2000, DJIA, Nasdaq 100), internet search volume reflecting investor's interests and implied volatility are used to improve forecast via a deep learning method of the LSTM. The LSTM method combined with search volume index produces better forecasts than existing standard methods of the vector autoregressive (VAR) and the vector error correction (VEC) models. It also beats the recently proposed vector error correction heterogeneous autoregressive (VECHAR) model which takes advantage of the cointegration relation between realized volatility and implied volatility.

• Journal of the Society of Disaster Information
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• v.19 no.1
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• pp.44-59
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• 2023

Purpose: In the event of mass casualties, triage must be done promptly and accurately so that as many patients as possible can be recovered and returned to the battlefield. However, medical personnel have received many tasks with less manpower, and the battlefield for classifying patients is too complex and uncertain. Therefore, we studied an artificial intelligence model that can assist and replace medical personnel on the battlefield. Method: The triage model is presented using reinforcement learning, a field of artificial intelligence. The learning of the model is conducted to find a policy that allows as many patients as possible to be treated, taking into account the condition of randomly set patients and the medical capability of the military hospital. Result: Whether the reinforcement learning model progressed well was confirmed through statistical graphs such as cumulative reward values. In addition, it was confirmed through the number of survivors whether the triage of the learned model was accurate. As a result of comparing the performance with the rule-based model, the reinforcement learning model was able to rescue 10% more patients than the rule-based model. Conclusion: Through this study, it was found that the triage model using reinforcement learning can be used as an alternative to assisting and replacing triage decision-making of medical personnel in the case of mass casualties.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.21 no.3
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• pp.302-310
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• 2014

Purpose: The purpose of this study was to compare the effectiveness of training using an intravenous (IV) arm model versus a computer simulator for IV injections. Method: Study was a quasi-experimental study conducted with 106 nursing students. Participants were divided into two groups: the IV Arm Group using a mannequin arm model (control group) and the Computer Simulator Group using the Virtual IV demonstration (experimental group). Theoretical lectures and video presentations on IV injections were given to both groups. Each group went through the training practice using the IV arm or computer simulator. After the completion of training, questionnaires were given to the students to evaluate their learning attitudes and experiences, self-confidence in IV injection, and satisfaction with the training materials. Results: Student satisfaction with the training materials including the reality, usefulness, and educational effects showed notable differences between the two groups with the Computer Simulator group reporting more positive effects that the IV Arm group. However, there was no statistical difference between the two groups in the categories of learning attitude, learning experience, or self-confidence. Conclusion: While there was a differences in strengths and weaknesses of the two methods, both methods should be considered for practice and further study needs to be done on educational effectiveness.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.373-373
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• 2021

Rainfall forecasting is an important issue that is applied in many areas, such as agriculture, flood warning, and water resources management. In this context, this study proposed a statistical and machine learning-based forecasting model for monthly rainfall. The Bayesian Gaussian process was chosen to optimize the hyperparameters of the Stacked Long Short-term memory (SLSTM) model. The proposed SLSTM model was applied for predicting monthly precipitation of Seoul station, South Korea. Data were retrieved from the Korea Meteorological Administration (KMA) in the period between 1960 and 2019. Four schemes were examined in this study: (i) prediction with only rainfall; (ii) with deseasonalized rainfall; (iii) with rainfall and minimum temperature; (iv) with deseasonalized rainfall and minimum temperature. The error of predicted rainfall based on the root mean squared error (RMSE), 16-17 mm, is relatively small compared with the average monthly rainfall at Seoul station is 117mm. The results showed scheme (iv) gives the best prediction result. Therefore, this approach is more straightforward than the hydrological and hydraulic models, which request much more input data. The result indicated that a deep learning network could be applied successfully in the hydrology field. Overall, the proposed method is promising, given a good solution for rainfall prediction.