• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.237-245
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• 2022

The prominence of IoTs (Internet of Things) and exponential advancement of computer networks has resulted in massive essential applications. Recognizing various cyber-attacks or anomalies in networks and establishing effective intrusion recognition systems are becoming increasingly vital to current security. MLTs (Machine Learning Techniques) can be developed for such data-driven intelligent recognition systems. Researchers have employed a TFDNNs (Tensor Flow Deep Neural Networks) and DCNNs (Deep Convolution Neural Networks) to recognize pirated software and malwares efficiently. However, tuning the amount of neurons in multiple layers with activation functions leads to learning error rates, degrading classifier's reliability. HTFDNNs ( Hybrid tensor flow DNNs) and MRNs (Modified Residual Networks) or Resnet CNNs were presented to recognize software piracy and malwares. This study proposes HTFDNNs to identify stolen software starting with plagiarized source codes. This work uses Tokens and weights for filtering noises while focusing on token's for identifying source code thefts. DLTs (Deep learning techniques) are then used to detect plagiarized sources. Data from Google Code Jam is used for finding software piracy. MRNs visualize colour images for identifying harms in networks using IoTs. Malware samples of Maling dataset is used for tests in this work.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.3
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• pp.201-207
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• 2021

Deep learning, a type of machine learning, performs learning while changing the weights as it progresses through each learning process. Tensor Flow and Keras provide the results of the end of the learning in graph form. Thus, If an error occurs, the result must be discarded. Consequently, existing technologies provide a function to roll back learning results, but the rollback function is limited to results up to five times. Moreover, they applied the concept of MLOps to track the deep learning process, but no rollback capability is provided. In this paper, we construct a system that manages the intermediate value of the learning process by blockchain to record the intermediate learning process and can rollback in the event of an error. To perform the functions of blockchain, the deep learning process and the rollback of learning results are designed to work by writing Smart Contracts. Performance evaluation shows that, when evaluating the rollback function of the existing deep learning method, the proposed method has a 100% recovery rate, compared to the existing technique, which reduces the recovery rate after 6 times, down to 10% when 50 times. In addition, when using Smart Contract in Ethereum blockchain, it is confirmed that 1.57 million won is continuously consumed per block creation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.7
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• pp.423-430
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• 2017

This study examined the effects of role rotation experience on learning flow, self leadership and debriefing satisfaction in nursing students. A non-equivalence control group quasi-experimental study was used and included as participants 203 junior nursing students at Y University. The experimental group (n=103) participated in the teaching class using a role rotation experience, while the control group (n=100) received conventional practice education. The outcome measurements were learning flow, self leadership, and debriefing satisfaction. The collected data were analyzed using a chi-test, and at-test using the SPSS WIN 21.0 program. The total score of learning flow and self leadership were similar in the two groups. On the other hand, in the case of the debriefing satisfaction (t=-2.70, p=.008), the experimental group ($4.24{\pm}0.51$) was remarkably higher than the control group ($4.03{\pm}0.60$). Although the changes regarding the learning flow and self leadership could not be identified, the debriefing satisfaction had been affected by the practice education using the role rotation experience. Therefore, to identify the effects of simulation education for further details, more research with diversified subjects and varied durations is needed.

• Journal of Gifted/Talented Education
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• v.26 no.1
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• pp.77-99
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• 2016

The purpose of this study was to examine the effect of GI-STEAM program on leadership, creative personality, and learning flow of elementary Gifted Students. GI-STEAM program was the convergence model of Group Investigation that belongs to Co-learning and STEAM framework of learning criterion. The participants were 16 gifted students in a Korean elementary school located in Gyeong-gi province. The experimental design was one group pretest-posttest design. After a pretest on leadership, creative personality, and learning flow was conducted, classes were carried out as GI-STEAM program for the gifted student and a post-test was conducted. The study results of the class that was conducted twelve times for two weeks are as follows. First, Individual area of leadership is meaningfully developed in statistics after GI-STEAM program. The sub-domains of leadership, such as the communication, organization management, society commitment and teamwork showed a statistically significant improvement. Second, the domain of creative personality didn't show meaningful difference after GI-STEAM program. However, the aesthetic in the sub-domains of the creative personality showed a statistically significant improvement. Third, learning flow was meaningfully developed in statistics after GI-STEAM program. The sub-domains of the leadership, such as the balance between challenge and ability, integration with behavior and consciousness, concrete feedback and Autotelic experience showed a statistically significant improvement. In conclusion, GI-STEAM is an effective program for improving ability of communication, aesthetic sensibility, which are core competency of 'creative-convergence' gifted students. For this reason, it is highly considered that various programs applying GI-STEAM should be developed.

• Journal of Korean Library and Information Science Society
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• v.46 no.3
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• pp.119-140
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• 2015

The domestic and foreign standards of school libraries suggest that school library should be run to give useful and pleasant experiences to students. This study applies Csikszentmihalyi's 'flow theory' to analyze the relationships between students' flow experience and their satisfaction about school libraries use. The variables selected in this study are students' satisfaction, flow experience, library skill, challenge of library use, types of library use and students' learning styles. The research model is designed by using these 6 variables in this study. The data are collected from 293 students and analyzed by structural equation modeling. The results of this study are as follows: The entire casual relationships show that library skills influence in types of library use, types of library use affect students' learning styles and students' learning styles influence in satisfaction through flow. To improve students' satisfaction about school library use, this study proposes effective ways related to teacher librarians' role performances to expand students' flow experience and increase their library skills.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.119-130
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• 2022

Crime is a common social problem that affects the quality of life. As the number of crimes increases, it is necessary to build a model to predict the number of crimes that may occur in a given period, identify the characteristics of a person who may commit a particular crime, and identify places where a particular crime may occur. Data privacy is the main challenge that organizations face when building this type of predictive models. Federated learning (FL) is a promising approach that overcomes data security and privacy challenges, as it enables organizations to build a machine learning model based on distributed datasets without sharing raw data or violating data privacy. In this paper, a federated long short- term memory (LSTM) model is proposed and compared with a traditional LSTM model. Proposed model is developed using TensorFlow Federated (TFF) and the Keras API to predict the number of crimes. The proposed model is applied on the Boston crime dataset. The proposed model's parameters are fine tuned to obtain minimum loss and maximum accuracy. The proposed federated LSTM model is compared with the traditional LSTM model and found that the federated LSTM model achieved lower loss, better accuracy, and higher training time than the traditional LSTM model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.58-64
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• 2021

As the number of registered vehicles increases, traffic congestion will worsen worse, which may act as an inhibitory factor for urban social and economic development. Through accurate traffic flow prediction, various AI techniques have been used to prevent traffic congestion. This paper uses the data from a VDS (Vehicle Detection System) as input variables. This study predicted traffic flow in five levels (free flow, somewhat delayed, delayed, somewhat congested, and congested), rather than predicting traffic flow in two levels (free flow and congested). The Catboost model, which is a machine-learning algorithm, was used in this study. This model predicts traffic flow in five levels and compares and analyzes the accuracy of the prediction with other algorithms. In addition, the preprocessed model that went through RandomizedSerachCv and One-Hot Encoding was compared with the naive one. As a result, the Catboost model without any hyper-parameter showed the highest accuracy of 93%. Overall, the Catboost model analyzes and predicts a large number of categorical traffic data better than any other machine learning and deep learning models, and the initial set parameters are optimized for Catboost.

• Journal of Digital Convergence
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• v.16 no.8
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• pp.221-231
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• 2018

The purpose of this study was to identify the effect of simulation integrated with problem based learning(SIM-PBL) on nursing students. A non-equivalent pre-post test of quasi-experimental design was used. 37 were assigned to an intervention group and 41 to a control group. SIM-PBL was applied to intervention group and simulation was applied to control group. Data were analyzed using the SPSS 20.0 program. There were significant increases in system thinking(t=2.14, p=.036), proactivity in problem solving(t=2.07, p=.042), and performance ability for medication(t=5.26, p=<.001) in the intervention group compared to the control group. However, there was no difference in learning flow between the intervention and control groups. In order to adapt to the clinical setting, it is necessary to consider the SIM-PBL needs to be considered in the curriculum of nursing students.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.269-274
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• 2022

With the advent of the Fourth Industrial Revolution modern society demands global convergence talent with expertise. The purpose of this study is to suggest the Person-organizational Fit can be enhanced only when university institutions enhance the learning flow and learning interest of Chinese students. Only when the suitability of Chinese students and Univ. is good will it be of great help to the educational performance and administrative management of international students, and the learning atmosphere and university image can be promoted. However, international students with excessive self-efficacy may have a negative effect on Person-organizational Fit to making it difficult to educate and manage students. On the other hand, for international students with low self-efficacy, the higher the immersion in learning, the higher the individual-organization (Univ) suitability.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.2
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• pp.19-28
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• 2019

The purpose of this study is to examine the trends on machine learning and deep learning research in the published journals from the Web of Science Database. To achieve the study purpose, we used the abstracts of 20,664 articles published between 1990 and 2017, which include the word 'machine learning', 'deep learning', and 'artificial neural network' in their titles. Twenty major research topics were identified from topic modeling analysis and they were inclusive of classification accuracy, machine learning, optimization problem, time series model, temperature flow, engine variable, neuron layer, spectrum sample, image feature, strength property, extreme machine learning, control system, energy power, cancer patient, descriptor compound, fault diagnosis, soil map, concentration removal, protein gene, and job problem. The analysis of the time-series linear regression showed that all identified topics in machine learning research were 'hot' ones.