• ETRI Journal
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• v.44 no.2
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• pp.232-243
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• 2022

Changes in household composition and the residential environment have had a considerable impact on the features of postal delivery regions in recent years, resulting in a large increase in the overall workload of domestic postal delivery services. In this paper, we provide complex analysis results for postal delivery areas using various unsupervised learning approaches. First, we extract highly influential features using several feature-engineering methods. Then, using quantitative and qualitative cluster analyses, we find the distinctive traits and semantics of postal delivery zones. Unsupervised learning approaches are useful for successfully grouping postal service zones, according to our findings. Furthermore, by comparing a postal delivery region to other areas in the same group, workload balancing was achieved.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.172-180
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• 2022

Crypto-mining malware (known as crypto-jacking) is a novel cyber-attack that exploits the victim's computing resources such as CPU and GPU to generate illegal cryptocurrency. The attacker get benefit from crypto-jacking by using someone else's mining hardware and their electricity power. This research focused on the possibility of detecting the potential crypto-mining malware in an environment by analyzing both static and dynamic approaches of deep learning. The Program Executable (PE) files were utilized with deep learning methods which are Long Short-Term Memory (LSTM). The finding revealed that LTSM outperformed both SVM and RF in static and dynamic approaches with percentage of 98% and 96%, respectively. Future studies will focus on detecting the malware using larger dataset to have more accurate and realistic results.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.91-100
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• 2023

As SDN devices and systems hit the market, security in SDN must be raised on the agenda. SDN has become an interesting area in both academics and industry. SDN promises many benefits which attract many IT managers and Leading IT companies which motivates them to switch to SDN. Over the last three decades, network attacks becoming more sophisticated and complex to detect. The goal is to study how traffic information can be extracted from an SDN controller and open virtual switches (OVS) using SDN mechanisms. The testbed environment is created using the RYU controller and Mininet. The extracted information is further used to detect these attacks efficiently using a machine learning approach. To use the Machine learning approach, a dataset is required. Currently, a public SDN based dataset is not available. In this paper, SDN based dataset is created which include legitimate and non-legitimate traffic. Classification is divided into two categories: binary and multiclass classification. Traffic has been classified with or without dimension reduction techniques like PCA and LDA. Our approach provides 98.58% of accuracy using a random forest algorithm.

• Journal of Science Education
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• v.42 no.2
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• pp.165-181
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• 2018

This study compares and analyzes the characteristics and effects of various educational contextual variables according to students' achievement level and gender groups based on the results of PISA 2015 science domain. PISA 2015 included additional variables about teaching-learning and affective characteristics in the field of science, because science was the main domain of PISA 2015. The results of the mediation analysis using a multiple group structural equation model showed that the environment and strategy for the teaching and learning had a positive effect on the affective characteristics, and also positively affected science achievement through the mediator of the affective characteristics. Particularly, the environment and strategy for the teaching and learning was the most effective in improving the affective characteristics for the low achievement group. It was found that the difference of the mediated effect between achievement level groups was statistically significant, but that between male and female students was not. Therefore, the appropriate the environment and strategy for the teaching and learning will need to be emphasized consistently to improve students' cognitive and affective achievement. The implications and suggestions of these results were discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.610-632
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• 2024

Mobile Learning (M-learning) adoption and success in supporting students' learning engagement mainly depend on many factors. Therefore, this study systematically reviews the literature, synthesizes and analyzes the predictors of M-learning adoption, and uses success for students' learning engagement. Literature from 2016 to 2023 in various databases is covered in this study. Based on the review's findings, the factors that influence students' learning engagement when it comes to M-learning usage and adoption, can be divided into technical, pedagogical, and social factors. More specifically, technical factors include mobile devices availability and quality, connectivity to the internet, and user-friendly interfaces, pedagogical factors include effective instructional design, teaching methods, and assessment strategies, and social factors include motivation of students, social interaction and perceived enjoyment - all these factors have a significant impact on the M-learning adoption and use success. The findings of the review also indicated that M-learning has a key role in enhancing the learning engagement of students through different ways, like increasing their motivation, attention, and participation in their process of learning, paving the way for interaction and building relationships opportunities with peers and instructors, which in turn, can lead to strengthening the learning environment. The implications of these findings extend beyond immediate educational contexts, offering vital insights for future educational technology strategies and policy decisions, particularly in addressing global educational challenges and embracing technological advancements in learning.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.1
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• pp.35-40
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• 2015

The paper proposes a meta-learning approach for building multi-classifier systems in a GA-based inductive learning environment. In our meta-learning approach, a classifier consists of a general classifier and a meta-classifier. We obtain a meta-classifier from classification results of its general classifier by applying a learning algorithm to them. The role of the meta-classifier is to evaluate the classification result of its general classifier and decide whether to participate into a final decision-making process or not. The classification system draws a decision by combining classification results that are evaluated as correct ones by meta-classifiers. We present empirical results that evaluate the effect of our meta-learning approach on the performance of multi-classifier systems.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.95-100
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• 2021

This paper introduces model-based meta reinforcement learning as a control for the manipulator of an underwater construction robot. Model-based meta reinforcement learning updates the model fast using recent experience in a real application and transfers the model to model predictive control which computes control inputs of the manipulator to reach the target position. The simulation environment for model-based meta reinforcement learning is established using MuJoCo and Gazebo. The real environment of manipulator control for underwater construction robot is set to deal with model uncertainties.

• Journal of Distribution Science
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• v.20 no.11
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• pp.121-129
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• 2022

Purpose: Artificial intelligence is widely used, particularly in the popular neural network theory called Deep learning. The improvement of computing speed and capability expedited the progress of Deep learning applications. The application of Deep learning in education has various effects and possibilities in creating and managing educational content and services that can replace human cognitive activity. Among Deep learning, Deep fake technology is used to combine and synchronize human faces with voices. This paper will show how to develop e-Learning content videos using those technologies and open-source tools. Research design, data, and methodology: This paper proposes 4 step development process, which is presented step by step on the Google Collab environment with source codes. This technology can produce various video styles. The advantage of this technology is that the characters of the video can be extended to any historical figures, celebrities, or even movie heroes producing immersive videos. Results: Prototypes for each case are also designed, developed, presented, and shared on YouTube for each specific case development. Conclusions: The method and process of creating e-learning video contents from the image, video, and audio files using Deep fake open-source technology was successfully implemented.

• Journal of The Korean Association For Science Education
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• v.32 no.7
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• pp.1139-1156
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• 2012

The purpose of this study was to investigate students' and mentors' perceptions of Research and Education (R&E) programs in science high schools and science academies. The sample included 1,466 science high school/science academy students and 310 mentors. They filled out the survey, which consisted of the perceptions of R&E performance procedures (Selecting a topic, Learning topic-related knowledge, Designing and performing the research study, and Evaluating and presenting results), and R&E environment (Research period, meeting opportunities with mentor/subject, learning/experimental environment). The results showed that differences existed in the perceptions of R&E performance procedures and R&E environment, especially on selecting topics and learning topic-related knowledge stages. At the end of the paper, suggestions were included for improving R&E.

• Archives of design research
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• v.18 no.3 s.61
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• pp.149-160
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• 2005

The Purpose of this study is to embody self-directed learning management system and contents which are considered of learner's interactivity and usability in e-learning environment; It is based on self directed teaching and learning strategies. It divides type of interaction centering on the learners into four categories: (1) learner and instructor (2) learner and learner (3) learner and contents (4) learner and learning management system. The specific elements of learning management system is set up and embodied to present the interaction strategies according to the above-mentioned patterns, to improve the self-directed learning ability and to facilitate an online communication. The learning contents based on the self directed learning strategies, design the interface in due consideration of the learners' usability based on six strategies such as simple navigation, consistency, intuitive interface, linkage, user supports and immediate feedback. This research makes up for the weak points in the self-directed functions of learning management system and links up with learning contents, therefore it has a value to improve learner's interactivity and usability. It is expected that the research results can be helpful in quality improvement of e-learning environment.