• International journal of advanced smart convergence
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• v.13 no.2
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• pp.258-266
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• 2024

E-learning systems have proliferated in recent years, particularly in the wake of the global COVID-19 pandemic. For kids, there isn't a specific online learning platform available, though. To do this, new conceptual models of training and learning software that are adapted to the abilities and preferences of end users must be created. Young pupils: those in kindergarten, preschool, and elementary school are unique subjects with little research history. From the standpoint of software technology, young students who have never had access to a computer system are regarded as specific users with high expectations for the functionality and interface of the software, social network connectivity, and instantaneous Internet communication. In this study, we suggested creating an electronic learning management system that is web-based and appropriate for primary school pupils. User-centered design is the fundamental technique that was applied in the development of the system that we are proposing. Test findings have demonstrated that students who are using the digital environment for the first time are studying more effectively thanks to the online learning management system.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.127-135
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• 2024

Recent studies have shown that inverse design using deep learning has the potential to rapidly generate the optimal design that satisfies the target performance without the need for iterative optimization processes. Unlike traditional methods, deep learning allows the network to rapidly generate a large number of solution candidates for the same objective after a single training, and enables the generation of diverse designs tailored to the objectives of inverse design. These inverse design techniques are expected to significantly enhance the efficiency and innovation of design processes in various fields such as aerospace, biology, medical, and engineering. We analyzes inverse design models that are mainly utilized in the nano and chemical fields, and proposes inverse design models based on supervised and unsupervised learning that can be applied to the engineering system. It is expected to present the possibility of effectively applying inverse design methodologies to the design optimization problem in the field of engineering according to each specific objective.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.161-168
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• 2024

As the size of big data models grows, distributed training is emerging as an essential element for large-scale machine learning tasks. In this paper, we propose ParamHub for distributed data training. During the training process, this agent utilizes the provided data to adjust various conditions of the model's parameters, such as the model structure, learning algorithm, hyperparameters, and bias, aiming to minimize the error between the model's predictions and the actual values. Furthermore, it operates autonomously, collecting and updating data in a distributed environment, thereby reducing the burden of load balancing that occurs in a centralized system. And Through communication between agents, resource management and learning processes can be coordinated, enabling efficient management of distributed data and resources. This approach enhances the scalability and stability of distributed machine learning systems while providing flexibility to be applied in various learning environments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2067-2081
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• 2024

Recently, image analysis research has been actively conducted due to the accumulation of big image data and the development of deep learning. Image analytics research has different characteristics from other data such as data size, real-time, image quality diversity, structural complexity, and security issues. In addition, a large amount of data is required to effectively analyze images with deep-learning models. However, in many fields, the data that can be collected is limited, so there is a need for meta learning based image analysis technology that can effectively train models with a small amount of data. This paper presents a comprehensive survey of meta-learning-based object-tracking techniques. This approach comprehensively explores object tracking methods and research that can achieve high performance in data-limited situations, including key challenges and future directions. It provides useful information for researchers in the field and can provide insights into future research directions.

• Journal of Internet of Things and Convergence
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• v.3 no.2
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• pp.15-20
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• 2017

The purpose of this study is to understand the concept of transformative learning and the meaning and process of learning. Transformative learning explains that learning takes place in three aspects. First, Instrumental learning takes place in the process of acquiring knowledge based on facts, such as hypothesis testing. Secondly, Communicative learning recognizes the meaning of other people's thoughts or social norms, cultures and values through language. Thirdly, emancipatory learning makes through critical self-reflection by understanding oneself on the basis of psychological and cultural assumptions. The implications of outplacement convergence education, is that main purpose of outplacement education is to build theory and shift the perspective to learning conversion in general operation practice. The content of the outplacement convergence education is that it should shift from instrumental learning to communicative and emancipatory learning.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.11
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• pp.2121-2142
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• 2011

In this paper, we propose an e-Learning process control model that aims to graphically describe and automatically generate the manifest of sequencing prerequisites in packaging SCORM's content aggregation models. In specifying the e-Learning activity sequencing, SCORM provides the concept of sequencing prerequisites to be manifested on each e-Learning activity of the corresponding tree-structured content organization model. However, the course developer is required to completely understand the SCORM's complicated sequencing prerequisites and other extensions. So, it is necessary to achieve an efficient way of packaging for the e-Learning content organization models. The e-Learning process control model proposed in this paper ought to be an impeccable solution for this problem. Consequently, this paper aims to realize a new concept of process-driven e-Learning content aggregating approach supporting the e-Learning process control model and to implement its e-Learning process modeling system graphically describing and automatically generating the SCORM's sequencing prerequisites. Eventually, the proposed model becomes a theoretical basis for implementing a SCORM-based e-Learning process management system satisfying the SCORM's sequencing prerequisite specifications. We strongly believe that the e-Learning process control model and its modeling system achieve convenient packaging in SCORM's content organization models and in implementing an e-Learning management system as well.

• School Mathematics
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• v.1 no.1
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• pp.235-243
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• 1999

This article is that we develop a learning materials using on the internet with JAVA in middle school mathematics. we construct the learning instruction simulation java program that students can use the applet on the internet for understanding the concepts of mathematics. We service the homepage at internet address. [http://edupark.kongiu.ac.kr]

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.11 no.3
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• pp.39-45
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• 2002

The computer technology and internet have the potential to provide a highly interactive and powerful learning environment for engineering disciplines. Many academic courses that teach engineering subjects have already begun incorporating virtual instruments as teaching and learning tools. This paper introduces the concept of the virtual instrument and reports some of the LabVIEW software applications in several universities. Finally the paper contemplates the future trends on the remote laboratory via the internet for engineering education.

• Journal of Internet Computing and Services
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• v.11 no.5
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• pp.71-80
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• 2010

The purpose of this research is to help IPTV (Internet Protocol Television) commercialization using newly produced educational contents in the area of entertainment and education which currently popular in the market. It is called, Stimulus $Tester^{TM}$, endow reaction time from the feedback of learning system, using a non-direct method, for example, a remote controller. Reaction time is the learning efficiency promotion mechanism that learner ascertain the learning condition of oneself by the time with solved questions from the solving the question in given time. Reaction time also play a key role that the learner may go through course which distribute the point to PC from Server. If this system is ready, we expect that the educational industry will gradually spread out. To verify the learning efficiency of this system, we concluded that the learning improvements, by an Internet-based and a paper-based test, of the increase by 51%, from 2.47min to 1.27min, during reaction of 7 days.

• Journal of Korea Multimedia Society
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• v.23 no.11
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• pp.1396-1405
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• 2020

Cyber threats such as forced personal information collection and distribution of malicious codes using malicious URLs continue to occur. In order to cope with such cyber threats, a security technologies that quickly detects malicious URLs and prevents damage are required. In a web environment, malicious URLs have various forms and are created and deleted from time to time, so there is a limit to the response as a method of detecting or filtering by signature matching. Recently, researches on detecting and predicting malicious URLs using machine learning techniques have been actively conducted. Existing studies have proposed various features and machine learning algorithms for predicting malicious URLs, but most of them are only suggesting specialized algorithms by supplementing features and preprocessing, so it is difficult to sufficiently reflect the strengths of various machine learning algorithms. In this paper, a system for predicting malicious URLs using multiple machine learning algorithms was proposed, and an experiment was performed to combine the prediction results of multiple machine learning models to increase the accuracy of predicting malicious URLs. Through experiments, it was proved that the combination of multiple models is useful in improving the prediction performance compared to a single model.