• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.2
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• pp.165-170
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• 2021

Machine learning has recently been applied to research in most areas. This is because the results of machine learning are not determined, but the learning of input data creates the objective function, which enables the determination of new data. In addition, the increase in accumulated data affects the accuracy of machine learning results. The data collected here is an important factor in machine learning. The proposed system is a convergence system of cloud systems and local fog systems for service delivery. Thus, the cloud system provides machine learning and infrastructure for services, while the fog system is located in the middle of the cloud and the user to collect and refine data. The data for this application shall be based on the Sensitive data generated by smart devices. The machine learning technique applied to this system uses SVM algorithm for classification and RNN algorithm for status recognition.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.4
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• pp.97-106
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• 2007

According to the 2004 Sloan Consortium Report, distance education is the fastest growing sector of higher education. This study suggests a research model, based on an e-Learning success model, the relationship of the e-learner's self-regulated learning strategy, computer self-efficacy, and system quality perception of the e-Learning environment. As a result, perceived usefulness, perceived ease of use, and service quality effect on e-learner's satisfaction. In addition to, self-regulated learning strategy based on computer self-efficacy is also important variable regarding e-learner's satisfaction.

• Journal of the Korea Society of Computer and Information
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• v.13 no.6
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• pp.69-76
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• 2008

The biggest strength of the Internet is to enable to access information without any limitation of time and space. As the Internet and IT technologies have been developed, various kinds of teaching ways in education field such as remote lectures, video lectures, and CAI(Computer Adapted Instruction) have emerged. In terms of education, evaluation can be a basic foundation to help teach students in the next learning stage according to each student's level. In addition, it is able to give the information of students'abilities and provides proper learning programs to teach students on a case-by-case basis. The purpose of the paper is to establish evaluation system on the WWW(World Wide Web) that can reflect learning activities part of students in their evaluation scores based on the two important learning theories, Behaviorism and constructivism, which are mainly used in evaluation procedures to judge learning ability of students. This system will give information about learners, and analyze the learning interest of learners. The proposed system enables teachers to evaluate learning ability of students through various kinds of information of learners, and to execute level-based education.

• Journal of Korea Multimedia Society
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• v.22 no.8
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• pp.852-859
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• 2019

With the development of IT technology and smart devices, various applications utilizing image information are being developed. In order to provide an intuitive interface for pronunciation recognition, there is a growing need for research on pronunciation recognition using mouth feature values. In this paper, we propose a system to distinguish Korean vowel pronunciations by detecting feature points of lips region in images and applying Bayesian based learning model. The proposed system implements the recognition system based on Bayes' theorem, so that it is possible to improve the accuracy of speech recognition by accumulating input data regardless of whether it is speaker independent or dependent on small amount of learning data. Experimental results show that it is possible to effectively distinguish Korean vowels as a result of applying probability based Bayesian classification using only visual information such as mouth shape features.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.1
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• pp.209-219
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• 2011

Most of the computer-based distance learning systems are asynchronous ones that allow students to download from the server the lecture previously given by a lecturer. While these asynchronous systems has the advantage that enable students to view the lecture with no time restriction, the study may not be effective due to the lack of support for real-time interaction between students and lecturers. Based on the student-lecturer-collaboration session model, this paper presents a collaboration session-centric synchronous distance learning system that supports real-time interaction between students and teachers, awareness information during lecture, and feedback from students. Basic feature of the proposed system include audio and video conferencing, text-based chat, and shared slide with annotation support.

• The Journal of Korean Association of Computer Education
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• v.7 no.2
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• pp.57-65
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• 2004

For the purpose of an effective instruction-learning process by systematic management of knowledge between instructor and learner in e-Learning, we have developed the authoring system in which the instructor is able to author easily on various lecture frames according to the instructional design theory. The authored contents with the relations among the learning objects based on SCORM standard would help learner to conceptualize the contents. A knowledge map is constructed on the relations among the learning objects using RDF of the semantic web. We introduce the ontology in which the instructor can make a dictionary of terminology by registering the words of the teaching area. The learning activity and comprehension of students can be assessed using each student's learning map along the interaction points which are introduced to present the individual learning by considering each student's capacity of understanding and achievement.

• Journal of Multimedia Information System
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• v.8 no.3
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• pp.183-190
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• 2021

In this paper, we implement a system for a fund recommendation based on the investment propensity and for a future fund price prediction. The investment propensity is classified by scoring user responses to series of questions. The proposed system recommends the funds with a suitable risk rating to the investment propensity of the user. The future fund prices are predicted by Prophet model which is one of the machine learning methods for time series data prediction. Prophet model predicts future fund prices by learning the parameters related to trend changes. The prediction by Prophet model is simple and fast because the temporal dependency for predicting the time-series data can be removed. We implement web pages for the fund recommendation and for the future fund price prediction.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.370-378
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• 2012

This paper proposes a method of constructing of hybrid web-based smart learning system to operable in a variety of mobile devices. To do this, the proposed system is developed a learning system with standardized and enhanced functions. In the proposed method, API specifications based on the standard functionality of smart learning system are created. And then, by building the API provider on a legacy system an organic linkage between the legacy system and the smart learning system is guaranteed. A standard API method is applied to data integration between the PC-based learning system and the smart learning system. The smart learning system interacts with legacy learning systems though Json/XML data forms via the https protocol. As a result, the legacy system using the proposed method dose not require major modifications and changes for a smart learning service.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.73-82
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• 2022

Early detection continues to be the mainstay of breast cancer control as well as the improvement of its treatment. Even so, the absence of cancer symptoms at the onset has early detection quite challenging. Therefore, various researchers continue to focus on cancer as a topic of health to try and make improvements from the perspectives of diagnosis, prevention, and treatment. This research's chief goal is development of a system with deep learning for classification of the breast cancer as non-malignant and malignant using mammogram images. The following two distinct approaches: the first one with the utilization of patches of the Region of Interest (ROI), and the second one with the utilization of the overall images is used. The proposed system is composed of the following two distinct stages: the pre-processing stage and the Convolution Neural Network (CNN) building stage. Of late, the use of meta-heuristic optimization algorithms has accomplished a lot of progress in resolving these problems. Teaching-Learning Based Optimization algorithm (TIBO) meta-heuristic was originally employed for resolving problems of continuous optimization. This work has offered the proposals of novel methods for training the Residual Network (ResNet) as well as the CNN based on the TLBO and the Genetic Algorithm (GA). The classification of breast cancer can be enhanced with direct application of the hybrid TLBO- GA. For this hybrid algorithm, the TLBO, i.e., a core component, will combine the following three distinct operators of the GA: coding, crossover, and mutation. In the TLBO, there is a representation of the optimization solutions as students. On the other hand, the hybrid TLBO-GA will have further division of the students as follows: the top students, the ordinary students, and the poor students. The experiments demonstrated that the proposed hybrid TLBO-GA is more effective than TLBO and GA.

• Asia pacific journal of information systems
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• v.32 no.2
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• pp.226-248
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• 2022

The existing model for sentiment analysis of product reviews learned from past data and new data was labeled based on training. But new data was never used by the existing system for making a decision. The proposed Aspect-based multi-agent Deep Reinforcement learning Sentiment Analysis (ADRSA) model learned from its very first data without the help of any training dataset and labeled a sentence with aspect category and sentiment polarity. It keeps on learning from the new data and updates its knowledge for improving its intelligence. The decision of the proposed system changed over time based on the new data. So, the accuracy of the sentiment analysis using deep reinforcement learning was improved over supervised learning and unsupervised learning methods. Hence, the sentiments of premium customers on a particular site can be explored to other customers effectively. A dynamic environment with a strong knowledge base can help the system to remember the sentences and usage State Action Reward State Action (SARSA) algorithm with Bidirectional Encoder Representations from Transformers (BERT) model improved the performance of the proposed system in terms of accuracy when compared to the state of art methods.