• The KIPS Transactions:PartB
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• v.11B no.1
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• pp.77-82
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• 2004

Reinforcement learning takes reward about selecting action when agent chooses some action and did state transition in Present state. this can be the important subject in reinforcement learning as temporal-credit assignment problems. In this paper, by new meta heuristic method to solve hard combinational optimization problem, examine Ant-Q learning method that is proposed to solve Traveling Salesman Problem (TSP) to approach that is based for population that use positive feedback as well as greedy search. And, suggest Ant-TD reinforcement learning method that apply state transition through diversification strategy to this method and TD-error. We can show through experiments that the reinforcement learning method proposed in this Paper can find out an optimal solution faster than other reinforcement learning method like ACS and Ant-Q learning.

• KIISE Transactions on Computing Practices
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• v.23 no.4
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• pp.244-249
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• 2017

Steganalysis is to detect information hidden by steganography inside general data such as images. There are stegoanalysis techniques that use machine learning (ML). Existing ML approaches to steganalysis are based on extracting features from stego images and modeling them. Recently deep learning-based methodologies have shown significant improvements in detection accuracy. However, all the existing methods, including deep learning-based ones, have a critical limitation in that they can only detect stego images that are created by a specific steganography method. In this paper, we propose a generalized steganalysis method that can model multiple types of stego images using deep learning. Through various experiments, we confirm the effectiveness of our approach and envision directions for future research. In particular, we show that our method can detect each type of steganography with the same level of accuracy as that of a steganalysis method dedicated to that type of steganography, thereby demonstrating the general applicability of our approach to multiple types of stego images.

• The Journal of the Korea Contents Association
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• v.14 no.2
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• pp.205-222
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• 2014

This study analyzed the effects of interactivity on the learning attitude in the tablet PC-based digital textbook environment. Most of digital textbook studies focused on comparison of learning effect between digital textbook and paper textbook. This study, instead, focused on the interaction between students and digital textbook, and examined the hypothesis that, in the digital textbook-based learning environment, interactivity factors affect learning attitude. The results showed that active control, two-way communication, and synchronicity have significant effects on the learning attitude. Those findings indicate that it's necessary to effectively realize interactivity in the process of developing digital textbooks. Also, important implication is not the fixed interactivity but how students perceive the digital textbook and make use of it. Therefore, for the interactive digital textbook, perceived user control, two-way communication, and synchronicity should be realized properly.

• Journal of The Korean Association of Information Education
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• v.14 no.4
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• pp.571-588
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• 2010

IPTV, a convergence service of broadcasting and communication, draws an attention as an educational media that provides various advantages including top quality of screen, easy access, and protection from harmful contents. In addition, a successful IPTV learning requires learners' active participations. Yet, the research on educational applications of IPTV is at the early stage focusing on teachers and developers' perspectives. Therefore, the present study conducted a survey on learners' current IPTV usage and intentions to use. Reflecting the results of the survey, we developed learner-centered IPTV learning scenarios based on self-regulated learning strategies and suggested its implementations.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.231-243
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• 2021

In Recent years the way we analyze the breast cancer has changed dramatically. Breast cancer is the most common and complex disease diagnosed among women. There are several subtypes of breast cancer and many options are there for the treatment. The most important is to educate the patients. As the research continues to expand, the understanding of the disease and its current treatments types, the researchers are constantly being updated with new researching techniques. Breast cancer survival rates have been increased with the use of new advanced treatments, largely due to the factors such as earlier detection, a new personalized approach to treatment and a better understanding of the disease. Many machine learning classification models have been adopted and modified to diagnose the breast cancer disease. In order to enhance the performance of classification model, our research proposes a model using A Hybrid Modified K-Means Clustering with Modified SVM (Support Vector Machine) Machine learning algorithm to create a new method which can highly improve the performance and prediction. The proposed Machine Learning model is to improve the performance of machine learning classifier. The Proposed Model rectifies the irregularity in the dataset and they can create a new high quality dataset with high accuracy performance and prediction. The recognized datasets Wisconsin Diagnostic Breast Cancer (WDBC) Dataset have been used to perform our research. Using the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset, We have created our Model that can help to diagnose the patients and predict the probability of the breast cancer. A few machine learning classifiers will be explored in this research and compared with our Proposed Model "A Hybrid Modified K-Means with Modified SVM Machine Learning Algorithm to Enhance the Cancer Prediction" to implement and evaluated. Our research results show that our Proposed Model has a significant performance compared to other previous research and with high accuracy level of 99% which will enhance the Cancer Prediction.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.3
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• pp.326-332
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• 2019

Recently, the deep learning technology has gained lots of attention which leads to its application to various fields. Especially, there are recent attempts to overcome the limit of wireless communications systems through the use of the deep learning. In this paper, we have verified the performance of deep learning based transmit power control scheme. Unlike previous transmit power control schemes where the optimal transmit power is derived by solving the optimization problem explicitly, in the deep learning based transmit power control, the general solver for the optimization problem is derived through the deep neural network (DNN). Especially, by using the spectral efficiency as the loss function of DNN, the training can be performed without needing labels. Through simulation based on Tensorflow, we confirm that the transmit power control based on deep learning can achieve the optimal performance while reducing the computational complexity by 1/200.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.3 no.2
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• pp.67-72
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• 2022

Ecological research relies on the interpretation of large amounts of visual data obtained from extensive wildlife surveys, but such large-scale image interpretation is costly and time-consuming. Using an artificial intelligence (AI) machine learning model, especially convolution neural networks (CNN), it is possible to streamline these manual tasks on image information and to protect wildlife and record and predict behavior. Ecological research using deep-learning-based object recognition technology includes various research purposes such as identifying, detecting, and identifying species of wild animals, and identification of the location of poachers in real-time. These advances in the application of AI technology can enable efficient management of endangered wildlife, animal detection in various environments, and real-time analysis of image information collected by unmanned aerial vehicles. Furthermore, the need for school education and social use on biodiversity and environmental issues using AI is raised. School education and citizen science related to ecological activities using AI technology can enhance environmental awareness, and strengthen more knowledge and problem-solving skills in science and research processes. Under these prospects, in this paper, we compare the results of our early 2013 study, which automatically identified African cichlid fish species using photographic data of them, with the results of reanalysis by CNN deep learning method. By using PyTorch and PyTorch Lightning frameworks, we achieve an accuracy of 82.54% and an F1-score of 0.77 with minimal programming and data preprocessing effort. This is a significant improvement over the previous our machine learning methods, which required heavy feature engineering costs and had 78% accuracy.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.116-124
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• 2022

Information and communication technologies used in the social sphere are born due to the development of computer technologies. The main task of the distance learning process in higher education institutions is not to provide information, but to teach how to obtain and use it. The purpose of the article: to identify innovative approaches in the training of specialists in higher education institutions in the context of distance learning. Various innovative approaches to organizing the work of students of higher educational institutions in the context of distance learning are considered. Based on the conducted research, it is concluded that each of the approaches described by us outlines the study of the phenomenon of professional training of a specialist in the condition of distance learning. All the described approaches significantly contribute to the improvement of professional training of specialists, encourage students to self-improvement, professional development and enrich their professional competence in modern conditions. The emergence and spread of innovative technologies means not only a change in the activity itself and its inherent means and mechanisms of its implementation, but also a significant restructuring of goals, value orientations, specific knowledge, skills and abilities. Therefore, the current stage of the development of civilization, scientific and technological progress requires the emergence of such specialists who would have broad humanitarian thinking, would have good psychological training, would be able to build professional activities according to laws that take into account the relationship between economic productivity and creativity, as well as the desire of the individual for constant renewal, self-realization. Only such qualities will help you master the specifics of innovative technologies well. We see the prospects in the study of innovative approaches to training specialists in higher education institutions in the condition of distance learning in foreign countries.

• Journal of the Korean Geographical Society
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• v.48 no.6
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• pp.994-1008
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• 2013

In this paper, we describe two exploratory examples in the use of Augmented Reality (AR) for geographical visualization regarding refinement of visual content in geography textbooks and learning motivation of geography students. Currently, teaching and learning materials with AR technology and their utilization in the geography classroom have become a new topic in geographical research themes, and this trend has increased. Adequate development and utilization of geographical materials is an important starting point for smart education research in geography. This paper describes the system and software, and the implication of marker AR applications for teaching and learning geography in the classroom. For the AR applications to be utilized in geography education, two marker based AR examples, virtual globe and visualization of topographical features, are presented and their utilization aspects are discussed. Finally, from the discussion stated in this paper, it can be inferred that AR is useful for exploring geographical materials, and marker based AR will contribute to progress in spatial science and geographical education research.

• Journal of Korean Elementary Science Education
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• v.39 no.1
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• pp.117-132
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• 2020

The purpose of this study is to investigate elementary school teachers' awareness of Artificial Intelligence (AI) and find out how to apply it in elementary science education. The survey was conducted online and involved 95 teachers working in the metropolitan area. The results of this study are as follows. First, teachers need to learn about the general characteristics of AI and how to apply it to education. Second, science classes had the highest preference for AI among elementary school subjects. Third, the preference for AI application by elementary science field was 68.4% for earth and space, 54.7% for exercise and energy, 32.6% for matter, 27.4% for life. Fourth, AI-based Science Education (AISE) teaching- learning strategies were developed based on AI characteristics and the changing perspective of elementary science education, AISE's teaching-learning strategies are five: 'automation', 'individualization', 'diversification', 'cooperation' and 'creativity' and teachers can use them in teaching design, class practice and evaluation stages. Finally, the creative problem-solving Doing Thinking Making Sharing (DTMS) model was devised to implement the creativity strategy in AISE. This model consists of four-steps teaching courses: Doing, Thinking, Making and Sharing based on the empirical learning theory. In the future, follow-up research is needed to verify the effectiveness of this model by applying it to elementary science education.