• Journal of Korea Society of Industrial Information Systems
• /
• v.23 no.4
• /
• pp.81-92
• /
• 2018

Bitcoin is a cryptographic digital currency and has been given a significant amount of attention in literature since it was first introduced by Satoshi Nakamoto in 2009. It has become an outstanding digital currency with a current market capitalization of approximately $60 billion. By 2019, it is expected to have over 5 million users. Nowadays, investing in Bitcoin is popular, and along with the advantages and disadvantages of Bitcoin, learning how to forecast is important for investors in their decision-making so that they are able to anticipate problems and earn a profit. However, most investors are reluctant to invest in bitcoin because it often fluctuates and is unpredictable, which may cost a lot of money. In this paper, we focus on solving the Bitcoin forecasting prediction problem based on deep learning structures and neural decomposition. First, we propose a deep learning-based framework for the bitcoin forecasting problem with deep feed forward neural network. Forecasting is a time-dependent data type; thus, to extract the information from the data requires decomposition as the feature extraction technique. Based on the results of the experiment, the use of neural decomposition and deep neural networks allows for accurate predictions of around 89%.

• Journal of the Korean association of regional geographers
• /
• v.12 no.2
• /
• pp.281-290
• /
• 2006

Map uses graphic language of dot, line and area to represent surface of the earth. Map has been adopted as tools for regional and cartography learning to improve graphicacy in geography education. Due to the rapid development in GIS and internet, practical use of maps has been extended in various study area. This Study is to develope web-based leaning materials for self-controled geography instruction. As learning materials for this aim, it has been constructed WebGIS for topography and thematic maps with boundary map of Chungbuk, digital map of Jochiwon(1:25,000), statistic data and field images. Function of WebGIS intend to improve skills on geo-information collection and spatial query, regional difference of spatial distribution. Individual learning using internet can make an improvement of learner centeredness and problem-solving. Finally, it will be expected to be suggest one of the education guide as blueprint in info-society.

• The Journal of Korean Association of Computer Education
• /
• v.10 no.6
• /
• pp.51-59
• /
• 2007

According to developments in info-communication, the recent educational paradigm asks not for a passive transmitter but an active constructor who can solve a variety of complicated problems in real situations. Such a change asks for an educational setting which includes sharing ideas and information rather than simply possessing them. Learning through presentation has many problems including few presentation opportunities as well as the reuse of presentation data. This study suggests such strategies as promoting interactions through presentations and the practical use of these strategies in class. For this, the role of the presentation data provider and learner, and strategies to implement the step by step learning support system have been suggested. Using presentations, as described in this study, allow for communication with students outside the original class time and location. The degree of learning students experience through presentations is expected to be high.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.12 no.2
• /
• pp.375-384
• /
• 2017

This paper is intended to provide a good lecture to computer utilizing courses in the Department of Nursing, therefore first analyzes the outlines of relevant qualifying examinations as well as the contents of the textbooks and syllabus that are currently taught in universities, and then design the curriculum by applying blended learning for effective proceedings of computer-utilizing course. The curriculum for computer-utilizing course which this paper suggests is based on blended learning which blends face-to-face classes with e-learning classes, and its two tracks of teaching and practice include weekly core areas, teaching goals, and subjects. Therefore, this curriculum is expected to lead to excellent learning outcomes as it will become a good teaching scheme for teachers and will motivate students to acquire license. and to find employment.

• Management & Information Systems Review
• /
• v.32 no.3
• /
• pp.43-59
• /
• 2013

The most important thing in training of organization is that how effectively it can be made most of the performance among the staff. It will be useless if the knowledge which gaining after training can not be applied. Therefore the transfer of learning is studied since it is important for decision of training. We studied the factors of transfer of learning and carried out a survey targeting the public officials of Gangwon province with the factors we made a study. We define the factor of both promoted and interrupted in training and suggest the way of improving it. The first, the modeling of competency can stimulate the desire of achievement and complete a course of training among staff of organizations. The second, the construction of training program and organizational culture just for Gangwon province can increase the satisfaction of training among the learners. The third, the establishment of management system after training can reinforce the capability making use of train. The sharing of each information with boss at the office can help to stimulate the function of feedback after training as well.

• Journal of the Korea Convergence Society
• /
• v.11 no.3
• /
• pp.1-7
• /
• 2020

This paper introduces two different techniques for dealing with incomplete data and algorithms for learning this data. The first method is to process the incomplete data by assigning the missing value with equal probability that the missing variable can have, and learn this data with the SVM. This technique ensures that the higher the frequency of missing for any variable, the higher the entropy so that it is not selected in the decision tree. This method is characterized by ignoring all remaining information in the missing variable and assigning a new value. On the other hand, the new method is to calculate the entropy probability from the remaining information except the missing value and use it as an estimate of the missing variable. In other words, using a lot of information that is not lost from incomplete learning data to recover some missing information and learn using deep learning. These two methods measure performance by selecting one variable in turn from the training data and iteratively comparing the results of different measurements with varying proportions of data lost in the variable.

• Journal of Convergence for Information Technology
• /
• v.9 no.10
• /
• pp.170-175
• /
• 2019

This study aimed to analyse the effects of academic self-efficacy of beauty specialized high school students on learning flow and provide basic data needed for their learning instruction. For the purpose, this study surveyed 327 students of beauty specialized high schools located in B metropolitan city and N city. The responses were analysed with the use of the SPSS WIN 21.0. The results are presented as follows: Academic self-efficacy had a significant effect on learning flow and in particular, self-control efficacy and task difficulty preference were important factors. Based on the results above, it is suggested that teachers should present data that is properly converged by techniques and academic knowledge according to levels and steps so that students can have experiences of academic achievements and be encouraged to have higher self-efficiency.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.1
• /
• pp.139-146
• /
• 2022

The purpose of this study was to confirm the relationship between the subjective symptoms of smartphone addiction and visual display terminal syndrome in nursing college students and learning flow in the COVID-19 pandemic. For the collection of materials, the materials of the final 134 people were analyzed by collecting the students at the nursing colleges located in S city and M city for convenience. The collected materials were subjected to descriptive statistics, t-test, ANOVA, and Pearson's correlation coefficients using the SPSS / WIN 26 program. As a result of this study, the learning flow of nursing college students was negative correlated to the tendency of smartphone addiction and the subjective symptoms of visual display terminal syndrome. Therefore, to improve the learning flow of nursing college students, it is necessary to reduce the symptoms of smartphone addictive use and visual display terminal syndrome. The need for intervention and development of various effective programs for smart phone addiction management and display terminal syndrome management was suggested.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.3
• /
• pp.763-768
• /
• 2023

In this paper, the existing statistical method (ARIMA) and machine learning method (Informer) were developed and compared to predict the distribution volume of pharmaceuticals. It was found that a machine learning-based model is advantageous for daily data prediction, and it is effective to use ARIMA for monthly prediction and switch to Informer as the data increases. The prediction error rate (RMSE) was reduced by 26.6% compared to the previous method, and the prediction accuracy was improved by 13%, resulting in a result of 86.2%. Through this thesis, we find that there is an advantage of obtaining the best results by ensembleing statistical methods and machine learning methods. In addition, machine learning-based AI models can derive the best results through deep learning operations even in irregular situations, and after commercialization, performance is expected to improve as the amount of data increases.

• Journal of the Korean Geosynthetics Society
• /
• v.23 no.1
• /
• pp.17-25
• /
• 2024

The Voids in the Mineral Aggregate (VMA) within asphalt mixtures play a crucial role in defining the mixture's structural integrity, durability, and resistance to environmental factors. Accurate prediction and optimization of VMA are essential for enhancing the performance and longevity of asphalt pavements, particularly in varying climatic and environmental conditions. This study introduces a novel machine learning framework leveraging ensemble machine learning model for predicting VMA in asphalt mixtures. By analyzing a comprehensive set of variables, including aggregate size distribution, binder content, and compaction levels, our framework offers a more precise prediction of VMA than traditional single-model approaches. The use of advanced machine learning techniques not only surpasses the accuracy of conventional empirical methods but also significantly reduces the reliance on extensive laboratory testing. Our findings highlight the effectiveness of a data-driven approach in the field of asphalt mixture design, showcasing a path toward more efficient and sustainable pavement engineering practices. This research contributes to the advancement of predictive modeling in construction materials, offering valuable insights for the design and optimization of asphalt mixtures with optimal void characteristics.