• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.5
• /
• pp.1377-1393
• /
• 2023

Social media is used for various purposes including entertainment, communication, information search, and voicing their thoughts and concerns about a service, product, or issue. The social media data can be used for information mining and getting insights from it. The World Health Organization has listed COVID-19 as a global epidemic since 2020. People from every aspect of life as well as the entire health system have been severely impacted by this pandemic. Even now, after almost three years of the pandemic declaration, the fear caused by the COVID-19 virus leading to higher depression, stress, and anxiety levels has not been fully overcome. This has also triggered numerous kinds of discussions covering various aspects of the pandemic on the social media platforms. Among these aspects is the part focused on vaccines developed by different countries, their features and the advantages and disadvantages associated with each vaccine. Social media users often share their thoughts about vaccinations and vaccines. This data can be used to determine the popularity levels of vaccines, which can provide the producers with some insight for future decision making about their product. In this article, we used Twitter data for the vaccine popularity detection. We gathered data by scraping tweets about various vaccines from different countries. After that, various machine learning and deep learning models, i.e., naive bayes, decision tree, support vector machines, k-nearest neighbor, and deep neural network are used for sentiment analysis to determine the popularity of each vaccine. The results of experiments show that the proposed deep neural network model outperforms the other models by achieving 97.87% accuracy.

• Journal of Intelligence and Information Systems
• /
• v.29 no.2
• /
• pp.129-148
• /
• 2023

Bitcoin is a blockchain technology-based digital currency that has been recognized as a representative cryptocurrency and a financial investment asset. Due to its highly volatile nature, Bitcoin has gained a lot of attention from investors and the public. Based on this popularity, numerous studies have been conducted on price and trend prediction using machine learning and deep learning. This study employed LSTM (Long Short Term Memory) and CNN (Convolutional Neural Networks), which have shown potential for predictive performance in the finance domain, to enhance the classification accuracy in Bitcoin price trend prediction. XAI(eXplainable Artificial Intelligence) techniques were applied to the predictive model to enhance its explainability and interpretability by providing a comprehensive explanation of the model. In the empirical experiment, CNN was applied to technical indicators and Google trend data to build a Bitcoin price trend prediction model, and the CNN model using both technical indicators and Google trend data clearly outperformed the other models using neural networks, SVM, and LSTM. Then SHAP(Shapley Additive exPlanations) was applied to the predictive model to obtain explanations about the output values. Important prediction drivers in input variables were extracted through global interpretation, and the interpretation of the predictive model's decision process for each instance was suggested through local interpretation. The results show that our proposed research framework demonstrates both improved classification accuracy and explainability by using CNN, Google trend data, and SHAP.

• Journal of Digital Convergence
• /
• v.17 no.9
• /
• pp.23-30
• /
• 2019

The development of content for language education using AR/VR technology is a necessary task to be pursued in line with commercialization of 5G. Research on service platform for systematic management and service is currently being carried out by global companies competitively, The unique language education service model for unique areas of culture has the right to pursue R & D jointly with Korea and China. In this study, we applied the developed "Korean language education service platform for Chinese people based on e-learning" to improve the acceptance of AR/VR contents and applied AR/VR technology to video-based language education contents. And to present a new paradigm of language education. Contents development is to develop AR-based vocabulary learning services, develop experiential learning contents for VR-based step-by-step situations, and gradually develop contents to enable beginner / intermediate / advanced language education services. The service platform enables management of learning management and learning contents, and complies with metadata attributes to complete a platform capable of accommodating large capacity AR/VR contents. In the future, systematic research will be carried out in order to develop as a portal for educational services through development of various contents using mixed reality technology.

• Journal of Information Technology Application
• /
• v.3 no.1
• /
• pp.1-21
• /
• 2001

The explosive growth of the Internet and the increasing popularity of the World Wide Web have generated significant interest in the development of electronic commerce in a global online marketplace. The rapid adoption of the Internet as a commercial medium is rapidly expanding the necessity of Web advertisement as a new communication channel. if proper Web advertisement could be suggested to the right user, then effectiveness of Web advertisement will be raised and it will help company to earn more profit. So, this article describes a personalized advertisement technique as a part of intelligent customer services for an electronic newspaper provide. Based on customers history of navigation on the electronic newspapers pages, which are divided into several sections such as politics, economics, sports, culture, and so on, appropriate advertisements (especially, banner ads) are chosen and displayed with the aid of machine learning techniques, when customers visit to the site. To verify feasibility of the technique, an application will be made to one of the most popular e-newspaper publishing company in Korea.

• Cross-Cultural Studies
• /
• v.26
• /
• pp.73-94
• /
• 2012

In the present context of systemic global crisis, this paper focuses on a sustainable society. Throughout the World there are vast members of the unemployes. A secure job lasting a lifetime has become more and more rare. Nowadays majority of jobs are part-time or temporary. $Andr{\acute{e}}$ Gorz found a solution in a policy of the progessive reduction in labor time. This is the potential which automated production opens up for a culture society. Nevertheless, Gorz's proposal is based on utopion ideals. This paper focuses on a dynamic force for a culture society, especially art, learning and the third sector. Adrienne Goehler underlines that a culture in the broad sense of the word produces economical and social productivity. In this connection Goehler give attention to 'Cultrual Creatives' and the Creative Class. Cultural creatives are comprised of people who have participated in the process of creating a new culture with enlightened creativity. The Creative Class is a class of workers whose job is to create economic growth through innovation. Creativity is important for a sustainable society. Gore and Rifkin both come close to the ecological thinking. Gore claims that ecosystem of nature have a self-organizing capacity. In this context tried to prove this article that ecosystem is closely connected with a creative environment.

• The Journal of Society for e-Business Studies
• /
• v.26 no.1
• /
• pp.43-65
• /
• 2021

With the advancement of information and communications technology, a means to recruit IT professional has fundamentally changed. Nowadays recruiters search for candidate information from the Web as well as traditional information sources such as résumés or interviews. Particularly, open-source software development (OSSD) platforms have become an opportunity for developers to demonstrate their IT capabilities, making it a way for recruiters to find the right candidates, whom they need. Therefore, this study aims to investigate the impact developers' profiles in an OSSD platform on their finding a job. This study examined four antecedents of developer information that can accelerate their job search: job-seeking status, personal-information posting, learning activities and knowledge contribution activities. For the empirical analysis, we developed a Web crawler and gathered a dataset on 4,005 developers from GitHub, which is a well-known OSSD platform. Proportional hazards regression was used for data analysis because shorter job-seeking period implies more successful result of job change. Our results indicate that developers, who explicitly posted their job-seeking status, had shorter job-seeking periods than those who did not. The other antecedents (i.e., personal-information posting, learning, and knowledge contribution activities) also contributed in reducing the job-seeking period. These findings imply values of OSSD platforms for recruiters to find proper candidates and for developers to successfully find a job.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_1
• /
• pp.1047-1056
• /
• 2022

Sea ice, frozen sea water, in the Artic is a primary indicator of global warming. Due to its importance to the climate system, shipping-route navigation, and fisheries, Arctic sea ice prediction has gained increased attention in various disciplines. Recent advances in artificial intelligence (AI), motivated by a desire to develop more autonomous and efficient future predictions, have led to the development of new sea ice prediction models as alternatives to conventional numerical and statistical prediction models. This study aims to evaluate the performance of the two-stream convolutional long-and short-term memory (TS-ConvLSTM) AI model, which is designed for learning both global and local characteristics of the Arctic sea ice changes, for the minimum September Arctic sea ice from 2001 to 2021, and to show the possibility for an operational prediction system. Although the TS-ConvLSTM model generally increased the prediction performance as training data increased, predictability for the marginal ice zone, 5-50% concentration, showed a negative trend due to increasing first-year sea ice and warming. Additionally, a comparison of sea ice extent predicted by the TS-ConvLSTM with the median Sea Ice Outlooks (SIOs) submitted to the Sea Ice Prediction Network has been carried out. Unlike the TS-ConvLSTM, the median SIOs did not show notable improvements as time passed (i.e., the amount of training data increased). Although the TS-ConvLSTM model has shown the potential for the operational sea ice prediction system, learning more spatio-temporal patterns in the difficult-to-predict natural environment for the robust prediction system should be considered in future work.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.15 no.6
• /
• pp.275-281
• /
• 2015

Korea's e-government is the world best system and the open government data field is also ranking number 1 by UN evaluation. Therefore Korea's ICT level is ranking number 1 in the world especially in ICT (Information Communication Technology). However, in terms of national ICT capabilities, national ICT competency education is tend to perform by the simple list of courses rather than detail definition of the specific skills or training. In this paper, we define the national ICT capabilities through research and analysis of the national ICT competency model and define the relevant curriculum. This is very important to develop national ICT competency as well as ICT technologies because other countries will develop their information by learning from Korea's ICT and e-government as a model. Therefore, we firstly study about the national ICT competency model and will be drawn from the courses required for national ICT competency model.

• Journal of Korea Water Resources Association
• /
• v.56 no.4
• /
• pp.245-259
• /
• 2023

The agricultural reservoir is a critical water supply system in South Korea, providing approximately 60% of the agricultural water demand. However, the reservoir faces several issues that jeopardize its efficient operation and management. To address this issues, we propose a novel deep-learning-based water level recognition model that uses CCTV image data to accurately estimate water levels in agricultural reservoirs. The model consists of three main parts: (1) dataset construction, (2) image segmentation using the U-Net algorithm, and (3) CCTV-based water level recognition using either CNN or ResNet. The model has been applied to two reservoirs G-reservoir and M-reservoir with observed CCTV image and water level time series data. The results show that the performance of the image segmentation model is superior, while the performance of the water level recognition model varies from 50 to 80% depending on water level classification criteria (i.e., classification guideline) and complexity of image data (i.e., variability of the image pixels). The performance of the model can be improved if more numbers of data can be collected.

• Journal of The Korean Association For Science Education
• /
• v.40 no.3
• /
• pp.237-251
• /
• 2020

Issues on climate change we are facing, such as global warming, are very important as it affects our lives directly. To overcome this, efforts to reduce greenhouse gases emissions (e.g., carbon dioxide) are necessary and these efforts should be based on our integrated understanding of carbon cycle. The purpose of this study is to examine the research trend on carbon cycle education and to suggest the value and direction of carbon cycle education for students who will be citizens of the future. We analyzed 52 carbon cycle education related studies collected from academic research databases (RISS, KCI, ERIC, Google Scholar, and others). As a result, we conclude that resources are still limited and more researches on verification and utilization of developed program, development of accurate and comprehensive tools for students' recognition and level assessment, developing educational model or teacher professional development, providing more appropriate curriculum resources, and the use of various topics or materials for carbon cycle education are necessary. Students' comprehensive understanding of the carbon cycle is important to actively react to the changes in the global environment. Therefore, to support such learning opportunities, resources that can be connected to students' daily experiences to improve students' understanding of carbon cycle and replace misconceptions based on the verification of existing programs should be provided in the classroom as well as the curriculum. In addition, sufficient exemplary cases in carbon cycle education including various materials and topics should be provided through professional development to support teachers teaching strategies with carbon cycle.