• The Korean Journal of Food & Health Convergence
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• v.10 no.1
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• pp.1-6
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• 2024

Purpose: The research aims to address the intricacies of AI and Big Data application within the food industry. This study explores the strategic implementation of AI and Big Data in the food industry. The study seeks to understand how these technologies can be employed to bolster consumer engagement and contribute to market expansion, while considering ethical implications. Research Method: This research employs a comprehensive approach, analyzing current trends, case studies, and existing academic literature. It focuses on the application of AI and Big Data in areas such as supply chain management, consumer behavior analysis, and personalized marketing strategies. Results: The study finds that AI and Big Data significantly enhance market analytics, consumer personalization, and market trend prediction. It highlights the potential of these technologies in creating more efficient supply chains, improving consumer satisfaction through personalization, and providing valuable market insights. Conclusion and Implications: The paper offers actionable insights and recommendations for the effective implementation of AI and Big Data strategies in the food industry. It emphasizes the need for ethical considerations, particularly in data privacy and the transparency of AI algorithms. The study also explores future trends, suggesting that AI and Big Data will continue to revolutionize the industry, emphasizing sustainability, efficiency, and consumer-centric practices.

• Journal of The Korean Association of Information Education
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• v.24 no.5
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• pp.463-471
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• 2020

Humanity faces the fourth industrial revolution, a time of technological revolution by the collaboration of various industries including the fields of artificial intelligence(AI) and big data. Many countries are focused on fostering AI talent to prevail in the coming technological revolution. While Korea also provides some strategies to enhance the cultivation of AI talent, it is still difficult for Korean undergraduate students to get involved in AI studies. Through on the implementation of 'Big data analysis based AI education', which allows an easier approach to AI education, this paper examined the changes in the attitudes of undergraduate students regarding general AI education. 'Big data analysis based AI education' was provided at undergraduate level for 5.5 weeks (15 hours). The attitudes of undergraduate students were analyzed by pre-postmortem. The results showed there was a significant improvement in confidence and self-directed in regard to receiving AI education. With these results, further active research to develop basic AI education that also increases confidence and self-initiative can be expected.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.471-477
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• 2021

In the field of display, a smart factory means more efficient display manufacturing using AI/BIG DATA technology not only for job automation, but also for existing process management, moving facilities, process abnormalities, and defect classification. In the past, when defects appeared in the display manufacturing process, the classification of defects and coping with process abnormalities were different, a lot of time was consumed for this. However, in the field of display manufacturing, advanced process equipment must be used, and it can be said that the competitiveness of the display manufacturing industry is to quickly identify the cause of defects and increase the yield. In this paper, we will summarize the cases in which smart factory AI/BIG DATA technology is applied to domestic display manufacturing, and analyze what advantages can be derived compared to existing methods. This information can be used as prior knowledge for improved smart factory development in the field of display manufacturing using AI/BIG DATA.

• Journal of Integrative Natural Science
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• v.14 no.4
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• pp.176-182
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• 2021

We live in the age of artificial intelligence, and big data and artificial intelligence education are no longer just for majors, but are required to be able to handle non-majors as well. Software and artificial intelligence education for non-majors is not just a general education, it creates talents who can understand and utilize them, and the quality of education is increasingly important. Through such education, we can nurture creative talents who can create and use new values by fusion with various fields of computing technology. Since 2015, many universities have been implementing software-oriented colleges and AI-oriented colleges to foster software-oriented human resources. However, it is not easy to provide AI basic statistics education of big data analysis deception to non-majors. Therefore, we would like to present a big data education model for non-majors in big data analysis so that big data analysis can be directly applied.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.377-379
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• 2023

Concept drift detection on data stream is the major issue to maintain the performance of the machine learning model. Since the online stream is to be a function of time, the classical statistic methods are hard to apply. In particular case of seasonal time series, a novel window strategy with Fourier analysis however, gives a chance to adapt the classical methods on the series. We explore the KS-test for an adaptation of the periodic time series and show that this strategy handles a complicate time series as an ordinary tabular dataset. We verify that the detection with the strategy takes the second place in time delay and shows the best performance in false alarm rate and detection accuracy comparing to that of arbitrary window sizes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.452-454
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• 2021

Currently, the security control market is operating based on AI technology. The reason for using AI is to detect large amounts of logs and big data between security equipment, and to alleviate time and human problems. However, problems are still occurring in the application of AI. The security control market is responding to many problems other than the problems introduced in this paper, and this paper attempts to deal with five problems. We would like to consider problems that arise in applying AI technology to security control environments such as 'AI model selection', 'AI standardization problem', 'Big data accuracy', 'Security Control Big Data Accuracy and AI Reliability', 'responsibility material problem', and 'lack of AI validity.'

• Journal of Digital Convergence
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• v.19 no.7
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• pp.95-111
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• 2021

Despite the innovation of AI and Big Data, theoretical research bout the effect of AI and Big Data on market competition is still in early stages; therefore, this paper analyzes the effect of AI, Big Data, and data sharing on an entry firm by using game theory. In detail, the firms' business environments are divided into internal and external ones. Then, AI algorithms are divided into algorithms for (1) customer marketing, (2) cost reduction without automation, and (3) cost reduction with automation. Big Data is also divided into external and internal data. this study shows that the sharing of external data does not affect the incumbent firm's algorithms for consumer marketing while lessening the entry firm's entry barrier. Improving the incumbent firm's algorithms for cost reduction (with and without automation) and external data can be an entry barrier for the entry firm. These findings can be helpful (1) to analyze the effect of AI, Big Data, and data sharing on market structure, market competition, and firm behaviors and (2) to design policy for AI and Big Data.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.287-296
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• 2024

Artificial intelligence (AI), the Internet of Things (IoT), and Big Data are playing important roles in improving or upgrading energy efficiency. Furthermore, their roles in energy efficiency are expected to become more and more essential. This study conducted a bibliometric comparative analysis on the features in the articles on the AI, the IoT, and the Big Data in energy efficiency by using the Web of Science database and compared the features in their trends in article publications, citations, countries, research areas, journals, and funding agencies from 2012 to 2022. This study attempted to make significant contributions by shedding new light on the following features. Among the AI, the IoT, and the Big Data in energy efficiency, the most articles were published and the most article citations were received in the AI in energy efficiency. China was found out to be the most leading country. Engineering and computer science were revealed to be the first research area. IEEE Access and IEEE Internet of Things were ranked with first journal. National Natural Science Foundation of China was the first research funding agency concerning the articles published in the AI, the IoT, and the Big Data in energy efficiency from 2012 to 2022.

• Genomics & Informatics
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• v.17 no.4
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• pp.44.1-44.3
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• 2019

Artificial intelligence (AI), big data, and ubiquitous robotic companions -the three most notable technologies of the 4th Industrial Revolution-are receiving renewed attention each day. Technologies that can be experienced in daily life, such as autonomous navigation, real-time translators, and voice recognition services, are already being commercialized in the field of information technology. In the biosciences field in Korea, such technologies have become known to the local public with the introduction of the AI doctor Watson in large number of hospitals. Additionally, AlphaFold, a technology resembling the AI AlphaGo for the game Go, has surpassed the limit on protein folding predictions-the most challenging problems in the field of protein biology. This report discusses the significance of AI technology and big data on the bioscience field. The introduction of automated robots in this field is not just only for the purpose of convenience but a prerequisite for the real sense of AI and the consequent accumulation of basic scientific knowledge.

• Electronics and Telecommunications Trends
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• v.35 no.7
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• pp.3-12
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• 2020

In this paper, we introduce ETRI AI Strategy #1, "Proactively Securing AI Core Technologies." The first goal of this strategy is to innovate artificial intelligence (AI) service technology to overcome the current limitations of AI technologies. Even though we saw a big jump in AI technology development recently due to the rise of deep learning (DL), DL still has technical limitations and problems. This paper introduces the four major parts of the advanced AI technologies that ETRI will secure to overcome the problems of DL and harmonize AI with the human world: post DL technology, human-AI collaboration technology, intelligence for autonomous things, and big data platform technology.