• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.420-422
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• 2023

본 연구는 웨어러블 디바이스를 활용하여 장애인 재활운동 보조를 위한 AI 기반의 맞춤형 서비스 개발을 소개한다. 해당 서비스는 웨어러블 디바이스를 장착한 상태로 운동 중인 사용자의 심박수, 소모 칼로리, 운동 시간 등의 센서 데이터를 수집 및 관리한다. 사용자 생체 데이터는 클라이언트 서버 간 실시간 통신으로 관리되며, django rest framework 로 구축된 서버에 저장된다. 제안 시스템을 통해 수집된 데이터는 시계열 군집화 분석을 위해 k-means clustering 과 k-shape clustering 을 활용하여 체력 평가의 핵심 지표인 심박수를 분석하였다. 특히, 상대적으로 운동이 어려운 장애인 사용자를 위한 맞춤형 운동능력 분석 및 해석에 대한 정보 제공이 가능하다.

• Asia pacific journal of information systems
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• v.32 no.3
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• pp.496-513
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• 2022

Artificial intelligence or big data technologies can benefit finance companies such as those in the insurance sector. With artificial intelligence, companies can develop better customer segmentation methods and eventually improve the quality of customer relationship management. However, the application of AI-based customer segmentation in the insurance industry seems to have been unsuccessful. Findings from our interviews with sales agents and customer service managers indicate that current customer segmentation in the Korean insurance company relies upon individual agents' heuristic decisions rather than a generalizable data-based method. We propose guidelines for AI-based customer segmentation for the insurance industry, based on the CRISP-DM standard data mining project framework. Our proposed guideline provides new insights for studies on AI-based technology implementation and has practical implications for companies that deploy algorithm-based customer relationship management systems.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.931-934
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• 2019

Research using artificial intelligence to generate captions for an image has been studied extensively. However, these systems are unable to create creative stories that include more than one sentence based on image content. A story is a better way that humans use to foster social cooperation and develop social norms. This paper proposes a framework that can generate a relatively short story to describe based on the context of an image. The main contributions of this paper are (1) An unsupervised framework which uses recurrent neural network structure and encoder-decoder model to construct a short story for an image. (2) A huge English novel dataset, including horror and romantic themes that are manually collected and validated. By investigating the short stories, the proposed model proves that it can generate more creative contents compared to existing intelligent systems which can produce only one concise sentence. Therefore, the framework demonstrated in this work will trigger the research of a more robust AI story writer and encourages the application of the proposed model in helping story writer find a new idea.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.2_2
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• pp.445-456
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• 2024

This study aims to suggest service improvement opportunities by analyzing user review data of the top three OTT service apps(Netflix, Coupang Play, and TVING) on Google Play Store. To achieve this objective, we proposed a framework for uncovering service opportunities through the analysis of negative user reviews from OTT service providers. The framework involves automating the labeling of identified topics and generating service improvement opportunities using topic modeling and prompt engineering, leveraging GPT-4, a generative AI model. Consequently, we pinpointed five dissatisfaction topics for Netflix and TVING, and nine for Coupang Play. Common issues include "video playback errors", "app installation and update errors", "subscription and payment" problems, and concerns regarding "content quality". The commonly identified service enhancement opportunities include "enhancing and diversifying content quality". "optimizing video quality and data usage", "ensuring compatibility with external devices", and "streamlining payment and cancellation processes". In contrast to prior research, this study introduces a novel research framework leveraging generative AI to label topics and propose improvement strategies based on the derived topics. This is noteworthy as it identifies actionable service opportunities aimed at enhancing service competitiveness and satisfaction, instead of merely outlining topics.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 1998.10a
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• pp.913-928
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• 1998

• Journal of Information Technology Services
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• v.20 no.3
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• pp.41-56
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• 2021

New drug discovery and development research enable clinical treatment that saves human life and improves the quality of life, but the possibility of success with new drugs is significantly low despite a long time of 14 to 16 years and a large investment of 2 to 3 trillion won in traditional methods. As artificial intelligence is expected to radically change the new drug development paradigm, artificial intelligence new drug discovery and development projects are underway in various forms of collaboration, such as joint research between global pharmaceutical companies and IT companies, and government-private consortiums. This study uses the TOE framework and the Value-based Adoption Model, and the technical, organizational, and environmental factors that should be considered for the acceptance of AI technology at the level of the new drug research organization are the value of artificial intelligence technology. By analyzing the explanatory power of the relationship between perception and intention to use, it is intended to derive practical implications. Therefore, in this work, we present a research model in which technical, organizational, and environmental factors affecting the introduction of artificial intelligence technologies are mediated by strategic value recognition that takes into account all factors of benefit and sacrifice. Empirical analysis shows that usefulness, technicality, and innovativeness have significantly affected the perceived value of AI drug development systems, and that social influence and technology support infrastructure have significant impact on AI Drug Discovery and Development systems.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.5
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• pp.159-174
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• 2023

In this paper, we proposed a methodology to effectively determine the selection and prioritization of new business and innovation projects using AI technology. AI technology is a technology that can upgrade the business of companies in various industries and increase the added value of the entire industry. However, there are various constraints and difficulties in the decision-making process of selecting and implementing AI projects in the enterprise. In this paper, we propose a new methodology for prioritizing AI projects using Morphology, AHP, and TOPSIS. The proposed methodology helps prioritize AI projects by simultaneously considering the technical feasibility of AI technology and real-world user requirements. In this study, we applied the proposal methodology to a real enterprise that wanted to prioritize multiple AI projects in the HR field and evaluated the results. The results confirm the practical applicability of the methodology and suggest ways to use it to help companies make decisions about AI projects. The significance of the methodology proposed in this study is that it is a framework for prioritizing multiple AI projects considered by a company in the most reasonable way by considering both business and technical factors at the same time.

• Journal of Information Technology Services
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• v.20 no.1
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• pp.29-40
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• 2021

North Korea's science and technology policies are being pursued under strong leadership and control by the central government. In particular, a large part of the research and development of science and technology related to the Fourth Industrial Revolution in North Korea is controlled and absorbed by the defense organizations under the national defense-oriented policy framework, among which North Korea is making national efforts to develop advanced technologies in artificial intelligence and actively utilize them in the military affairs. The future weapon system based on AI will have superior performance and destructive power that is different from modern weapons systems, which is likely to change the paradigm of the future battlefield, so a thorough analysis and prediction of the level of AI militarization technology, the direction of development, and AI-based weapons system in North Korea is needed. In addition, research and development of South Korea's corresponding weapon systems and military science and technology are strongly required as soon as possible. Therefore, in this paper, we will analyze the level of AI technology, the direction of AI militarization, and the AI-based weapons system in North Korea, and discuss the AI military technology and corresponding weapon systems that South Korea military must research and develop to counter the North Korea's. The next study will discuss the analysis of AI militarization technologies not only in North Korea but also in neighboring countries in Northeast Asia such as China and Russia, as well as AI weapon systems by battlefield function, detailed core technologies, and research and development measures.

• Journal of Information Technology Services
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• v.21 no.2
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• pp.61-84
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• 2022

With the recent application of deep learning to Natural Language Processing (NLP), the performance of NLP has improved significantly and NLP is emerging as a core competency of organizations. However, when encountering NLP use cases that are sporadically reported through various online and offline channels, it is often difficult to come up with a big picture of how to understand and interpret them or how to connect them to business. This study presents a framework for systematically analyzing NLP use cases, considering the characteristics of NLP techniques applicable to almost all industries and business functions, environmental changes in the era of the Fourth Industrial Revolution, and the effectiveness of adopting NLP reflecting all business functional areas. Through solving research questions based on the framework, the usefulness of it is validated. First, by accumulating NLP use cases and pivoting them around the business function dimension, we derive how NLP techniques are used in each business functional area. Next, by synthesizing related surveys and reports to the accumulated use cases, we draw implications for each business function and major NLP techniques. This work promotes the creation of innovative business scenarios and provides multilateral implications for the adoption of NLP by systematically viewing NLP techniques, industries, and business functional areas. The use case analysis framework proposed in this study presents a new perspective for research on new technology use cases. It also helps explore strategies that can dramatically improve organizational performance through a holistic approach that encompasses all business functional areas.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.141-150
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• 2021

Based on Artificial Intelligence technology, AI-enabled warfare is expected to become the main issue in the future warfare. Natural language processing technology is a core technology of AI technology, and it can significantly contribute to reducing the information burden of underrstanidng reports, information objects and intelligences written in natural language by commanders and staff. In this paper, we propose a Language model-based Multi-source Information Integration (LAMII) framework to reduce the information overload of commanders and support rapid decision-making. The proposed LAMII framework consists of the key steps of representation learning based on language models in self-supervsied way and document integration using autoencoders. In the first step, representation learning that can identify the similar relationship between two heterogeneous sentences is performed using the self-supervised learning technique. In the second step, using the learned model, documents that implies similar contents or topics from multiple sources are found and integrated. At this time, the autoencoder is used to measure the information redundancy of the sentences in order to remove the duplicate sentences. In order to prove the superiority of this paper, we conducted comparison experiments using the language models and the benchmark sets used to evaluate their performance. As a result of the experiment, it was demonstrated that the proposed LAMII framework can effectively predict the similar relationship between heterogeneous sentence compared to other language models.