• 정보화정책
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• 제29권2호
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• pp.3-36
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• 2022

지금까지의 인공지능 연구는 컴퓨터 분야의 새로운 알고리즘에 관한 것이 대부분이며, 인공지능의 활용 사례연구도 주로 인간과의 대결에서 승리한 것을 보여 주고 있다. 사회와 기업의 지속적인 관심 속에 학계에서도 단순 기술적 측면의 인공지능 연구에서 벗어나 인공지능의 활용적 측면, 특히 조직·전략과의 연계, 인공지능의 활용 리스크 등의 문제에서 이론을 정립하려는 노력이 최근 시도되고 있다. 본 문헌 연구에서는 2015년부터 2022년 현재까지 인공지능의 활용에 관한 연구를 인공지능 활용 분야, 인공지능 프로젝트 관리 그리고 인공지능의 활용 리스크 측면에서 조사하였다. 또한 세부 분석을 위하여 인용 수 20개 이상의 785개 연구에 대하여 세부 분야로 분류하여 조사하였다. 연구 결과 아직 많은 인공지능의 활용연구는 산업 또는 기업 업무별 과거 데이터를 중심으로 한 프로토타이핑 프로젝트 연구에 치우쳐져 있었다. 향후 인공지능 활용을 위한 조직 구조, 프로젝트 선정과 적용과정 등의 연구가 인공지능 활용의 리스크 연구와 함께 필요할 것으로 보인다.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2023년도 제67차 동계학술대회논문집 31권1호
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• pp.469-471
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• 2023

본 논문에서는 기존 중고거래 플랫폼들의 불편한 점들을 해소하고 사용자들이 효율적이고 편리한 중고거래를 할 수 있도록 도와주는 플랫폼을 개발했다. 조사를 통해 기존 중고거래 플랫폼은 허위 매물, 시세 파악의 어려움, 사기 피해 등의 문제점이 존재한다는 사실을 인식했다. 문제 해결을 위해 파이썬을 활용하여 주요 중고거래 플랫폼의 상품 데이터를 수집했다. 이에 IQR을 적용하여 가격의 이상치를 판별했다. 가격 비교와 허위 매물 판별이 용이하게 되는 장점이 있다. 또한 이상치를 제거한 상품들의 시세를 계산하여 데이터를 차트로 시각화했다. 플랫폼과 지역마다 상이한 중고 상품의 신뢰성 있는 시세를 파악할 수 있고 중고거래 사기 피해를 방지할 수 있도록 사용자에게 주요 사기 수법, 뉴스 등의 정보를 제공한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권6호
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• pp.1518-1539
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• 2024

Artificial intelligence-enabled business models aim to improve decision-making, operational efficiency, innovation, and productivity. The presented systematic literature review is conducted to highlight elucidating the utilization of artificial intelligence (AI) methods and techniques within AI-enabled businesses, the significance and functions of AI-enabled organizational models and frameworks, and the design parameters employed in academic research studies within the AI-enabled business domain. We reviewed 39 empirical studies that were published between 2010 and 2023. The studies that were chosen are classified based on the artificial intelligence business technique, empirical research design, and SLR search protocol criteria. According to the findings, machine learning and artificial intelligence were reported as popular methods used for business process modelling in 19% of the studies. Healthcare was the most experimented business domain used for empirical evaluation in 28% of the primary research. The most common reason for using artificial intelligence in businesses was to improve business intelligence. 51% of main studies claimed to have been carried out as experiments. 53% of the research followed experimental guidelines and were repeatable. For the design of business process modelling, eighteen AI mythology were discovered, as well as seven types of AI modelling goals and principles for organisations. For AI-enabled business models, safety, security, and privacy are key concerns in society. The growth of AI is influencing novel forms of business.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 추계학술발표대회
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• pp.1001-1004
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• 2021

Pain is an unpleasant experience for the patient. The recognition and assessment of pain help tailor the treatment to the patient, and they are also challenging in the medical. In this paper, we propose an approach for pain recognition through a deep neural network applied to pre-processed physiological. The proposed approach applies the idea of shortcut connections to concatenate the spatial information of a convolutional neural network and the temporal information of a recurrent neural network. In addition, our proposed approach applies the attention mechanism and achieves competitive performance on the BioVid Heat Pain dataset.

• Journal of Information Processing Systems
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• 제18권4호
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• pp.535-548
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• 2022

Using a rich resource language to classify sentiments in a language with few resources is a popular subject of research in natural language processing. Burmese is a low-resource language. In light of the scarcity of labeled training data for sentiment classification in Burmese, in this study, we propose a method of transfer learning for sentiment analysis of a language that uses the feature transfer technique on sentiments in English. This method generates a cross-language word-embedding representation of Burmese vocabulary to map Burmese text to the semantic space of English text. A model to classify sentiments in English is then pre-trained using a convolutional neural network and an attention mechanism, where the network shares the model for sentiment analysis of English. The parameters of the network layer are used to learn the cross-language features of the sentiments, which are then transferred to the model to classify sentiments in Burmese. Finally, the model was tuned using the labeled Burmese data. The results of the experiments show that the proposed method can significantly improve the classification of sentiments in Burmese compared to a model trained using only a Burmese corpus.

• 한국IT서비스학회지
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• 제12권3호
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• pp.127-149
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• 2013

Is the social web environment in which production, distribution and consumption of information occurs from users an environment where manifestation of collective intelligence is easily made? Or is the social web environment a condition that incites people to depend on the groupthink due to biased information? It is important to conduct empirical studies on the possibility of social media as a tool of collective intelligence under the situation where conflicting opinions prevail. However, most of the existing studies related to this were limited to an exploratory research rather than an empirical research. In this regard, this study attempted to examine if the social media can perform a part as a platform of the collective intelligence empirically. Based on the experimental results, it can be safely said that the communication methods of social media showed its usefulness in both 'intellectual capacity of the group' and 'problem-solving skill of the group.'

• 한국IT서비스학회지
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• 제18권1호
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• pp.201-217
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• 2019

Cars have become a necessity in modern life. It is widely used to transport people or products to a destination conveniently. However, the addition of a navigation service that provides route information and more makes driving more convenient and safer. Recent developments in the mobile app ecosystem encourages people to adopt not only an installation-type car navigation, but also a mobile app navigation, supporting connected car concepts. It should be noted that mobile apps with mobile Internet can be a significant linkage between information acquired online and offline business. This study demonstrates the impact of the app use experience for a driver in the context of applying artificial intelligence service. As a result, the introduction of artificial intelligence services has a statistically significant moderating effect on the use of mobile navigation apps. This seminal research is valuable as it evaluates the role of artificial intelligence applied to mobile navigation apps.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권12호
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• pp.4308-4325
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• 2021

The importance and necessity of artificial intelligence, particularly machine learning, has recently been emphasized. In fact, artificial intelligence, such as intelligent surveillance cameras and other security systems, is used to solve various problems or provide convenience, providing solutions to problems that humans traditionally had to manually deal with one at a time. Among them, information security is one of the domains where the use of artificial intelligence is especially needed because the frequency of occurrence and processing capacity of dangerous codes exceeds the capabilities of humans. Therefore, this study intends to examine the definition of artificial intelligence and machine learning, its execution method, process, learning algorithm, and cases of utilization in various domains, particularly the cases and contents of artificial intelligence technology used in the field of information security. Based on this, this study proposes a method to apply machine learning technology to the method of classifying and detecting malware that has rapidly increased in recent years. The proposed methodology converts software programs containing malicious codes into images and creates training data suitable for machine learning by preparing data and augmenting the dataset. The model trained using the images created in this manner is expected to be effective in classifying and detecting malware.

• 디지털융복합연구
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• 제9권2호
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• pp.53-63
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• 2011

소셜네트워크를 통해 각 개인들은 정보를 공유하고, 이러한 공유된 정보와 지식을 바탕으로 집단지성은 형성되고 성장한다. 본 연구는 집단의 능력이라 할 수 있는 집단지성을 측정하고, 집단지성의 형성과정을 관찰하는 것이 목적으로, 5개의 실험집단을 형성하여 기업 경영게임을 실시하게 하였고 집단이 게임을 이용해 수행해야 하는 과업을 주었다. 또한 각 구성원간 소통할 수 있는 네트워크 공간을 만들어 주었으며, 기업경영게임을 통한 과업을 실시하는 동안 네트워크 공간에서 각 구성원간 상호작용과 피드백의 빈도 및 집단의 참여율을 관찰하였다. 연구결과 집단마다 상이한 게임성과를 얻었으며, 5개의 집단의 게임성과의 평균을 기준으로 성과가 높은 집단과 낮은집단으로 나누어 t-test한 결과 상호작용과 피드백의 빈도 및 집단지성참여에 유의미한 차이를 보였다.

• Journal of Information Processing Systems
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• 제15권5호
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• pp.1192-1200
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• 2019

Recently, artificial intelligence techniques have been widely used in the computer science field, such as the Internet of Things, big data, cloud computing, and mobile computing. In particular, resource management is of utmost importance for maintaining the quality of services, service-level agreements, and the availability of the system. In this paper, we review and analyze various ways to meet the requirements of cloud resource management based on artificial intelligence. We divide cloud resource management techniques based on artificial intelligence into three categories: fog computing systems, edge-cloud systems, and intelligent cloud computing systems. The aim of the paper is to propose an intelligent resource management scheme that manages mobile resources by monitoring devices' statuses and predicting their future stability based on one of the artificial intelligence techniques. We explore how our proposed resource management scheme can be extended to various cloud-based systems.