• 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.

• 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.

• 디지털융복합연구
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• 제19권3호
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• pp.81-88
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• 2021

현대사회는 다양한 형태의 위기에 노출되어 있다. 특히, 9·11테러 이후로 각 국가는 비군사적 위기에 대한 관리의 비중이 점차 커지고 있다. 이에 본 연구에서는 제4차 산업혁명시대에서 국가위기관리를 위해 인공지능(AI)을 활용하는 방안에 관한 고찰을 목적으로 한다. 이를 위해 인간의 의사결정을 지원해주기 위해 운용되고 연구개발(R&D) 중인 인공지능(AI)의 실효성을 분석하여 인공지능(AI)을 국가위기관리에 활용 가능성을 살펴보았다. 연구결과, 인공지능(AI)은 데이터에 근거한 객관적인 상황 판단과 최적의 대응 방안을 정책결정권자에게 제시해주어 급박한 위기 상황에서 정책결정권자의 결정행위를 지원해주는 것이 가능하여 인공지능(AI)을 국가위기관리에 활용하는 것이 효율적임을 알 수 있었다. 이러한 연구결과는 신속하고 효율적인 국가위기 대응을 위해 인공지능(AI) 활용의 가능성을 제시해 준다.

• 한국병원경영학회지
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• 제28권3호
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• pp.27-38
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• 2023

Objectives: This study explores the impact of age groups on social presence and trust among users of medical artificial intelligence chatbots. Furthermore, we investigate the existence of gender differences within these relationships. Method: We collected data through a survey from people who had interacted with general hospital chatbot services, either by making reservations or seeking consultations. Multiple linear regression analysis was conducted to examine the relationship between general characteristics of study population and social presence and trust of artificial intelligence chatbots. Additionally, we conducted stratified analysis to confirm the presence of gender differences within these relationship. Results: Among 300 participants, those aged 50 and older had higher social presence of artificial intelligence chatbots and greater trust of artificial intelligence chatbots (social presence, 𝛽=0.543, p=0.003; trust, 𝛽=0.787, p=0.000). In stratified by sex, women aged 50 and older had higher social presence and trust of artificial intelligence chatbots compared to those in their 30s age group (social presence, 𝛽 = 0.925, p=0.002; trust, 𝛽=0.645, p=:0.007). However, there was no statistically significant relationship between age and chatbot social presence and trust in men. Conclusion: This study demonstrates that advanced age plays a significant roles in users' social presence and trust in medical artificial intelligence chatbots. Futhermore, our findings reveal gender differences with women aged 50 and older showing the most substantial levels of social presence and trust. Therefore, it is expected that this finding can serve as valuable evidence to enhance the satisfaction of medical institution service users, offering crucial insights into the effective utilization of chatbot services.

• 품질경영학회지
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• 제46권3호
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• pp.677-694
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• 2018

Purpose: The purpose of this study is to investigate various prior studies on artificial intelligence and to examine the concept and characteristics of various prior studies of existing service quality. And this paper is to study the concept and characteristics of artificial intelligence services and propose suitable quality measurement items. Methods: The research method of this paper is to examine previous research related to existing artificial intelligence and to analyze characteristics related to service quality. Results: This paper examines the concept and characteristics of artificial intelligence service in a new era by examining previous studies related to artificial intelligence and derives quality measurement items. Conclusion: In the future, it is necessary to verify the validity of the quality measurement items of artificial intelligence service. Therefore, it is necessary to elicit and verify the main quality measurement items through the investigation of the expert group.

• 반도체디스플레이기술학회지
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• 제21권2호
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• pp.133-137
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• 2022

In the smart industry where data plays an important role, cloud computing is being used in a complex and advanced way as a convergence technology because it has and fits well with its strengths. Accordingly, in order to utilize artificial intelligence rather than human beings for quality management of cloud computing services, a consistent standardization method of data collected from various nodes in various areas is required. Therefore, this study analyzed technologies and cases for incorporating artificial intelligence into specific services through previous studies, suggested a plan to use artificial intelligence to comprehensively standardize data in quality management of cloud computing services, and then verified it through case studies. It can also be applied to the artificial intelligence learning model that analyzes the risks arising from the data formalization method presented in this study and predicts the quality risks that are likely to occur. However, there is also a limitation that separate policy development for service quality management needs to be supplemented.

• 지식경영연구
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• 제19권1호
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• pp.97-118
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• 2018

The finance-investment industry is currently focusing on research related to artificial intelligence and big data, moving beyond conventional theories of financial engineering. However, the case of equity optimization portfolio by using an artificial intelligence, big data, and its performance is rarely realized in practice. Thus, the purpose of this study is to propose process improvements in equity selection, information analysis, and portfolio composition, and lastly an improvement in portfolio returns, with the case of an equity optimization model based on quantitative research by an artificial intelligence. This paper is an empirical study of the portfolio based on an artificial intelligence technology of "D" asset management, which is the largest domestic active-quant-fiduciary management in accordance with the purpose of this paper. This study will apply artificial intelligence to finance, analyzing financial and demand-supply information and automating factor-selection and weight of equity through machine learning based on the artificial neural network. Also, the learning the process for the composition of portfolio optimization and its performance by applying genetic algorithms to models will be documented. This study posits a model that the asset management industry can achieve, with continuous and stable excess performance, low costs and high efficiency in the process of investment.

• Asia pacific journal of information systems
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• 제32권4호
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• pp.916-944
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• 2022

Artificial Intelligence has been a conceptual area for several decades. It has been studied extensively through experiments by the Information Systems community. When Information Systems supported with Information Technology became all pervasive in business and other allied areas, gradually the advancements in Artificial Intelligence also emerged as innovations across domains. Artificial Intelligence by definition is expected to substitute Human Intelligence, thereby making a huge space for innovation. In fact, all processes effected by human intelligence are liable to be replaced by AI which in itself is a massive innovation space. This paper will study the publication's repository (Scopus and Google Scholar from 1983 till 2021) in the area of Artificial Intelligence and innovation, then analyze the trend to gain insight into the evolution of AI as a vehicle for innovation.

• 디지털산업정보학회논문지
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• 제15권3호
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• pp.55-67
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• 2019

In this study, I researched whether consumers would adopt artificial intelligence speakers. A study was conducted on the motivations that arise when consumers want to use artificial intelligence speakers. Key motivational factors include needs and wants, and emotion is also included in the hypothesis as influencing the intended use. These factors have modeled the motivational process in which consumers want to use artificial intelligence speakers. In the empirical study, the survey was conducted and the survey data was analyzed by applying the method of analysis of the structural equation model. As a result of empirical research, consumers' expectations to meet their general needs for artificial intelligence speakers affected their expectations to meet their wants and their favorable perceptions. And consumers' expectations of meeting their quasi-desire for artificial intelligence speakers have affected their expectations of meeting the wants and affected their perception of favorability. Finally, consumers' expectations for satisfying their wants and their perception of favorability affected their intention to use artificial intelligence speakers. The implications of this study is that it helps to formulate strategies for information technology products with combined functionality. The specific components of motivation can play an important role in increasing consumers' intention to use artificial intelligence speakers.

• Journal of information and communication convergence engineering
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• 제19권3호
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• pp.142-147
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• 2021

In this paper, we propose an intelligent research fund management system that applies artificial intelligence technology to an integrated research fund management system. By defining research fund management rules as work rules, a detection model learned using deep learning is designed, through which the disapproval status is presented for each research fund usage history. The disapproval detection system of the RCMS implemented in this study predicts whether the newly registered usage details are recognized or disapproved using an artificial intelligence model designed based on the use of an 8.87 million research fund registered in the RCMS. In addition, the item-detail recommendation system described herein presents the usage details according to the usage history item newly registered by the artificial intelligence model through a correlation between the research cost usage details and the item itself. The accuracy of the recommendation was shown to be 97.21%.