• Journal of the Semiconductor & Display Technology
• /
• v.20 no.4
• /
• pp.83-88
• /
• 2021

Cloud services have become the core infrastructure of the digital economy as a basis for collecting, storing, and processing large amounts of data to trigger artificial intelligence-based services and industrial innovation. Recently, cloud services have been spotlighted as a means of responding to corporate crises and changes in the work environment in a national disaster caused by COVID-19. While the cloud is attracting attention, the speed of adoption and diffusion of cloud services is not being actively carried out due to the lack of trust among users and uncertainty about security, performance, and cost. This study compares and analyzes the "Cloud Service Quality and Performance Management System" and the "Cloud Service Certification System" and suggests complementary points and improvement measures for the cloud service quality and performance management system.

• Journal of the Korean Society of Radiology
• /
• v.18 no.3
• /
• pp.267-273
• /
• 2024

Using a cloud-based vertex AI platform that can develop an artificial intelligence learning model without coding, this study easily developed an artificial intelligence learning model by the non-professional general public and confirmed its clinical applicability. Nine dental diseases and 2,999 root disease X-ray images released on the Kaggle site were used for the learning data, and learning, verification, and test data images were randomly classified. Image classification and multi-label learning were performed through hyper-parameter tuning work using a learning pipeline in vertex AI's basic learning model workflow. As a result of performing AutoML(Automated Machine Learning), AUC(Area Under Curve) was found to be 0.967, precision was 95.6%, and reproduction rate was 95.2%. It was confirmed that the learned artificial intelligence model was sufficient for clinical diagnosis.

• Journal of the Korea Convergence Society
• /
• v.11 no.9
• /
• pp.21-29
• /
• 2020

The Topic Modeling research, the methodology for deduction keyword within literature, has become active with the explosion of data from digital society transition. The research objective is to investigate research trends in D.N.A.(Data, Network, Artificial Intelligence) field using DTM(Dynamic Topic Model). DTM model was applied to the 1,519 of research projects with SW·A.I technology classifications among ICT(Information and Communication Technology) field projects between 6 years(2015~2020). As a result, technology keyword for D.N.A. field; Big data, Cloud, Artificial Intelligence, extended keyword; Unstructured, Edge Computing, Learning, Recognition was appeared every year, and accordingly that the above technology is being researched inclusively from other projects can be inferred. Finally, it is expected that the result from this paper become useful for future policy·R&D planning and corporation's technology·marketing strategy.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.8
• /
• pp.3821-3841
• /
• 2019

Cloud radio access networks (C-RANs) have been regarded in recent times as a promising concept in future 5G technologies where all DSP processors are moved into a central base band unit (BBU) pool in the cloud, and distributed remote radio heads (RRHs) compress and forward received radio signals from mobile users to the BBUs through radio links. In such dynamic environment, automatic decision-making approaches, such as artificial intelligence based deep reinforcement learning (DRL), become imperative in designing new solutions. In this paper, we propose a generic framework of autonomous cell activation and customized physical resource allocation schemes for energy consumption and QoS optimization in wireless networks. We formulate the problem as fractional power control with bandwidth adaptation and full power control and bandwidth allocation models and set up a Q-learning model to satisfy the QoS requirements of users and to achieve low energy consumption with the minimum number of active RRHs under varying traffic demand and network densities. Extensive simulations are conducted to show the effectiveness of our proposed solution compared to existing schemes.

• Journal of the Korea Society of Computer and Information
• /
• v.23 no.12
• /
• pp.81-87
• /
• 2018

Cloud computing is a computing environment in which users borrow as many IT resources as they need to, and use them over the network at any point in time. This is the concept of leasing and using as many IT resources as needed to lower IT resource usage costs and increase efficiency. Recently, cloud computing is emerging to provide stable service and volume of data along with major technological developments such as the Internet of Things, artificial intelligence and big data. However, for a more secure cloud environment, the importance of perimeter security such as shared resources and resulting secure data storage and access control is growing. This paper analyzes security threats in cloud computing environments and proposes a security architecture for effective response.

• Smart Media Journal
• /
• v.10 no.1
• /
• pp.99-107
• /
• 2021

In addition to the 4th Industrial Revolution and Industry 4.0, the recent megatrends in the ICT field are Big-data, IoT, Cloud Computing, and Artificial Intelligence. Therefore, rapid digital transformation according to the convergence of various industrial areas and ICT fields is an ongoing trend that is due to the development of technology of AI services suitable for the era of the 4th industrial revolution and the development of subdivided technologies such as (Business Intelligence), IA (Intelligent Analytics, BI + AI), AIoT (Artificial Intelligence of Things), AIOPS (Artificial Intelligence for IT Operations), and RPA 2.0 (Robotic Process Automation + AI). This study aims to integrate and advance various machine learning services of infrastructure-side GPU, CDA (Connected Data Architecture) framework, and AI based on mass distributed Abyss storage in accordance with these technical situations. Also, we want to utilize AI business revenue model in various industries.

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.4
• /
• pp.105-112
• /
• 2020

In the fourth industrial revolution represented by hyper-connected and intelligence, cloud computing is drawing attention as a technology to realize big data and artificial intelligence technologies. The proliferation of cloud computing has also increased the number of threats. In this paper, we propose one way to effectively monitor to the resources assigned to clients by the IaaS service provider. The method we propose in this paper is to model the use of resources allocated to cloud systems using ARIMA algorithm, and it identifies abnormal situations through the use and trend analysis. Through experiments, we have verified that the client service provider can effectively monitor using the proposed method within the minimum amount of access to the client systems.

• Journal of the Semiconductor & Display Technology
• /
• v.20 no.2
• /
• pp.115-120
• /
• 2021

In Korea, where the world's first cloud computing development law was created, cloud service technology has been developing so far, and the industries to which artificial intelligence and big data technologies can be applied based on this are increasing. It is important for domestic and overseas cloud operators to secure many partners in order to provide optimal services to users. It is also important to systematically develop the partner's technology and discover new partners. In particular, the public, medical, and financial sectors are industrial fields that are difficult for domestic as well as global cloud service providers to expand without the help of partners. This study analyzes partner policies for industries caused by domestic regulations through domestic and foreign cases, and aims to establish partner management policies optimized for the domestic environment.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.2
• /
• pp.833-849
• /
• 2021

Cloud accounting represents a new area of accounting information systems. Past research has often focused on accounting information systems and its antecedents, rather than factors that adopt cloud accounting system. The purpose of this paper is to explain the factors that influence the adoption of cloud accounting in the financial sectors. This paper applied the technology acceptance model (TAM), technology-organization-environment, and the De Lone and Mc Lean model, coupled with proposed factors relevant to cloud accounting. The proposed model was empirically evaluated using survey data from 187 managers (financial managers, IT department managers, audit managers, heads of accounting departments, and head of internal control departments) in Jordanian bank branches. Based on the SEM results, top management support, organizational competency, service quality, system quality, perceived usefulness, and perceived ease of use had a positive relationship with the intention of using cloud accounting. Cloud accounting adoption positively affected cloud accounting usage. This paper contributes to a theoretical understanding of factors that activate the adoption of cloud accounting. For financial firms in general the results enable them to better develop cloud accounting framework. The paper verifies the factors that affect the adoption of cloud accounting and the proposed cloud accounting model.

• Journal of Information Technology Applications and Management
• /
• v.30 no.6
• /
• pp.91-111
• /
• 2023

This study aims to identify the influencing factors of intention to use cloud services using the SEM-ANN two-step approach. In previous studies of SEM-ANN, SEM presented R² and ANN presented MSE(mean squared error), so analysis performance could not be compared. In this study, R² and MSE were calculated and presented by SEM and ANN, respectively. Then, analysis performance was compared and feature importances were compared by sensitivity analysis. As a result, the ANN default model improved R² by 2.87 compared to the PLS model, showing a small Cohen's effect size. The ANN optimization model improved R² by 7.86 compared to the PLS model, showing a medium Cohen effect size. In normalized feature importances, the order of importances was the same for PLS and ANN. The contribution of this study, which links structural equation modeling to artificial intelligence, is that it verified the effect of improving the explanatory power of the research model while maintaining the order of importance of independent variables.