• Journal of Platform Technology
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• v.10 no.3
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• pp.51-59
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• 2022

Machine learning, which has recently innovatively developed, has become an important technology that can solve various optimization problems. In this paper, we introduce the latest research papers that solve the problem of channel sharing in heterogeneous networks using machine learning, analyze the characteristics of mainstream approaches, and present a guide to future research directions. Existing studies have generally adopted Q-learning since it supports fast learning both on online and offline environment. On the contrary, conventional studies have either not considered various coexistence scenarios or lacked consideration for the location of machine learning controllers that can have a significant impact on network performance. One of the powerful ways to overcome these disadvantages is to selectively use a machine learning algorithm according to changes in network environment based on the logical network architecture for machine learning proposed by ITU.

• Journal of information and communication convergence engineering
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• v.18 no.2
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• pp.82-87
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• 2020

This paper discusses how to design a game server that is scalable, adaptable, and re-buildable with components. Furthermore, it explains how various implementation issues were resolved. To support adaptability, the server comprises three layers: network, user, and database. To ensure independence between the layers, each layer was designed to communicate with each other only via message queues. In this architecture, each layer can have an arbitrary number of threads; thus, scalability is guaranteed for each layer. The network layer uses input/output completion ports(IOCP), which shows the best performance on the Windows platform, it can handle up to 5,000 simultaneous connections on a typical entry-level computer, despite being built with a single-threaded user layer. To completely separate the database from the game server, the SQL code was not directly embedded in the database layer.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2012.05a
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• pp.586-591
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• 2012

The solution of maintenance decision support system for the gas turbine engine, which is currently operating in GUNSAN combined cycle power plant, was developed and is consist of online monitoring module, periodic performance trending module, optimal compressor washing interval analysis module and hot component management module. Also, GUI platform was applied to this solution for the user to monitoring the analyzed result of engine performance condition and then to make a decision of the consequent maintenance action. In online condition monitoring module, the performance degradation of engine is provided by the analysis of difference between the real time measurement data compared to exist engine performance. The optimal compressor washing interval module produced the washing interval of maximum net profit value by researching the maintenance expense and the loss profit value corresponds to the performance degradation with economic assessment algorithm. Thus, this solution support the user to enable the optimal maintenance and operation of gas turbine engine with overall analysis of engine condition and main information.

• Journal of Information Technology Services
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• v.22 no.4
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• pp.111-122
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• 2023

Since the organization of civil servants has been divided and stratified according to the characteristics of the bureaucracy, it is inevitable that the organization and personnel will increase when new tasks arise. Even in the process of informatization, only the processing method was brought online while leaving the existing business processing procedures as they were, so there was no reduction in manpower through informatization. In order to maintain or upgrade the current administrative services while reducing the number of civil servants, it is inevitable to use AI technology. By using data and AI to integrate the 'powers and responsibilities assigned to the officials in charge', manpower can be reduced, and the reduced costs can be reinvested in the collection, analysis, and utilization of on-site data to further promote intelligent informatization. In this study, as a way for the government's success in intelligent informatization innovation, we proposed a 'Civil Servants-AI Collaboration Platform'. This Platform based on the civil servant proposal system as a reward system and the characteristics of intelligent informatization that are different from the informatization. By establishing a 'Civil Servants-AI Collaboration Platform', the performance evaluation system of the short-term evaluation method by superiors can be improved to a data-driven always-on evaluation method, thereby alleviating the rigid hierarchy of government organizations. In addition, through the operation of Collaboration Platform, it will become common to define and solve problems using data and AI, and the intelligence informatization of government organizations will be activated.

• The Journal of Information Systems
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• v.28 no.1
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• pp.73-96
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• 2019

Purpose The purpose of this study is to examine the effect of communication-structure characteristics on performance in online collaboration using the data from Github, one of representative open source software development platforms. We analyze the impact of in-degree/out-degree centralization and reciprocity of communication network on collaboration performance in each project. In addition, we investigate the moderating effect of owner types, an individual developer or an organization. Design/methodology/approach We collect the data of 838 Github projects, and conduct social network analysis for measuring in-degree/out-degree centralization and reciprocity as independent variables. With these variables, hierarchical regression analysis is employed on the relationship between the characteristics of communication structure and collaborative performance. Findings Our results show that for the project owned by an organization, the centralized structure of communication is not associated with the collaboration performance. In addition, the reciprocity is positively related to the collaboration performance. On the other hand, for the project owned by an individual developer, the centralized structure of communication is positively related to the performance, and the reciprocity does not show the positive relationship on the performance.

• Journal of Korea Multimedia Society
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• v.21 no.5
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• pp.617-625
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• 2018

Phishing website has become a crucial concern in cyber security applications. It is performed by fraudulently deceiving users with the aim of obtaining their sensitive information such as bank account information, credit card, username, and password. The threat has led to huge losses to online retailers, e-business platform, financial institutions, and to name but a few. One way to build anti-phishing detection mechanism is to construct classification algorithm based on machine learning techniques. The objective of this paper is to compare different classifier ensemble approaches, i.e. random forest, rotation forest, gradient boosted machine, and extreme gradient boosting against single classifiers, i.e. decision tree, classification and regression tree, and credal decision tree in the case of website phishing. Area under ROC curve (AUC) is employed as a performance metric, whilst statistical tests are used as baseline indicator of significance evaluation among classifiers. The paper contributes the existing literature on making a benchmark of classifier ensembles for web phishing detection.

• Journal of Internet Computing and Services
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• v.17 no.4
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• pp.173-180
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• 2016

SOA has been adopted by enormous number of organizations world-wide. This study has investigated significant factors which affect SOA adoption in positive and negative manner. Unlike the previous similar studies, where they focused on qualitative analysis for SOA adoption, this study conducted a quantitative analysis to investigate the relationship between the adoption of SOA and the performance of IT organizations. In order to conduct the research, an online questionnaire was created and distributed among SOA experts through the social networking platform of professionals, LinkedIn. Total one hundred and four (104) respondents from thirty (30) different countries participated in this study. The results of this study indicate that there are both positive and negative influences upon SOA adoption. The positive influences includes: governance, strategy, culture and communication, business and IT alignment and ROI; whereas complexity, security concerns, and costs have negatively affected SOA adoption.

• Journal of Multimedia Information System
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• v.5 no.2
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• pp.99-104
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• 2018

Phishing website has become a crucial concern in cyber security applications. It is performed by fraudulently deceiving users with the aim of obtaining their sensitive information such as bank account information, credit card, username, and password. The threat has led to huge losses to online retailers, e-business platform, financial institutions, and to name but a few. One way to build anti-phishing detection mechanism is to construct classification algorithm based on machine learning techniques. The objective of this paper is to compare different classifier ensemble approaches, i.e. random forest, rotation forest, gradient boosted machine, and extreme gradient boosting against single classifiers, i.e. decision tree, classification and regression tree, and credal decision tree in the case of website phishing. Area under ROC curve (AUC) is employed as a performance metric, whilst statistical tests are used as baseline indicator of significance evaluation among classifiers. The paper contributes the existing literature on making a benchmark of classifier ensembles for web phishing detection.

• The Journal of the Korea Contents Association
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• v.20 no.12
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• pp.36-48
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• 2020

App market has continuously been growth since its launch. The market revenues will reach about 1,000 billion US dollars in 2019. App is a core service for smartphone. Currently, there are more than 1.5 million mobile apps in App platform calling out for attention. So, if you are looking at developing a successful app, you need to have a solid marketing and distribution strategy. Online word of mouth(eWOM) is one of the most effective, powerful App marketing method. eWOM affect potential consumers' decision making, and this effect can spread rapidly through online social network. Despite the increasing research on word of mouth, only few studies have focused on content analysis. Most of studies focused on the causes and acceptance of eWOM and eWOM performance measurement. This study aims to content analysis of mobile apps review In 2013, Google researchers announced Word2Vec. This method has overcome the weakness of previous studies. This is faster and more accurate than traditional methods. This study found out the relationship between mobile app reviews and checked for reactions by Word2vec.

• The Journal of Industrial Distribution & Business
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• v.11 no.10
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• pp.27-38
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• 2020

Purpose: The public sectors including government and public organizations have put an efforts to improve the quality of people's lives by providing enhanced services. The purpose of this paper is to investigate the factors that affect job attitude, job satisfaction, and job performance in the public sector, that are rarely examined by previous studies. Research Design, data, and methodology: The following research questions have been proposed: i) how do payroll system, personnel management system, cooperative working environment, and self-efficacy affect job attitude?; and ii) how does job attitude affect job satisfaction and performance? This paper used a survey through an online platform and collected data randomly from five classified public institutions. This study applied regression analysis and ANOVA. Results: This study found that cooperative working environment and self-efficacy had significant impacts on job attitude, while payroll system and personnel management system did not affect job attitude. Overall job attitude affected both job satisfaction and performance. Conclusions: The results provide policy implications to the public sector which factors should be considered to improve job attitude, job satisfaction, and job performance. The results also provide managerial implications how such efforts ultimately improve service quality to the citizens.