• Journal of Information Processing Systems
• /
• v.15 no.5
• /
• pp.1192-1200
• /
• 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.

• Proceedings of the KIEE Conference
• /
• 1993.11a
• /
• pp.26-28
• /
• 1993

The application of artificial intelligence technologies to power systems has been an active research topic for about a decade. The purpose of this paper is to provide a brief review of the current status of applications of artificial intelligence (AI) techniques to power systems. In this paper, AI techniques, such as knowlege-based expert systems, artificial neural networks and fuzzy systems are reviewed in the view of the applications to power systems.

• The Journal of Bigdata
• /
• v.8 no.1
• /
• pp.35-47
• /
• 2023

It is crucial to develop effective and efficient big data analytics methods for problem-solving in the field of business in order to improve the performance of data analytics and reduce costs and risks in the analysis of customer data. In this study, a big data-driven data analysis system using artificial intelligence techniques is designed to increase the accuracy of big data analytics along with the rapid growth of the field of data science. We present a key direction for big data analysis systems through missing value imputation, outlier detection, feature extraction, utilization of explainable artificial intelligence techniques, and exploratory data analysis. Our objective is not only to develop big data analysis techniques with complex structures of business data but also to bridge the gap between the theoretical ideas in artificial intelligence methods and the analysis of real-world data in the field of business.

• Korean Journal of Cognitive Science
• /
• v.15 no.4
• /
• pp.61-66
• /
• 2004

I think constructivism can be considered as the new count proposal for understanding human to Cartesian rationalism. Constructivism has the common area not only with recent results of evolutionary biology, evolutionary psychology. brain science, system theory, and complex adaptive system but also with recent trends of humanities, and social science. In artificial intelligence, the studies which can be considered as constructivistic methods is going on. In this paper, from a constructivistic pint of view, to broaden the concept of intelligence in artificial intelligenve, I will examine constructivistic methodologies to intelligent machine and look about the artificial intelligence techniques which are constructivistic. Throughout such a discussion I want to promote the integral understanding of various kinds of mind theories and techniques, and pave the way of general intelligence in artificial intelligence.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2013.10a
• /
• pp.211-213
• /
• 2013

In these days, the delusions of Big Data and apprehension about them are coming into the picture in many business fields. General techniques for preservation, analysis, and utilization of Big Data are falling short of useful techniques for the volume of fast-increasing data. However, there are some assertions that the power of analysis and prediction of Artificial Intelligence would intensify the power of Big Data analysis. This paper studies on business cases to try to graft the Artificial Intelligence technique onto Big Data analysis. We first research on various techniques of Artificial Intelligence and relations between Artificial Intelligence and Big Data. And then, we perform case studies of Big Data with using Artificial Intelligence and propose some roles of Big Data in the future.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.21 no.4
• /
• pp.57-62
• /
• 2021

As the parallel computing capability for artificial intelligence improves, the field of artificial intelligence technology is expanding in various industries. In particular, artificial intelligence is being introduced to process data generated from IoT sensors that have enoumous data. However, the limitation exists when applying the AI techniques on IoT network because IoT has time series data, where the importance of data changes over time. In this paper, we propose time-weighted and user-state based artificial intelligence processing techniques to effectively process IoT sensor data. This technique aims to effectively classify IoT sensor data through a data pre-processing process that personalizes time series data and places a weight on the time series data before artificial intelligence learning and use status of personal data. Based on the research, it is possible to propose a method of applying artificial intelligence learning in various fields.

• Journal of Korean Library and Information Science Society
• /
• v.21
• /
• pp.57-86
• /
• 1994

The purpose of the study is to understand the research trends in the hypertext information retrieval. Around 30 related papers were investigated, from which three distinctive streams of research trends are grasped: 1) a trend of incorporating the traditional retrieval models, especially the query-based searching model into the hypermedia system. 2) a trend of a n.0, pplying the hypermedia system as an interface to the OPAC system, 3) a trend of incorporating the artificial intelligence techniques into the hypermedia techniques. The research on the hypermedia is going on, and the research directions will be increasingly intend to incorporate the traditional retrieval models and artificial intelligence techniques into the hypermedia system.

• Journal of information and communication convergence engineering
• /
• v.17 no.1
• /
• pp.21-40
• /
• 2019

This paper presents a comprehensive survey of various artificial intelligence (AI) techniques implemented in cognitive radio engine to improve cognition capability in cognitive radio networks (CRNs). AI enables systems to solve problems by emulating human biological processes such as learning, reasoning, decision making, self-adaptation, self-organization, and self-stability. The use of AI techniques is studied in applications related to the major tasks of cognitive radio including spectrum sensing, spectrum sharing, spectrum mobility, and decision making regarding dynamic spectrum access, resource allocation, parameter adaptation, and optimization problem. The aim is to provide a single source as a survey paper to help researchers better understand the various implementations of AI approaches to different cognitive radio designs, as well as to refer interested readers to the recent AI research works done in CRNs.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.4
• /
• pp.187-195
• /
• 2023

This study propose to develop artificial intelligence liberal arts courses for college students in the humanities and social sciences majors using the entry artificial intelligence model. A group of experts in computer, artificial intelligence, and pedagogy was formed, and the final artificial intelligence liberal arts course was developed using previous research analysis and Delphi techniques. As a result of the study, the educational topics were largely composed of four categories: image classification, image recognition, text classification, and sound classification. The training consisted of 1) Understanding the principles of artificial intelligence, 2) Practice using the entry artificial intelligence model, 3) Identifying the Ethical Impact, and 4) Based on learned, team idea meeting to solve real-life problems. Through this course, understanding the principles of the core technology of artificial intelligence can be directly implemented through the entry artificial intelligence model, and furthermore, based on the experience of solving various real-life problems with artificial intelligence, and it can be expected to contribute positively to understanding technology, exploring the ethics needed in the artificial intelligence era.

• Journal of Intelligence and Information Systems
• /
• v.8 no.2
• /
• pp.159-175
• /
• 2002

Web mining is an emerging science of applying modem data mining technologies to the problem of extracting valid, comprehensible, and actionable information from large databases of web in e-Business environment and of using it to make crucial e-Business decisions. In this paper, we present the noble framework of data visualization system based on web mining for analyzing the characteristics of on-line customers in e-Business. We also propose the framework of forecasting system for providing the forecasting information of sales/purchase through the use of web mining based on artificial intelligence techniques such as back-propagation network, memory-based reasoning, and self-organizing map.