• Journal of Information Processing Systems
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• v.15 no.5
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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.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.26-28
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• 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
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• v.8 no.1
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• pp.35-47
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• 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
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• v.15 no.4
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• pp.61-66
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• 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.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.108-117
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• 2024

The selection and recommendation of a suitable job applicant from the pool of thousands of applications are often daunting jobs for an employer. The recommendation and selection process significantly increases the workload of the concerned department of an employer. Thus, Resume Classification System using the Natural Language Processing (NLP) and Machine Learning (ML) techniques could automate this tedious process and ease the job of an employer. Moreover, the automation of this process can significantly expedite and transparent the applicants' selection process with mere human involvement. Nevertheless, various Machine Learning approaches have been proposed to develop Resume Classification Systems. However, this study presents an automated NLP and ML-based system that classifies the Resumes according to job categories with performance guarantees. This study employs various ML algorithms and NLP techniques to measure the accuracy of Resume Classification Systems and proposes a solution with better accuracy and reliability in different settings. To demonstrate the significance of NLP & ML techniques for processing & classification of Resumes, the extracted features were tested on nine machine learning models Support Vector Machine - SVM (Linear, SGD, SVC & NuSVC), Naïve Bayes (Bernoulli, Multinomial & Gaussian), K-Nearest Neighbor (KNN) and Logistic Regression (LR). The Term-Frequency Inverse Document (TF-IDF) feature representation scheme proven suitable for Resume Classification Task. The developed models were evaluated using F-Score_M, Recall_M, Precission_M, and overall Accuracy. The experimental results indicate that using the One-Vs-Rest-Classification strategy for this multi-class Resume Classification task, the SVM class of Machine Learning algorithms performed better on the study dataset with over 96% overall accuracy. The promising results suggest that NLP & ML techniques employed in this study could be used for the Resume Classification task.

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

• Clinical Endoscopy
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• v.57 no.1
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• pp.11-17
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• 2024

Although magnifying endoscopy with narrow-band imaging is the standard diagnostic test for gastric cancer, diagnosing gastric cancer using this technology requires considerable skill. Artificial intelligence has superior image recognition, and its usefulness in endoscopic image diagnosis has been reported in many cases. The diagnostic performance (accuracy, sensitivity, and specificity) of artificial intelligence using magnifying endoscopy with narrow band still images and videos for gastric cancer was higher than that of expert endoscopists, suggesting the usefulness of artificial intelligence in diagnosing gastric cancer. Histological diagnosis of gastric cancer using artificial intelligence is also promising. However, previous studies on the use of artificial intelligence to diagnose gastric cancer were small-scale; thus, large-scale studies are necessary to examine whether a high diagnostic performance can be achieved. In addition, the diagnosis of gastric cancer using artificial intelligence has not yet become widespread in clinical practice, and further research is necessary. Therefore, in the future, artificial intelligence must be further developed as an instrument, and its diagnostic performance is expected to improve with the accumulation of numerous cases nationwide.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.57-62
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• 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
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• v.21
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• pp.57-86
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• 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
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• v.17 no.1
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• pp.21-40
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• 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.