• Journal of Information Processing Systems
• /
• 제15권5호
• /
• 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.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1993년도 정기총회 및 추계학술대회 논문집 학회본부
• /
• 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.

• 한국빅데이터학회지
• /
• 제8권1호
• /
• 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.

• 인지과학
• /
• 제15권4호
• /
• pp.61-66
• /
• 2004

서양철학의 근간을 이루는 데카르트의 이성주의적 인간이해의 반성으로부터 등장한 구성주의는 지능을 포함한 인간이해의 새로운 대안이 될 수 있을 것으로 생각한다. 구성주의는 진화생물학, 진화심리학, 뇌과학, 시스템이론, 복잡계 이론의 성과뿐만 아니라 나아가 인문사회학의 경향과도 설명을 공유할 수 있는 많은 부분이 있다. 또한 인공지능 분야에서도 구성주의적 방법이라고 할 수 있는 연구가 진행되고 있다. 이 글에서는 구성주의적 관점에서 인공지능에서 다루는 지능에 대한 이해의 지평을 넓히고 이를 기반으로 한 방법론에 대한 검토와 그러한 경향에 있는 일부 인공지능 기술을 살펴보고자 한다. 이러한 논의를 통하여 여러 가지 관점의 마음에 대한 이론과 기술을 상호보완적으로 이해하고 다소 등한히 되고 있는 인공지능의 보편지능(general intelligence)의 토대로 삼고자 한다.

• International Journal of Computer Science & Network Security
• /
• 제24권7호
• /
• pp.108-117
• /
• 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.

• 한국정보통신학회:학술대회논문집
• /
• 한국정보통신학회 2013년도 추계학술대회
• /
• pp.211-213
• /
• 2013

최근에 많은 기업현장에서, 빅데이터에 대한 착각과 이해가 현실화되고 있다. 빅데이터의 보존, 분석, 활용을 위한 일반적인 기술이 빠르게 증가하는 데이터의 양에 효과적으로 대응하기 위해서는 기능이 매우 제한적이다. 하지만, 인공지능이 빅데이터 분석력을 증가할 수 있는 몇 개의 가정이 존재한다. 본 연구에서는 인공지능 기술을 빅데이터 분석에 접목시키려는 노력을 보인 실무사례에 대해 연구하려고 한다. 우선 인공지능의 다양한 기술과 인공지능과 빅데이터 간의 관계에 대한 연구를 하고, 인공지능을 이용한 빅데이터 기업사례 분석을 수행하겠으며, 미래 빅데이터에 대한 역할도 언급하고자 한다.

• Clinical Endoscopy
• /
• 제57권1호
• /
• pp.11-17
• /
• 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.

• 한국인터넷방송통신학회논문지
• /
• 제21권4호
• /
• pp.57-62
• /
• 2021

인공지능을 위한 병렬연산 능력이 향상됨에 따라 인공지능 적용 분야가 다양한 방향으로 확대되고 있다. 특히 방대한 데이터를 처리해야 하는 IoT센서의 데이터를 처리하기 위해 인공지능이 도입되고 있다. 하지만 시간에 따른 데이터의 중요도가 달라지는 IoT 시계열 데이터 특성상 기존의 인공지능 학습 기법을 그대로 적용하기에는 한계점이 있다. 본 과제에서는 IoT 센서 데이터를 효과적으로 처리하기 위해 시간가중치기반 및 사용자 상태값 기반 인공지능 처리기법을 연구한다. 상기 기법을 통해 기존 인공지능 학습을 적용시키는 것 보다 높은 센서 정확도를 확보 할 수 있게 된다. 이에 더해, 해당 연구를 기반으로 다양한 분야에서 인공지능 학습을 적용하는 방안을 제시하고, 지속적인 연구를 통해 다양한 분야로의 확장을 기대할 수 있다.

• 한국도서관정보학회지
• /
• 제21권
• /
• 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
• /
• 제17권1호
• /
• 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.