• 한국컴퓨터정보학회논문지
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• 제26권10호
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• pp.199-208
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• 2021

본 논문에서는 머신러닝 플랫폼을 활용하여 STEAM 교육을 하는 인공지능 융합교육 프로그램이 초등학생의 융합인재소양과 학습몰입에 미치는 영향에 대해 분석하였다. 동질집단인 초등학교 6학년 44명을 실험집단과 통제집단으로 나누고 통제집단에는 일반 교과 융합 수업 10차시를, 실험집단에는 머신러닝 포 키즈(Machine learning for Kids)를 활용한 STEAM 기반 인공지능 융합 수업 10차시를 적용하였다. 인공지능 융합교육 프로그램은 2015 개정 교육과정의 목표, 성취기준 및 내용 요소를 분석하여 융합교육 프로그램 설계를 위한 교과목 및 수업 내용을 선정하였고, 소프트웨어 수업 모델, 다양한 교수·학습 전략 등을 활용한 교수·학습 과정안 및 학습지를 개발하였다. 융합인재소양 검사와 학습몰입 검사 결과, 실험집단과 통제집단 간에 유의미한 차이가 있는 것으로 나타났다. 특히 인공지능 기능을 확장시킨 코딩 환경이 학습자들의 몰입과 융합인재소양에 긍정적인 영향을 미친다는 것을 확인할 수 있었다. 융합인재소양의 하위 요소 중 융합, 창의 영역과 같은 개인적인 역량을 발휘하는 부분에서 유의미한 차이가 나타났으며, 학습몰입의 하위 요소 중 도전과 능력의 조화, 명확한 목표, 과제에 대한 집중, 자기 목적적 경험 영역에서 유의미한 차이가 나타났다. 추후 더욱 확장된 연구가 이루어진다면, 미래를 대비하는 더욱 효과적인 교육을 위한 기초 연구가 될 수 있을 것이다.

• International Journal of Advanced Culture Technology
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• 제10권4호
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• pp.412-419
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• 2022

The amount of digital text data is growing exponentially, and many machine learning solutions are being used to monitor and manage this data. Artificial intelligence and machine learning are used in many areas of our daily lives, but the underlying processes and concepts are not easy for most people to understand. At a time when many experts are needed to run a machine learning solution, no-code machine learning tools are a good solution. No-code machine learning tools is a platform that enables machine learning functions to be performed without engineers or developers. The latest No-Code machine learning tools run in your browser, so you don't need to install any additional software, and the simple GUI interface makes them easy to use. Using these platforms can save you a lot of money and time because there is less skill and less code to write. No-Code machine learning tools make it easy to understand artificial intelligence and machine learning. In this paper, we examine No-Code machine learning tools and compare their features.

• 한국학교수학회논문집
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• 제23권2호
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• pp.223-237
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• 2020

4차 산업혁명 시대를 대표하는 인공지능 기술은 이제 우리 삶에 깊숙이 관여되고 있고 미래 교육은 이러한 인공지능의 원리와 활용에 대한 학생들의 역량 함양을 중시하고 있다. 따라서 본 연구의 목적은 인공지능 역량과 가장 밀접한 교과인 수학에서 다루어야 하는 인공지능 관련 교육 내용을 고찰하는데 있다. 이를 위해 인공지능의 핵심 기술인 기계학습(machine learning)의 원리를 수학기반으로 학습할 수 있는 인공지능 교과를 수학과의 과목으로 신설할 것과, '인공지능과 데이터 과학을 위한 수학' 교과에서 다루어야 하는 주요 수학 내용들을 제안하였다.

• 디지털산업정보학회논문지
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• 제7권4호
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• pp.89-99
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• 2011

Digital edutainment games and educational content to the concept of combining learning and to draw conclusions that can be varied and fun learning program. Yet effective design. There's a lot of discussion about the systematic and scientific research, explore the difficult, but so is the growing field of endless possibility. Merely a linguistic capabilities of human intelligence and mathematical ability were measured primarily on issues raised in the traditional intelligence tests, and emerged a variety of multiple intelligences theory of human intelligence classified into 8 types and characteristics of each intelligence activities and guidelines for faculty are presented. These lessons are based on multiple intelligences theory professor activities through the design study for students to form learning activities to meet effectively and systematically conducted classes, and student-specific classes can be designed. In this study, multiple intelligences theory, based on children's edutainment content by linguistic intelligence, and intrapersonal intelligence body-kinestic intelligent and can learn by linking to content that was designed. Children interested in animation and gaming content through the feeling that you can become stiff in Korean alphabet education to solve the quests were designed to be a natural puleonagal.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권1호
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• pp.305-326
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• 2019

Discriminative correlation filter (DCF) based tracking algorithms have recently shown impressive performance on benchmark datasets. However, amount of recent researches are vulnerable to heavy occlusions, irregular deformations and so on. In this paper, we intend to solve these problems and handle the contradiction between accuracy and real-time in the framework of tracking-by-detection. Firstly, we propose an innovative strategy to combine the template and color-based models instead of a simple linear superposition and rely on the strengths of both to promote the accuracy. Secondly, to enhance the discriminative power of the learned template model, the spatial regularization is introduced in the learning stage to penalize the objective boundary information corresponding to features in the background. Thirdly, we utilize a discriminative multi-scale estimate method to solve the problem of scale variations. Finally, we research strategies to limit the computational complexity of our tracker. Abundant experiments demonstrate that our tracker performs superiorly against several advanced algorithms on both the OTB2013 and OTB2015 datasets while maintaining the high frame rates.

• 한국게임학회 논문지
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• 제20권6호
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• pp.63-74
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• 2020

최근, 사람 대신 인공지능 에이전트를 이용하여 게임 테스트를 자동화하는 연구가 관심을 모으고 있다. 본 논문은 게임 밸런싱 자동화를 위한 선행 연구로써 사람과 인공지능으로부터 플레이 데이터를 수집하고 이들의 유사도를 분석하고자 한다. 이때, 사람과 유사한 플레이를 할 수 있는 인공지능의 생성을 위해 학습 단계에서 제약사항을 추가하였다. 플레이 데이터는 14명의 사람과 60개의 인공지능을 대상으로 플리피버드 게임을 각각 10회 실시하여 획득하였다. 수집한 데이터는 코사인 유사도 방법으로 이동 궤적, 액션 위치, 죽은 위치를 비교 분석하였다. 분석 결과 사람과의 유사도가 0.9 이상인 인공지능 에이전트를 찾을 수 있었다.

• Korean Journal of Radiology
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• 제25권6호
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• pp.550-558
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• 2024

Hepatocellular carcinoma (HCC) is a biologically heterogeneous tumor characterized by varying degrees of aggressiveness. The current treatment strategy for HCC is predominantly determined by the overall tumor burden, and does not address the diverse prognoses of patients with HCC owing to its heterogeneity. Therefore, the prognostication of HCC using imaging data is crucial for optimizing patient management. Although some radiologic features have been demonstrated to be indicative of the biologic behavior of HCC, traditional radiologic methods for HCC prognostication are based on visually-assessed prognostic findings, and are limited by subjectivity and inter-observer variability. Consequently, artificial intelligence has emerged as a promising method for image-based prognostication of HCC. Unlike traditional radiologic image analysis, artificial intelligence based on radiomics or deep learning utilizes numerous image-derived quantitative features, potentially offering an objective, detailed, and comprehensive analysis of the tumor phenotypes. Artificial intelligence, particularly radiomics has displayed potential in a variety of applications, including the prediction of microvascular invasion, recurrence risk after locoregional treatment, and response to systemic therapy. This review highlights the potential value of artificial intelligence in the prognostication of HCC as well as its limitations and future prospects.

• Clinical Endoscopy
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• 제51권6호
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• pp.547-551
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• 2018

Unlike wired endoscopy, capsule endoscopy requires additional time for a clinical specialist to review the operation and examine the lesions. To reduce the tedious review time and increase the accuracy of medical examinations, various approaches have been reported based on artificial intelligence for computer-aided diagnosis. Recently, deep learning-based approaches have been applied to many possible areas, showing greatly improved performance, especially for image-based recognition and classification. By reviewing recent deep learning-based approaches for clinical applications, we present the current status and future direction of artificial intelligence for capsule endoscopy.

• International Journal of Computer Science & Network Security
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• 제22권11호
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• pp.357-366
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• 2022

Artificial intelligence (AI) is the replication of human intelligence by computer systems and machines using tools like machine learning, deep learning, expert systems, and natural language processing. AI can be applied in administrative settings to automate repetitive processes, analyze and forecast data, foster social communication skills among staff, reduce costs, and boost overall operational effectiveness. In order to understand how AI is being used for administrative duties in various organizations, this paper gives a critical dialogue on the topic and proposed a framework for using artificial intelligence in organizations. Additionally, it offers a list of specifications, attributes, and requirements that organizations planning to use AI should consider.

• International Journal of Computer Science & Network Security
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• 제23권3호
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• pp.144-150
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• 2023

With progressive advancements in Man-made consciousness (computer based intelligence) and Profound Learning (DL), contributing altogether to Normal Language Handling (NLP), the precision and nature of Machine Interpretation (MT) has worked on complex. There is a discussion, but that its no time like the present the human interpretation became immaterial or excess. All things considered, human flaws are consistently dealt with by its own creations. With the utilization of brain networks in machine interpretation, its been as of late guaranteed that keen frameworks can now decipher at standard with human interpreters. In any case, simulated intelligence is as yet not without any trace of issues related with handling of a language, let be the intricacies and complexities common of interpretation. Then, at that point, comes the innate predispositions while planning smart frameworks. How we plan these frameworks relies upon what our identity is, subsequently setting in a one-sided perspective and social encounters. Given the variety of language designs and societies they address, their taking care of by keen machines, even with profound learning abilities, with human proficiency looks exceptionally far-fetched, at any rate, for the time being.