• 한국수학교육학회지시리즈A:수학교육
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• 제63권2호
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• pp.369-385
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• 2024

디지털 기술의 발전과 함께 교육에서도 다양한 디지털 자료가 활용되고 있다. 교사가 디지털 자료를 적절히 사용하기 위해서는 먼저 해당 자료가 수업에 적합한지 판단하고 그 질을 평가할 수 있어야 한다. 본 연구는 예비교사들이 인공지능 기반 수학 수업 지원시스템인 똑똑! 수학탐험대를 어떻게 평가하는지 탐색하였다. 선행연구를 기반으로 개발된 평가틀을 바탕으로 똑똑! 수학탐험대에 대해서 콘텐츠의 질, 수학 교수, 기술 사용, 수학교육과정과의 부합성을 평가하였다. 연구결과, 예비교사들은 똑똑! 수학탐험대를 전반적으로 긍정적으로 평가하였다. 예비교사들의 평가 경향은 세 집단으로 분류되었으며, 준거별 구체적인 특징은 집단에 따라 다르게 나타났다. 연구 결과를 바탕으로 수학교육에서 디지털 기술 및 인공지능 사용에 대한 예비교사의 평가 능력을 개선하기 위한 시사점을 제안하였다.

• 한국콘텐츠학회논문지
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• 제19권3호
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• pp.138-154
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• 2019

본 연구에서는 미디어의 이용과 충족 이론을 토대로 소비자의 AI 스피커의 사용 행태를 탐색하였다. 이를 위해 AI 스피커 사용 경험이 있는 국내 소비자 330명을 대상으로 AI 스피커의 속성 인식과 이용의 동기적 요인을 도출하였다. 연구의 결과, 사용자의 AI 스피커 이용 동기 요인은 사회성 유지 및 현실 일탈, 정보습득 및 학습, 유희와 휴식, 유용성 추구의 4개 하위 차원으로 구성되는 것으로 나타났다. 또한, AI 스피커 사용자의 연령대는 주로 30대이며, 혁신적 성향이 높은 사용자로 확인되었다. 아울러, AI 스피커 사용 동기요인 중 사회성 유지 및 현실 일탈 요인은 성별과 연령에 따라서, 그리고 정보 습득 및 학습, 유희와 휴식, 유용성 추구 요인은 연령과 혁신성 수준에 따라 유의한 차이가 있었다. 본 연구는 AI 스피커의 특성을 반영한 사용자 이용 동기를 도출함으로써 향후 AI 스피커를 활용한 전략적 콘텐츠 운용 및 서비스 제공에 도움이 될 수 있다는 실무적 시사점을 갖는다.

• International Journal of Internet, Broadcasting and Communication
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• 제11권2호
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• pp.59-66
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• 2019

The emergence of new IT technologies and convergence industries, such as artificial intelligence, bigdata and the Internet of Things, is another chance for South Korea, which has established itself as one of the world's top IT powerhouses. On the other hand, however, privacy concerns that may arise in the process of using such technologies raise the task of harmonizing the development of new industries and the protection of personal information at the same time. In response, the government clearly presented the criteria for deidentifiable measures of personal information and the scope of use of deidentifiable information needed to ensure that bigdata can be safely utilized within the framework of the current Personal Information Protection Act. It strives to promote corporate investment and industrial development by removing them and to ensure that the protection of the people's personal information and human rights is not neglected. This study discusses the strategy of deidentifying personal information protection based on the analysis of fake news. Using the strategies derived from this study, it is assumed that deidentification information that is appropriate for deidentification measures is not personal information and can therefore be used for analysis of big data. By doing so, deidentification information can be safely utilized and managed through administrative and technical safeguards to prevent re-identification, considering the possibility of re-identification due to technology development and data growth.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• 제30권4호
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• pp.145-152
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• 2019

Objectives: The detection of autism spectrum disorder (ASD) is based on behavioral observations. To build a more objective datadriven method for screening and diagnosing ASD, many studies have attempted to incorporate artificial intelligence (AI) technologies. Therefore, the purpose of this literature review is to summarize the studies that used AI in the assessment process and examine whether other behavioral data could potentially be used to distinguish ASD characteristics. Methods: Based on our search and exclusion criteria, we reviewed 13 studies. Results: To improve the accuracy of outcomes, AI algorithms have been used to identify items in assessment instruments that are most predictive of ASD. Creating a smaller subset and therefore reducing the lengthy evaluation process, studies have tested the efficiency of identifying individuals with ASD from those without. Other studies have examined the feasibility of using other behavioral observational features as potential supportive data. Conclusion: While previous studies have shown high accuracy, sensitivity, and specificity in classifying ASD and non-ASD individuals, there remain many challenges regarding feasibility in the real-world that need to be resolved before AI methods can be fully integrated into the healthcare system as clinical decision support systems.

• International Journal of Computer Science & Network Security
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• 제24권1호
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• pp.9-16
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• 2024

AI or Artificial Intelligence has been a significant tool used in the organisational backgrounds for an effective improvement in the management methods. The processing of the information and the analysis of the data for the further achievement of heightened efficiency can be performed by AI through its data analytics measures. In the medical field, AI has been integrated for an improvement within the management of the medical services and to note a rise in the levels of customer satisfaction. With the benefits of reasoning and problem solving, AI has been able to initiate a range of benefits for both the consumers and the medical personnel. The main benefits which have been noted in the integration of AI would be integrated into the study. The issues which are noted with the integrated AI usage for the medical sector would also be identified in the study. Medical Image Processing has been seen to integrate 3D image datasets with the medical industry, in terms of Computed Tomography (CT) or Magnetic Resonance Imaging (MRI). The usage of such medical devices have occurred in the diagnosis of the patients, the development of guidance towards medical intervention and an overall increase in the medical efficiency. The study would focus on such different tools, adhered with AI for increased medical improvement.

• Asian Journal of Innovation and Policy
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• 제12권2호
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• pp.261-267
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• 2023

In the paper of 'Natural Selection Favors AIs over Humans,' Dan Hendrycks applies principles of Darwinian evolution to forecast potential trajectories of AI development. He proposes that competitive pressures within corporate and military realms could lead to AI replacing human roles and exhibiting self-interested behaviors. However, such claims carry the risk of oversimplifying the complex issues of competition and natural selection without clear criteria for judging whether AI is selfish or altruistic, necessitating a more in-depth analysis and critique. Other studies, such as ''The Threat of AI and Our Response: The AI Charter of Ethics in South Korea,' offer diverse opinions on the natural selection of artificial intelligence, examining major threats that may arise from AI, including AI's value judgment and malicious use, and emphasizing the need for immediate discussions on social solutions. Such contemplation is not merely a technical issue but also significant from an ethical standpoint, requiring thoughtful consideration of how the development of AI harmonizes with human welfare and values. It is also essential to emphasize the importance of cooperation between artificial intelligence and humans. Hendrycks's work, while speculative, is supported by historical observations of inevitable evolution given the right conditions, and it prompts deep contemplation of these issues, setting the stage for future research focused on AI safety, regulation, and ethical considerations.

• International Journal of Computer Science & Network Security
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• 제23권10호
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• pp.49-56
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• 2023

Computer architecture serves as a link between application requirements and underlying technology capabilities such as technical, mathematical, medical, and business applications' computational and storage demands are constantly increasing. Machine learning these days grown and used in many fields and it performed better than traditional computing in applications that need to be implemented by using mathematical algorithms. A mathematical algorithm requires more extensive and quicker calculations, higher computer architecture specification, and takes longer execution time. Therefore, there is a need to improve the use of computer hardware such as CPU, memory, etc. optimization has a main role to reduce the execution time and improve the utilization of computer recourses. And for the importance of execution time in implementing machine learning supervised module linear regression, in this paper we focus on optimizing machine learning algorithms, for this purpose we write a (Diabetes prediction program) and applying on it a Practical Swarm Optimization (PSO) to reduce the execution time and improve the utilization of computer resources. Finally, a massive improvement in execution time were observed.

• 한국군사과학기술학회지
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• 제27권3호
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• pp.319-328
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• 2024

Semi-supervised learning is a good way to train a classification model using a small number of labeled and large number of unlabeled data. We applied semi-supervised learning to a synthetic aperture radar(SAR) image classification model with a limited number of datasets that are difficult to create. To address the previous difficulties, semi-supervised learning uses a model trained with a small amount of labeled data to generate and learn pseudo labels. Besides, a lot of number of papers use a single fixed threshold to create pseudo labels. In this paper, we present a semi-supervised synthetic aperture radar(SAR) image classification method that applies different thresholds for each class instead of all classes sharing a fixed threshold to improve SAR classification performance with a small number of labeled datasets.

• 한국IT서비스학회지
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• 제20권4호
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• pp.81-93
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• 2021

In the era of artificial intelligence, the use of 'artificial intelligence-based services' has been diversified by combining various smart devices, big data, and voice recognition technology with artificial intelligence. From the perspective of IT services, these services are important technology that cause a paradigm shift from display-centered to voice-centered, and from passive to active IT-based services. This study seeks to find a solution to the current situation where AI assistant service is still in its beginning stage, despite having been ten years since its release and having a growing number of consumer touch points. Accordingly, we categorized the functions of AI assistant services and identified the degree of importance and satisfaction of services recognized by actual users. In order to define the 'ideal' services of AI assistant, seven experts from AI assistant-related industry have participated in the interview. Based on this result, we investigated the importance and satisfaction of services perceived by actual users of AI assistant services. As a result of IPA (Importance Performance Analysis). we find out which services are potentially 'keep', 'concentrate', 'low priority', or 'overkill' and provide various implications from the findings.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2022년도 추계학술발표대회
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• pp.481-483
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• 2022

With the advent of the era of artificial intelligence, more and more fields have begun to use artificial intelligence technology, especially the medical field. Cancer is one of the biggest problems in the medical field. [1] If it can be detected early and treated early, the possibility of cure will be greatly increased. Malignant skin cancer, as one of the types of cancer with the highest fatality rate in recent years has problems such as relying on the experience of doctors and being unable to be detected and detected in time. Therefore, if artificial intelligence technology can be used to help doctors in early detection of skin cancer, or to allow everyone to detect skin lesions or spots anytime, anywhere, it will have great practical significance. In this paper we used attention residual learning convolutional neural network (ARL-CNN) model [2] to classify skin cancer pictures.