• Convergence Security Journal
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• v.23 no.5
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• pp.117-123
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• 2023

Recently, as the demand for Generative Artificial Intelligence (AI) and artificial intelligence has increased, the seriousness of misuse and abuse has emerged. However, intelligent CCTV, which maximizes detection of abnormal behavior, is of great help to prevent crime in the military and police. AI performs learning as taught by humans and then proceeds with self-learning. Since AI makes judgments according to the learned results, it is necessary to clearly understand the characteristics of learning. However, it is often difficult to visually judge strange and abnormal behaviors that are ambiguous even for humans to judge. It is very difficult to learn this with the eyes of artificial intelligence, and the result of learning is very many False Positive, False Negative, and True Negative. In response, this paper presented standards and methods for clarifying the learning of AI's strange and abnormal behaviors, and presented learning measures to maximize the judgment ability of intelligent CCTV's False Positive, False Negative, and True Negative. Through this paper, it is expected that the artificial intelligence engine performance of intelligent CCTV currently in use can be maximized, and the ratio of False Positive and False Negative can be minimized..

• Communications of Mathematical Education
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• v.35 no.4
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• pp.445-473
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• 2021

The purpose of this study is to derive implications for the design of the next curriculum by analyzing the textbooks designed as a new subject in the 2015 revised curriculum. In the mathematics curriculum documents of , 'related learning elements' are presented instead of 'learning elements'. 'Related learning elements' are defined as mathematical concepts or principles that can be used in the context of artificial intelligence, but there are no specific restrictions on the amount and scope of dealing with 'related learning elements'. Accordingly, the aspects of 'related learning elements' reflected in the textbooks were analyzed focusing on the textbook format, the amount and scope of contents, and the ways of using technological tools. There were differences in the format of describing 'related learning elements' in the textbook by textbook and the amount and scope of handling mathematics concepts. Although similar technological tools were dealt with in each textbook so that 'related learning elements' could be used in the context of artificial intelligence, the focus was on computations and interpretation of results. In order to fully reflect the intention of the curriculum in textbooks, a systematic discussion on 'related learning elements' will be necessary. Additionally, in order for students to experience the use of mathematics in artificial intelligence, substantialized activities that can set and solve problems using technological tools should be included in textbooks.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.3
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• pp.233-244
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• 2023

Although artificial intelligence and machine learning technologies have been used in various fields, problems with personal information protection have arisen based on centralized data collection and processing. Federated learning has been proposed to solve this problem. Federated learning is a process in which clients who own data in a distributed data environment learn a model using their own data and collectively create an artificial intelligence model by centrally collecting learning results. Unlike the centralized method, Federated learning has the advantage of not having to send the client's data to the central server. In this paper, we quantitatively present the performance improvement when federated learning is applied using the building change detection learning data. As a result, it has been confirmed that the performance when federated learning was applied was about 29% higher on average than the performance when it was not applied. As a future work, we plan to propose a method that can effectively reduce the number of federated learning rounds to improve the convergence time of federated learning.

• East Asian mathematical journal
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• v.39 no.2
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• pp.229-250
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• 2023

The purpose of this study is to investigate the impact of project learning using chatbots on artificial intelligence literacy. The subjects of the study were a total of 41 students from 1st to 3rd grade of general high school in Gyeonggi-do. Classes were held after school for a total of 6 hours, and the contents of the classes consisted of the concept and characteristics of artificial intelligence, the concept and expression of knowledge, OBT application for Kakao i open builder, guidance on how to create chatbots, and chatbot production practice. As a result of the pre- and post-test of the experimental group, the quantitative value of artificial intelligence literacy increased in all three grades. In the case of second-year students who set up a comparison group, when compared with the results of the comparison group, there was a significant positive effect on the AI literacy result, and female students were found to be more effective than male students.

• Journal of the Korean School Mathematics Society
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• v.23 no.2
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• pp.223-237
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• 2020

Artificial intelligence technology, which represents the era of the 4th Industrial Revolution, is now deeply involved in our lives, and future education places great emphasis on building students' capabilities for the principles and uses of artificial intelligence. Therefore, the purpose of this study is to develop the contents of AI related education in mathematics, which the relationship is closely connected to each other. To this end, I propose establishing two novel AI-related contents in mathematics education. One subject is related to learning the principle of machine learning based on mathematics foundation. In addition, I draw the core math contents dealt in following subject called 'Basic Mathematics for AI and Data Science.'

• Journal of Korea Game Society
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• v.20 no.6
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• pp.63-74
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• 2020

Recently, research on automating game tests using artificial intelligence agents instead of humans is attracting attention. This paper aims to collect play data from human and artificial intelligence and analyze their similarity as a preliminary study for game balancing automation. At this time, constraints were added at the learning stage in order to create artificial intelligence that can play similar to humans. Play datas obtained 14 people and 60 artificial intelligence by playing Flippy bird games 10 times each. The collected datas compared and analyzed for movement trajectory, action position, and dead position using the cosine similarity method. As a result of the analysis, an artificial intelligence agent with a similarity of 0.9 or more with humans was found.

• Korean Journal of Radiology
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• v.25 no.6
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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.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.213-221
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• 2021

With the advent of the 4th industrial revolution, the need for artificial intelligence education has increased. The online learning environment caused by COVID-19 made it possible to use variety of artificial intelligence platforms. In this study, an aritificial intelligence class plan was developed and proposed to achieve the goal of artificial intelligence education using an AI platform. The AI platform used is AI for Oceans, With the theme of creating a program for the environment, designed a 6-hour project class using Novel Engineering-based on STEAM model. Students experience AI for Oceans enough time and learn supervised learning by experience. Based on understanding of supervised learning, students design their own programs for the environment using Entry's AI blocks. In this study, for AI convergence education, this lesson was developed and presented with the goal of acquiring the creative problem solving ability and integrated thinking ability by using the principles of artificial intelligence to solve problems.

• Journal of Information Technology Services
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• v.15 no.4
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• pp.85-95
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• 2016

Recently, deep learning and machine learning have attracted considerable attention and many supporting frameworks appeared. In artificial intelligence field, a large body of research is underway to apply the relevant knowledge for complex problem-solving, necessitating the application of various learning algorithms and training methods to artificial intelligence systems. In addition, there is a dearth of performance evaluation of decision making agents. The decision making agent that can find optimal solutions by using reinforcement learning methods designed through this research can collect raw pixel data observed from dynamic environments and make decisions by itself based on the data. The decision making agent uses convolutional neural networks to classify situations it confronts, and the data observed from the environment undergoes preprocessing before being used. This research represents how the convolutional neural networks and the decision making agent are configured, analyzes learning performance through a value-based algorithm and a policy-based algorithm : a Deep Q-Networks and a Policy Gradient, sets forth their differences and demonstrates how the convolutional neural networks affect entire learning performance when using pixel data. This research is expected to contribute to the improvement of artificial intelligence systems which can efficiently find optimal solutions by using features extracted from raw pixel data.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.33 no.3
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• pp.142-155
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• 2022

Diagnosis using voice is non-invasive and can be implemented through various voice recording devices; therefore, it can be used as a screening or diagnostic assistant tool for laryngeal voice disease to help clinicians. The development of artificial intelligence algorithms, such as machine learning, led by the latest deep learning technology, began with a binary classification that distinguishes normal and pathological voices; consequently, it has contributed in improving the accuracy of multi-classification to classify various types of pathological voices. However, no conclusions that can be applied in the clinical field have yet been achieved. Most studies on pathological speech classification using speech have used the continuous short vowel /ah/, which is relatively easier than using continuous or running speech. However, continuous speech has the potential to derive more accurate results as additional information can be obtained from the change in the voice signal over time. In this review, explanations of terms related to artificial intelligence research, and the latest trends in machine learning and deep learning algorithms are reviewed; furthermore, the latest research results and limitations are introduced to provide future directions for researchers.