• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.474-477
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• 2021

Recently, in order to prevent the proliferation of COVID-19, which began in earnest in 2020, an increasing number of places have been measuring the temperature and required to wear a mask. However, as wearing a mask and checking the temperature are typically measured directly by a person or by a single individual positioned in front of the machine, standards may vary based on the person's manual measurement method, wasting workforce. While standing in front of a device often measures the maximum temperature of the face, the standard of fever is also unclear. Both approaches can create bottleneck situations when checking large numbers of people. Furthermore, it is unable to conduct periodic measurements and tracking because the measuring machines are generally put only at the entrance. Thus, this study suggests a method for preventing the spread of infectious diseases by automatically identifying and displaying unmasked people and those with fever in real-time using a general camera, a thermal imaging camera, and an artificial intelligence algorithm.

• Journal of the HCI Society of Korea
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• v.14 no.2
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• pp.61-69
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• 2019

Recently, as a new ICT trend, emerging chatbots are actively introduced in the field of finance. Chatbot conducts services through the interaction of communication with users. The purpose of this study is to investigate the effect of interaction dialogue type on the efficiency, usability, sensibility and perceived security of financial service chatbot. Based on theoretical considerations, I have divided into closed conversation, open conversation, and mixed conversation type based on the conversation style based on the implementation method of chatbot. Three types of Financial Chatbot prototypes were made and the experiments were conducted after account inquiry, account transfer, Q & A financial task execution. As a result of experimental research analysis, chatbot's interaction dialogue type was found to affect efficiency and usability. Users have shown that the interaction of closed conversations and mixed conversations is an intuitive interface that allows financial services to be easily manipulated without error. This study will be used as a resource to improve the user experience that requires deep understanding of financial chatbot users who should consider both the emotional element of artificial intelligence that provides services through natural conversation and the functional elements that perform financial business can be.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.867-871
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• 2022

Amid the recent rapid trend change, the change in design has a great impact on the sales of fashion companies, so it is inevitable to be careful in choosing new designs. With the recent development of the artificial intelligence field, various machine learning is being used a lot in the fashion market to increase consumers' preferences. To contribute to increasing reliability in the development of new products by quantifying abstract concepts such as preferences, we generate new images that do not exist through three adversarial generative neural networks (GANs) and numerically compare abstract concepts of preferences using pre-trained convolution neural networks (CNNs). Deep convolutional generative adversarial networks (DCGAN), Progressive growing adversarial networks (PGGAN), and Dual Discriminator generative adversarial networks (DANs), which were trained to produce comparative, high-level, and high-level images. The degree of similarity measured was considered as a preference, and the experimental results showed that D2GAN showed a relatively high similarity compared to DCGAN and PGGAN.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.453-455
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• 2022

Metadata is data that explains attributes and features to the data as structured data. Among them, video metadata refers to data extracted from information constituting the video for accurate content-based search. Recently, as the number of users using video content increases, the number of OTT providers is also increasing, and the role of metadata is becoming more important for OTT providers to recommend a large amount of video content to individual users or to search appropriately. In this paper, a study was conducted on a method of automatically extracting metadata for mood attributes through sound effects of images. In order to classify the sound effect of the video and generate metadata about the attributes of the mood, I would like to propose a method of establishing a terminology dictionary for the mood and extracting information through supervised learning.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.307-332
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• 2022

One of the most intensively conducted research areas in business application study is a bankruptcy prediction model, a representative classification problem related to loan lending, investment decision making, and profitability to financial institutions. Many research demonstrated outstanding performance for bankruptcy prediction models using artificial intelligence techniques. However, since most machine learning algorithms are "black-box," AI has been identified as a prominent research topic for providing users with an explanation. Although there are many different approaches for explanations, this study focuses on explaining a bankruptcy prediction model using a counterfactual example. Users can obtain desired output from the model by using a counterfactual-based explanation, which provides an alternative case. This study introduces a counterfactual generation technique based on a genetic algorithm (GA) that leverages both domain knowledge (i.e., causal feasibility) and feature importance from a black-box model along with other critical counterfactual variables, including proximity, distribution, and sparsity. The proposed method was evaluated quantitatively and qualitatively to measure the quality and the validity.

• Journal of Biomedical Engineering Research
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• v.11 no.2
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• pp.305-314
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• 1990

In this paper we have implemented and tested TPN which is system to supply sufficent nutrition to nutritionally deficient patient by means of ES (expert system) a kind of A.1 (artificial intelligence) . This system affords to evaluation of nutritional state of patient which is essential to physi- cian. who performs TPN, decision of performing TPN and management of patient-data & calculation of information needing to making TPN fluid. The features were as follolv 1. we input data, take ideal weight of patient and 24hr's creatlnln In urine according to chart in system compare TSF (triceps skin fold), MAC (mid-arm circumference), AMC (arm muscle circumference) to 5th, 15th, 50th percentile and evaluate the nutritional state of patient. 2. Calculation of protein & nonprotein calorie needing to treament of patient can be made exactly by stress factor, activity factor and body temperature. 3. patient's personal recording needing to management of patient date name of chief doc- tor, name of department of admission, chart number, history can by taken very easily. 4. The way of system operating is pull-down Menu one, It can be processing very efficiently. 5. Date processing in system, we can manage memory volume of computer verlr efficiently using of dynamic allocation variables. 6. We can make it very easy to edit & revise the input data, processed data is saved to diskette in 2 files (TDF, THF) , these are semipermanent preservation.

• Smart Media Journal
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• v.8 no.3
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• pp.95-103
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• 2019

The global manufacturing industry has reached the limit to growth due to a long-term recession, the rise of labor cost and raw material. As a solution to these difficulties, we promote the 4th Industry Revolution based on ICT and sensor technology. Following this trend, this paper proposes the design of a model using manufacturing data in the protection film process for smart manufacturing innovation. In the protective film process, the manufacturing data of temperature, pressure, humidity, and motion and thermal image are acquired by various sensors for the raw material blending, stirring, extrusion, and inspection processes. While the acquired manufacturing data is stored in mass storage, A.I. platform provides time-series image analysis and its visualization.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.1
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• pp.55-64
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• 2022

This study analyzes the success factors of OTT original content through big data, and intends to suggest scenarios, casting, fun, and moving elements when producing the next work. In addition, I would like to offer suggestions for the success of 'Squid Game Season 2'. The success factor of 'Squid Game' through big data is first, it is a simple psychological experimental game. Second, it is a retro strategy. Third, modern visual beauty and color. Fourth, it is simple aesthetics. Fifth, it is the platform of OTT Netflix. Sixth, Netflix's video recommendation algorithm. Seventh, it induced Binge-Watch. Lastly, it can be said that the consensus was high as it was related to the time to think about 'death' and 'money' in a pandemic situation. The suggestions for 'Squid Game Season 2' are as follows. First, it is a fusion of famous traditional games of each country. Second, it is an AI-based planned MD product production and sales strategy. Third, it is casting based on artificial intelligence big data. Fourth, secondary copyright and copyright sales strategy. The limitations of this study were analyzed only through external data. Data inside the Netflix platform was not utilized. In this study, if AI big data is used not only in the OTT field but also in entertainment and film companies, it will be possible to discover better business models and generate stable profits.

• The Journal of Bigdata
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• v.7 no.1
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• pp.49-61
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• 2022

In sports, the proportion of data analysis in team management such as team strategy planning and marketing is increasing. In KBO(Korea Baseball Organization) league, in particular, plans such as recruiting players and fostering players are established to devise team strategies for the next year, such as FA and trade, at the end of a season. For these reasons, it is very important to predict players' performance for the next year. In this study, the target was limited to only the batter and tried to find out how to predict whether the performance of the next year will improve. As a standard record for rising and falling, OPS(On-Base Plus Slugging), which is easy to calculate and has a high relationship with team score, was used. In this study, 40 years of regular season data from 1982 to 2021 were used as data, and 11 machine learning classification models were used as experimental methods. Predicting the rise and fall of OPS, RBF SVM, Neural Net, Gaussian Process, and AdaBoost were more accurate than other classification models, and age did not significantly affect accuracy.

• Education of Primary School Mathematics
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• v.26 no.2
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• pp.99-113
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• 2023

In this study, I explored the possibility of using ChatGPT math education. For this purpose, students' problem-solving outputs and conversation data between pre-service teachers and a student were selected as an analysis case. A case was analyzed using ChatGPT and compared with the results of mathematics education experts. The results that ChatGPT analyzed students' problem-solving strategies and mathematical thinking skills were similar to those of math education experts. ChatGPT was able to analyze teacher questions with evaluation criteria, and the results were similar to those of math education experts. ChatGPT could also respond with mathematical theory as a source of evaluation criteria. These results demonstrate the potential of ChatGPT to analyze students' thinking and teachers' practice in mathematics education. However, there are limitations in properly applying the evaluation criteria or providing inaccurate information, so the further review of the derived information is required.