• Journal of Korean Society for Quality Management
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• v.50 no.1
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• pp.43-61
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• 2022

Purpose: The purpose of this study was verifying factors that affect to intention to use AI financial services and finding a way of building an user oriented AI ecology. Methods: This study used the UTAUT (Unified Theory of Acceptance and Use of Technology) model with independent variables such as performance expectancy, effort expectancy, social influence, facilitating conditions, trust, personal innovativeness and AI understanding as moderating variable. The data was collected through online & offline survey with questionnaire from 330 financial customers. Results: As a result, the analysis suggested that the performance expectancy, social influence, facilitating conditions, personal innovativeness are statistically significant to the intention to use AI. It was also found that AI knowledge of users differently influence the intention to use through the moderating effect on the facilitating conditions. Conclusion: Performance expectancy, social influence, facilitating conditions, personal innovativeness have positive causation to the intention to use in AI financial service. On the facilitating conditions, unlike other variables, it was found that the user's intention to use was different by the level of AI understanding. It means that customers could have the strong intention to use AI even though they don't have enough pieces of knowledge on the factors. Customers seem to be of recognition that the technology has certain benefits for themselves. The facilitating factors are significantly affected by AI understanding and differently effect on the intention to use AI.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.330-331
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• 2021

Newly, the fourth industrial revolution is a way of describing the blurring of boundaries between the physical, digital, and biological worlds. It's a fusion of advances in AI (artificial intelligence), robotics, the IoT (internet of things), 3d printing, genetic engineering, quantum computing, and other technologies. At the world economic forum in Davos, switzerland, in january 2016, chairman professor (klaus schwab) proposed the fourth industrial revolution for the first time. In order to apply the AHP (analytic hierarchy process) analysis method, the first stage factors were designed as Digital Technology, Physics Technology and Biological Technology. In addition, the second stage factors were organized into 8 detailed services presented in the conceptual model. Thus, we present the theoretical and practical implications of these results.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.1188-1191
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• 2021

The function of the black box can be combined with a car to check the video through a cloud server, reduce the hassle of checking the video through a memory card, check the black box image in real time through a PC and smartphone, and check the user's Excel, brake operation status, and handle control record at the time of the accident. In addition, the goal was to accurately identify vehicle accidents and simplify accident handling through artificial intelligence object recognition of black box images using cloud services. Measures can be prepared to preserve images even if the black box itself loses, such as fire, flooding, or damage that occurs in an accident. It has been confirmed that the exact situation before and after the accident can be grasped immediately by providing object recognition and log recording functions under actual driving experimental conditions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.357-359
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• 2021

Railway system as a mass transportation is under progress to smart railway system beyond high speed and automation era. Communication network technology including 5G-R(5th Generation - Railway) mobile communication technology and information convergence technology of Big Data, Deep Learnig, AI(Artificial Intelliegnce) and Block Chain have to be used for implementation and operation of this smart railway system. In this paper, a communication network structure is suggested for this smart railway system. This suggested smart railway commnuication network structure is composed with layered structure of plane unit for safety operation of high speed railway, railway system management and customer services, and also have some complexed function of each plane. Results of this study can be used for smart railway communication network implementation, operation and managements, development of railway communication standards.

• Convergence Security Journal
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• v.22 no.2
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• pp.9-19
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• 2022

As the expansion of digital transformation, we are more exposed to the threat of cyber attacks, and many institution or company is operating a signature-based intrusion prevention system at the forefront of the network to prevent the inflow of attacks. However, in order to provide appropriate services to the related ICT system, strict blocking rules cannot be applied, causing many false events and lowering operational efficiency. Therefore, many research projects using artificial intelligence are being performed to improve attack detection accuracy. Most researches were performed using a specific research data set which cannot be seen in real network, so it was impossible to use in the actual system. In this paper, we propose a technique for classifying major attack keywords in the security event log collected from the actual system, assigning a weight to each key keyword, and then performing a similarity check using TF-IDF to determine whether an actual attack has occurred.

• Information Systems Review
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• v.23 no.4
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• pp.69-86
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• 2021

As artificial intelligence technology advances, personalized recommendation systems using big data have attracted huge attention. In the case of beauty products, product preferences are clearly divided depending on customers' skin types and sensitivity along with individual tastes, so it is necessary to provide customized recommendation services based on accumulated customer data. Therefore, by employing deep learning methods, this study proposes a neural network-based recommendation model utilizing both product search history and context information such as gender, skin types and skin worries of customers. The results show that our model with context information outperforms collaborative filtering-based recommender system models using customer search history.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.3
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• pp.403-416
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• 2024

Malware is being commercialized and sold on the black market, primarily driven by financial incentives. With the increasing demand driven by these sales, the scope of attacks via malware has expanded. In response, there has been a surge in research efforts leveraging artificial intelligence for detection and classification. However, adversaries are integrating various anti-analysis techniques into their malware to thwart analytical efforts. In this study, we introduce the "Malware Analysis with Dynamic Extraction (MADE)" framework, a hybrid binary analysis tool devised to procure datasets from advanced malware incorporating Anti-Analysis techniques. The MADE framework has the proficiency to autonomously execute dynamic analysis on binaries, encompassing those laden with Anti-VM and Anti-Debugging defenses. Experimental results substantiate that the MADE framework can effectively circumvent over 90% of diverse malware implementations using Anti-Analysis techniques and can adeptly extract relevant datasets.

• Smart Media Journal
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• v.13 no.6
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• pp.44-53
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• 2024

Companies in modern society are increasingly recognizing sentiment classification as a crucial task, emphasizing the importance of accurately understanding consumer opinions opinions across various platforms such as social media, product reviews, and customer feedback for competitive success. Extensive research is being conducted on sentiment classification as it helps improve products or services by identifying the diverse opinions and emotions of consumers. In sentiment classification, fine-tuning with large-scale datasets and pre-trained language models is essential for enhancing performance. Recent advancements in artificial intelligence have led to high-performing sentiment classification models, with the ELECTRA model standing out due to its efficient learning methods and minimal computing resource requirements. Therefore, this paper proposes a method to enhance sentiment classification performance through efficient fine-tuning of various datasets using the KoELECTRA model, specifically trained for Korean.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.1-9
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• 2024

In this paper, we propose BRAIDS, a boot storm mitigation plan consisting of an AI-based VDI usage prediction system and a virtual machine boot scheduler system, to alleviate boot storms and improve service stability. Virtual Desktop Infrastructure (VDI) is an important technology for improving an organization's work productivity and increasing IT infrastructure efficiency. Boot storms that occur when multiple virtual desktops boot simultaneously cause poor performance and increased latency. Using the xgboost algorithm, existing VDI usage data is used to predict future VDI usage. In addition, it receives the predicted usage as input, defines a boot storm considering the hardware specifications of the VDI server and virtual machine, and provides a schedule to sequentially boot virtual machines to alleviate boot storms. Through the case study, the VDI usage prediction model showed high prediction accuracy and performance improvement, and it was confirmed that the boot storm phenomenon in the virtual desktop environment can be alleviated and IT infrastructure can be utilized efficiently through the virtual machine boot scheduler.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.453-458
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• 2024

Optical Character Recognition is the technology that recognizes text in images and converts them into digital format. Deep learning-based OCR is being used in many industries with large quantities of recorded data due to its high recognition performance. To improve medical services, deep learning-based OCR was actively introduced by the medical industry. In this paper, we discussed trends in OCR engines and medical OCR and provided a roadmap for development of medical OCR. By using natural language processing on detected text data, current medical OCR has improved its recognition performance. However, there are limits to the recognition performance, especially for non-standard handwriting and modified text. To develop advanced medical OCR, databaseization of medical data, image pre-processing, and natural language processing are necessary.