• The Journal of Korean Academic Society of Nursing Education
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• v.29 no.3
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• pp.234-246
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• 2023

Purpose: This study aimed to develop a case-based nursing education program using generative artificial intelligence and to assess its usability and applicability in nursing curriculums. Methods: The program was developed by following the five steps of the ADDIE model: analysis, design, development, implementation, and evaluation. A panel of five nursing professors served as experts to implement and evaluate the program. Results: Utilizing ChatGPT, six program modules were designed and developed based on experiential learning theory. The experts' evaluations confirmed that the program was suitable for case-based learning, highly usable, and applicable to nursing education. Conclusion: Generative artificial intelligence was identified as a valuable tool for enhancing the effectiveness of case-based learning. This study provides insights and future directions for integrating generative artificial intelligence into nursing education. Further research should be attempted to implement and evaluate this program with nursing students.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.1-14
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• 2024

While text-to-image models have made remarkable progress in image synthesis, certain models, particularly generative diffusion models, have exhibited a noticeable bias to- wards generating images related to the culture of some developing countries. This paper introduces an empirical investigation aimed at mitigating the bias of image generative model. We achieve this by incorporating symbols representing Saudi culture into a stable diffusion model using the Dreambooth technique. CLIP score metric is used to assess the outcomes in this study. This paper also explores the impact of varying parameters for instance the quantity of training images and the learning rate. The findings reveal a substantial reduction in bias-related concerns and propose an innovative metric for evaluating cultural relevance.

• Korean Journal of Radiology
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• v.24 no.8
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• pp.715-718
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• 2023

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.2
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• pp.301-321
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• 2024

Generative artificial intelligence is currently developing rapidly and expanding industrially. The development of generative AI is expected to improve productivity in most industries. However, there is a probability for exploitation of generative AI, and cases that actually lead to crime are emerging. Compared to the fast-growing AI, there is no legislation to regulate the generative AI. In the case of Korea, the crimes and risks related to generative AI has not been clearly classified for legislation. In addition, research on the responsibility for illegal data learned by generative AI or the illegality of the generated data is insufficient in existing research. Therefore, this study attempted to classify crimes related to generative AI for domestic legislation into generative AI for target crimes, generative AI for tool crimes, and other crimes based on ECRM. Furthermore, it suggests technical countermeasures against crime and risk and measures to improve the legal system. This study is significant in that it provides realistic methods by presenting technical countermeasures based on the development stage of AI.

• Korean Journal of Radiology
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• v.25 no.3
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• pp.224-242
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• 2024

The emergence of Chat Generative Pre-trained Transformer (ChatGPT), a chatbot developed by OpenAI, has garnered interest in the application of generative artificial intelligence (AI) models in the medical field. This review summarizes different generative AI models and their potential applications in the field of medicine and explores the evolving landscape of Generative Adversarial Networks and diffusion models since the introduction of generative AI models. These models have made valuable contributions to the field of radiology. Furthermore, this review also explores the significance of synthetic data in addressing privacy concerns and augmenting data diversity and quality within the medical domain, in addition to emphasizing the role of inversion in the investigation of generative models and outlining an approach to replicate this process. We provide an overview of Large Language Models, such as GPTs and bidirectional encoder representations (BERTs), that focus on prominent representatives and discuss recent initiatives involving language-vision models in radiology, including innovative large language and vision assistant for biomedicine (LLaVa-Med), to illustrate their practical application. This comprehensive review offers insights into the wide-ranging applications of generative AI models in clinical research and emphasizes their transformative potential.

• Korean Journal of Radiology
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• v.25 no.5
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• pp.493-498
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• 2024

• ETRI Journal
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• v.44 no.6
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• pp.1004-1019
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• 2022

For autonomous driving without high-definition maps, we present a model capable of generating multiple plausible paths from egocentric images for autonomous vehicles. Our generative model comprises two neural networks: feature extraction network (FEN) and path generation network (PGN). The FEN extracts meaningful features from an egocentric image, whereas the PGN generates multiple paths from the features, given a driving intention and speed. To ensure that the paths generated are plausible and consistent with the intention, we introduce an attentive discriminator and train it with the PGN under a generative adversarial network framework. Furthermore, we devise an interaction model between the positions in the paths and the intentions hidden in the positions and design a novel PGN architecture that reflects the interaction model for improving the accuracy and diversity of the generated paths. Finally, we introduce ETRIDriving, a dataset for autonomous driving, in which the recorded sensor data are labeled with discrete high-level driving actions, and demonstrate the state-of-the-art performance of the proposed model on ETRIDriving in terms of accuracy and diversity.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.1
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• pp.7-10
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• 2023

This study proposes an AI-based shoe recommendation model based on user clothing image data to solve the problem of the global fashion industry, which is worsening due to factors such as the economic downturn. Shoes are an important part of modern fashion, and this research aims to improve user satisfaction and contribute to economic growth through a generative AI-based shoe recommendation service. By utilizing generative AI in the personalized consumer market, we show the feasibility, efficiency, and improvements through an accessible web-based implementation. In conclusion, this study provides insights to help fulfill consumer needs in the ever-changing fashion market by implementing a generative AI-based shoe recommendation model.

• International Journal of High-Rise Buildings
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• v.12 no.3
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• pp.203-208
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• 2023

The implementation of artificial intelligence (AI) design for tall building structures is an essential solution for addressing critical challenges in the current structural design industry. Generative AI technology is a crucial technical aid because it can acquire knowledge of design principles from multiple sources, such as architectural and structural design data, empirical knowledge, and mechanical principles. This paper presents a set of AI design techniques for building structures based on two types of generative AI: generative adversarial networks and graph neural networks. Specifically, these techniques effectively master the design of vertical and horizontal component layouts as well as the cross-sectional size of components in reinforced concrete shear walls and frame structures of tall buildings. Consequently, these approaches enable the development of high-quality and high-efficiency AI designs for building structures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4625-4647
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• 2020

The Generative Adversarial Networks, as one of the most creative deep learning models in recent years, has achieved great success in computer vision and natural language processing. It uses the game theory to generate the best sample in generator and discriminator. Recently, many deep learning models have been applied to the security field. Along with the idea of "generative" and "adversarial", researchers are trying to apply Generative Adversarial Networks to the security field. This paper presents the development of Generative Adversarial Networks. We review traditional generation models and typical Generative Adversarial Networks models, analyze the application of their models in natural language processing and computer vision. To emphasize that Generative Adversarial Networks models are feasible to be used in security, we separately review the contributions that their defenses in information security, cyber security and artificial intelligence security. Finally, drawing on the reviewed literature, we provide a broader outlook of this research direction.