• Imaging Science in Dentistry
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• v.53 no.2
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• pp.175-176
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• 2023

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.403-404
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• 2023

In an era where everything is digitalized using AI(Artificial Intelligence), such as the ChatGPT craze, the evacuation guidance system still uses an analog and fixed method, so there is a limit to quick response in case of fire. In order to overcome this, we introduce a digitally transformed evacuation guidance system using AI and discuss its effectiveness.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.890-891
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• 2023

해당 프로젝트 주제는 AR 여행 기록 다이어리로 사용자 다이어리 내용에 감정 분석을 적용해 이모지를 달력에 표시한다. 네이버 클라우드 Sentiment와 ChatGPT를 활용하며, 다양한 감정 범주를 고려한 정확도 향상을 추구하고 입력 데이터에 따라 긍/부정 분석을 수행한다. 중립과 놀람은 모호함으로 분리하여 감정 분석을 실시한다.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.173-180
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• 2024

Along with its importance, the real estate market poses risks of various fraudulent activities. Recently, a surge in real estate-related scams, such as lease fraud, has caused great financial damage to many ordinary people. These problems are often caused by the complexity of real estate transactions and information imbalance. Therefore, there is an urgent need to secure reliability and improve transparency in the transaction process. In this paper, to solve this real estate fraud problem, we propose a chatbot system using digital technology and artificial intelligence, especially GPT (Generative Pre-Trained Transformer). This system serves to protect users from fraud by providing them with precautions and confirmations in the lease transaction process. In addition, GPT-based chatbots respond to questions from users in time, contributing to reducing uncertainty in the transaction process and increasing reliability.

• Education of Primary School Mathematics
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• v.27 no.3
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• pp.303-320
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• 2024

This study presents the development and performance evaluation of a custom GPT-based chatbot tailored to provide solutions following Polya's problem-solving stages. A beta version of the chatbot was initially deployed to assess its mathematical capabilities, followed by iterative error identification and correction, leading to the final version. The completed chatbot demonstrated an accuracy rate of approximately 89.0%, correctly solving an average of 57.8 out of 65 image-based problems from a 6th-grade elementary mathematics textbook, reflecting a 4 percentage point improvement over the beta version. For a subset of 50 problems, where images were not critical for problem resolution, the chatbot achieved an accuracy rate of approximately 91.0%, solving an average of 45.5 problems correctly. Predominant errors included problem recognition issues, particularly with complex or poorly recognizable images, along with concept confusion and comprehension errors. The custom chatbot exhibited superior mathematical performance compared to the general-purpose ChatGPT. Additionally, its solution process can be adapted to various grade levels, facilitating personalized student instruction. The ease of chatbot creation and customization underscores its potential for diverse applications in mathematics education, such as individualized teacher support and personalized student guidance.

• Journal of the Korean Society for information Management
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• v.40 no.3
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• pp.77-98
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• 2023

Large Language Models (LLMs) are becoming the major trend in the natural language processing field. These models were built based on research data, but information such as types, limitations, and risks of using research data are unknown. This research would present how to analyze and evaluate the LLMs that were built with research data: LLaMA or LLaMA base models such as Alpaca of Stanford, Vicuna of the large model systems organization, and ChatGPT from OpenAI from the perspective of research data. This quality evaluation focuses on the validity, functionality, and reliability of Data Quality Management (DQM). Furthermore, we adopted the Holistic Evaluation of Language Models (HELM) to understand its evaluation criteria and then discussed its limitations. This study presents quality evaluation criteria for LLMs using research data and future development directions.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.31-39
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• 2023

The evolution of Natural Language Processing (NLP) and the rise of large language models (LLM) like ChatGPT have paved the way for specialized question-answering (QA) systems tailored to specific domains. This study outlines a system harnessing the power of LLM in conjunction with document search algorithms to interpret and address user inquiries using documents from the Korea Atomic Energy Research Institute (KAERI). Initially, the system refines multiple documents for optimized search and analysis, breaking the content into managable paragraphs suitable for the language model's processing. Each paragraph's content is converted into a vector via an embedding model and archived in a database. Upon receiving a user query, the system matches the extracted vectors from the question with the stored vectors, pinpointing the most pertinent content. The chosen paragraphs, combined with the user's query, are then processed by the language generation model to formulate a response. Tests encompassing a spectrum of questions verified the system's proficiency in discerning question intent, understanding diverse documents, and delivering rapid and precise answers.

• Convergence Security Journal
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• v.23 no.3
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• pp.29-36
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• 2023

With the recent surge in exposure to fake advertising phishing sites in search engines, the damage caused by poor search quality and personal information leakage is increasing. In particular, the seriousness of the problem is worsening faster as the possibility of automating the creation of advertising phishing sites through tools such as ChatGPT increases. In this paper, the source code of fake advertising phishing sites was statically analyzed to derive structural commonalities, and among them, a detection crawler that filters sites step by step based on foreign domains and redirection was developed to confirm that fake advertising posts were finally detected. In addition, we demonstrate the need for new guide lines by verifying that the redirection page of fake advertising sites is divided into three types and returns different sites according to each situation. Furthermore, we propose new detection guidelines for fake advertising phishing sites that cannot be detected by existing detection methods.

• Knowledge Management Research
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• v.24 no.4
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• pp.153-176
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• 2023

Generative AI, especially conversational AI like ChatGPT, has recently gained traction as a technological alternative for automating customer service. However, there is still a lack of research on whether current generative AI technologies can effectively replace traditional human managers in customer service automation, and whether they are advantageous in some situations and disadvantageous in others, depending on the conditions and environment. To answer the question, "Can generative AI replace human managers in customer service activities?", this study conducted experiments and surveys on customer online reviews of a food delivery platform. We applied the perspective of the elaboration likelihood model to generate hypotheses about whether there is a difference between positive and negative online reviews, and analyzed whether the hypotheses were supported. The analysis results indicate that for positive reviews, generative AI can effectively replace human managers. However, for negative reviews, complete replacement is challenging, and human managerial intervention is considered more desirable. The results of this study can provide valuable practical insights for organizations looking to automate customer service using generative AI.

• Smart Media Journal
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• v.13 no.3
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• pp.36-44
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• 2024

Recently, large-scale artificial intelligence (AI) such as ChatGPT have been developed, and as AI is used across various industrial fields, attention is focused on AI chips (semiconductors). AI chips refer to chips designed for calculations for AI algorithms, and many companies at domestic and abroad, such as NVIDIA, Tesla, and ETRI, are developing AI chips. In this paper, we survey research trends on nine types of AI chips. Currently, many attempts have been made to improve the computational performance of most AI chips, and semiconductors for specific purposes are also being designed. In order to compare various AI semiconductors, each chip is analyzed in terms of operation unit, speed, power, and energy efficiency. We introduce currently existing optimization methodologies for AI computation. Based on this, future research directions for AI semiconductors are presented in this paper.