• Fisheries and Aquatic Sciences
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• v.5 no.4
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• pp.308-310
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• 2002

Effect of 4-nonylphenol (4-NP), endocrine disrupting compounds (EDCs), on glutamate oxaloacetate transaminase (GOT) and glutamate pyruvate transaminase (GPT) were investigated in the plasma of juvenile rockfish, Sebastes schlegeli. Fish were injected with 4­NP (10, 50, 100 and 200 mg/kg body weight) in $70\%$ ethanol twice at 3-day intervals and plasma sampling were extracted at 7 days after the last injection. Controls received solvent only. 4-NP significant increased GOT in a dose-dependent manner. GPT was markedly elevated to $61\%$ (P<0.05) and $82\%$ (P<0.01) than that of the control at the 4-NP doses of 100 and 200mg, respectively. These results suggest that the estrogenic activity of 4-NP increase plasma GOT and GPT by toxic effect on hepatocyte.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.186-191
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• 2023

This study would investigate the generative AIs currently in service in the era of hyperscale AIs and explore measures for the use of generative AIs, focusing on 'ChatGPT,' which has received attention as a leader of generative AIs. Among the various generative AIs, this study selected ChatGPT, which has rich application cases to conduct research, investigation, and use. This study investigated the concept, learning principle, and features of ChatGPT, identified the algorithm of conversational AI as one of the specific cases and checked how it is used. In addition, by comparing various cases of the application of conversational AIs such as Google's Bard and MS's NewBing, this study sought efficient ways to utilize them through the collected cases and conducted research on the limitations of conversational AI and precautions for its use. If connected to city-related databases, it can provide information on city infrastructure, transportation systems, and public services, so residents can easily get the information they need. We want to apply this research to enrich the lives of our citizens.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.491-492
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• 2023

본 논문에서는 일상생활의 편의성을 높이기 위한 새로운 AI 애플리케이션 'Chat Chef'를 제안 한다. 이 앱은 사용자의 냉장고 속 재료 정보를 바탕으로 ChatGPT를 이용하여 요리 레시피를 추천하는 기능을 제공한다. 사용자는 앱을 통해 냉장고 내의 재료들을 사진으로 촬영하면, 이미지 인식을 위해 YOLOv7를 이용하여 감자, 당근, 양파 등과 같은 식료품들을 약 3,000장의 이미지 데이터를 학습하여 인식하며, 바코드를 인식하여 제품들 목록을 데이터베이스에 저장한다. 제안한 'Chat Chef' 앱은 재료 목록과 ChatGPT API를 이용하여 사용자에게 개인화된 레시피를 제공하며, 요리 과정에 대한 정보를 제공한다. 이와 같이 ChatGPT와 같은 AI 기술을 활용하여 실생활에 적용할 수 있는 활용 방안을 제시한다.

• Korean Journal of Community Nutrition
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• v.3 no.4
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• pp.574-582
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• 1998

This study was carried out to discover the effects of eating habits and health-related life style on blood pressure, $\gamma$-Glutamic acid Peptide Transferase($\gamma$-GPT), blood glucose and High Density Lipoprotein-Cholesterol(HDL-C). 185 subjects(85 male, 100 female) were selected, who were living in the Cheonju area aged 40#s to 60#s. The mean systollic blood pressure(SBP), diastollic blood pressure (DBP), $\gamma$-GPT, fasting blood sugar(FBS) and HDL-C for all the subjects were 118mmHg, 77mmHg, 281U/l, 90mg/dl and 45mg/dl, respectively. The SBP and DBP for subuects over 60 years old were 126mmHg and 81mmHg and were significantly higher than subjects in their 40#s and 50#s(p<0.001, p<0.005). The HDL-C of the group that rarely ate breakfast was 57mg/dl and that was significantly higher than the 44mg/dl scored by those who ate breakfast everyday(p<0.05). The SBP for subjects having a snack 2-3 times/week was 125mmHg and that was significantly higher than the 114mmHg of those having a snack everyday(p<0.05). The $\gamma$-GPT for subjects consuming alcohol everyday was 44IU/L and that was significantly higher than 18IU/I of the non-drinking group(p<0.001). The $\gamma$-GPT of light smokers was 53IU/I and that was significantly higher than the 22IU/I for non-smoking participants(p<0.001). The DBP, SBP, $\gamma$-GPT, FBS and HDL-C related to exercise not significantly different. The SBP(p<0.001) and DBP(p-0.01) between age group was positively correlated. The $\gamma$-GPT between drinking frequency(p<0.001), drinking quantity(p<0.05), and smoking(p<0.05) was also positively correlated. The FBS between exercises had a negative correlation(p<0.05), and the HDL-C between breakfast had a negative correlation(p<0.05). These results indicate that decreasing drinking and smoking, when combined with appropriate exercise, will decrease the $\gamma$-GPT and fasting blood sugar level, and help preventing adult diseases.

• Journal of the East Asian Society of Dietary Life
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• v.18 no.5
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• pp.702-710
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• 2008

The principal objective of this study was to assess the effects of eating habits and health-related lifestyle on blood pressure, $\gamma$-Glutamic acid Peptide Transferase ($\gamma$-GPT), glucose and HDL-Cholesterol (HDL-C). All subjects (261 male, 252 female) were from the Iksan area of Korea, and were at least 50 years of age. The mean systolic blood pressure (SBP), diastolic blood pressure (DBP) and HDL-C for all the subjects was 131.3 mmHg, 78.5 mmHg, and 43.1 mg/dl, respectively. The HDL-C of the $50{\sim}64$ year-old group was higher than that of the over-75-year-old group. The "regular breakfast" group evidenced a lower SBP, $\gamma$-GPT, and higher HDL-C than the "seldom breakfast" group (p<0.001, p<0.001, p<0.01). SBP in the "snacking everyday" group was higher than that of the "seldom snacking" group (p<0.001). As for the frequency of using alcohol, SBP and $\gamma$-GPT for the group using alcohol everyday were higher than those of the non-drinking group (p<0.001, p<0.001), SBP and DBP were higher and $\gamma$-GPT was lower in the group that regularly drank more than 4 glasses of Soju than in the non-drinking group (p<0.001, p<0.05, p<0.001). SBP, DBP, and $\gamma$-GPT for the "heavy smoker" group were higher than those of the non-smoker group (p<0.01, p<0.01, p<0.05). The HDL-C was lower in the "heavy smoker" group than in the "non-smoker" group (p<0.05). The SBP with exercise was as follows: Group 1 ($0.022{\sim}0.073\;kcal/min/kg$) was lower than that of Group 3 ($0.144{\sim}0.161\;kcal/min/kg$) and Group 4 (0.161 kcal/min/kg) (p<0.001). To conclude: advancing age, snacking, and frequent alcohol consumption increased blood pressure; the lowest blood pressure was detected in the group that ate breakfast everyday and in the group that engaged in more frequent exercise; Moreover, $\gamma$-GPT was higher and HDL-C was lower in the smokers' group than in the non-smokers' group. Considering the results of this study, there appears to be an urgent need to instruct aging adults about eating breakfast everyday, reducing smoking, using less or no alcohol, and getting proper and regular exercise.

• The Transactions of the Korea Information Processing Society
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• v.13 no.3
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• pp.148-155
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• 2024

Despite the increasing importance of irony and sarcasm detection in the field of natural language processing, research on the Korean language is relatively scarce compared to other languages. This study aims to experiment with various models for irony detection in Korean text. The study conducted irony detection experiments using KoBERT, a BERT-based model, and ChatGPT. For KoBERT, two methods of additional training on sentiment data were applied (Transfer Learning and MultiTask Learning). Additionally, for ChatGPT, the Few-Shot Learning technique was applied by increasing the number of example sentences entered as prompts. The results of the experiments showed that the Transfer Learning and MultiTask Learning models, which were trained with additional sentiment data, outperformed the baseline model without additional sentiment data. On the other hand, ChatGPT exhibited significantly lower performance compared to KoBERT, and increasing the number of example sentences did not lead to a noticeable improvement in performance. In conclusion, this study suggests that a model based on KoBERT is more suitable for irony detection than ChatGPT, and it highlights the potential contribution of additional training on sentiment data to improve irony detection performance.

• Informatization Policy
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• v.31 no.1
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• pp.3-31
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• 2024

This study aimed to examine public concerns in South Korea considering the country's unique context, triggered by the advent of generative artificial intelligence such as ChatGPT. To achieve this, comments from 102 YouTube video news related to ethical issues were collected using a Python scraper, and morphological analysis and preprocessing were carried out using Textom on 15,735 comments. These comments were then analyzed using a Correlated Topic Model (CTM). The analysis identified six primary topics within the comments: "Legal and Ethical Considerations"; "Intellectual Property and Technology"; "Technological Advancement and the Future of Humanity"; "Potential of AI in Information Processing"; "Emotional Intelligence and Ethical Regulations in AI"; and "Human Imitation."Structuring these topics based on a correlation coefficient value of over 10% revealed 3 main categories: "Legal and Ethical Considerations"; "Issues Related to Data Generation by ChatGPT (Intellectual Property and Technology, Potential of AI in Information Processing, and Human Imitation)"; and "Fear for the Future of Humanity (Technological Advancement and the Future of Humanity, Emotional Intelligence, and Ethical Regulations in AI)."The study confirmed the coexistence of various concerns along with the growing interest in generative AI like ChatGPT, including worries specific to the historical and social context of South Korea. These findings suggest the need for national-level efforts to ensure data fairness.

• Journal of Medicine and Life Science
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• v.20 no.2
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• pp.53-59
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• 2023

This study explores the potential use of artificial intelligence (AI)-based services, specifically ChatGPT-3.5, in medical education. The application of this technology is acknowledged as a valuable tool for simulating authentic clinical scenarios and enhancing learners' diagnostic and communication skills. To construct a case, students received ChatGPT training using a clinical ethics casebook titled "Clinical Ethics Cases and Commentaries for Medical Students and Physicians." Subsequently, a role-play script was generated based on this training. The initial draft of the script was reviewed by two medical professors and was further optimized using ChatGPT-3.5. Consequently, a comprehensive role-play script, accurately reflecting real-world clinical situations, was successfully developed. This study demonstrates the potential for effectively integrating AI technology into medical education and provides a solution to overcome limitations in developing role-play scripts within conventional educational settings. However, the study acknowledges that AI cannot always generate flawless role-play scripts and recognizes the necessity of addressing these limitations and ethical concerns. The research explores both the potential and limitations of employing AI in the early stages of medical education, suggesting that future studies should focus on overcoming these limitations while further investigating the potential applications of AI in this field.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.541-544
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• 2023

LLM(Largescale Language Model)의 성능 향상을 위한 비용 효율적인 방법으로 ChatGPT, GPT-4와 같은 초거대 모델의 output에 대해 SLM(Small Language Model)을 finetune하는 방법이 주목받고 있다. 그러나, 이러한 접근법은 주로 범용적인 지시사항 모델을 위한 학습 방법으로 사용되며, 제한된 특정 도메인에서는 추가적인 성능 개선의 여지가 있다. 본 연구는 특정 도메인(Writing Assistant)에서의 성능 향상을 위한 새로운 방법인 Self-Guided Approach를 제안한다. Self-Guided Approach는 (1) LLM을 활용해 시드 데이터에 대해 도메인 특화된 metric(유용성, 관련성, 정확성, 세부사항의 수준별) 점수를 매기고, (2) 점수가 매겨진 데이터와 점수가 매겨지지 않은 데이터를 모두 활용하여 supervised 방식으로 SLM을 미세 조정한다. Vicuna에서 제안된 평가 방법인, GPT-4를 활용한 자동평가 프레임워크를 사용하여 Self-Guided Approach로 학습된 SLM의 성능을 평가하였다. 평가 결과 Self-Guided Approach가 Self-instruct, alpaca와 같이, 생성된 instruction 데이터에 튜닝하는 기존의 훈련 방법에 비해 성능이 향상됨을 확인했다. 다양한 스케일의 한국어 오픈 소스 LLM(Polyglot1.3B, PolyGlot3.8B, PolyGlot5.8B)에 대해서 Self-Guided Approach를 활용한 성능 개선을 확인했다. 평가는 GPT-4를 활용한 자동 평가를 진행했으며, Korean Novel Generation 도메인의 경우, 테스트 셋에서 4.547점에서 6.286점의 성능 향상이 발생했으며, Korean scenario Genration 도메인의 경우, 테스트 셋에서 4.038점에서 5.795 점의 성능 향상이 발생했으며, 다른 유사 도메인들에서도 비슷한 점수 향상을 확인했다. Self-Guided Approach의 활용을 통해 특정 도메인(Writing Assistant)에서의 SLM의 성능 개선 가능성을 확인했으며 이는 LLM에 비용부담을 크게 줄이면서도 제한된 도메인에서 성능을 유지하며, LLM을 활용한 응용 서비스에 있어 실질적인 도움을 제공할 수 있을 것으로 기대된다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.3-4
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• 2023

자연어처리는 수많은 정보가 수집된 전자의무기록의 비정형 데이터에서 유의미한 정보나 패턴 등을 추출해 의료진의 의사결정을 지원하고, 환자에게 더 나은 진단이나 치료 등을 지원할 수 있어 큰 잠재력을 가지고 있다. 그러나 전자의무기록은 개인정보와 같은 민감한 정보가 다수 포함되어 있어 접근하기 어렵고, 이로 인해 충분한 양의 데이터를 확보하기 어렵다. 따라서 본 논문에서는 신뢰할 수 있는 의료 합성 텍스트를 생성하기 위해 ChatGPT 기반 의료 텍스트 증강 도구를 개발하였다. 이는 사용자가 입력한 실제 의료 텍스트로 의료 합성 데이터를 생성한다. 이를 위해, 적합한 프롬프트와 의료 텍스트에 대한 전처리 방법을 탐색하였다. ChatGPT 기반 의료 텍스트 증강 도구는 입력 텍스트의 핵심 키워드를 잘 유지하였고, 사실에 기반한 의료 합성 텍스트를 생성할 수 있다는 것을 확인할 수 있었다.