• Fourth Industrial Review
• /
• v.3 no.1
• /
• pp.1-12
• /
• 2023

Purpose - In Korea, where 64% of the land is forested, mountaineering is a leisure activity enjoyed by the majority of the people. As new technologies named the 4th industrial revolution spread more after the Covid-19 pandemic, we propose a human and technology convergence curriculum for mountaineering and trekking education to enjoy safety in the field of mountaineering and trekking using cutting-edge technology. Research design, data, and methodology - After examining the current state of the mountaineering industry and preceding studies on mountaineering and camping, and learning about BAC the 100 famous mountains, mountaineering gamification, and Gamification We designed an AI convergence curriculum using. Result - Understanding the topography and characteristics of mountains in Korea, acquiring mountaineering information through AI convergence, selecting mountaineering equipment suitable for the season, terrain, and weather, setting educational goals to safely climb, and deriving term project results. A total of 15 A curricula for teaching was proposed. Conclusion - Artificial intelligence technology is applied to the field of mountaineering and trekking and used as a tool, and it is expected that the base of mountaineering will be expanded through safe, efficient, fun, and sustainable education. Through this study, it is expected that the AI convergence education curriculum for mountaineering and trekking will be developed and advanced through several studies.

• Microbiology and Biotechnology Letters
• /
• v.51 no.1
• /
• pp.69-82
• /
• 2023

Nitrilases are a hydrolase group of enzymes that catalyzes nitrile compounds and produce industrially important organic acids. The current objective is to optimize nitrilase production using statistical methods assisted with artificial intelligence (AI) tool from novel nitrile degrading isolate. A nitrile hydrolyzing bacteria Bacillus subtilis AGAB-2 (GenBank Ascension number- MW857547) was isolated from industrial effluent waste through an enrichment culture technique. The culture conditions were optimized by creating an orthogonal design with 7 variables to investigate the effect of the significant factors on nitrilase activity. On the basis of obtained data, an AI-driven support vector machine was used for the fitted regression, which yielded new sets of predicted responses with zero mean error and reduced root mean square error. The results of the above global optimization were regarded as the theoretical optimal function conditions. Nitrilase activity of 9832 ± 15.3 U/ml was obtained under optimized conditions, which is a 5.3-fold increase in compared to unoptimized (1822 ± 18.42 U/ml). The statistical optimization method involving Plackett Burman Design and Response surface methodology in combination with an AI tool created a better response prediction model with a significant improvement in enzyme production.

• Smart Media Journal
• /
• v.10 no.2
• /
• pp.48-54
• /
• 2021

Sepsis is one of the leading causes of mortality globally, and it costs billions of dollars annually. However, treating septic patients is currently highly challenging, and more research is needed into a general treatment method for sepsis. Therefore, in this work, we propose a reinforcement learning method for learning the optimal treatment strategies for septic patients. We model the patient physiological time series data as the input for a deep recurrent Q-network that learns reliable treatment policies. We evaluate our model using an off-policy evaluation method, and the experimental results indicate that it outperforms the physicians' policy, reducing patient mortality up to 3.04%. Thus, our model can be used as a tool to reduce patient mortality by supporting clinicians in making dynamic decisions.

• International Journal of Advanced Culture Technology
• /
• v.8 no.3
• /
• pp.186-191
• /
• 2020

Artificial Intelligence (AI) technology is used in a variety of fields because it can make inferences and plans through learning processes. In the field of coding technologies, AI has been introduced as a tool for personalized and customized education to provide new educational environments. Also, it can be used as a virtual assistant in coding operations for easier and more efficient coding. Currently, as coding education becomes mandatory around the world, students' interest in programming is heightened. The purpose of coding education is to develop the ability to solve problems and fuse different academic fields through computational thinking and creative thinking to cultivate talented persons who can adapt well to the Fourth Industrial Revolution era. However, new non-computer science major students who take software-related subjects as compulsory liberal arts subjects at university came to experience many difficulties in these subjects, which they are experiencing for the first time. AI based coding technologies can be used to solve their difficulties and to increase the learning effect of non-computer majors who come across software for the first time. Therefore, this study examines the current state of AI based coding technologies and suggests the direction of future coding education.

• Smart Media Journal
• /
• v.10 no.2
• /
• pp.22-29
• /
• 2021

In the past decade, deep learning has been applied to various medical image analysis tasks. Skeletal bone age estimation is clinically important as it can help prevent age-related illness and pave the way for new anti-aging therapies. Recent research has applied deep learning techniques to the task of bone age assessment and achieved positive results. In this paper, we propose a bone age prediction method using a deep convolutional neural network. Specifically, we first train a classification model that automatically localizes the most discriminative region of an image and crops it from the original image. The regions of interest are then used as input for a regression model to estimate the age of the patient. The experiments are conducted on a whole-body scintigraphy dataset that was collected by Chonnam National University Hwasun Hospital. The experimental results illustrate the potential of our proposed method, which has a mean absolute error of 3.35 years. Our proposed framework can be used as a robust supporting tool for clinicians to prevent age-related diseases.

• Informatization Policy
• /
• v.31 no.3
• /
• pp.34-47
• /
• 2024

Artificial intelligence(AI) is regarded as a key tool that can significantly contribute to innovation and improve productivity as digital transformation continues to spread rapidly. Currently, however, there is lack of understanding and empirical research on the factors that influence the adoption of AI by companies. In particular, most studies have been conducted by foreign researchers analyzing data from foreign companies, and domestic studies have limitations in terms of objectivity and timeliness. This study employs econometric methods to identify the determinants of AI adoption at the firm level. To this end, we derive the technological, organizational, and environmental context factors from the perspective of the Technology-Organization-Environment(TOE) framework as a representative theory of technology adoption factors. We then conduct a logistic regression analysis using data from 11,601 Korean firms. This study not only expands the research literature by supplementing the limitations of previous studies in Korea but also provides timely evidence and implications through empirical analysis.

• Journal of Gastric Cancer
• /
• v.23 no.3
• /
• pp.388-399
• /
• 2023

Gastric cancer remains a significant global health concern, coercing the need for advancements in imaging techniques for ensuring accurate diagnosis and effective treatment planning. Artificial intelligence (AI) has emerged as a potent tool for gastric-cancer imaging, particularly for diagnostic imaging and body morphometry. This review article offers a comprehensive overview of the recent developments and applications of AI in gastric cancer imaging. We investigated the role of AI imaging in gastric cancer diagnosis and staging, showcasing its potential to enhance the accuracy and efficiency of these crucial aspects of patient management. Additionally, we explored the application of AI body morphometry specifically for assessing the clinical impact of gastrectomy. This aspect of AI utilization holds significant promise for understanding postoperative changes and optimizing patient outcomes. Furthermore, we examine the current state of AI techniques for the prognosis of patients with gastric cancer. These prognostic models leverage AI algorithms to predict long-term survival outcomes and assist clinicians in making informed treatment decisions. However, the implementation of AI techniques for gastric cancer imaging has several limitations. As AI continues to evolve, we hope to witness the translation of cutting-edge technologies into routine clinical practice, ultimately improving patient care and outcomes in the fight against gastric cancer.

• Sleep Medicine and Psychophysiology
• /
• v.30 no.1
• /
• pp.22-27
• /
• 2023

Objectives: The purpose of this study was to develop a screening tool that is simple and easy to use for assessing sleep problems, including hypersomnolence, restless legs syndrome, and insomnia. We also examined the reliability and validity of this tool. Methods: We developed the Sleep Problem Screening Questionnaire (SPSQ), which consists of three sub-sections: insomnia (SPSQi), hypersomnolence (SPSQh), and restless legs syndrome (SPSQr). Subsequently, the participants, consisting of 222 patients with insomnia disorder and 78 healthy individuals, completed both the SPSQ and the comparative scale (Korean version of the Insomnia Severity Index). The analysis was then conducted using this data. Results: The SPSQ demonstrated good convergent and discriminant validity, as well as satisfactory internal consistency. A cutoff score of 6 on the SPSQi was found to be optimal for distinguishing individuals with insomnia. Conclusion: The results of this study suggest that the SPSQ is a reliable and valid tool for screening sleep problems among general adult population. However, there is a limitation as a comparison and validation with scales related to restless legs syndrome and hypersomnolence were not conducted.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.3
• /
• pp.21-41
• /
• 2024

This study explores the relationship between users' attachment styles and their interactions with ChatGPT (Chat Generative Pre-trained Transformer), an advanced language model developed by OpenAI. As artificial intelligence (AI) becomes increasingly integrated into everyday life, it is essential to understand how individuals with different attachment styles engage with AI chatbots in order to build a better user experience that meets specific user needs and interacts with users in the most ideal way. Grounded in attachment theory from psychology, we are exploring the influence of attachment style on users' interaction with ChatGPT, bridging a significant gap in understanding human-AI interaction. Contrary to expectations, attachment styles did not have a significant impact on ChatGPT usage or reasons for engagement. Regardless of their attachment styles, hesitated to fully trust ChatGPT with critical information, emphasizing the need to address trust issues in AI systems. Additionally, this study uncovers complex patterns of attachment styles, demonstrating their influence on interaction patterns between users and ChatGPT. By focusing on the distinctive dynamics between users and ChatGPT, our aim is to uncover how attachment styles influence these interactions, guiding the development of AI chatbots for personalized user experiences. The introduction of the Perceived Partner Responsiveness Scale serves as a valuable tool to evaluate users' perceptions of ChatGPT's role, shedding light on the anthropomorphism of AI. This study contributes to the wider discussion on human-AI relationships, emphasizing the significance of incorporating emotional intelligence into AI systems for a user-centered future.

• Imaging Science in Dentistry
• /
• v.50 no.3
• /
• pp.193-198
• /
• 2020

Purpose: This study investigated knowledge, attitudes, and perceptions regarding the future of artificial intelligence (AI) for radiological diagnosis among dental specialists in central India. Materials and Methods: An online survey was conducted consisting of 15 closed-ended questions using Google Forms and circulated among dental professionals in central India. The survey consisted of questions regarding participants' recognition of and attitudes toward AI, their opinions on directions of AI development, and their perceptions regarding the future of AI in oral radiology. Results: Of the 250 participating dentists, 68% were already familiar with the concept of AI, 69% agreed that they expect to use AI for making dental diagnoses, 51% agreed that the major function of AI would be the interpretation of complicated radiographic scans, and 63% agreed that AI would have a future in India. Conclusion: This study concluded that dental specialists were well aware of the concept of AI, that AI programs could be used as an adjunctive tool by dentists to increasing their diagnostic precision when interpreting radiographs, and that AI has a promising role in radiological diagnosis.