• The Korean Journal of Applied Statistics
• /
• v.32 no.4
• /
• pp.543-559
• /
• 2019

Data often include missing values due to various reasons. If the missing data mechanism is not MCAR, analysis based on fully observed cases may an estimation cause bias and decrease the precision of the estimate since partially observed cases are excluded. Especially when data include many variables, missing values cause more serious problems. Many imputation techniques are suggested to overcome this difficulty. However, imputation methods using parametric models may not fit well with real data which do not satisfy model assumptions. In this study, we review imputation methods using nonlinear models such as kernel, resampling, and spline methods which are robust on model assumptions. In addition, we suggest utilizing imputation classes to improve imputation accuracy or adding random errors to correctly estimate the variance of the estimates in nonlinear imputation models. Performances of imputation methods using nonlinear models are compared under various simulated data settings. Simulation results indicate that the performances of imputation methods are different as data settings change. However, imputation based on the kernel regression or the penalized spline performs better in most situations. Utilizing imputation classes or adding random errors improves the performance of imputation methods using nonlinear models.

• Journal of the Korean Society of Earth Science Education
• /
• v.16 no.1
• /
• pp.153-165
• /
• 2023

This study investigates learners' environmental literacy, classifies the results by factors of environmental literacy, and then investigates the differences in the students' environmental behavior practice experiences according to the classification by factor. The study was conducted with 47 6th grade students from D elementary school located in P metropolitan city as the subject of final analysis, and environmental literacy questionnaires and environmental behavior practice experience questionnaires were used as the main data. As a result of the study, the learners were classified into three groups according to the factors of environmental literacy, and they were respectively named as the "High environmental literacy group", "low environmental literacy group", and "Low Function and Affectif group". A Word network was formed using the descriptions of environmental behavior practice experiences for each cluster, and a Degree Centrality Analysis was performed to visualize and then analyze. As a result of the analysis, "High environmental literacy group" was confirmed, 1) recognized the subjects of environmental action practice as individuals and families, 2) described his experience of environmental action practice in relation to all elements of environmental literacy, and had a relatively pessimistic view. "low environmental literacy group", and "Low Function and Affectif group" were confirmed 1) perceive the subject of environmental behavior practice as a relatively social problem, 2) the description of the experience of environmental behavior practice is relatively biased specific factors, and the "Low Function and Affectif group" is particularly focused on the knowledge element. And 3) it was confirmed that they were aware of climate change from a relatively optimistic perspective. Based on this conclusion, suggestions were made from the perspective of environmental education.

• Journal of The Korean Association For Science Education
• /
• v.43 no.3
• /
• pp.307-319
• /
• 2023

This study aims to explain the key concepts and principles of text-based generative artificial intelligence (AI) that has been receiving increasing interest and utilization, focusing on its application in science education. It also highlights the potential and limitations of utilizing generative AI in science education, providing insights for its implementation and research aspects. Recent advancements in generative AI, predominantly based on transformer models consisting of encoders and decoders, have shown remarkable progress through optimization of reinforcement learning and reward models using human feedback, as well as understanding context. Particularly, it can perform various functions such as writing, summarizing, keyword extraction, evaluation, and feedback based on the ability to understand various user questions and intents. It also offers practical utility in diagnosing learners and structuring educational content based on provided examples by educators. However, it is necessary to examine the concerns regarding the limitations of generative AI, including the potential for conveying inaccurate facts or knowledge, bias resulting from overconfidence, and uncertainties regarding its impact on user attitudes or emotions. Moreover, the responses provided by generative AI are probabilistic based on response data from many individuals, which raises concerns about limiting insightful and innovative thinking that may offer different perspectives or ideas. In light of these considerations, this study provides practical suggestions for the positive utilization of AI in science education.

• Korean Journal of Environment and Ecology
• /
• v.32 no.5
• /
• pp.469-477
• /
• 2018

Hanabusaya asiatica is an endemic species whose distribution is limited in the mid-eastern part of the Korean peninsula. Due to its narrow range and small population, it is necessary to protect its habitats by identifying it as Key Biodiversity Areas (KBAs) adopted by the International Union for Conservation of Nature (IUCN). In this paper, we estimated potential natural habitats for H. asiatica using maximum entropy model (MaxEnt) and identified candidate sites for KBA based on the model results. MaxEnt is a machine learning algorithm that can predict habitats for species of interest unbiasedly with presence-only data. This property is particularly useful for the study area where data collection via a field survey is unavailable. We trained MaxEnt using 38 locations of H. asiatica and 11 environmental variables that measured climate, topography, and vegetation status of the study area which encompassed all locations of the border region between South and North Korea. Results showed that the potential habitats where the occurrence probabilities of H. asiatica exceeded 0.5 were $778km^2$, and the KBA candidate area identified by taking into account existing protected areas was $1,321km^2$. Of 11 environmental variables, elevation, annual average precipitation, average precipitation in growing seasons, and the average temperature in the coldest month had impacts on habitat selection, indicating that H. asiatica prefers cool regions at a relatively high elevation. These results can be used not only for identifying KBAs but also for the reference to a protection plan for H. asiatica in preparation of Korean reunification and climate change.

• Journal of Intelligence and Information Systems
• /
• v.27 no.3
• /
• pp.231-252
• /
• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.

• Radiation Oncology Journal
• /
• v.22 no.3
• /
• pp.192-199
• /
• 2004

Purpose: To determine the patterns on evaluation and treatment in the patient with early breast cancer treated with conservative surgery and radiotherapy and to improve the radiotherapy techiniques, nationwide survey was peformed. Materials and Methods: A web-based database system for korean Patterns of Care Study (PCS) for 6 common cancers was developed. Two hundreds sixty-one randomly selected records of eligible patients treated between 1998$\~$1999 from 15 hospitals were reviewed. Results: The patients ages ranged from 24 to 85 years(median 45 years). Infiltrating ductal carcinoma was most common histologic type (88.9$\%$) followed by medullary carcinoma (4.2$\%$) and infiltrating lobular carcinoma (1.5$\%$). Pathologic T stage by AJCC was T1 in 59.7$\%$ of the casses, T2 in 29.5$\%$ of the cases, Tis in 8.8$\%$ of the cases. Axillary lymph node dissection was peformed I\in 91.2$\%$ of the cases and 69.7$\%$ were node negative. AJCC stage was 0 in 8.8$\%$ of the cases, stage I in 44.9$\%$ of the cases, stage IIa in 33.3$\%$ of the cases, and stage IIb in 8.4$\%$ of the cases. Estrogen and progesteron receptors were evaluated in 71.6$\%$, and 70.9$\%$ of the patients, respectively. Surgical methods of breast-conserving surgery was excision/lumpectomy in 37.2$\%$, wide excision in 11.5$\%$, quadrantectomy in 23$\%$ and partial mastectomy in 27.5$\%$ of the cases. A pathologically confirmed negative margin was obtained in 90.8$\%$ of the cases. Pathological margin was involved with tumor in 10 patients and margin was close (less than 2 mm) in 10 patients. All the patients except one recieved more than 90$\%$ of the planned radiotherapy dose. Radiotherapy volume was breast only In 88$\%$ of the cases, breast+supraclavicular fossa (SCL) in 5$\%$ of the cases, and breast+ SCL+ posterior axillary boost in 4.2%$\%$of the cases. Only one patient received isolated internal mammary lymph node irradiation. Used radiation beam was Co-60 in 8 cases, 4 MV X-ray in 115 cases, 6 MV X-ray in 125 cases, and 10 MV X-ray in 11 cases. The radiation dose to the whole breast was 45$\~$59.4 Gy (median 50.4) and boost dose was 8$\~$20 Gy (median 10 Gy). The total radiation dose delivered was 50.4$\~$70.4 Gy (median 60.4 Gy). Conclusion: There was no major deviation from current standard in the patterns of evaluation and treatment for the patients with early breast cancer treated with breast conservation method. Some varieties were identified in boost irradiation dose. Separate analysis for the datails of radiotherapy planning will be followed and the outcome of treatment is needed to evaluate the process.