• Journal of the Korean Society for Library and Information Science
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• v.51 no.4
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• pp.99-120
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• 2017

In order to develop a semiautomatic support system that allows researchers concerned to efficiently analyze the technical trends for the ever-growing industry and market. This paper introduces a couple of Korean sentence generation models that can automatically generate definitional statements as well as descriptions of technical terms and concepts. The proposed models are based on a deep learning model called LSTM (Long Sort-Term Memory) capable of effectively labeling textual sequences by taking into account the contextual relations of each item in the sequences. Our models take technical terms as inputs and can generate a broad range of heterogeneous textual descriptions that explain the concept of the terms. In the experiments using large-scale training collections, we confirmed that more accurate and reasonable sentences can be generated by CHAR-CNN-LSTM model that is a word-based LSTM exploiting character embeddings based on convolutional neural networks (CNN). The results of this study can be a force for developing an extension model that can generate a set of sentences covering the same subjects, and furthermore, we can implement an artificial intelligence model that automatically creates technical literature.

• Journal of Digital Convergence
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• v.18 no.9
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• pp.391-402
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• 2020

In today's era of media convergence, users can watch a drama by choosing a story like a game or enjoy a game directed like a movie. These two media material interactive storytelling, and there have been discussions about them for many years. The purposes of this study are to propose a new branch structure for interactive storytelling and find a new perspective for the method of proceeding with a branch structure. The main research targeted a game that materialized interactive storytelling best. Based on the findings, the study proposed a volume branch structure including a plot border and adjuster concept for interactive storytelling to keep the story narrative and help the users feel interactions freely. This structure can be materialized only with real-time edited videos in the game media and will be possible in the application cases of a physical engine that continues to develop and artificial intelligence that has not been introduced in games actively.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.6
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• pp.57-69
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• 2001

Expert system is one of AI area. It simulates the human's way of thinking to give solutions of problem in many applications. Most expert system consists of many components such as inference engine, knowledge base, and so on. Especially the performance of expert system depend on the control of efficiency of inference engine. Inference engine has to get features; first, if possible to minimize restrictions when it constructed the knowledge base. second, it has to serve various kinds of inferencing methods. In this paper we propose knowledge scheme for representing domain knowledge in ease, knowledge implementation technique for inferencing, and integrated knowledge-base engine with blackboard and inference engine. And we describe a expert system prototype that implemented in this paper using proposed methods, it perform diagnose about heavy industrial device. The fault diagnosis system prototype has been studied in this paper will be practical foundation in the research area of knowledge based system.

• Journal of the Korea Society for Simulation
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• v.26 no.2
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• pp.59-65
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• 2017

Along with the development of M&S, modeling research utilizing AI technology is attracting attention because of the fact that the needs of fields including human decision making such as defense M&S are increased. Obviously AI is a way to solve complex problems. However, AI did not consider logical time such as input time and processing time required by M&S. Therefore, in this paper we proposed a "time-based expert system" which redesigned the representative AI technology rule-based expert system. It consists of a rule structure "IF-THEN-AFTER" and an inference engine, takes logical time into consideration. We also tried logical analysis using a simple example. As a result of the analysis, the proposal Time-based Expert System proved that the result changes according to the input time point and inference time.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.2
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• pp.65-76
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• 2022

Recently, not only traditional statistical techniques but also machine learning algorithms have been used to make more accurate bankruptcy predictions. But the insolvency rate of companies dealing with financial institutions is very low, resulting in a data imbalance problem. In particular, since data imbalance negatively affects the performance of artificial intelligence models, it is necessary to first perform the data imbalance process. In additional, as artificial intelligence algorithms are advanced for precise decision-making, regulatory pressure related to securing transparency of Artificial Intelligence models is gradually increasing, such as mandating the installation of explanation functions for Artificial Intelligence models. Therefore, this study aims to present guidelines for eXplainable Artificial Intelligence-based corporate bankruptcy prediction methodology applying SMOTE techniques and LIME algorithms to solve a data imbalance problem and model transparency problem in predicting corporate bankruptcy. The implications of this study are as follows. First, it was confirmed that SMOTE can effectively solve the data imbalance issue, a problem that can be easily overlooked in predicting corporate bankruptcy. Second, through the LIME algorithm, the basis for predicting bankruptcy of the machine learning model was visualized, and derive improvement priorities of financial variables that increase the possibility of bankruptcy of companies. Third, the scope of application of the algorithm in future research was expanded by confirming the possibility of using SMOTE and LIME through case application.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.999-1008
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• 2023

Early prediction of chronic diseases such as diabetes is an important issue, and improving the accuracy of diabetes prediction is especially important. Various machine learning and deep learning-based methodologies are being introduced for diabetes prediction, but these technologies require large amounts of data for better performance than other methodologies, and the learning cost is high due to complex data models. In this study, we aim to verify the claim that DNN using the pima dataset and k-fold cross-validation reduces the efficiency of diabetes diagnosis models. Machine learning classification methods such as decision trees, SVM, random forests, logistic regression, KNN, and various ensemble techniques were used to determine which algorithm produces the best prediction results. After training and testing all classification models, the proposed system provided the best results on XGBoost classifier with ADASYN method, with accuracy of 81%, F1 coefficient of 0.81, and AUC of 0.84. Additionally, a domain adaptation method was implemented to demonstrate the versatility of the proposed system. An explainable AI approach using the LIME and SHAP frameworks was implemented to understand how the model predicts the final outcome.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.1
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• pp.41-50
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• 2024

Korea is facing a significant problem with historically low fertility rates, which is becoming a major social issue affecting the economy, labor force, and national security. This study analyzes the factors contributing to the regional gap in fertility rates and derives policy implications. The government and local authorities are implementing a range of policies to address the issue of low fertility. To establish an effective strategy, it is essential to identify the primary factors that contribute to regional disparities. This study identifies these factors and explores policy implications through machine learning and explainable artificial intelligence. The study also examines the influence of media and public opinion on childbirth in Korea by incorporating news and online community sentiment, as well as sentiment fear indices, as independent variables. To establish the relationship between regional fertility rates and factors, the study employs four machine learning models: multiple linear regression, XGBoost, Random Forest, and Support Vector Regression. Support Vector Regression, XGBoost, and Random Forest significantly outperform linear regression, highlighting the importance of machine learning models in explaining non-linear relationships with numerous variables. A factor analysis using SHAP is then conducted. The unemployment rate, Regional Gross Domestic Product per Capita, Women's Participation in Economic Activities, Number of Crimes Committed, Average Age of First Marriage, and Private Education Expenses significantly impact regional fertility rates. However, the degree of impact of the factors affecting fertility may vary by region, suggesting the need for policies tailored to the characteristics of each region, not just an overall ranking of factors.

• Journal of the Korean Society of Radiology
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• v.81 no.6
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• pp.1305-1333
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• 2020

Deep learning has recently achieved remarkable results in the field of medical imaging. However, as a deep learning network becomes deeper to improve its performance, it becomes more difficult to interpret the processes within. This can especially be a critical problem in medical fields where diagnostic decisions are directly related to a patient's survival. In order to solve this, explainable artificial intelligence techniques are being widely studied, and an attention mechanism was developed as part of this approach. In this paper, attention techniques are divided into two types: post hoc attention, which aims to analyze a network that has already been trained, and trainable attention, which further improves network performance. Detailed comparisons of each method, examples of applications in medical imaging, and future perspectives will be covered.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.141-160
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• 2019

Agile methodologies in software development, including the development of artificial intelligence software, have been widespread over the past several years. In spite of the popularity of agile methodologies in practice, there is a lack of empirical evidence to identify determinants of success of software projects in which agile methods are used. To understand the role of clients in software project where agile methods are used, we examine the effect of client-side factors, including lack of user involvement, unrealistic client expectations, and constant changes of requirements on project success from practitioners' perspective. Survey methods are used in this study. Data were collected by means of online survey to IT professionals who have experience with software development methodologies, and ordered logit regression is used to analyze the survey data. Results of our study imply the following managerial findings. First, user involvement is critical to project success to take advantage of agile methods. Second, it is interesting that, with an agile method, constant changes of client's requirements is not a negative factor but a positive factor of project success. Third, unrealistic client expectations do negatively affect project success even with agile methods.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.6
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• pp.642-648
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• 2019

Vessels sail according to the COLREG to prevent a collision. However, it is difficult to apply COLREG under special situation as heavy traffic, at this time personal skills of the operator are required. In this case, traffic control is required through the maritime traffic monitoring system. Therefore, maritime traffic management is globally implemented by VTS. In this system, VTS of icer uses the VTS system to assess risks and recommends possible safety operation to vessels with radio systems. This study considers that the risk analysis method with AI (Artificial Intelligence) technology from the operator's aspect. In addition, the research explains the Maritime Traffic Safety Monitoring System, Including AR (Augmented Reality) technology to increase vessel control efficiency. This system is able to predict hazards and risk priorities, and it leads to sequential elimination of dangerous situations. Especially, the hazard situations can be analyzed from operator's perspective of each vessel instead of the VTS officer's aspect, which is more practical than the conventional method. Furthermore, the result of analysis enables to comprehend quantitative hazardous areas and support recommended routes to avoid a collision. As a result, I firmly believe that the system will support to prevent a collision in complex traffic waters. In particular, it could be adopted as a collision prevention system for Maritime Autonomous Surface Ship, which occupies a significant proportion in Maritime 4th industrial revolution.