• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.51-58
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• 2022

In this paper, we propose a decision tree-based machine learning model that leads to food exchange table renewal by classifying food groups through machine learning for existing food and food data found by web crawling. The food exchange table is the standard for food exchange intake when composing a diet such as diet and diet, as well as patients who need nutritional management. The food exchange table, which is the standard for the composition of the diet, takes a lot of manpower and time in the process of revision through the National Health and Nutrition Survey, making it difficult to quickly reflect food changes according to new foods or trends. Since the proposed technique classifies newly added foods based on the existing food group, it is possible to organize a rapid food exchange table reflecting the trend of food. As a result of classifying food into the proposed model in the study, the accuracy of the food group in the food exchange table was 97.45%, so this food classification model is expected to be highly utilized for the composition of a diet that suits your taste in hospitals and nursing homes.

• Journal of Information Technology Applications and Management
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• v.30 no.2
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• pp.1-18
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• 2023

Recently, as technology development has accelerated and product life cycles have been shortened, it is necessary to derive key product features from customers in the R&D planning and evaluation stage. More companies want differentiated competitiveness by providing consumer-tailored products based on big data and artificial intelligence technology. To achieve this, the need to correctly grasp the required quality, which is a requirement of consumers, is increasing. However, the existing methods are centered on suppliers or domain experts, so there is a gap from the actual perspective of consumers. In other words, product attributes were defined by suppliers or field experts, but this may not consider consumers' actual perspective. Accordingly, the demand for deriving the product's main attributes through reviews containing consumers' perspectives has recently increased. Therefore, we propose a review data analysis-based required quality methodology containing customer requirements. Specifically, a pre-training language model with a good understanding of Korean reviews was established, consumer intent was correctly identified, and key contents were extracted from the review through a combination of KeyBERT and topic modeling to derive the required quality for each product. RevBERT, a Korean review domain-specific pre-training language model, was established through further pre-training. By comparing the existing pre-training language model KcBERT, we confirmed that RevBERT had a deeper understanding of customer reviews. In addition, all processes other than that of selecting the required quality were linked to the automation process, resulting in the automation of deriving the required quality based on data.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.279-291
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• 2023

In recent years, language model-based generative AI technologies have made remarkable progress. In particular, it has attracted a lot of attention due to its increasing potential in various fields such as summarization and code writing. As a reflection of this interest, the number of patent applications related to generative AI has been increasing rapidly. In order to understand these trends and develop strategies accordingly, future forecasting is key. Predictions can be used to better understand the future trends in the field of technology and develop more effective strategies. In this paper, we analyzed patents filed to date to identify the direction of development of language model-based generative AI. In particular, we took an in-depth look at research and invention activities in each country, focusing on application trends by year and detailed technology. Through this analysis, we tried to understand the detailed technologies contained in the core patents and predict the future development trends of generative AI.

• Advanced Industrial SCIence
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• v.3 no.1
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• pp.1-16
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• 2024

The purpose of this study is to develop a machine learning-based AI convergence class model and class design principles that can foster data literacy in high school students, and to develop detailed guidelines accordingly. We developed a machine learning-based teaching model, design principles, and detailed guidelines through research on prior literature, and applied them to 15 students at a specialized high school in Seoul. As a result of the study, students' data literacy improved statistically significantly (p<.001), so we confirmed that the model of this study has a positive effect on improving learners' data literacy, and it is expected that it will lead to related research in the future.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.307-332
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• 2022

One of the most intensively conducted research areas in business application study is a bankruptcy prediction model, a representative classification problem related to loan lending, investment decision making, and profitability to financial institutions. Many research demonstrated outstanding performance for bankruptcy prediction models using artificial intelligence techniques. However, since most machine learning algorithms are "black-box," AI has been identified as a prominent research topic for providing users with an explanation. Although there are many different approaches for explanations, this study focuses on explaining a bankruptcy prediction model using a counterfactual example. Users can obtain desired output from the model by using a counterfactual-based explanation, which provides an alternative case. This study introduces a counterfactual generation technique based on a genetic algorithm (GA) that leverages both domain knowledge (i.e., causal feasibility) and feature importance from a black-box model along with other critical counterfactual variables, including proximity, distribution, and sparsity. The proposed method was evaluated quantitatively and qualitatively to measure the quality and the validity.

• Journal of Intelligence and Information Systems
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• v.9 no.1
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• pp.227-249
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• 2003

Prediction of corporate failure using past financial data is a well-documented topic. Early studies of bankruptcy prediction used statistical techniques such as multiple discriminant analysis, logit and probit. Recently, however, numerous studies have demonstrated that artificial intelligence such as neural networks can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. In building neural network model, the selection of independent and dependent variables should be approached with great care and should be treated as model construction process. Irrespective of the efficiency of a teaming procedure in terms of convergence, generalization and stability, the ultimate performance of the estimator will depend on the relevance of the selected input variables and the quality of the data used. Approaches developed in statistical methods such as correlation analysis and stepwise selection method are often very useful. These methods, however, may not be the optimal ones for the development of neural network model. In this paper, we propose a genetic algorithms approach to find an optimal or near optimal input variables fur neural network modeling. The proposed approach is demonstrated by applications to bankruptcy prediction modeling. Our experimental results show that this approach increases overall classification accuracy rate significantly.

• Journal of Broadcast Engineering
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• v.23 no.5
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• pp.669-681
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• 2018

CNN (Convolution Neural Network) is one of the most important techniques to identify the kind of objects in the captured pictures. Whereas the conventional models have been used for low resolution images, the technique to recognize the high resolution images becomes crucial in the field of artificial intelligence. In this paper, we proposed an efficient CNN model based on dilated convolution and thresholding techniques to increase the recognition ratio and to decrease the computational complexity. The simulation results show that the proposed algorithm outperforms the conventional method and the thresholding technique enhances the performance of the proposed model.

• Journal of Engineering Education Research
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• v.23 no.2
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• pp.14-23
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• 2020

Based on the constructivist learning environments model and the learner-centered psychological principles, STEAM education program with the theme of eliminating smog was developed. Through the program, senior elementary school students will learn and apply the convergence knowledge of science, technology, engineering, arts and mathematics such as the human body's respiratory system (S), immune system (S), big data (M, T), computer programming(M), and aduino sensor utilization (E) directly to solve the problem. After expert validity testing, we found that developed program meet the standards of STEAM education program development and can develop creative thinking skills to find and solve problems in students' daily lives. In addition, this study is meaningful in providing a reference example for the development of STEAM education programs that enhance convergence knowledge in the future.

• 한국IT서비스학회:학술대회논문집
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• 2005.05a
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• pp.372-380
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• 2005

소프트웨어 개발노력 추정에 대한 연구는 소프트웨어가 복잡해지고 범위가 크게 증가함에 따라서 그 중은 지속적으로 부각되고 있다. 관련 프로젝트를 발주하는 업체나, 이를 수주하고 개발을 진행하는 업체에게 원가를 고려하는 측면에서 매우 중요한 부분을 차지하고 있다. 이러한 개발노력 추정을 위하여 다양한 접근 방식들이 고려되어지고 있는데, 그중에서 많이 활용되어지고 있는 방식은 소프트웨어 규모에 기반을 둔 LOC(Line Of Code) 기반 COCOMO (Constructive Cost Model) 모델이나 기능점수(Function Point)를 기반으로 한 회귀분석 모델, 인공지능(Artificial Intelligence)을 활용한 신경망(Neural Network) 모델, 사례분석기법 (CBR, Case Based Reasoning) 등이 있다. 이중에서 최근에 기능점수를 활용한 개발노력 추정에 관한 연구들이 활발히 진행되고 있으나 개발노력 추정에는 소프트웨어 규모의 척도인 기능점수 뿐만 아니라, 개발환경을 구성하는 여러 가지 측면에 대한 고려가 추가되어져야 한다. 이에 본 논문은 최신의 소프트웨어 개발 사례들에 대하여 기능점수 및 추가적인 개발환경 요소들을 면밀히 분석하고, 분석한 내용에 대해서 전문가들의 설문을 통한 빈도분석 및 로지스틱 회귀분석, 데이터마이닝 기법인 신경망 분석 등을 활용하여 개발노력 추정 모델을 구축함으로써, 소프트웨어 개발의 다양한 측면의 중요성을 강조하고, 정확한 추정의 방안을 제시 하고자 노력 하였다.

• Journal of Korean Association for Spatial Structures
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• v.5 no.3 s.17
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• pp.117-122
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• 2005

This paper investigated the optimum design of truss structures based on Genetic Algorithms (GA's). With GA's characteristic of running side by side, the overall optimization and feasible operation, the optimum design model of truss structures was established. Elite models were used to assure that the best units of the previous generation had access to the evolution of current generation. Using of non-uniformity mutation brought the obvious mutation at earlier stage and stable mutation in the later stage; this benefited the convergence of units to the best result. In addition, to avoid GA's drawback of converging to local optimization easily, by the limit value of each variable was changed respectively and the genetic operation was performed two times, so the program could work more efficiently and obtained more precise results. Finally, by simulating evolution process of nature biology of a kind self-organize, self-organize, artificial intelligence, this paper established continuous structural optimization model for ten bars cantilever truss, and obtained satisfactory result of optimum design. This paper further explained that structural optimization is practicable with GA's, and provided the theoretic basis for the GA's optimum design of structural engineering.