• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.11-30
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• 2019

Smartcards are used as the basic data for utilizing the various transportation policies and evaluations, etc. and provided the transportation basic statistics index. However, the main problem of the smartcard data is that the most of users do not take the alighting tag at the stop, so there is a limit to the scope of use for the total O-D trip data because incomplete O-D traffic data of transportation card users. In this study, a decision tree of learning model is estimated for the alighting stop of smartcard users. The model estimation accuracy in range less than 2 stops interval was 89.7% on average. By eliminating the incompleteness alighting stop of smartcard data through this model, it is expected to be used as the basic data for various transportation analyses and evaluations.

• Journal of Korean Biological Nursing Science
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• v.20 no.2
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• pp.103-113
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• 2018

Purpose: This study was performed to assess problems associated with sleep (short and long sleep duration) and to identify risky subgroups with sleep problems among adult cancer survivors. The study is based on the Korea National Health and Nutrition Examination Survey (KNHANES VI and VII) from 2013 to 2016. Methods: The sociodemographic and clinical data of 504 Korean cancer survivors aged 20-64 years was extracted from the KNHANES VI and VII database. Descriptive statistics for complex samples was used, and decision-tree analyses were performed using the SPSS WIN 24.0 program. Results: The mean age for survivors was approximately 51 years. The mean sleep duration was 6.97 hours; 36.2% of participants had short (< 7 hours) and 9.9% had long (> 8 hours) sleep duration. From the decision-trees analyses, the characteristics of the adult cancer survivors related to sleep problems were presented with six different pathways. Sleep problems were analyzed according to the survivors' sociodemographic information (age, education, living status, and occupation), clinical characteristics (body mass index, hypercholesterolemia, and anemia) and health-related quality of life (HRQoL). The HRQoL (${\leq}0.5$ or > 0.5 cutoff point) was a significant predictor of the participants' sleep problems because all six pathways were started from this predictor in the model. Conclusion: Health care professionals could use the decision-tree model for screening adult cancer survivors with sleep problems in clinical or community settings. Nursing interventions considering these specific individual characteristics and HRQoL level should be developed to have adequate sleep duration for Korean adult cancer survivors.

• Journal of Internet Computing and Services
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• v.25 no.4
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• pp.7-21
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• 2024

There are various machine learning techniques such as Reinforcement Learning, Deep Learning, Neural Network Learning, and so on. In recent, Large Language Models (LLMs) are popularly used for Generative AI based on Reinforcement Learning. It makes decisions with the most optimal rewards through the fine tuning process in a particular situation. Unfortunately, LLMs can not provide any explanation for how they reach the goal because the training is based on learning of black-box AI. Reinforcement Learning as black-box AI is based on graph-evolving structure for deriving enhanced solution through adjustment by human feedback or reinforced data. In this research, for mutually exclusive decision-making, Mutually Exclusive Learning (MEL) is proposed to provide explanations of the chosen goals that are achieved by a decision on both ends with specified conditions. In MEL, decision-making process is based on the tree-based structure that can provide processes of pruning branches that are used as explanations of how to achieve the goals. The goal can be reached by trade-off among mutually exclusive alternatives according to the specific contextual conditions. Therefore, the tree-based structure is adopted to provide feasible solutions with the explanations based on the pruning branches. The sequence of pruning processes can be used to provide the explanations of the inferences and ways to reach the goals, as Explainable AI (XAI). The learning process is based on the pruning branches according to the multi-dimensional contextual conditions. To deep-dive the search, they are composed of time window to determine the temporal perspective, depth of phases for lookahead and decision criteria to prune branches. The goal depends on the policy of the pruning branches, which can be dynamically changed by configured situation with the specific multi-dimensional contextual conditions at a particular moment. The explanation is represented by the chosen episode among the decision alternatives according to configured situations. In this research, MEL adopts the tree-based learning model to provide explanation for the goal derived with specific conditions. Therefore, as an example of mutually exclusive problems, employment process is proposed to demonstrate the decision-making process of how to reach the goal and explanation by the pruning branches. Finally, further study is discussed to verify the effectiveness of MEL with experiments.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1343-1346
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• 2005

In the cold rolling mill, coil breakage that generated in rolling process makes the various types of troubles such as the degradation of productivity and the damage of equipment. Recent researches were done by the mechanical analysis such as the analysis of roll chattering or strip inclining and the prevention of breakage that detects the crack of coil. But they could cover some kind of breakages. The prediction of Coil breakage was very complicated and occurred rarely. We propose to build effective prediction modes for coil breakage in rolling process, based on data mining model. We proposed three prediction models for coil breakage: (1) decision tree based model, (2) regression based model and (3) neural network based model. To reduce model parameters, we selected important variables related to the occurrence of coil breakage from the attributes of coil setup by using the methods such as decision tree, variable selection and the choice of domain experts. We developed these prediction models and chose the best model among them using SEMMA process that proposed in SAS E-miner environment. We estimated model accuracy by scoring the prediction model with the posterior probability. We also have developed a software tool to analyze the data and generate the proposed prediction models either automatically and in a user-driven manner. It also has an effective visualization feature that is based on PCA (Principle Component Analysis).

• 한국노년학
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• v.38 no.2
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• pp.351-364
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• 2018

The purpose of this study is to learn about the decision factors affecting utilization of elderly welfare center of the elderly living in Gimpo city. The reason of the study is that the elderly welfare center as a provider of general welfare services could not only thinking about the state policy but also need to consider about the inherent role and function of the elderly. Especially for these elders living in rural areas, although the number of elderly welfare centers of the whole country has greatly increased in last 10 years, the effect and function of the facility are almost the same and they are still lack of leisure activities. This issue become a serious problem nowadays. For the above reasons, this article conducts a social survey of 360 elderly people over the age of 65 who lives in the Gimpo city which is a rural-urban type city. The research method is to examine the relationship between the predisposing factors, enabling factors and need factors of Andersen's behavior model with binary logistic regression analysis and the decision tree analysis. The result of binary logistic regression shows the most of factors of Andersen's model is significant. The factors of age, gender, education level in predisposing factors; monthly income in enabling factors and the reserve for old life, the preparation of economic activity for old life in need factors are significant. Then the result of decision tree analysis shows the interaction between factors; when the education level in predisposing factors is higher, the possibility of using of elderly welfare center becomes bigger. Also as the level of healthy promoting preparation in the need factors gets lower, the possibility of using of elderly welfare center still becomes bigger. Although differences were found in the interpretation of the results of regression analysis and decision tree analysis, the results of this study can still provide support for the necessity of elderly welfare centers providing integrated welfare services.

• Journal of Service Research and Studies
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• v.3 no.1
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• pp.87-93
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• 2013

Breast cancer is known as a disease that occurs in a lot of developed countries. However, in recent years, the incidence of Korea's modern woman is increased steadily. As well known, breast cancer usually occurs in women over 50. In the case of Korea, however, the incidence of 40s with young women is increased steadily than the West. Therefore, it is a very urgent task to build a manual to the accurate diagnosis of breast cancer in adult women in Korea. In this paper, we show how using data mining techniques to predict breast cancer. Data mining refers to the process of finding regular patterns or relationships among variables within the database. To this, sophisticated analysis using the model, you will find useful information that is easily revealed. In this paper, through experiments Deicion Tree Naive Bayes analysis techniques were compared using analysis techniques to diagnose breast cancer. Two algorithms was analyzed by applying C4.5 algorithm. Deicison Tree classification accuracy was fairly good. Naive Bayes classification method showed better accuracy compared to the Decision Tree method.

• Korean Journal of Construction Engineering and Management
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• v.22 no.5
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• pp.55-65
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• 2021

Compared with other industries, construction industry shows a higher rate of death accidents and recently companies' legal responsibilities are to be increasingly enforced. The trend causes tremendous concerns for construction firms and increases the importance of forecasting and pro-actively managing death accidents in construction fields. The objective of the study is to develop a predictive analytics model for forecasting death accidents in building projects based on a decision tree technique, which enables to forecast the probabilities of death accidents by trade. The use of the model helps to decrease risks of legal punishments and to assist the safe execution of building projects by forecasting and pro-actively managing death accidents.

• Journal of Digital Convergence
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• v.16 no.12
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• pp.217-229
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• 2018

This paper aims at applying the data mining such as decision tree, neural network, and logistic regression to an unsold apartment complex in Wirye new town and developing the model forecasting the result of initial sale contract by house unit. Raw data are divided into training data and test data. The order of predictability in training data is neural network, decision tree, and logistic regression. On the contrary, the results of test data show that logistic regression is the best model. This means that logistic regression has more data adaptability than neural network which is developed as the model optimized for training data. Determinants of initial sale are the location of floor, direction, the location of unit, the proximity of electricity and generator room, subscriber's residential region and the type of subscription. This suggests that using two models together is more effective in exploring determinants of initial sales. This paper contributes to the development of convergence field by expanding the scope of data mining.

• 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 Korea Multimedia Society
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• v.25 no.6
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• pp.783-792
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• 2022

Versatile Video Coding (VVC) is the latest video coding standard, which had been developed by the Joint Video Experts Team (JVET) of ITU-T Video Coding Experts Group (VCEG) and ISO/IEC Moving Picture Experts Group (MPEG) in 2020. Although VVC can provide powerful coding performance, it requires tremendous computational complexity to determine the optimal block structures during the encoding process. In this paper, we propose a fast ternary tree decision method using two neural networks with 7 nodes as input vector based on the multi-layer perceptron structure, names STH-NN and STV-NN. As a training result of neural network, the STH-NN and STV-NN achieved accuracies of 85% and 91%, respectively. Experimental results show that the proposed method reduces the encoding complexity up to 25% with unnoticeable coding loss compared to the VVC test model (VTM).