• Journal of the Korean Society of Environmental Restoration Technology
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• v.12 no.2
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• pp.50-60
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• 2009

A spatial decision making process is needed when a local government tries to make polices and plans for eco-tourism in urban and regional site scale. This study aimed to suggest an assessment model to support spatial decision making on planning and making polices for eco-tourism. The model composes 6 stages of 'setting up ecogeographic territories'. 'value analysis method as ecotourism resources' 'synthetic assessing', 'grading values', 'selecting main resources for ecotourism' and 'spatial decision making support'. Applying the model to Shiheung city in Kyounggi province, validity was secured. By using the model, it was possible to make some decisions effectively such as selection of ecotourism resources, decision of the priorities of polices for ecotourism, and setting up the type of ecotourism to be introduced. In addition, by visualizing high valued resources and areas for ecotourism it w possible to support to make plans and policies effectively.

• Journal of the Korean Association of Geographic Information Studies
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• v.2 no.3
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• pp.81-90
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• 1999

The functional change of university forest have led to need more complicated techniques for forest management strategies, and more information about forest and natural environment. Therefore the systematic tools, like the so-called Forest Information System to which apply the techniques of geographic information system, are eagerly required for collecting, editing, managing, analyzing the various data about forest and environment, and for supporting the decision-making process. The digital mapping, which could be a primary step to construct the Forest Information System, was carried out using the many kinds of thematic spatial data referring to the Seongju Experimental Forest of Taegu University. As a result, various digital maps including forest type, soil type and so on were constructed. And then we made an user-interface system to link the attributive data in management plan to the thematic spatial data. This system was regarded as the effective tool capable of the more rapid query, analysis and update of related data for systematic management of university forest. Moreover, it would be a useful tool of decision-making in devising, assessing and operating the plan of forest management and development. But there would be much room for supplementation and improvement to make the more convenient and powerful system for the external demands, therefore more concerns and efforts in collecting, revising and updating the data is continuously required.

• Journal of Korean Society of Forest Science
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• v.112 no.2
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• pp.230-247
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• 2023

This study was conducted on migrants in 5 cities and counties near the Jirisan Dulle-gil Trail, designated as a National Forest Trail, to find out how the National Forest Trail affects the quality of life after migrants from urban to mountain villages. The group that used the Jirisan Dulle-gil Trail before and/or after the migration showed higher levels of impact on the migration decision, life satisfaction, and behavioral intention than the group that did not use the trail. The group that was affected by the Jirisan Dulle-gil Trail in deciding on the migration also showed higher usage satisfaction with the Jirisan Dulle-gil Trail, life satisfaction, satisfaction with the migration, and behavioral intention than the unaffected group. There were also significant differences in the quality of life according to the migration area, location satisfaction among the migration satisfaction levels, and behavioral intention. In conclusion, it was confirmed that the Jirisan Dulle-gil Trail plays an important role in the decision to migrate to mountain villages and the quality of life after the migration. The results of this study are expected to be used as basic data to present policies related to National Forest Trails that can contribute to the development of mountain villages and countermeasures against population extinction in mountain villages.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.3
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• pp.397-411
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• 2023

The number of deteriorated bridges with a service period of more than 30 years has been rapidly increasing in Korea. Accordingly, the importance of advanced maintenance technologies through the predictions of age-induced deterioration degree, condition, and performance of bridges is more and more noticed. The prediction method of the safety grade of bridges was proposed in this study using the classification models of the Decision Tree and the Random Forest based on machine learning. As a result of analyzing these models for the 8,850 bridges located in national roads with various evaluation indexes such as confusion matrix, balanced accuracy, recall, ROC curve, and AUC, the Random Forest largely showed better predictive performance than that of the Decision Tree. In particular, random under-sampling in the Random Forest showed higher predictive performance than that of other sampling techniques for the C and D grade bridges, with the recall of 83.4%, which need more attention to maintenance because of the significant deterioration degree. The proposed model can be usefully applied to rapidly identify the safety grade and to establish an efficient and economical maintenance plan of bridges that have not recently been inspected.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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• 2011.10a
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• pp.289-296
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• 2011

Recently, much of forest biomass has been obtained from the national forest management operation. Unfortunately, Korean Forest Services has a plan to use this forest biomass as energy fuels for wood pellets. Considering unhappy situation that about 80% of wood pulps has been imported, it is regarded as unwise decision. If forest biomass can be used to make pulps or other valuable woody products, we are able to double its economic value than the raw materials for wood pellets. In this study, we explored alternative raw materials for wood biomass used to make wood pellets. For this, fresh technology such as torrefaction was applied with the other lignocellulosic biomass.

• Journal of Korea Multimedia Society
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• v.21 no.5
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• pp.617-625
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• 2018

Phishing website has become a crucial concern in cyber security applications. It is performed by fraudulently deceiving users with the aim of obtaining their sensitive information such as bank account information, credit card, username, and password. The threat has led to huge losses to online retailers, e-business platform, financial institutions, and to name but a few. One way to build anti-phishing detection mechanism is to construct classification algorithm based on machine learning techniques. The objective of this paper is to compare different classifier ensemble approaches, i.e. random forest, rotation forest, gradient boosted machine, and extreme gradient boosting against single classifiers, i.e. decision tree, classification and regression tree, and credal decision tree in the case of website phishing. Area under ROC curve (AUC) is employed as a performance metric, whilst statistical tests are used as baseline indicator of significance evaluation among classifiers. The paper contributes the existing literature on making a benchmark of classifier ensembles for web phishing detection.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.747-749
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• 2003

For forest fire monitoring in relatively cool area like Siberia, design of Decision Support System (DSS) is proposed. The DSS is consisted of three different algorithms to detect potential fires from NOAA AVHRR image. The algorithm developed by CCRS (Canada Center for Remote Sensing) uses fixed thresholds for multi-channel information like one by ESA (European Space Agency). The algorithm of IGBP (International Geosphere Biosphere Program) involves contextual information in deriving fire pixels. CCRS and IGBP algorithms are rather liberal compared to more conservative ESA algorithm. Fire pixel information from the three algorithms is presented to the user. The user considers all these information in making decision about the location fire takes place.

• Journal of Multimedia Information System
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• v.5 no.2
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• pp.99-104
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• 2018

Phishing website has become a crucial concern in cyber security applications. It is performed by fraudulently deceiving users with the aim of obtaining their sensitive information such as bank account information, credit card, username, and password. The threat has led to huge losses to online retailers, e-business platform, financial institutions, and to name but a few. One way to build anti-phishing detection mechanism is to construct classification algorithm based on machine learning techniques. The objective of this paper is to compare different classifier ensemble approaches, i.e. random forest, rotation forest, gradient boosted machine, and extreme gradient boosting against single classifiers, i.e. decision tree, classification and regression tree, and credal decision tree in the case of website phishing. Area under ROC curve (AUC) is employed as a performance metric, whilst statistical tests are used as baseline indicator of significance evaluation among classifiers. The paper contributes the existing literature on making a benchmark of classifier ensembles for web phishing detection.

• 대한상한금궤의학회지
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• v.14 no.1
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• pp.41-50
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• 2022

Objective : The purpose of this study is to explore the most suitable machine learning model algorithm for Shanghanlun diagnostic system classification using natural language processing (NLP). Methods : A total of 201 data items were collected from 『Shanghanlun』 and 『Clinical Shanghanlun』, 'Taeyangbyeong-gyeolhyung' and 'Eumyangyeokchahunobokbyeong' were excluded to prevent oversampling or undersampling. Data were pretreated using a twitter Korean tokenizer and trained by logistic regression, ridge regression, lasso regression, naive bayes classifier, decision tree, and random forest algorithms. The accuracy of the models were compared. Results : As a result of machine learning, ridge regression and naive Bayes classifier showed an accuracy of 0.843, logistic regression and random forest showed an accuracy of 0.804, and decision tree showed an accuracy of 0.745, while lasso regression showed an accuracy of 0.608. Conclusions : Ridge regression and naive Bayes classifier are suitable NLP machine learning models for the Shanghanlun diagnostic system classification.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.652-662
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• 2023

Identifying highly discriminating genes is a critical step in tumor recognition tasks based on microarray gene expression profile data and machine learning. Gene selection based on tree models has been the subject of several studies. However, these methods are based on a single-tree model, often not robust to ultra-highdimensional microarray datasets, resulting in the loss of useful information and unsatisfactory classification accuracy. Motivated by the limitations of single-tree-based gene selection, in this study, ensemble gene selection methods based on multiple-tree models were studied to improve the classification performance of tumor identification. Specifically, we selected the three most representative tree models: ID3, random forest, and gradient boosting decision tree. Each tree model selects top-n genes from the microarray dataset based on its intrinsic mechanism. Subsequently, three ensemble gene selection methods were investigated, namely multipletree model intersection, multiple-tree module union, and multiple-tree module cross-union, were investigated. Experimental results on five benchmark public microarray gene expression datasets proved that the multiple tree module union is significantly superior to gene selection based on a single tree model and other competitive gene selection methods in classification accuracy.