• Asia Marketing Journal
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• v.20 no.1
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• pp.49-68
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• 2018

It is appropriate and useful to interpret some product classification schemes as buyer behavior models; such classifications permit investigations of discrepancies between classification predictions and actual buyer behavior. We review existing product classifications and identify underlying behavioral assumptions of various classification schemes that have been used in the marketing discipline for more than nine decades. Recognizing the irrelevance of existing product classifications for current products, we propose a new reclassification framework by incorporating transparency concepts. Based on this extended product classification, we highlight the potential roles of product classification study as an important link between consumer research and marketing strategy, emphasizing behavioral implications.

• Journal of the Korean Society of Safety
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• v.16 no.3
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• pp.117-124
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• 2001

This paper introduces m analysis framework and procedure for the support of the cognitive error analysis of emergency tasks in nuclear poler plants. The framework provides a new perspective in the utilization of influencing factors into error prediction. The framework can be characterized by two features. First, influencing factors that affect the occurrence of human error me classified into three groups, i.e., task characteristic factors(TCF), situation factors(SF), and performance assisting factors(PAF). This classification aims to support error prediction from the viewpoint of assessing the adequacy of PAF under given TCF and SF. Second, the assessment of influencing factors is made by each cognitive function. Through this, influencing factors assessment and error prediction can be made in an integrative way according to each cognitive function. In addition, it helps analysts identify vulnerable cognitive functions and error factors, and obtain specific nor reduction strategies. The proposed framework was applied to the error analysis of the bleed and feed operation of nuclear emergency tasks.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.1
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• pp.17-27
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• 2021

In this paper, we develop a galaxy image classification method based on hand-crafted features and machine learning techniques. Additionally, we provide an empirical analysis to reveal which combination of the techniques is effective for galaxy image classification. To achieve this, we developed a framework which consists of four modules such as preprocessing, feature extraction, feature post-processing, and classification. Finally, we found that the best technique for galaxy image classification is a method to use a median filter, ORB vector features and a voting classifier based on RBF SVM, random forest and logistic regression. The final method is efficient so we believe that it is applicable to embedded environments.

• Korean Journal of Human Ecology
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• v.21 no.6
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• pp.1129-1144
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• 2012

The purpose of this study was to investigate the gender difference in adolescent's problem behavior and depression, and The main aim of this study was to develop a fundamental web design to provide information content that would be easy for average consumers to understand based on the classification of information related to food safety. Based on the information obtained through in-depth interviews, the researchers developed an information classification system that meets the needs of consumers, and which serve as a basic framework for a homepage for a food safety information center. A total of 62 food items in 6 areas were selected based on reports of food safety related events occurring between 1998-2009 (KFDA 2008). The classification system of risk factors such as chemical risk factors and biological risk factors were categorized. The specific features of the information content for individual food items provided for classification based on evaluation by professional food scientists and the importance of risk factors. By providing a consumer participation section and company participation section, it was anticipated that the food safety information center would be able to act as a moderator for food safety information communication among consumers, the food industry, and the government. Based on the development of a classification system and framework, a design plan and tree-map for the internet site was developed.

• Korean Journal of Remote Sensing
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• v.31 no.3
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• pp.205-214
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• 2015

In this study, a novel land-cover classification framework for multi-temporal SAR data is presented that can combine multiple features extracted through data transforms and multiple classifiers. At first, data transforms using principle component analysis (PCA) and 3D wavelet transform are applied to multi-temporal SAR dataset for extracting new features which were different from original dataset. Then, three different classifiers including maximum likelihood classifier (MLC), neural network (NN) and support vector machine (SVM) are applied to three different dataset including data transform based features and original backscattering coefficients, and as a result, the diverse preliminary classification results are generated. These results are combined via a majority voting rule to generate a final classification result. From an experiment with a multi-temporal ENVISAT ASAR dataset, every preliminary classification result showed very different classification accuracy according to the used feature and classifier. The final classification result combining nine preliminary classification results showed the best classification accuracy because each preliminary classification result provided complementary information on land-covers. The improvement of classification accuracy in this study was mainly attributed to the diversity from combining not only different features based on data transforms, but also different classifiers. Therefore, the land-cover classification framework presented in this study would be effectively applied to the classification of multi-temporal SAR data and also be extended to multi-sensor remote sensing data fusion.

• Journal of Preventive Medicine and Public Health
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• v.29 no.4 s.55
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• pp.877-897
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• 1996

In recent years, the Korean Medical Association has undertaken the feat of establishing the Korean Standard Terminology of Medical Procedures with the dedicated help of 32 medical academic societies. However, because the project is being conducted by several different circles, it has yet to see a clear system of classification. This thesis, therefore, proposes the three principles of scientific properties, usefulness and ideology as the basis for classification system and has developed the Classification System of Medical Procedures in Korea upon their foundation. The methodology and organization of this thesis as follows. First, by adopting scientific classification system of Feinstein(1988), an analysis of the classification systems of the medical procedures in the United States, Japan, Taiwan, WHO was carried out to reveal the framework and the basic principles in each system. Second, the direction of classification system has been constructed by applying the normative principle of medical field in order to show the future direction of the medical field and realize its ideology. Third, a finalized framework for the classification system will be presented as based on the direction of classification system. Of the three basis principles mentioned above, the analysis on the principles of usefulness was left out of this thesis due to the difficulty of establishing specific standards of analysis. The results of the study are as follows. The overall structure of the thesis is aimed at showing the 'Prevention-Therapy-Rehabilitation' quality of comprehensive health care and consists of six chapters; I. Prevention and Health Promotion II. Evaluation and Management III. Diagnostic Procedures IV. Endoscopy V. Therapeutic Procedures VI. Rehabilitation Chapter three Diagnostic Procedures is divided into four parts : Functional Diagnosis, Visual Diagnosis, Pathological Diagnosis, Biopsy and Sampling. Chapter five Therapeutic Procedures is divided into Psychiatry, Non-Invasive Therapy, Invasive Therapy, Anaesthesia and Radiation Oncology. Of these sub-divisions, Functional Diagnosis, Biopsy and Sampling, Endoscopy and Invasive Therapy employs the anatomical system of classification. On the other hand, Visual Diagnosis, Pathological Diagnosis, Anesthesia and Diagnostic Radiology, namely those divisions in which there is little or no overlapping in services with other divisions, used the classification system of its own division. The classification system introduced in this thesis can be further supplemented through the use of the cluster analysis by incorporating the advice and assistance of other specialists.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.4021-4037
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• 2018

In network intrusion detection research, two characteristics are generally considered vital to building efficient intrusion detection systems (IDSs): an optimal feature selection technique and robust classification schemes. However, the emergence of sophisticated network attacks and the advent of big data concepts in intrusion detection domains require two more significant aspects to be addressed: employing an appropriate big data computing framework and utilizing a contemporary dataset to deal with ongoing advancements. As such, we present a comprehensive approach to building an efficient IDS with the aim of strengthening academic anomaly detection research in real-world operational environments. The proposed system has the following four characteristics: (i) it performs optimal feature selection using information gain and branch-and-bound algorithms; (ii) it employs machine learning techniques for classification, namely, Logistic Regression, Naïve Bayes, and Random Forest; (iii) it introduces bulk synchronous parallel processing to handle the computational requirements of large-scale networks; and (iv) it utilizes a real-time contemporary dataset generated by the Information Security Centre of Excellence at the University of Brunswick (ISCX-UNB) to validate its efficacy. Experimental analysis shows the effectiveness of the proposed framework, which is able to achieve high accuracy, low computational cost, and reduced false alarms.

• Knowledge Management Research
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• v.22 no.1
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• pp.105-120
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• 2021

Recently, there has been a dramatic change in real estate markets with the development of information technology. The word, Proptech, is defined as the real estate transaction innovation motivated by various types of information technology including artificial intelligence, sensing technology and big data. The objective of this study is to provide a value-creation framework for Proptech business based on the understanding of how and what types of values are created and shared, which gives organization to develop strategies and business models. And a new classification scheme of Proptech business is also suggested based on the recognition of created values along the development of Proptech business. Then, the proposed matrix is applied to derive the business value such as intangibility value, relational value and enhancement value with the case analysis on the each components of Proptech business.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.1
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• pp.73-81
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• 2012

For planning and developing rural regions, it is very important to understand and utilize regional characteristics including social, demographic, and economic aspects. The purpose of this study is to find effective analysis techniques and provide a procedure design for mining regional characteristics in South Korea through reviewing and analyzing 41 related studies. The engaged research methods can be classified into five categories (PCA+CA, PCA, CA, GIS, and PCA+GIS) with the combination of three methodologies: principal component analysis (PCA), cluster analysis (CA), and geographical information system (GIS). The combination of PCA and CA occupied about 40 % of research methods used in related studies. The analysis tool of Korean Rural Information Supporting System (KRISS) is designed based on the outcomes of this study and applied to classify the regional capacity of agriculture using agricultural census data (2000) for evaluating its applicability.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.161-176
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• 2022

There has been active research in image classification using deep learning convolutional neural network (CNN) models. ImageNet large-scale visual recognition challenge (ILSVRC) (2010-2017) was one of the most important competitions that boosted the development of efficient deep learning algorithms. This paper introduces and compares six monumental models that achieved high prediction accuracy in ILSVRC. First, we provide a review of the models to illustrate their unique structure and characteristics of the models. We then compare those models under a unified framework. For this reason, additional devices that are not crucial to the structure are excluded. Four popular data sets with different characteristics are then considered to measure the prediction accuracy. By investigating the characteristics of the data sets and the models being compared, we provide some insight into the architectural features of the models.