• Title/Summary/Keyword: decision tree

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Effective R & D Management using Data Mining Classification Techniques (데이터마이닝 분류기법을 이용한 효과적인 연구관리에 관한 연구)

  • 황석해;문태수;이준한
    • Journal of Information Technology Application
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    • v.3 no.2
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    • pp.1-24
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    • 2001
  • This purpose of this study is to drive important criteria for improving customer relationship of R institute using data mining techniques. The focus of this research is to consider patterns and interactions of research variables from research management database of R institute, and to classify the outside organizations and the inside organizations for research contract organizations, and to decide the directions of customer relationship management through analyzing the research type and research cost of research topics. In order to drive criteria variables through pattern analysis of the research database, decision tree algorithm is employed. The results show that determinant variables of 17 input variables are research period, overhead cost, R & D cost as variables to classify the outside and inside contract organization.

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Self-efficacy and Compliance in Patients with Chronic Heart Failure: The Effect of a Self-management Program using Decision Tree (의사결정 틀을 이용한 만성 심부전 환자의 자기관리프로그램이 자기효능, 자기관리 이행에 미치는 효과)

  • Kim, Cho-Ja;Kim, Gi-Yon;Jang, Yeon-Soo
    • Korean Journal of Adult Nursing
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    • v.16 no.2
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    • pp.316-326
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    • 2004
  • Purpose: The purpose of this study was to identify effects of a self-management program on self-efficacy and compliance in patients with CHF. Hypothesis: 1) Patients with CHF who are provided with a self-management program will show higher self-efficacy scores than a control group. 2) Patients who are provided with a self-management program will show higher compliance scores than a control group. Method: This study was designed as a nonequivalent non-synchronized pre-posttest control group. There were eight patients in the experimental group, and twelve in the control group. According to NYHA classification, all patients belonged under the classesII to IV. Data were collected using the instruments developed by the researchers. Data were analyzed using descriptive statistics and Mann Whitney U test. Result: There were significant differences in self-efficacy scores and compliance scores between the experimental and control group. Conclusion: By utilizing the program, patients were able to monitor their symptoms routinely, comply with therapeutic regimen, and feel better able to positively influence their disease. Therefore, better compliance means fewer readmissions of patients with CHF.

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Adaptive Security Management Model based on Fuzzy Algorithm and MAUT in the Heterogeneous Networks (이 기종 네트워크에서 퍼지 알고리즘과 MAUT에 기반을 둔 적응적 보안 관리 모델)

  • Yang, Seok-Hwan;Chung, Mok-Dong
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.47 no.1
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    • pp.104-115
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    • 2010
  • Development of the system which provides services using diverse sensors is expanding due to the widespread use of ubiquitous technology, and the research on the security technologies gaining attention to solve the vulnerability of ubiquitous environment's security. However, there are many instances in which flexible security services should be considered instead of strong only security function depending on the context. This paper used Fuzzy algorithm and MAUT to be aware of the diverse contexts and to propose context-aware security service which provides flexible security function according to the context.

Predicting Factors on Performance Confidence of Cardiopulmonary Resuscitation in Community Members (지역사회 주민의 심폐소생술 수행 자신감 예측요인)

  • Lee, Su-Jin
    • Journal of Digital Contents Society
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    • v.19 no.9
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    • pp.1699-1705
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    • 2018
  • This study is a descriptive investigatory study that secondarily analyzes the community health survey in order to identify the characteristics of confidence regarding the execution of cardiopulmonary resuscitation among community members of Korea. Study subjects included 357,176 people who were aware of cardiopulmonary resuscitation based on 2014 and 2016 community health survey. The collected data were analyzed for composite sample frequency and decision-making tree using SPSS WIN 25.0 program. According to the results of this study, a confidence regarding execution of cardiopulmonary resuscitation of the community members was higher if the subject has experienced cardiopulmonary resuscitation education, trained on mannequin within the past 2 years, received cardiopulmonary resuscitation education within the past 2 years, is of male sex, and is 41.5 years of age or younger.

A Framework for Semantic Interpretation of Noun Compounds Using Tratz Model and Binary Features

  • Zaeri, Ahmad;Nematbakhsh, Mohammad Ali
    • ETRI Journal
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    • v.34 no.5
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    • pp.743-752
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    • 2012
  • Semantic interpretation of the relationship between noun compound (NC) elements has been a challenging issue due to the lack of contextual information, the unbounded number of combinations, and the absence of a universally accepted system for the categorization. The current models require a huge corpus of data to extract contextual information, which limits their usage in many situations. In this paper, a new semantic relations interpreter for NCs based on novel lightweight binary features is proposed. Some of the binary features used are novel. In addition, the interpreter uses a new feature selection method. By developing these new features and techniques, the proposed method removes the need for any huge corpuses. Implementing this method using a modular and plugin-based framework, and by training it using the largest and the most current fine-grained data set, shows that the accuracy is better than that of previously reported upon methods that utilize large corpuses. This improvement in accuracy and the provision of superior efficiency is achieved not only by improving the old features with such techniques as semantic scattering and sense collocation, but also by using various novel features and classifier max entropy. That the accuracy of the max entropy classifier is higher compared to that of other classifiers, such as a support vector machine, a Na$\ddot{i}$ve Bayes, and a decision tree, is also shown.

Learning Text Chunking Using Maximum Entropy Models (최대 엔트로피 모델을 이용한 텍스트 단위화 학습)

  • Park, Seong-Bae;Zhang, Byoung-Tak
    • Annual Conference on Human and Language Technology
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    • 2001.10d
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    • pp.130-137
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    • 2001
  • 최대 엔트로피 모델(maximum entropy model)은 여러 가지 자연언어 문제를 학습하는데 성공적으로 적용되어 왔지만, 두 가지의 주요한 문제점을 가지고 있다. 그 첫번째 문제는 해당 언어에 대한 많은 사전 지식(prior knowledge)이 필요하다는 것이고, 두번째 문제는 계산량이 너무 많다는 것이다. 본 논문에서는 텍스트 단위화(text chunking)에 최대 엔트로피 모델을 적용하는 데 나타나는 이 문제점들을 해소하기 위해 새로운 방법을 제시한다. 사전 지식으로, 간단한 언어 모델로부터 쉽게 생성된 결정트리(decision tree)에서 자동적으로 만들어진 규칙을 사용한다. 따라서, 제시된 방법에서의 최대 엔트로피 모델은 결정트리를 보강하는 방법으로 간주될 수 있다. 계산론적 복잡도를 줄이기 위해서, 최대 엔트로피 모델을 학습할 때 일종의 능동 학습(active learning) 방법을 사용한다. 전체 학습 데이터가 아닌 일부분만을 사용함으로써 계산 비용은 크게 줄어 들 수 있다. 실험 결과, 제시된 방법으로 결정트리의 오류의 수가 반으로 줄었다. 대부분의 자연언어 데이터가 매우 불균형을 이루므로, 학습된 모델을 부스팅(boosting)으로 강화할 수 있다. 부스팅을 한 후 제시된 방법은 전문가에 의해 선택된 자질로 학습된 최대 엔트로피 모델보다 졸은 성능을 보이며 지금까지 보고된 기계 학습 알고리즘 중 가장 성능이 좋은 방법과 비슷한 성능을 보인다 텍스트 단위화가 일반적으로 전체 구문분석의 전 단계이고 이 단계에서의 오류가 다음 단계에서 복구될 수 없으므로 이 성능은 텍스트 단위화에서 매우 의미가 길다.

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Design and Implementation of Real-Time Vehicle Safety System based on Wireless Sensor Networks (무선 센서 네트워크 기반의 실시간 차량 안전 시스템 설계 및 구현)

  • Hong, YouSik;Oh, Sei-JIn;Kim, Cheonshik
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.8 no.2
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    • pp.57-65
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    • 2008
  • Wireless sensor networks achieve environment monitoring and controlling through use of small devices of low cost and low power. Such network is comprised of several sensor nodes, each having a microprocessor, sensor, actuator and wired/wireless transceiver inside a small device. In this paper, we employ the sensor networks in order to design and implement a real-time vehicle safety system. Such system can inform the safe velocity in a specific weather condition to drivers in advance through analyzing the weather data collected from sensor networks. As a result, the drivers can prevent effectively accidents by controlling their car speed.

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Automated condition assessment of concrete bridges with digital imaging

  • Adhikari, Ram S.;Bagchi, Ashutosh;Moselhi, Osama
    • Smart Structures and Systems
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    • v.13 no.6
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    • pp.901-925
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    • 2014
  • The reliability of a Bridge management System depends on the quality of visual inspection and the reliable estimation of bridge condition rating. However, the current practices of visual inspection have been identified with several limitations, such as: they are time-consuming, provide incomplete information, and their reliance on inspectors' experience. To overcome such limitations, this paper presents an approach of automating the prediction of condition rating for bridges based on digital image analysis. The proposed methodology encompasses image acquisition, development of 3D visualization model, image processing, and condition rating model. Under this method, scaling defect in concrete bridge components is considered as a candidate defect and the guidelines in the Ontario Structure Inspection Manual (OSIM) have been adopted for developing and testing the proposed method. The automated algorithms for scaling depth prediction and mapping of condition ratings are based on training of back propagation neural networks. The result of developed models showed better prediction capability of condition rating over the existing methods such as, Naïve Bayes Classifiers and Bagged Decision Tree.

A Halal Food Classification Framework Using Machine Learning Method for Enhancing Muslim Tourists (무슬림 관광객 증대를 위한 머신러닝 기반의 할랄푸드 분류 프레임워크)

  • Kim, Sun-A;Kim, Jeong-Won;Won, Dong-Yeon;Choi, Yerim
    • The Journal of Information Systems
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    • v.26 no.3
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    • pp.273-293
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    • 2017
  • Purpose The purpose of this study is to introduce a framework that helps Muslims to determine whether a food can be consumed. It can complement existing Halal food classification services having a difficulty of constructing Halal food database. Design/methodology/approach The proposed framework includes two components. First, OCR(Optical Character Recognition) technique is utilized to read the food additive information. Second, machine learning methods were used to trained and predicted to determine whether a food can be consumed using the provided information. Findings Among the compared machine learning methods, SVM(Support Vector Machine), DT(Decision Tree), and NB(Naive Bayes), SVM with linear kernel and DT had excellent performance in the Halal food classification. The framework which adopting the proposed framework will enhance the tourism experiences of Muslim tourists who consider keeping the Islamic law most importantly. Furthermore, it can eventually contribute to the enhancement of smart tourism ecosystem.

A Method of Predicting Service Time Based on Voice of Customer Data (고객의 소리(VOC) 데이터를 활용한 서비스 처리 시간 예측방법)

  • Kim, Jeonghun;Kwon, Ohbyung
    • Journal of Information Technology Services
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    • v.15 no.1
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    • pp.197-210
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    • 2016
  • With the advent of text analytics, VOC (Voice of Customer) data become an important resource which provides the managers and marketing practitioners with consumer's veiled opinion and requirements. In other words, making relevant use of VOC data potentially improves the customer responsiveness and satisfaction, each of which eventually improves business performance. However, unstructured data set such as customers' complaints in VOC data have seldom used in marketing practices such as predicting service time as an index of service quality. Because the VOC data which contains unstructured data is too complicated form. Also that needs convert unstructured data from structure data which difficult process. Hence, this study aims to propose a prediction model to improve the estimation accuracy of the level of customer satisfaction by combining unstructured from textmining with structured data features in VOC. Also the relationship between the unstructured, structured data and service processing time through the regression analysis. Text mining techniques, sentiment analysis, keyword extraction, classification algorithms, decision tree and multiple regression are considered and compared. For the experiment, we used actual VOC data in a company.