• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제7권2호
• /
• pp.91-95
• /
• 2007

Main goal of web recommendation system is to study how user behavior on a website can be predicted by analyzing web log data which contain the visited web pages. Many researches of the web recommendation system have been studied. To construct web recommendation system, web mining is needed. Especially, web usage analysis of web mining is a tool for recommendation model. In this paper, we propose web recommendation system using grid based support vector machines for improvement of web recommendation system. To verify the performance of our system, we make experiments using the data set from our web server.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제6권2호
• /
• pp.173-177
• /
• 2006

This paper investigates the asymmetric costs of false errors to enhance the detection systems performance. The proposed method utilizes the network model to consider the cost ratio of false errors. By comparing false positive errors with false negative errors this scheme achieved better performance on the view point of both security and system performance objectives. The results of our empirical experiment show that the network model provides high accuracy in detection. In addition, the simulation results show that effectiveness of probe detection is enhanced by considering the costs of false errors.

• 한국지능시스템학회논문지
• /
• 제25권3호
• /
• pp.217-222
• /
• 2015

클러스터링 기법은 탐색적 자료 분석 기법으로 알려진 중요한 데이터마이닝 기법 중 하나로서 패턴 인식, 원격 탐사 등의 분야에 사용되고 있다. 이 방법을 이용하여 데이터의 기본 구조를 추출하고, 개체의 군집화 혹은 군집의 계층을 조직한다. 기상 레이더는 대기 중에 존재하는 물체에서 반사되는 신호를 이용하여 관측을 수행하고, 해당 좌표에 데이터를 저장하는 원리로 동작하는데, 이를 분석하기 위해서는 흩어져있는 레이더 데이터를 유사도를 바탕으로 강수에코와 비강수에코를 구분하여 군집화 할 필요가 있다. 따라서 본 논문에서는 클러스터링 기법을 레이더 데이터에 적용하는 방법에 대한 연구를 수행하였다. 또한, 강수에코와 비강수에코가 인접해 있을 경우 발생할 수 있는 문제를 해결하기 위하여 퍼지 로직과 계층적 클러스터링 기법을 접목하여 유사도를 판별하는 방법에 대한 연구를 수행하였다. 실제 사례를 바탕으로 본 논문에서 제안한 클러스터링 기법을 적용한 결과, 강수에코와 비강수에코가 인접해 있는 경우 기존 기법보다 좋은 결과를 도출하는 것을 확인할 수 있었다.

• Asia pacific journal of information systems
• /
• 제11권4호
• /
• pp.113-133
• /
• 2001

We propose utilizing a fuzzy cognitive map(FCM) to integrate tacit knowledge and explicit knowledge both of which are crucial to the success of knowledge management. Recently, explicit knowledge is getting more available as CRM and data mining approaches become popular as the advent of using database and the Internet technology. However, for the knowledge management to be successful, tacit knowledge should be seamlessly integrated with explicit knowledge seamlessly. The problem hindering such effort is how to find a vehicle facilitating transformation of explicit knowledge into tacit knowledge, and vice versa. FCM has been important method for representing tacit knowledge as a form of explict knowledge. In this respect, we suggest the detailed process about how to integrate explicit knowledge and tacit knowledge by using FCM. We gathered extensive set of data from the credit card company, and applied our proposed method. Results showed that our approach is robust and promising for the field of integrating two different kinds of knowledge.

• 한국컴퓨터정보학회논문지
• /
• 제23권11호
• /
• pp.25-30
• /
• 2018

Pattern classification system is often an important component of intelligent systems. In this paper, we present a pattern classification system consisted of the feature selection module, knowledge base construction module and decision module. We introduce a feature impact evaluation selection method based on fuzzy cluster analysis considering computational approach and generalization capability of given data characteristics. A fuzzy neural network, OFUN-NET based on unsupervised learning data mining technique produces knowledge base for representative clusters. 240 blemish pattern images are prepared and applied to the proposed system. Experimental results show the feasibility of the proposed classification system as an automating defect inspection tool.

• 제어로봇시스템학회논문지
• /
• 제11권10호
• /
• pp.830-836
• /
• 2005

Application of microarray technologies which monitor simultaneously the expression pattern of thousands of individual genes in different biological systems results in a tremendous increase of the amount of available gene expression data and have provided new insights into gene expression during drug development, within disease processes, and across species. There is a great need of data mining methods allowing straightforward interpretation, visualization and analysis of the relevant information contained in gene expression profiles. Specially, classifying biological samples into known classes or phenotypes is an important practical application for microarray gene expression profiles. Gene expression profiles obtained from tissue samples of patients thus allowcancer classification. In this research, molecular classification of microarray gene expression data is applied for multi-class cancer using computational biology such gene selection, principal component analysis and fuzzy clustering. The proposed method was applied to microarray data from leukemia patients; specifically, it was used to interpret the gene expression pattern and analyze the leukemia subtype whose expression profiles correlated with four cases of acute leukemia gene expression. A basic understanding of the microarray data analysis is also introduced.

• 한국인터넷방송통신학회논문지
• /
• 제23권4호
• /
• pp.127-135
• /
• 2023

요즘, 우울증 및 스트레스로 자살하는 환자가 급증하고 있다. 뿐만 아니라, 스트레스 및 우울증이 오래 지속되면, 심장병 및 뇌 질환, 고혈압 등을 유발할 수 있는 위험한 요소로 질환이다. 그러나, 아무리 현대 의학이 발전하였지만, 우울증 및 심장병 환자에게는 특별한 약이나 치료제가 없는 매우 난감한 상황이다. 그러므로, 세계 여러 나라에서, 심전도 및 산소포화도, 뇌파 분석 기능을 이용해서 우울증 위험환자 및 자살 위험환자를 조기에 판단하는 연구가 활발하게 이루어지고 있다. 본 논문에서는, 이러한 문제점을 분석하기 위해서, 심장병 가설데이터를 수립해서, 심장병 위험환자를 판단하는 컴퓨터 모의실험을 수행하였다. 특히, 심장병 발생 예측을 을 10% 이상 향상하게 시키기 위해서, 퍼지 추론을 사용하는 모의실험을 수행하였다.

• Interdisciplinary Bio Central
• /
• 제3권2호
• /
• pp.7.1-7.6
• /
• 2011

Background: Published manuscripts are the main source of biological knowledge. Since the manual examination is almost impossible due to the huge volume of literature data (approximately 19 million abstracts in PubMed), intelligent text mining systems are of great utility for knowledge discovery. However, most of current text mining tools have limited applicability because of i) providing abstract-based search rather than sentence-based search, ii) improper use or lack of ontology terms, iii) the design to be used for specific subjects, or iv) slow response time that hampers web services and real time applications. Results: We introduce an advanced text mining system called PubMine that supports intelligent knowledge discovery based on diverse bio-ontologies. PubMine improves query accuracy and flexibility with advanced search capabilities of fuzzy search, wildcard search, proximity search, range search, and the Boolean combinations. Furthermore, PubMine allows users to extract multi-dimensional relationships between genes, diseases, and chemical compounds by using OLAP (On-Line Analytical Processing) techniques. The HUGO gene symbols and the MeSH ontology for diseases, chemical compounds, and anatomy have been included in the current version of PubMine, which is freely available at http://pubmine.kobic.re.kr. Conclusions: PubMine is a unique bio-text mining system that provides flexible searches and analysis of biological entity relationships. We believe that PubMine would serve as a key bioinformatics utility due to its rapid response to enable web services for community and to the flexibility to accommodate general ontology.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제12권1호
• /
• pp.1-5
• /
• 2012

Data reduction has been used widely in data mining for convenient analysis. Principal component analysis (PCA) and factor analysis (FA) methods are popular techniques. The PCA and FA reduce the number of variables to avoid the curse of dimensionality. The curse of dimensionality is to increase the computing time exponentially in proportion to the number of variables. So, many methods have been published for dimension reduction. Also, data augmentation is another approach to analyze data efficiently. Support vector machine (SVM) algorithm is a representative technique for dimension augmentation. The SVM maps original data to a feature space with high dimension to get the optimal decision plane. Both data reduction and augmentation have been used to solve diverse problems in data analysis. In this paper, we compare the strengths and weaknesses of dimension reduction and augmentation for classification and propose a classification method using data reduction for classification. We will carry out experiments for comparative studies to verify the performance of this research.

• 대한원격탐사학회지
• /
• 제34권1호
• /
• pp.45-74
• /
• 2018

Landslide is a natural hazard that threats lives and properties in many areas around the world. Landslides are difficult to recognize, particularly in rainforest regions. Thus, an accurate, detailed, and updated inventory map is required for landslide susceptibility, hazard, and risk analyses. The inconsistency in the results obtained using different features selection techniques in the literature has highlighted the importance of evaluating these techniques. Thus, in this study, six techniques of features selection were evaluated. Very-high-resolution LiDAR point clouds and orthophotos were acquired simultaneously in a rainforest area of Cameron Highlands, Malaysia by airborne laser scanning (LiDAR). A fuzzy-based segmentation parameter (FbSP optimizer) was used to optimize the segmentation parameters. Training samples were evaluated using a stratified random sampling method and set to 70% training samples. Two machine-learning algorithms, namely, Support Vector Machine (SVM) and Random Forest (RF), were used to evaluate the performance of each features selection algorithm. The overall accuracies of the SVM and RF models revealed that three of the six algorithms exhibited higher ranks in landslide detection. Results indicated that the classification accuracies of the RF classifier were higher than the SVM classifier using either all features or only the optimal features. The proposed techniques performed well in detecting the landslides in a rainforest area of Malaysia, and these techniques can be easily extended to similar regions.