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Journal of Intelligence and Information Systems,
Vol. 8, No. 2, December 2002
Moving Object Tracking Using Co-occurrence Features of Objects
Seongdon Kim, Seongah Chin, and Moonwon Choo
Vol. 8, No. 2, Page: 1 ~ 13
Keywords : Object tracking, Motion tracking, Object motion detection
Abstract
In this paper, we propose an object tracking system which can be convinced of moving area shaped on objects through color sequential images, decided moving directions of foot messengers or vehicles of image sequences. In static camera, we suggests a new evaluating method extracting co-occurrence matrix with feature vectors of RGB after analyzing and blocking difference images, which is accessed to field of camera view for motion. They are energy, entropy, contrast, maximum probability, inverse difference moment, and correlation of RGB color vectors. we describe how to analyze and compute corresponding relations of objects between adjacent frames. In the clustering, we apply an algorithm of FCM(fuzzy c means) to analyze matching and clustering problems of adjacent frames of the featured vectors, energy and entropy, gotten from previous phase. In the matching phase, we also propose a method to know correspondence relation that can track motion each objects by clustering with similar area, compute object centers and cluster around them in case of same objects based on membership function of motion area of adjacent frames.
An Artificial Intelligence-based Data Mining Approach to Extracting Strategies for Reducing the Churning Rate in Credit Card Industry
Kun Chang Lee, Namho Chung, and Kyung-Shik Shin
Vol. 8, No. 2, Page: 15 ~ 35
Keywords : Data Mining, Churning rate, Neural network, C5.0
Abstract
Data mining has received a lot of attention from practitioners. That is partly because it allows company to extract a set of useful knowledge about customers from database, thereby retaining current customers and magneting potential customers. This logic is especially essential in the field of credit card industry, where just 5% increase of number of customers is hewn to cause 120% increase in profit. The problem is how to retain current customers and even make them more loyal to company. However, previous studies lacked proposing extensive strategies of reducing the churning rate. In this sense, this study attempts to suggest such strategies by applying neural network, logistic regression, and C5.0 techniques to credit card data. We used a real data set of four years from 1997 to 2000, which were gathered from a credit card company. Experimental results revealed that our approach could yield robust strategies for retaining customers by reducing the churning rate.
A Comparition on the Knowledge Management Level of Small Firms
Byung-Young Kang
Vol. 8, No. 2, Page: 37 ~ 49
Keywords : Knowledge Management, Intellectual Capital
Abstract
The purpose of this research is to investigate the level of knowledge management in Korea small firms. The research scheme was experimented through a questionnaire survey answered by 150 firms. The research model was composed of five groups : 1) knowledge management and business strategic, 2) a culture and structure for knowledge management, 3) learning process and community 4) information technology to support knowledge management 5) a reward and performance measurement. The results of this research indicated that the level of knowledge management is different according to the characteristic of small firms. The result of the empirical analysis can be summarized as follows : First, the business culture for knowledge management is not performed pertinently. Second, the learning process for knowledge management and a reward and performance measurement is insufficient. Third, the characteristics of a fm should be considered for measuring the level of knowledge management.
Case-based Optimization Modeling
Yong Sik Chang, and Jae Kyu Lee
Vol. 8, No. 2, Page: 51 ~ 69
Abstract
In the supply chain environment on the web, collaborative problem solving and case-based modeling has been getting more important, because it is difficult to cope with diverse problem requirements and inefficient to manage many models as well. Hence, the approach on case-based modeling is required. This paper provides a framework that generates a goal model based on multiple cases, modeling knowledge, and forward chaining and it also develops a search algorithm through sensitivity analysis to reduce the modeling effort.
Development of Integrated Planning System for Efficient Container Terminal Operation
Jae-Yeong Shin, and Chae-Min Lee
Vol. 8, No. 2, Page: 71 ~ 89
Keywords : Container Terminal, Berth Scheduling, Discharging and Loading Planning, Rule-based System
Abstract
In this paper, an integrated planning system is introduced for the efficient operation of container terminal. It consists of discharging and loading planning, yard planning, and berth scheduling subsystem. This interface of this system is considered for user's convenience, and the rule-based system is suggested and developed in order to make planning with automatic procedures, warning functions for errors.
Recommender System using Association Rule and Collaborative Filtering
Ki-Hyun Lee, Byung-Jin Ko, and Geun-Sik Jo
Vol. 8, No. 2, Page: 91 ~ 103
Keywords : Recommender System, Association Rule, Collaborative Filterining
Abstract
A collaborative filtering which supports personalized services of users has been common use in existing web sites for increasing the satisfaction of users. A collaborative filtering is demanded that items are estimated more than specified number. Besides, it tends to ignore information of other users as recommending them on the basis of information of partial users who have similar inclination. However, there are valuable hidden information into other users' one. In this paper, we use Association Rule, which is common wide use in Data Mining, with collaborative filtering for the purpose of discovering those information. In addition, this paper proved that Association Rule applied to Recommender System has a effects to recommend users by the relation between groups. In other words, Association Rule based on the history of all users is derived from. and the efficiency of Recommender System is improved by using Association Rule with collaborative filtering.
A Study on the Recognition of an English Calling Card by using Contour Tracking Algorithm and Enhanced ART1
Kwangbaek Kim, Cheolki Kim, and Jeungwon Kim
Vol. 8, No. 2, Page: 105 ~ 115
Keywords : English Calling Card, Contour Tracking algorithm, ART1
Abstract
This paper proposed a recognition method of english calling card using both 4-directed contour tracking algorithm and enhanced ART1 algorithm. After we extract candidate character string region using horizontal smearing and 4-directed contour tracking method, we extract character string region through comparison of character region and non-character region using horizontal and vertical ratio and area in english calling card. In extracted character string region, we extract each character using horizontal smearing and contour tracking algorithm, and recognize each character by enhanced ART1 algorithm. The proposed ART1 algorithm is enhanced by dynamic control of similarity using fuzzy sum connective operator. The result indicate that the proposed method is superior in performance.
Feature Selection for Case-Based Reasoning using the Order of Selection and Elimination Effects of Individual Features
Jae Sik Lee, and Hyuk Hee Lee
Vol. 8, No. 2, Page: 117 ~ 137
Abstract
A CBR(Case-Based Reasoning) system solves the new problems by adapting the solutions that were used to solve the old problems. Past cases are retained in the case base, each in a specific form that is determined by features. Features are selected for the purpose of representing the case in the best way. Similar cases are retrieved by comparing the feature values and calculating the similarity scores. Therefore, the performance of CBR depends on the selected feature subsets. In this research, we measured the Selection Effect and the Elimination Effect of each feature. The Selection Effect is measured by performing the CBR with only one feature, and the Elimination Effect is measured by performing the CBR without only one feature. Based on these measurements, the feature subsets are selected. The resulting CBR showed better performance in terms of accuracy and efficiency than the CBR with all features.
A Personalized Recommendation Methodology based on Collaborative Filtering
Jae-Kyeong Kim, Ji-Hae Suh, Do-Hyun Ahn, and Yoon-Ho Cho
Vol. 8, No. 2, Page: 139 ~ 157
Keywords : Recommendation system, Personalization, Collaborative filtering, Decision Tree, Data Mining
Abstract
The rapid growth of e-commerce has made both companies and customers face a new situation. Whereas companies have become to be harder to survive due to more and more competitions, the opportunity for customers to choose among more and more products has increased. So, the recommender systems that recommend suitable products to the customer have an important position in E-commerce. This research introduces collaborative filtering based recommender system which helps customers find the products they would like to purchase by producing a list of top-N recommended products. The suggested methodology is based on decision tree, product taxonomy, and association rule mining. Decision tree is used to select target customers, who have high possibility of purchasing recommended products. We applied the recommender system to a Korean department store. The methodology is evaluated with the analysis of a real department store case and is compared with other methodologies.
Web Mining for successful e-Business based on Artificial Intelligence Techniques
Jang Hee Lee, Sung Jin Yu, and Sang Chan Park
Vol. 8, No. 2, Page: 159 ~ 175
Keywords : e-Commerce, e-Business, web mining, Data Visualization System
Abstract
Web mining is an emerging science of applying modem data mining technologies to the problem of extracting valid, comprehensible, and actionable information from large databases of web in e-Business environment and of using it to make crucial e-Business decisions. In this paper, we present the noble framework of data visualization system based on web mining for analyzing the characteristics of on-line customers in e-Business. We also propose the framework of forecasting system for providing the forecasting information of sales/purchase through the use of web mining based on artificial intelligence techniques such as back-propagation network, memory-based reasoning, and self-organizing map.
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