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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Journal of Intelligence and Information Systems
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Korea Inteligent Information System Society
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Volume & Issues
Volume 11, Issue 3 - Dec 2005
Volume 11, Issue 2 - Nov 2005
Volume 11, Issue 1 - Jun 2005
Selecting the target year
Biometrics for Person Authentication: A Survey
Ankur, Agarwal ; Pandya, A.-S. ; Lho, Young-Uhg ; Kim, Kwang-Baek ;
Journal of Intelligence and Information Systems , volume 11, issue 1, 2005, Pages 1~15
As organizations search fur more secure authentication methods (Dr user access, e-commerce, and other security applications, biometrics is gaining increasing attention. Biometrics offers greater security and convenience than traditional methods of personal recognition. In some applications, biometrics can replace or supplement the existing technology. In others, it is the only viable approach. Several biometric methods of identification, including fingerprint hand geometry, facial, ear, iris, eye, signature and handwriting have been explored and compared in this paper. They all are well suited for the specific application to their domain. This paper briefly identifies and categorizes them in particular domain well suited for their application. Some methods are less intrusive than others.
Implementation of the Classification using Neural Network in Diagnosis of Liver Cirrhosis
Park, Byung-Rae ;
Journal of Intelligence and Information Systems , volume 11, issue 1, 2005, Pages 17~33
This paper presents the proposed a classifier of liver cirrhotic step using MR(magnetic resonance) imaging and hierarchical neural network. The data sets for classification of each stage, which were normal, 1type, 2type and 3type, were analysis in the number of data was 231. We extracted liver region and nodule region from T1-weight MR liver image. Then objective interpretation classifier of liver cirrhotic steps. Liver cirrhosis classifier implemented using hierarchical neural network which gray-level analysis and texture feature descriptors to distinguish normal liver and 3 types of liver cirrhosis. Then proposed Neural network classifier learned through error back-propagation algorithm. A classifying result shows that recognition rate of normal is
, 1type is
, 2type is
, 3type is
. The recognition ratio very high, when compared between the result of obtained quantified data to that of doctors decision data and neural network classifier value. If enough data is offered and other parameter is considered this paper according to we expected that neural network as well as human experts and could be useful as clinical decision support tool for liver cirrhosis patients.
A Method for Generating and Evaluating Multi-Attribute Proposals in Automated Negotiation Systems
Choi, Hyung-Rim ; Kim, Hyun-Soo ; Hong, Soon-Goo ; Park, Young-Jae ; Park, Yong-Sung ; Yoo, Dong-Yeol ;
Journal of Intelligence and Information Systems , volume 11, issue 1, 2005, Pages 35~51
The wide spread of Internet and rapid development of e-commerce-related technology have brought sweeping changes on the traditional commercial transactions. Accordingly, many efforts to transform these transactions electronically under e-commerce environment have been carried out. As most transactions are usually made through negotiations, the function of automated negotiation is also required in the e-commerce environment. This paper aims to develop the method to generate and evaluate the multi-attribute negotiation proposals for automated negotiation systems. To this end the related articles are reviewed and the method dealing with e-negotiation strategy is suggested. In this method, the seller generates his or her own negotiation proposal and then evaluates the buyer's proposal based on SAW (Simple Additive Weighting Method), one of the MADM (Multi Attribute Decision Making) methods. To verify the suggested method, a case study is conducted in the order-based manufacturing environment.
Development of a Context Middleware supporting Context-Awareness in Ubiquitous Computing Environment
Shim, Choon-Bo ; Shin, Yong-Won ;
Journal of Intelligence and Information Systems , volume 11, issue 1, 2005, Pages 53~63
Adaptive services need to become as mobile as their users and be extended to take advantage of the constantly changing context in which they are accessed. Context-awareness is a technology to facilitate information acquisition and execution by supporting interoperability between users and devices based on users' context. The objective of this study is to develop a middleware fer dealing with context-awareness in ubiquitous computing. To achieve it, our middleware plays an important role in recognizing a moving node with mobility by using a bluetooth wireless communication technology as well as in executing an appropriate execution module according to the context acquired from a context server. In addition, for verifying the usefulness of the Proposed middleware, we develop an application system which Provides a music playing service based on context information by using our context middleware.
A Framework of an Expert System's Knowledge for the Diagnosis in Art Psychotherapy
Kim, Seong-In ; Yoo, Seok ; Myung, Ro-Hae ; Kim, Sheung-Kown ;
Journal of Intelligence and Information Systems , volume 11, issue 1, 2005, Pages 65~93
Expert system implementation of human expert's diagnosis in art psychotherapy requires extensive knowledge on: (1) characteristics in a drawing; (2) psychological symptoms in a client; (3) relationships between the characteristics and the symptoms; (4) decision process; (5) knowledge elicitation and aquisition methods. Experts from many different fields provide such knowledge, ranging from art therapists who is on the spot, psychiatrists, psychologists, artists to knowledge engineers who know how to implement the decision system to a computer. The problems that make the implementation difficult are the expert's complex decision process and the ambiguity, the inconsistency and even the contradiction in the huge volume of the knowledge. Modeling the expert's decision process, we develope a framework of the system and then analyze and classify the knowledge. With the proposed classification, we present a suitable method of knowledge elicitation and aquisition. Then, we describe the subsets of knowledge in a unified structure using the ontology concept and Protege 2000 as a tool. Finally, we apply the system to a real case to show its usability and suitability.
The Improving Method of Facial Recognition Using the Genetic Algorithm
Bae, Kyoung-Yul ;
Journal of Intelligence and Information Systems , volume 11, issue 1, 2005, Pages 95~105
As the security system using facial recognition, the recognition performance depends on the environments (e. g. face expression, hair style, age and make-up etc.) For the revision of easily changeable environment, it's generally used to set up the threshold, replace the face image which covers the threshold into images already registered, and update the face images additionally. However, this usage has the weakness of inaccuracy matching results or can easily active by analogous face images. So, we propose the genetic algorithm which absorbs greatly the facial similarity degree and the recognition target variety, and has excellence studying capacity to avoid registering inaccuracy. We experimented variable and similar face images (each 30 face images per one, total 300 images) and performed inherent face images based on ingredient analysis as face recognition technique. The proposed method resulted in not only the recognition improvement of a dominant gene but also decreasing the reaction rate to a recessive gene.
A Web Personalized Recommender System Using Clustering-based CBR
Hong, Tae-Ho ; Lee, Hee-Jung ; Suh, Bo-Mil ;
Journal of Intelligence and Information Systems , volume 11, issue 1, 2005, Pages 107~121
Recently, many researches on recommendation systems and collaborative filtering have been proceeding in both research and practice. However, although product items may have multi-valued attributes, previous studies did not reflect the multi-valued attributes. To overcome this limitation, this paper proposes new methodology for recommendation system. The proposed methodology uses multi-valued attributes based on clustering technique for items and applies the collaborative filtering to provide accurate recommendations. In the proposed methodology, both user clustering-based CBR and item attribute clustering-based CBR technique have been applied to the collaborative filtering to consider correlation of item to item as well as correlation of user to user. By using multi-valued attribute-based clustering technique for items, characteristics of items are identified clearly. Extensive experiments have been performed with MovieLens data to validate the proposed methodology. The results of the experiment show that the proposed methodology outperforms the benchmarked methodologies: Case Based Reasoning Collaborative Filtering (CBR_CF) and User Clustering Case Based Reasoning Collaborative Filtering (UC_CBR_CF).
Effect of Rule Identification in Acquiring Rules from Web Pages
Kang, Ju-Young ; Lee, Jae-Kyu ; Park, Sang-Un ;
Journal of Intelligence and Information Systems , volume 11, issue 1, 2005, Pages 123~151
In the world of Web pages, there are oceans of documents in natural language texts and tables. To extract rules from Web pages and maintain consistency between them, we have developed the framework of XRML(extensible Rule Markup Language). XRML allows the identification of rules on Web pages and generates the identified rules automatically. For this purpose, we have designed the Rule Identification Markup Language (RIML) that is similar to the formal Rule Structure Markup Language (RSML), both as pares of XRML. RIML is designed to identify rules not only from texts, but also from tables on Web pages, and to transform to the formal rules in RSは syntax automatically. While designing RIML, we considered the features of sharing variables and values, omitted terms, and synonyms. Using these features, rules can be identified or changed once, automatically generating their corresponding RSML rules. We have conducted an experiment to evaluate the effect of the RIML approach with real world Web pages of Amazon.com, BamesandNoble.com, and Powells.com We found that
of the rules can be detected on the Web pages, and the completeness of generated rule components is
. This is good proof that XRML can facilitate the extraction and maintenance of rules from Web pages while building expert systems in the Semantic Web environment.
Real-time Graph Search for Space Exploration
Choi, Eun-Mi ; Kim, In-Cheol ;
Journal of Intelligence and Information Systems , volume 11, issue 1, 2005, Pages 153~167
In this paper, we consider the problem of exploring unknown environments with a mobile robot or an autonomous character agent. Traditionally, research efforts to address the space exploration problem havefocused on the graph-based space representations and the graph search algorithms. Recently EXPLORE, one of the most efficient search algorithms, has been discovered. It traverses at most min
edges where d is the deficiency of a edges and n is the number of edges and n is the number of vertices. In this paper, we propose DFS-RTA* and DFS-PHA*, two real-time graph search algorithms for directing an autonomous agent to explore in an unknown space. These algorithms are all built upon the simple depth-first search (DFS) like EXPLORE. However, they adopt different real-time shortest path-finding methods for fast backtracking to the latest node, RTA* and PHA*, respectively. Through some experiments using Unreal Tournament, a 3D online game environment, and KGBot, an intelligent character agent, we analyze completeness and efficiency of two algorithms.
Cluster-Based Selection of Diverse Query Examples for Active Learning
Kang, Jae-Ho ; Ryu, Kwang-Ryel ; Kwon, Hyuk-Chul ;
Journal of Intelligence and Information Systems , volume 11, issue 1, 2005, Pages 169~189
In order to derive a better classifier with a limited number of training examples, active teaming alternately repeats the querying stage fur category labeling and the subsequent learning stage fur rebuilding the calssifier with the newly expanded training set. To relieve the user from the burden of labeling, especially in an on-line environment, it is important to minimize the number of querying steps as well as the total number of query examples. We can derive a good classifier in a small number of querying steps by using only a small number of examples if we can select multiple of diverse, representative, and ambiguous examples to present to the user at each querying step. In this paper, we propose a cluster-based batch query selection method which can select diverse, representative, and highly ambiguous examples for efficient active learning. Experiments with various text data sets have shown that our method can derive a better classifier than other methods which only take into account the ambiguity as the criterion to select multiple query examples.
Product Recommender System for Online Shopping Malls using Data Mining Techniques
Kim, Kyoung-Jae ; Kim, Byoung-Guk ;
Journal of Intelligence and Information Systems , volume 11, issue 1, 2005, Pages 191~205
This paper presents a novel product recommender system as a tool fur differentiated marketing service of online shopping malls. Ihe proposed model uses genetic algorithnt one of popular global optimization techniques, to construct a personalized product recommender systen The genetic algorinun may be useful to recommendation engine in product recommender system because it produces optimal or near-optimal recommendation rules using the customer profile and transaction data. In this study, we develop a prototype of WeLbased personalized product recommender system using the recommendation rules fi:om the genetic algorithnL In addition, this study evaluates usefulness of the proposed model through the test fur user satisfaction in real world.