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Journal of Intelligence and Information Systems,
Vol. 11, No. 1, June 2005
Biometrics for Person Authentication: A Survey
Ankur Agarwal, A.S Pandya, Young-Uhg Lho, and Kwang-Baek Kim
Vol. 11, No. 1, Page: 1 ~ 15
Keywords : Biometrics, Personal Authentication, Fingerprints, Signature
Abstract
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
Byung Rae Park
Vol. 11, No. 1, Page: 17 ~ 33
Keywords : Liver cinhosis, Neural network, Magnetic Resonance image
Abstract
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 100%, 1type is 82.8%, 2type is 87.1%, 3type is 84.2%. 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
Hyung Rim Choi, Hyun Soo Kim, Soon Goo Hong, Young Jae Park, Yong Sung Park, and Dong Yeol Yoo
Vol. 11, No. 1, Page: 35 ~ 51
Keywords : e-commerce, negotiation, automated negotiation systems
Abstract
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
Choon-Bo Shim, and Yong-Won Shin
Vol. 11, No. 1, Page: 53 ~ 63
Keywords : Ubiquitous Computing, Context-Awareness, Context Middleware
Abstract
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
Seong-in Kim, Seok Yoo, Ro Hae Myung, and Sheung-Kown Kim
Vol. 11, No. 1, Page: 65 ~ 73
Keywords : Expert system Art Psychotherapy, Knowledge elicitation and acquisition, Ontology
Abstract
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
Kyoung-Yul Bae
Vol. 11, No. 1, Page: 95 ~ 105
Keywords : Biometrics, Facial Recognition, GA-based Eigenface Algorithm
Abstract
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
Taeho Hong, Hee-Jung Lee, and Bomil Suh
Vol. 11, No. 1, Page: 107 ~ 121
Abstract
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
Juyoung Kang, Jae Kyu Lee, and Sang-un Park
Vol. 11, No. 1, Page: 123 ~ 151
Keywords : Rule Identification, Rule Acquisition, Knowledge Enginering, Expert System, Knowledge Acquisition
Abstract
Real-time Graph Search for Space Exploration
Eunmi Choi, and Incheol Kim
Vol. 11, No. 1, Page: 153 ~ 167
Keywords : Space Exploration, Real-time Graph Search, RTA* Algorithm, Depth-First Search
Abstract
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$min(mn, d^2+m)$ 수식 이미지 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
Jaeho Kang, Kwang Ryel Ryu, and Hyuk-Chul Kwon
Vol. 11, No. 1, Page: 169 ~ 189
Keywords : Active learning, Text classification, Batch query selection
Abstract
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.
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