• Journal of Korea Society of Industrial Information Systems
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• v.15 no.3
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• pp.1-9
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• 2010

In recent year, the cores of ubiquitous environment are sensor networks and RFID systems. RFID system transmits the electronic information of the tag to the reader by using RF signal. Collision happens in RFID system when there are many matched tags, and it degrades the tag identification performance. Such a system needs algorithm which is able to arbitrate tag collision. This paper suggests a hybrid method which reduces collision between the tags, and can quickly identify the tag. The proposed method operates based on certainty, which takes an advantage of tree based algorithm, and to reduce collision it selects transmission time slot by using tag ID. The simulation results show the suggested method has higher performance in the number of queries and collision compared to other tree based and hybrid algorithms.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.190-194
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• 2023

By looking the importance of communication, data delivery and access in various sectors including governmental, business and individual for any kind of data, it becomes mandatory to identify faults and flaws during cyber communication. To protect personal, governmental and business data from being misused from numerous advanced attacks, there is the need of cyber security. The information security provides massive protection to both the host machine as well as network. The learning methods are used for analyzing as well as preventing various attacks. Machine learning is one of the branch of Artificial Intelligence that plays a potential learning techniques to detect the cyber-attacks. In the proposed methodology, the Decision Tree (DT) which is also a kind of supervised learning model, is combined with the different cross-validation method to determine the accuracy and the execution time to identify the cyber-attacks from a very recent dataset of different network attack activities of network traffic in the UNSW-NB15 dataset. It is a hybrid method in which different types of attributes including Gini Index and Entropy of DT model has been implemented separately to identify the most accurate procedure to detect intrusion with respect to the execution time. The different DT methodologies including DT using Gini Index, DT using train-split method and DT using information entropy along with their respective subdivision such as using K-Fold validation, using Stratified K-Fold validation are implemented.

• The Journal of Korean Association of Computer Education
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• v.21 no.6
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• pp.83-92
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• 2018

The subjective question is appropriate for evaluation of deep thinking, but it is not easy to score. Since, regardless of same scoring criterion, the graders are able to produce different scores, we need the objective automatic evaluation system. However, the system has the problem of Korean analysis and comparison. This paper suggests the Korean syntactic analysis and subjective grading system using the syntactic-semantic tree comparator. This system is the hybrid grading system of word based and syntactic-semantic tree based grading. This system grades the answers on the subjective question using the syntactic-semantic comparator. This proposed system has the good result. This system will be utilized in Korean syntactic-semantic analysis, subjective question grading, and document classification.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.125-140
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• 2013

We call a data set in which the number of records belonging to a certain class far outnumbers the number of records belonging to the other class, 'imbalanced data set'. Most of the classification techniques perform poorly on imbalanced data sets. When we evaluate the performance of a certain classification technique, we need to measure not only 'accuracy' but also 'sensitivity' and 'specificity'. In a customer churn prediction problem, 'retention' records account for the majority class, and 'churn' records account for the minority class. Sensitivity measures the proportion of actual retentions which are correctly identified as such. Specificity measures the proportion of churns which are correctly identified as such. The poor performance of the classification techniques on imbalanced data sets is due to the low value of specificity. Many previous researches on imbalanced data sets employed 'oversampling' technique where members of the minority class are sampled more than those of the majority class in order to make a relatively balanced data set. When a classification model is constructed using this oversampled balanced data set, specificity can be improved but sensitivity will be decreased. In this research, we developed a hybrid model of support vector machine (SVM), artificial neural network (ANN) and decision tree, that improves specificity while maintaining sensitivity. We named this hybrid model 'hybrid SVM model.' The process of construction and prediction of our hybrid SVM model is as follows. By oversampling from the original imbalanced data set, a balanced data set is prepared. SVM_I model and ANN_I model are constructed using the imbalanced data set, and SVM_B model is constructed using the balanced data set. SVM_I model is superior in sensitivity and SVM_B model is superior in specificity. For a record on which both SVM_I model and SVM_B model make the same prediction, that prediction becomes the final solution. If they make different prediction, the final solution is determined by the discrimination rules obtained by ANN and decision tree. For a record on which SVM_I model and SVM_B model make different predictions, a decision tree model is constructed using ANN_I output value as input and actual retention or churn as target. We obtained the following two discrimination rules: 'IF ANN_I output value <0.285, THEN Final Solution = Retention' and 'IF ANN_I output value ${\geq}0.285$, THEN Final Solution = Churn.' The threshold 0.285 is the value optimized for the data used in this research. The result we present in this research is the structure or framework of our hybrid SVM model, not a specific threshold value such as 0.285. Therefore, the threshold value in the above discrimination rules can be changed to any value depending on the data. In order to evaluate the performance of our hybrid SVM model, we used the 'churn data set' in UCI Machine Learning Repository, that consists of 85% retention customers and 15% churn customers. Accuracy of the hybrid SVM model is 91.08% that is better than that of SVM_I model or SVM_B model. The points worth noticing here are its sensitivity, 95.02%, and specificity, 69.24%. The sensitivity of SVM_I model is 94.65%, and the specificity of SVM_B model is 67.00%. Therefore the hybrid SVM model developed in this research improves the specificity of SVM_B model while maintaining the sensitivity of SVM_I model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.1A
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• pp.24-35
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• 2008

We propose a tree-based integrated network of infrastructure network and mobile ad hoc network to effectively support Mobile IP for mobile ad hoc networks and also proposed a network management protocol for formation and management of the integrated network and a tree-based routing protocol suitable for the integrated network. The integrated network has fixed gateways(IGs) that connect two hybrid networks and the mobile nodes in the network form a small sized trees based on the mobile nodes that are in the communication distance with a IG. A new node joins an arbitrary tree and is registered with its HA and FA along tree path. In addition, the proposed protocol establishes a route efficiently by using the tree information managed in every node. We examined the effectiveness of the tree-based integrated network for some possible network deployment scenarios and compared our routing protocol against the Mobile IP supported AODV protocol.

• The KIPS Transactions:PartC
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• v.10C no.5
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• pp.611-616
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• 2003

Recently, the HFC-CATV network stand in a substructure of superhighway information network. Because of sharing up to 500 of subscribes, the Collision Resolution Algorithm needs in the upstream channel of HFC-CATV network. In order to provide Quality of Service (QoS) to users with real-time data such as voice, video and interactive service, the research of Collision Resolution Algorithm must include an effective priority scheme. In IEEE 802.14, the Collision Resolution Algorithm has high request delay because of static PNA(Priority New Access) slots structure and different priority traffics with the same probability. In order to resolve this problem, this paper proposed dynamic priority collision resolution algorithm with ternary tree algorithm. It has low request delay according to an increase of traffic load because high priority traffic first resolve and new traffic content with different probability. In the result of the simulation, it demonstrated that the proposed algorithm needs lower request delay than that of ternary tree algorithm with static PNA slots structure.

• Journal of the Korea Society of Computer and Information
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• v.19 no.3
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• pp.45-54
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• 2014

In this paper, an improved two-step P2P traffic classification scheme is proposed to overcome the limitations of the existing methods. The first step is a signature-based classifier at the packet-level. The second step consists of pattern heuristic rules and a statistics-based classifier at the flow-level. With pattern heuristic rules, the accuracy can be improved and the amount of traffic to be classified by statistics-based classifier can be reduced. Based on the analysis of different decision tree algorithms, the statistics-based classifier is implemented with REPTree. In addition, the ensemble algorithm is used to improve the performance of statistics-based classifier Through the verification with the real datasets, it is shown that our hybrid scheme provides higher accuracy and lower overhead compared to other existing schemes.

• The KIPS Transactions:PartC
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• v.14C no.1 s.111
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• pp.1-6
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• 2007

In order to operate a secure network, it is very important for the network to raise positive detection as well as lower negative detection for reducing the damage from network intrusion. By using SVM on the intrusion detection field, we expect to improve real-time detection of intrusion data. However, due to classification based on calculating values after having expressed input data in vector space by SVM, continuous data type can not be used as any input data. Therefore, we present the hybrid model between SVM and decision tree method to make up for the weak point. Accordingly, we see that intrusion detection rate, F-P error rate, F-N error rate are improved as 5.6%, 0.16%, 0.82%, respectively.

• Journal of Digital Contents Society
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• v.9 no.3
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• pp.509-516
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• 2008

We propose the Hybrid Rendering model to support multi-resolution for computer graphics. LOD method for computer graphics system considering performance of device environment and end-user preference usually adopts mesh resolution, mipmap in texture rendering, or oct-tree data structure in ray tracing. The hybrid rendering model, as a local shading model combining Gouraud shading model and a flat shading model, applies a proper shading method to each of polygons consisting of an object. This method can be an effective alternative to reduce real-time rendering time so that it can be utilized in real time adaptive service of computer graphic contents among various device environments under ubiquitous environments.

• The KIPS Transactions:PartD
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• v.11D no.6
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• pp.1221-1230
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• 2004

In mobile environments, the locations of moving objects such as vehicles, airplanes and users of wireless devices continuously change over time. For efficiently processing moving object information, the database system should be able to deal with large volume of data, and manage indexing efficiently. However, previous research on indexing method mainly focused on query performance, and did not pay attention to update operation for moving objects. In this paper, we propose a novel moving object indexing method, named ACAR-Tree. For processing efficiently frequently updating of moving object location information as well as query performance, the proposed method is based on fixed grid structure with auxiliary R-Tree. This hybrid structure is able to overcome the poor update performance of R-Tree which is caused by reorganizing of R-Tree. Also, the proposed method is able to efficiently deal with skewed-. or gaussian distribution of data using auxiliary R-Tree. The experimental results using various data size and distribution of data show that the proposed method has reduced the size of index and improve the update and query performance compared with R-Tree indexing method.