• Journal of Korea Multimedia Society
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• v.22 no.2
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• pp.203-209
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• 2019

The performance of an intelligent classifier for detecting malwares added to multimedia contents based on machine learning is highly dependent on the properties of feature set. Especially, in order to determine the malicious code in real time the size of feature set should be as short as possible without reducing the accuracy. In this paper, we introduce an optimal feature selection method to satisfy both high detection rate and the minimum length of feature set against the feature set provided by PEFeatureExtractor well known as a feature extraction tool. For the evaluation of the proposed method, we perform the experiments using Windows Portable Executables 32bits.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.176-179
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• 2022

In this paper, we consider a sectorized wireless powered sensor network (WPSN), wherein sensor nodes are clustered based on sectors and transmit data to the cluster head (CH) using energy harvested from a hybrid access point. We construct a system model for this sectorized WPSN and find optimal coordinates of CH that maximize the achievable transmission rate of sensing data. To obtain the optimal CH with low overhead, we perform an asymptotic geometric analysis (GA). Simulation results show that the proposed GA-based CH selection method is close to the optimal performance exhibited by exhaustive search with a low feedback overhead.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.11-16
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• 2023

Vehicular networks are part of the next generation wireless and smart Intelligent Transportation Systems (ITS). In the future, autonomous vehicles will be an integral part of ITS and will provide safe and reliable traveling features to the users. The reliability and security of data transmission in vehicular networks has been a challenging task. To manage data transmission in vehicular networks, road networks are divided into clusters and a cluster head is selected to handle the data. The selection of cluster heads is a challenge as vehicles are mobile and their connectivity is dynamically changing. In this paper, a novel secure cluster head selection algorithm is proposed for secure and reliable data sharing. The idea is to use the secrecy rate of each vehicle in the cluster and adaptively select the most secure vehicle as the cluster head. Simulation results show that the proposed scheme improves the reliability and security of the transmission significantly.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.344-357
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• 2015

With the increase in a number of access technologies and data rates, a continuous connection among different networks is demand of the future wireless networks. In the last decade, user connectivity among different access networks remained a challenging job. Therefore, in this article, we proposed a user-centric and user-perspective based network selection mechanism for fast handover management in heterogeneous wireless networks. The proposed scheme selects the most appropriate network among available networks on the basis of resources i.e. cost, data rate, and link quality. Initially, we load the Media Independent Information Service (MIIS) with the information of cost and data rate provided by different network operators. Similarly, Mobile Node (MN) is also loaded with the user preferred cost and data rate for different applications. The MN obtains the information of cost and data rate from MIIS server upon a predefined threshold, and make a decision for handover according to its current cost and data rate. Furthermore, we employ an optimal threshold mechanism for initiation of the handover execution phase to minimize false handover indications. The proposed scheme is based on a survey for network selection and its implementation in C programming language to validate its performance and accuracy. The simulation result shows that the proposed scheme performs superior then the schemes present in the current literature.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9B
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• pp.1755-1766
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• 1999

A region-based video codec based on the H.263+ standard is examined and its associated novel rate control scheme is proposed in this work. The region-based coding scheme is a hybrid method that consists of the traditional block DCT coding and the object-based coding. Basically, we adopt H.263+ as the platform, and develop a fast macroblock-based segmentation method to implement the region-based video codec. The proposed rate control solution includes rate control in three levels: encoding frame selection, frame-layer rate control and macroblock-layer rate control. The goal is to enhance the visual quality of decoded frames at low bit rates. The efficiency of proposed rate control scheme applied to the region-based video codes is demonstrated via several typical test sequences.

• Journal of the Korea Society for Simulation
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• v.22 no.4
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• pp.139-147
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• 2013

This paper proposes a novel method for feature selection for mass spectrometric proteomic data based on Random Forest. The method includes an effective preprocessing step to filter a large amount of redundant features with high correlation and applies a tournament strategy to get an optimal feature subset. Experiments on three public datasets, Ovarian 4-3-02, Ovarian 7-8-02 and Prostate shows that the new method achieves high performance comparing with widely used methods and balanced rate of specificity and sensitivity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.389-405
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• 2014

Outdated Channel State Information (CSI) was proved to have negative effect on performance in two-way relay networks. The diversity order of widely used opportunistic relay selection (ORS) was degraded to unity in networks with outdated CSI. This paper proposed a multiple relay selection scheme for amplify-and-forward (AF) based two-way relay networks (TWRN) with outdated CSI. In this scheme, two sources exchange information through more than one relays. We firstly select N best relays out of all candidate relays with respect to signal-noise ratio (SNR). Then, the ratios of the SNRs on the rest of the candidate relays to that of the Nth highest SNR are tested against a normalized threshold ${\mu}{\in}[0,1]$, and only those relays passing this test are selected in addition to the N best relays. Expressions of outage probability, average bit error rate (BER) and ergodic channel capacity were obtained in closed-form for the proposed scheme. Numerical results and Simulations verified our theoretical analyses, and showed that the proposed scheme had significant gains comparing with conventional ORS.

• Genomics & Informatics
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• v.14 no.4
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• pp.187-195
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• 2016

In genetic association studies with high-dimensional genomic data, multiple group testing procedures are often required in order to identify disease/trait-related genes or genetic regions, where multiple genetic sites or variants are located within the same gene or genetic region. However, statistical testing procedures based on an individual test suffer from multiple testing issues such as the control of family-wise error rate and dependent tests. Moreover, detecting only a few of genes associated with a phenotype outcome among tens of thousands of genes is of main interest in genetic association studies. In this reason regularization procedures, where a phenotype outcome regresses on all genomic markers and then regression coefficients are estimated based on a penalized likelihood, have been considered as a good alternative approach to analysis of high-dimensional genomic data. But, selection performance of regularization procedures has been rarely compared with that of statistical group testing procedures. In this article, we performed extensive simulation studies where commonly used group testing procedures such as principal component analysis, Hotelling's $T^2$ test, and permutation test are compared with group lasso (least absolute selection and shrinkage operator) in terms of true positive selection. Also, we applied all methods considered in simulation studies to identify genes associated with ovarian cancer from over 20,000 genetic sites generated from Illumina Infinium HumanMethylation27K Beadchip. We found a big discrepancy of selected genes between multiple group testing procedures and group lasso.

• Journal of KIISE:Software and Applications
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• v.36 no.5
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• pp.396-400
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• 2009

During periods of large data transmission, routing selection methods are used to efficiently manage data traffic and improve the speed of transmission. One approach in routing selection is AntNet that applies the Ant algorithm in transmissions with uniform probability. However, this approach uses random selection, which can cause excessive data transmission rates and fail to optimize data This paper presents the use of the Genetic Algorithm (GA) to efficiently route and disperse data transmissions, during periods with "unnecessary weight increases for random selection". This new algorithm for improved performance provides highly accurate estimates of the transmission time and the transmission error rate.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.12
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• pp.870-872
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• 2014

In this paper, we devise a cost function by considering the energy consumption rate and the remaining energy of a peer. Then, we propose a parent peer selection method that chooses the least cost peer in the system in a distributed manner. On the contrary to the conventional method that makes each peer select the least cost neighbor as a parent peer, the proposed method chooses a parent peer using the swarm intelligence formed among a set of peers. Therefore, the proposed method could extent distributedly the number of peers searched for parent peer selection. Thus, compared to the conventional method, the proposed method increases the probability of being a parent peer as the cost of a peer becomes smaller with less operational load.