• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.25-30
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• 2018

It is extremely lacking and urgently required that the method of constructing the Gene Regulatory Network (GRN) from RNA-Sequencing data (RNA-Seq) because of Big-Data and GRN in Big-Data has obtained substantial observation as the interactions among relevant featured genes and their regulations. We propose newly the computational comparative feature patterns selection method by implementing a minimum-redundancy maximum-relevancy (MRMR) filter the support vector machine-recursive feature elimination (SVM-RFE) with Intensity-dependent normalization (DEGSEQ) as a preprocessor for emphasizing equal preciseness in RNA-seq in Big-Data. We found out the proposed algorithm might be more scalable and convenient because of all libraries in R package and be more improved in terms of the time consuming in Big-Data and minimum-redundancy maximum-relevancy of a set of feature patterns at the same time.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.915-916
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• 2014

With the upcoming era of internet of things, the study of sensor network has been paid attention. Raspberry pi is a tiny versatile computer system which is able to act as a sensor node in hadoop cluster network. In this paper, we deployed 5 Raspberry pi's to construct an experimental testbed of hadoop sensor network with 5-node map-reduce hadoop software framework. We compared and analyzed the network architecture in terms of efficiency, resource management, and throughput using various parameters. We used a learning machine with support vector machine as test workload. In our experiments, Raspberry pi fulfilled the role of distributed computing sensor node in the sensor network.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.12
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• pp.2298-2304
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• 2017

Online signature verification is one of the simple and efficient method of identity verification and has less resistance than other biometric technologies. In training to build a verification model, negative samples are required to build the model, but in most practical applications it is not easy to get negative samples - forgery signatures. In this paper, proposed is a method using someone else's signatures as negative samples. In verification, shape-based features extracted from the time-sequenced signature data are extracted and a support vector machine is used to verify. SVM tries to map a feature vector to a high dimensional space and to draw a linear boundary in the high dimensional space. SVM is one of the best classifiers and has been applied to various applications. Using general handwriting data, i.e., someone else's signatures which have little in common with positive samples improved the verification rate experimentally, which means that signature verification without negative samples is possible.

• Database Research
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• v.34 no.3
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• pp.3-13
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• 2018

Text classification is one of the text mining technologies that classifies a given textual document into its appropriate categories and is used in various fields such as spam email detection, news classification, question answering, emotional analysis, and chat bot. In general, the text classification system utilizes machine learning algorithms, and among a number of algorithms, naïve Bayes and support vector machine, which are suitable for text data, are known to have reasonable performance. Recently, with the development of deep learning technology, several researches on applying deep neural networks such as recurrent neural networks (RNN) and convolutional neural networks (CNN) have been introduced to improve the performance of text classification system. However, the current text classification techniques have not yet reached the perfect level of text classification. This paper focuses on the fact that the text data is expressed as a vector only with the word dimensions, which impairs the semantic information inherent in the text, and proposes a neural network architecture based upon the semantic tensor space model.

• Journal of Digital Convergence
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• v.16 no.3
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• pp.145-156
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• 2018

Unlike the past, when distributors distributed games through retail stores, they are now selling digital content, which is based on online distribution channels. This study analyzes the effects of eWOM (electronic Word of Mouth) on sales volume of game sold on Steam, an online digital content distribution channel. Recently, data mining techniques based on Big Data have been studied. In this study, emotion index of eWOM is derived by emotional analysis which is a text mining technique that can analyze the emotion of each review among factors of eWOM. Emotional analysis utilizes Naive Bayes and SVM classifier and calculates the emotion index through the SVM classifier with high accuracy. Regression analysis is performed on the dependent variable, sales variation, using the emotion index, the number of reviews of each game, the size of eWOM, and the user score of each game, which is a rating of eWOM. Regression analysis revealed that the size of the independent variable eWOM and the emotion index of the eWOM were influential on the dependent variable, sales variation. This study suggests the factors of eWOM that affect the sales volume when Korean game companies enter overseas markets based on steam.

• The Journal of the Korea Contents Association
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• v.8 no.10
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• pp.11-19
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• 2008

In this paper, we propose a prediction method of lung cancer susceptibility using an importance evaluation of SNP data and the SVM learning, a gene data concerning getting sick with the lung cancer. Since the number of negative data is much larger that of positive data, which are to be used in the SVM learning, for each positive data, a negative data is first searched which has the same sex and the minimum age difference with the positive data. The searched negative data is then coupled with the positive data. For the importance evaluation of each SNP data, an equation which calculates the influence of each SNP data on the prediction of getting sick is adopted. The SNP data are sorted according to the evaluated importance. In experiments, we observed the prediction accuracy which varies according to the number of sorted SNP data used in the learning. LOOCV test results showed that the proposed method yields the prediction accuracy of maximum 65.0% for test data.

• Journal of Korea Multimedia Society
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• v.10 no.3
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• pp.391-400
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• 2007

According to the recent advances in multimedia computing, storage and searching technology have made large volume of music contents become prevalent. Also there has been increasing needs for the study on efficient categorization and searching technique for music contents management. In this paper, a new classifying method using the local information of music content and music tone feature is proposed. While the conventional classifying algorithms are based on entire information of music content, the algorithm proposed in this paper focuses on only the specific local information, which can drastically reduce the computing time without losing classifying accuracy. In order to improve the classifying accuracy, it uses a new classification feature based on music tone. The proposed method has been implemented as a part of MuSE (Music Search/Classification Engine) which was installed on various systems including commercial PDAs and PCs.

• Journal of the Korea Society of Computer and Information
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• v.17 no.12
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• pp.179-185
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• 2012

This paper proposes a design of RSIDS(RST and SVM based Intrusion Detection System) using RST(Rough Set Theory) and SVM(Support Vector Machine) algorithm. The RSIDS consists of PrePro(PreProcessing) module, RRG(RST based Rule Generation) module, and SAD(SVM based Attack Detection) module. The PrePro module changes the collected information to the data format of RSIDS. The RRG module analyzes attack data, generates the rules of attacks, extracts attack information from the massive data by using these rules, and transfers the extracted attack information to the SAD module. The SAD module detects the attacks by using it, which the SAD module notifies to a manager. Therefore, compared to the existing SVM, the RSIDS improved average ADR(Attack Detection Ratio) from 77.71% to 85.28%, and reduced average FPR(False Positive ratio) from 13.25% to 9.87%. Thus, the RSIDS is estimated to have been improved, compared to the existing SVM.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.10 no.3
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• pp.221-228
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• 2016

In this paper, we introduce our simulation software for EEG signal accuracy improvement. Users can check and train own EEG signal accuracy using our simulation software. Subjects were shown emotional imagination condition with landscape photography and logical imagination condition with a mathematical problem to subject. We use that EEG signal data, and apply Independent Component Analysis algorithm for noise removal. So we can have beta waves(${\beta}$, 14-30Hz) data through Band Pass Filter. We extract feature using Root Mean Square algorithm and That features are classified through Support Vector Machine. The classification result is 78.21% before EEG signal accuracy improvement training. but after successive training, the result is 91.67%. So user can improve own EEG signal accuracy using our simulation software. And we are expecting efficient use of BCI system based EEG signal.