• The Journal of Information Technology
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• v.5 no.4
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• pp.129-137
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• 2002

In this paper, a public key knapsack cryptosystem algorithm is based on the security to a difficulty of polynomial factorization in computer communication networks is proposed. For the proposed public key knapsack cryptosystem, a polynomial vector Q(x,y,z) is formed by transform of superincreasing vector P, a polynomial g(x,y,z) is selected. Next then, the two polynomials Q(x,y,z) and g(x,y,z) is decided on the public key. The enciphering first selects plaintext vector. Then the ciphertext R(x,y,z) is computed using the public key polynomials and a random integer $\alpha$. For the deciphering of ciphertext R(x,y,z), the plaintext is determined using the roots x, y, z of a polynomial g(x,y,z)=0 and the increasing property of secrety key vector. Therefore a public key knapsack cryptosystem is based on the security to a difficulty of factorization of a polynomial g(x,y,z)=0 with three variables. The propriety of the proposed public key cryptosystem algorithm is verified with the computer simulation.

• 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 Information Technology and Architecture
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• v.9 no.3
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• pp.333-344
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• 2012

This paper proposes a system which recommends movies using information from social network services containing personal interest and taste. Method for establishing data is as follows. The system gathers movies' information from web sites and user's information from social network services such as Facebook and twitter. The data from social network services is categorized into six steps of emotion level for more accurate processing following users' emotional states. Gathered data will be established into vector space model which is ideal for analyzing and deducing the information with the system which is suggested in this paper. The existing similarity measurement method for movie recommendation is presentation of vector information about emotion level and similarity measuring method on the coordinates using Cosine measure. The deducing method suggested in this paper is two-phase arithmetic operation as follows. First, using general cosine measurement, the system establishes movies list. Second, using similarity measurement, system decides recommendable movie list by vector operation from the coordinates. After Comparative Experimental Study on the previous recommendation systems and new one, it turned out the new system from this study is more helpful than existing systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.153-172
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• 2022

In this study, the authors intend to analyze factors contributing to research performance using Backpropagation Neural Network and Support Vector Machine. The analyzing factors contributing to lecturer research performance start from defining the features. The next stage is to collect datasets based on defining features. Then transform the raw dataset into data ready to be processed. After the data is transformed, the next stage is the selection of features. Before the selection of features, the target feature is determined, namely research performance. The selection of features consists of Chi-Square selection (U), and Pearson correlation coefficient (CM). The selection of features produces eight factors contributing to lecturer research performance are Scientific Papers (U: 154.38, CM: 0.79), Number of Citation (U: 95.86, CM: 0.70), Conference (U: 68.67, CM: 0.57), Grade (U: 10.13, CM: 0.29), Grant (U: 35.40, CM: 0.36), IPR (U: 19.81, CM: 0.27), Qualification (U: 2.57, CM: 0.26), and Grant Awardee (U: 2.66, CM: 0.26). To analyze the factors, two data mining classifiers were involved, Backpropagation Neural Networks (BPNN) and Support Vector Machine (SVM). Evaluation of the data mining classifier with an accuracy score for BPNN of 95 percent, and SVM of 92 percent. The essence of this analysis is not to find the highest accuracy score, but rather whether the factors can pass the test phase with the expected results. The findings of this study reveal the factors that have a significant impact on research performance and vice versa.

• Journal of the Korea Society of Computer and Information
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• v.23 no.7
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• pp.19-26
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• 2018

This paper presents a structuring process of unstructured social network service (SNS) messages on rail services. We crawl messages about rail services posted on SNS and extract keywords indicating date and time, rail operating company, station name, direction, and rail service types from each message. Among them, the rail service types are classified by machine learning according to predefined rail service types, and the rest are extracted by regular expressions. Words are converted into vector representations using Word2Vec and a conventional Convolutional Neural Network (CNN) is used for training and classification. For performance measurement, our experimental results show a comparison with a TF-IDF and Support Vector Machine (SVM) approach. This structured information in the database and can be easily used for services for railway users.

• Annual Conference on Human and Language Technology
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• 2009.10a
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• pp.56-60
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• 2009

오늘날 인터넷은 개인의 감정, 의견을 서로 공유할 수 있는 공간이 되고 있다. 하지만 인터넷에는 너무나 방대한 문서가 존재하기 때문에 다른 사용자들의 감정, 의견 정보를 개인의 의사 결정에 활용하기가 쉽지 않다. 최근 들어 감정이나 의견을 자동으로 추출하기 위한 연구가 활발하게 진행되고 있으며, 감정 분석에 관한 기존 연구들은 대부분 어구의 극성(polarity) 정보가 있는 감정 사전을 사용하고 있다. 하지만 인터넷에는 나날이 신조어가 새로 생기고 언어 파괴 현상이 자주 일어나기 때문에 사전에 기반한 방법은 한계가 있다. 본 논문은 감정 분석 문제를 긍정과 부정으로 구분하는 이진 분류 문제로 본다. 이진 분류 문제에서 탁월한 성능을 보이는 Support Vector Machines(SVM)을 사용하며, 문서들 간의 유사도 계산을 위해 문장의 부분 문자열을 비교하는 문자열 커널을 사용한다. 실험 결과, 실제 영화평에서 제안된 모델이 비교 대상으로 삼은 Bag of Words(BOW) 모델보다 안정적인 성능을 보였다.

• Journal of Korea Multimedia Society
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• v.22 no.8
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• pp.832-843
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• 2019

Prostate cancer is a high-risk with a high incidence and is a disease that occurs only in men. Accurate diagnosis of cancer is necessary as the incidence of cancer patients is increasing. Prostate cancer is also a disease that is difficult to predict progress, so it is necessary to predict in advance through prognosis. Therefore, in this paper, grade classification is attempted based on texture feature extraction. There are two main methods of classification: Uses One-way Analysis of Variance (ANOVA) to determine whether texture features are significant values, compares them with all texture features and then uses only one classification i.e. Benign versus. The second method consisted of more detailed classifications without using ANOVA for better analysis between different grades. Results of both these methods are compared and analyzed through the machine learning models such as Support Vector Machine and K-Nearest Neighbor. The accuracy of Benign versus Grade 4&5 using the second method with the best results was 90.0 percentage.

• Annual Conference of KIPS
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• 2020.11a
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• pp.971-974
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• 2020

Infants express their physical and emotional needs to the outside world mainly through crying. However, most of parents find it challenging to understand the reason behind their babies' cries. Failure to correctly understand the cause of a baby' cry and take appropriate actions can affect the cognitive and motor development of newborns undergoing rapid brain development. In this paper, we propose an infant cry recognition system based on deep transfer learning to help parents identify crying babies' needs the same way a specialist would. The proposed system works by transforming the waveform of the cry signal into log-mel spectrogram, then uses the VGGish model pre-trained on AudioSet to extract a 128-dimensional feature vector from the spectrogram. Finally, a softmax function is used to classify the extracted feature vector and recognize the corresponding type of cry. The experimental results show that our method achieves a good performance exceeding 0.96 in precision and recall, and f1-score.

• Spatial Information Research
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• v.15 no.4
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• pp.385-395
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• 2007

As the growth of performance of the computer and the development of the Internet are exponential, sharing and using the information illegally have also increased to the same proportion. In this paper, we proposed a novel method on the vector map data among digital contents. Vector map data are used for GIS, navigation and web-based services etc. We embedded watermark into the coordinate of the vector map data using bit operation and extracted the watermark. This method helps to protect the copyright of the vector map data. This watermarking method is a spatial domain method and it embeds the watermark within an allowable error. Our experiment shows that the watermark produced by this method is resistant to simplification and translation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.40-52
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• 2020

In this paper, we consider a precoder design problem for multiuser interference channel. Most of the conventional schemes for precoder design utilize a signal-to-interference-plus-noise ratio (SINR) as a cost function. However, since the SINR metric of a desired transmitter-receiver pair is a function of precoding vectors of all transmitters in the multiuser interference channel, an analytic closed-form solution is not available for the precoding vector of a desired transmitter that maximizes the SINR metric. To eliminate coupling between the precoding vectors of all transmitters and to find a closed-form solution for the precoding vector of the desired transmitter, we use a signal-to-leakage-plus-noise ratio (SLNR) instead as a cost function because the SLNR at a transmitter is a function of the precoding vector of the desired transmitter only. In addition, channel estimation errors for undesired links are considered when designing the precoding vector because they are inevitable in a multiuser interference channel. In this case, we propose a design scheme for the precoding vector that is robust to the channel estimation error. In the proposed scheme, the precoding vector is designed to maximize the worst-case SLNR. Through computer simulation, we show that the proposed scheme has better performance than the conventional scheme in terms of SLNR, SINR, and sum rate of all users.