• The Journal of Information Systems
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• v.26 no.4
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• pp.17-37
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• 2017

Purpose Customer Loyalty is the most important factor of customer relationship management (CRM). Especially in retailing industry, where customers have many options of where to spend their money. Classifying loyal customers through customers' data can help retailing companies build more efficient marketing strategies and gain competitive advantages. This study aims to construct classification models of distinguishing the loyal customers within a Korean retailing company using data mining techniques with R language. Design/methodology/approach In order to classify retailing customers, we used combination of support vector machines (SVMs) and other classification algorithms of machine learning (ML) with the support of recursive feature elimination (RFE). In particular, we first clean the dataset to remove outlier and impute the missing value. Then we used a RFE framework for electing most significant predictors. Finally, we construct models with classification algorithms, tune the best parameters and compare the performances among them. Findings The results reveal that ML classification techniques can work well with CRM data in Korean retailing industry. Moreover, customer loyalty is impacted by not only unique factor such as net promoter score but also other purchase habits such as expensive goods preferring or multi-branch visiting and so on. We also prove that with retailing customer's dataset the model constructed by SVMs algorithm has given better performance than others. We expect that the models in this study can be used by other retailing companies to classify their customers, then they can focus on giving services to these potential vip group. We also hope that the results of this ML algorithm using R language could be useful to other researchers for selecting appropriate ML algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.5
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• pp.925-938
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• 2010

Depending on network conditions, a communication network could be overloaded when media are transmitted. Research has been carried out to lessen network overloading, such as by filtering, load distribution, frame dropping, and other methods. Among these methods, one of the most effective is frame dropping, which reduces specified video frames for bandwidth diminution. In frame dropping, B-frames are dropped and then I- and P-frames are dropped, based on the dependency among the frames. This paper proposes a scheme for protecting copyrights by encryption, when frame dropping is applied to reduce the bandwidth of media based on the MPEG-4 file format. We designed two kinds of frame dropping: the first stores and then sends the dropped files and the other drops frames in real time when transmitting. We designed three kinds of encryption methods using the DES algorithm to encrypt MPEG-4 data: macro block encryption in I-VOP, macro block and motion vector encryption in P-VOP, and macro block and motion vector encryption in I-, P-VOP. Based on these three methods, we implemented a digital rights management solution for MPEG-4 data streaming. We compared the results of dropping, encryption, decryption, and the quality of the video sequences to select an optimal method, and found that there was no noticeable difference between the video sequences recovered after frame dropping and the ones recovered without frame dropping. The best performance in the encryption and decryption of frames was obtained when we applied the macro block and motion vector encryption in I-, P-VOP.

• BMB Reports
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• v.55 no.12
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• pp.615-620
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• 2022

The murine leukemia virus-based semi-retroviral replicating vectors (MuLV-based sRRV) had been developed to improve safety and transgene capacity for cancer gene therapy. However, despite the apparent advantages of the sRRV, improvements in the in vivo transduction efficiency are still required to deliver therapeutic genes efficiently for clinical use. In this study, we established a gibbon ape leukemia virus (GaLV) envelope-pseudotyped semi-replication-competent retrovirus vector system (spRRV) which is composed of two transcomplementing replication-defective retroviral vectors termed MuLV-Gag-Pol and GaLV-Env. We found that the spRRV shows considerable improvement in efficiencies of gene transfer and spreading in both human glioblastoma cells and pre-established human glioblastoma mouse model compared with an sRRV system. When treated with ganciclovir after intratumoral injection of each vector system into pre-established U-87 MG glioblastomas, the group of mice injected with spRRV expressing the herpes simplex virus type 1-thymidine kinase (HSV1-tk) gene showed a survival rate of 100% for more than 150 days, but all control groups of mice (HSV1-tk/PBS-treated and GFP/GCV-treated groups) died within 45 days after tumor injection. In conclusion, these findings sug-gest that intratumoral delivery of the HSV1-tk gene by the spRRV system is worthy of development in clinical trials for the treatment of malignant solid tumors.

• Asian-Australasian Journal of Animal Sciences
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• v.26 no.7
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• pp.1030-1037
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• 2013

Early detection of anomalies is an important issue in the management of group-housed livestock. In particular, failure to detect oestrus in a timely and accurate way can become a limiting factor in achieving efficient reproductive performance. Although a rich variety of methods has been introduced for the detection of oestrus, a more accurate and practical method is still required. In this paper, we propose an efficient data mining solution for the detection of oestrus, using the sound data of Korean native cows (Bos taurus coreanea). In this method, we extracted the mel frequency cepstrum coefficients from sound data with a feature dimension reduction, and use the support vector data description as an early anomaly detector. Our experimental results show that this method can be used to detect oestrus both economically (even a cheap microphone) and accurately (over 94% accuracy), either as a standalone solution or to complement known methods.

• Journal of the Korean Society for Precision Engineering
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• v.33 no.2
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• pp.129-138
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• 2016

This paper presents a natural corners-based SLAM (Simultaneous Localization and Mapping) with a robust data association algorithm in a real unknown environment. Corners are extracted from raw laser sensor data, which are chosen as landmarks for correcting the pose of mobile robot and building the map. In the proposed data association method, the extracted corners in every step are separated into several groups with small numbers of corners. In each group, local best matching vector between new corners and stored ones is found by joint compatibility, while nearest feature for every new corner is checked by individual compatibility. All these groups with local best matching vector and nearest feature candidate of each new corner are combined by partial compatibility with linear matching time. Finally, SLAM experiment results in an indoor environment based on the extracted corners show good robustness and low computation complexity of the proposed algorithms in comparison with existing methods.

• Journal of Ocean Engineering and Technology
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• v.24 no.1
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• pp.47-52
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• 2010

This study presents a modified scheme for the radar image simulation of sea waves. A simulated radar image was obtained by taking into account the dot product of the directed vector from the radar and the normal vector of the sea surface. Moreover, to calculate the radar image, we used the radar received power and radar cross section. To demonstrate the effectiveness of the proposed scheme, the wave spectrum from field data was utilized to obtain the simulated sea waves. The radar image was simulated using numerically generated sea waves. The wave statistics from the simulation agrees comparatively with those of the original field data acquired by real radar measurements.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.1
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• pp.62-69
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• 2011

A novel QEGT codebook index searching algorithm for long tenn evolution (LTE) system is proposed. The proposed algorithm divides the Q precoding vectors into M groups, and selects the optimal precoding vector from the selected group at the receiver. This algorithm reduced the calculation for searching the optimal precoding vector index compared to the previous algorithms. The index searching algorithm is implemented for TI's TMS320C6713 DSP board. When the number of transmit antenna is 4, the number of clock cycles is reduced to 25%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.5170-5188
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• 2015

This paper proposes a novel progressive secret image-hiding scheme based on the inpainting technique, the vector quantization technique (VQ) and the exploiting modification direction (EMD) technique. The proposed scheme first divides the secret image into non-overlapping blocks and categorizes the blocks into two groups: complex and smooth. The blocks in the complex group are compressed by VQ with PCA sorted codebook to obtain the VQ index table. Instead of embedding the original secret image, the proposed method progressively embeds the VQ index table into the cover images by using the EMD technique. After the receiver recovers the complex parts of the secret image by decoding the VQ index table from the shadow images, the smooth parts can be reconstructed by using the inpainting technique based on the content of the complex parts. The experimental results demonstrate that the proposed scheme not only has the advantage of progressive data hiding, which involves more shadow images joining to recover the secret image so as to produce a higher quality steganography image, but also can achieve high hiding capacity with acceptable recovered image quality.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.9
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• pp.1269-1282
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• 1993

In this paper, we propose to method of recognizing printed chinese characters which combine the coventional deterministic methods and the neural networks. Firstly, we extract four directional vector of strokes from chinese characters. Secondly, we make the mesh of the center of gravity in the vector and then constitute the H x8 feature matrix using black pixel lenth from each meshs. This normalized feature matrix value offer as the input of neural network for classifying into the 14 character types. And this calssified character classify again into Busu group by the Busu recognizing neural network. Finally, we recognize each characters using the distance of similarity between input characters and reference characters. The usefulness of the proposed algorithm is evaluated by experimenting with recognizing the chinese characters.

• Journal of Information Science Theory and Practice
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• v.8 no.2
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• pp.6-17
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• 2020

Word similarity is often measured to enhance system performance in the information retrieval field and other related areas. This paper reports on an experimental comparison of values for word similarity measures that were computed based on 50 intentionally selected words from a Reuters corpus. There were three targets, including (1) co-occurrence-based similarity measures (for which a co-occurrence frequency is counted as the number of documents or sentences), (2) context-based distributional similarity measures obtained from a latent Dirichlet allocation (LDA), nonnegative matrix factorization (NMF), and Word2Vec algorithm, and (3) similarity measures computed from the tf-idf weights of each word according to a vector space model (VSM). Here, a Pearson correlation coefficient for a pair of VSM-based similarity measures and co-occurrence-based similarity measures according to the number of documents was highest. Group-average agglomerative hierarchical clustering was also applied to similarity matrices computed by individual measures. An evaluation of the cluster sets according to an answer set revealed that VSM- and LDA-based similarity measures performed best.