• The Journal of Information Systems
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• v.18 no.3
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• pp.375-399
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• 2009

In this study, we propose a integrated model of logistic regression, artificial neural networks, support vector machines(SVM), with case-based reasoning(CBR). To predict respondents in the direct marketing is the binary classification problem as like bankruptcy prediction, IDS, churn management and so on. To solve the binary problems, we employed logistic regression, artificial neural networks, SVM. and CBR. CBR is a problem-solving technique and shows significant promise for improving the effectiveness of complex and unstructured decision making, and we can obtain excellent results through CBR in this study. Experimental results show that the classification accuracy of integration model using CBR is superior to logistic regression, artificial neural networks and SVM. When we apply the customer response model to predict respondents in the direct marketing, we have to consider from the view point of profit/cost about the misclassification.

• Communications for Statistical Applications and Methods
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• v.26 no.3
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• pp.315-323
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• 2019

Panel data sets have recently been developed in various areas, and many recent studies have analyzed panel, or longitudinal data sets. Often a dichotomous dependent variable occur in survival analysis, biomedical and epidemiological studies that is analyzed by a generalized linear mixed effects model (GLMM). The most common estimation method for the binary panel data may be the maximum likelihood (ML). Many statistical packages provide ML estimates; however, the estimates are computed from numerically approximated likelihood function. For instance, R packages, pglm (Croissant, 2017) approximate the likelihood function by the Gauss-Hermite quadratures, while Rchoice (Sarrias, Journal of Statistical Software, 74, 1-31, 2016) use a Monte Carlo integration method for the approximation. As a result, it can be observed that different packages give different results because of different numerical computation methods. In this note, we discuss the pros and cons of numerical methods compared with the exact computation method.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.11
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• pp.853-864
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• 2017

This paper proposes a false alarm rate reduction technique for detection of small targets in a terrestrial environment. CFAR algorithm is useful in homogeneous background, but it is not easy to detect targets in non-homogeneous background. In particular, when the clutter power is not significantly different from the target signal, it is difficult to detect the target due to high false alarm rate. To solve these difficulties, this study presents the false alarm rate reduction technique based on CFAR algorithm, matched filter and binary integration technique. The parameters are studied through the theoretical analysis and the validity of the proposed study is examined by the test results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.2
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• pp.92-99
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• 2014

This paper proposes the method of facial expression recognition based on decision tree structure. In the image of facial expression, ASM(Active Shape Model) and LBP(Local Binary Pattern) make the local features of a facial expressions extracted. The discriminant features gotten from local features make the two facial expressions of all combination classified. Through the sum of true related to classification, the combination of facial expression and local region are decided. The integration of branch classifications generates decision tree. The facial expression recognition based on decision tree shows better recognition performance than the method which doesn't use that.

• Korean Journal of Plant Resources
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• v.10 no.2
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• pp.107-113
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• 1997

Transformants of White Clover(Trifolium repens L.) were efficiently produced from immature seed derived callus cocultivated with Agrobacterium twnefaciens LBA4404 harboring plant binary vector. pBI121, using acetosyringone. The mean frequencies of transformants on the two kanamycin-containing media were 16 to 19% when the immature seed-derived calli were infected with bacteria cultured in the presence of 100$\mu$M acetosyringone compared with 7% in media without acetosyringone. Transgenic white clover was subject to molecular analysis for integration into plant nuclear genome and expression of $\beta$-glucuronidase(GUS) gene. PCR and Northern blot analyses demonstrated that GUS gene was integrated into white clover nuclear genome and expressed into its mRNA. The expression of GUS gene into its protein was confirmed by spectrophotometric assay of GUS activity.

• Journal of Electrochemical Science and Technology
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• v.12 no.4
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• pp.440-452
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• 2021

Enhancement of durability and reduction of maintenance cost of concrete, with the implementation of various approaches, has always been a matter of concern to researchers. The integration of pozzolans as a substitute for cement into the concrete is one of the most desirable technique. Silica fume (SF) and colloidal nanosilica (CS) have received a great deal of interest from researchers with their significant performance in improving the durability of concrete. The synergistic role of the micro and nano-silica particles in improving the main characteristics of cemented materials needs to be investigated. This work aims to examine the utility of partial substitution of cement by SF and CS in binary and ternary blends in the improvement of the durability characteristics linked to resistance for electrochemical corrosion using electrical resistivity and half-cell potential analysis and chloride penetration trough rapid chloride penetration test. Furthermore, the effects of this silica mixture on the compressive strength of concrete under normal and aggressive environment have also been investigated. Based on the maximum compression strength of the concrete, the optimal cement substituent ratios have been obtained as 12% SF and 1.5% CS for binary blends. The optimal CS and SF combination mixing ratios has been obtained as 1.0% and 12% respectively for ternary blends. The ternary blends with substitution of cement by optimal percentage of CS and SF exhibited decreased rate for electrochemical corrosion. The strength and durability studies were found in consistence with the microstructural analysis signifying the beneficiary role of CS and SF in upgrading the performance of concrete.

• Journal of Plant Biotechnology
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• v.6 no.2
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• pp.69-75
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• 2004

Optimal protocol for efficient genetic transformation has been defined to aid future strategies of genetic engineering in pigeon pea with agronomically important genes. Transgenic pigeonpea plants were successfully produced through Agrobacterium tumefaciens-mediated genetic transformation method using cotyledonary node explants by employing defined culture media. The explants were co-cultivated with A. tumefaciens strain C-58 harboring the binary plasmid, pCAMBIA-1301 [con-ferring $\beta$-glucuronidase(GUS) activity and resistance to hygromycin] and cultured on selection medium (regeneration medium supplemented with hygromycin) to select putatively transformed shoots. The shoots were then rooted on root induction medium and transferred to pots containing sand and soil mixture in the ratio of 1:1. About 22 putative TO transgenic plants have been produced. Stable expression and integration of the transgenes in the putative transgenics were confirmed by GUS assay, PCR and Southern blot hybridization with a transformation efficiency of over 45%. Stable integration and expression of the marker gene has been confirmed in the TO and T1 transgenics through PCR, and Southern hybridization.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.127-136
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• 2021

Real-time human recognition is a challenging task, as the images are captured in an unconstrained environment with different poses, makeups, and styles. This limitation is addressed by generating several facial images with poses, makeup, and styles with a single reference image of a person using Generative Adversarial Networks (GAN). In this paper, we propose deep learning-based human recognition using integration of GAN and Spatial Domain Techniques. A novel concept of human recognition based on face depiction approach by generating several dissimilar face images from single reference face image using Domain Transfer Generative Adversarial Networks (DT-GAN) combined with feature extraction techniques such as Local Binary Pattern (LBP) and Histogram is deliberated. The Euclidean Distance (ED) is used in the matching section for comparison of features to test the performance of the method. A database of millions of people with a single reference face image per person, instead of multiple reference face images, is created and saved on the centralized server, which helps to reduce memory load on the centralized server. It is noticed that the recognition accuracy is 100% for smaller size datasets and a little less accuracy for larger size datasets and also, results are compared with present methods to show the superiority of proposed method.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.12
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• pp.17-23
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• 1997

A 3-D vision system is developed to evaluate blanks' line up and monitor gap and thickness difference between blanks in tailored blank welding system. A structured lighting method is used for 3-D vision recognition. Images of sheared portion in blanks are irregular according to roughness of blank surface, shape of sheared geometry and blurring. It is difficult to get accurate and reliable informations in the case of using binary image processing or contour detection techniques in real time for such images. We propoe a new energy integration method robust to blurring and changes of illumination. The method is computationally simple, and uses feature restoration concept, different to another digital image restoration methods which aim image itself restoration and may be used in conventional applications using structured line lighting technique. Experimental results show this system measuring repeatability is .+-. pixel for gap and thickness difference in static and dynamic tests. The data are expected to be useful for preview gap control.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.89-96
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• 2023

Intrusion detection has been widely studied in both industry and academia, but cybersecurity analysts always want more accuracy and global threat analysis to secure their systems in cyberspace. Big data represent the great challenge of intrusion detection systems, making it hard to monitor and analyze this large volume of data using traditional techniques. Recently, deep learning has been emerged as a new approach which enables the use of Big Data with a low training time and high accuracy rate. In this paper, we propose an approach of an IDS based on cloud computing and the integration of big data and deep learning techniques to detect different attacks as early as possible. To demonstrate the efficacy of this system, we implement the proposed system within Microsoft Azure Cloud, as it provides both processing power and storage capabilities, using a convolutional neural network (CNN-IDS) with the distributed computing environment Apache Spark, integrated with Keras Deep Learning Library. We study the performance of the model in two categories of classification (binary and multiclass) using CSE-CIC-IDS2018 dataset. Our system showed a great performance due to the integration of deep learning technique and Apache Spark engine.