• Journal of Information Technology Applications and Management
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• v.14 no.1
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• pp.161-178
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• 2007

Nowadays, Customer Relationship Management (CRM) has been a competitive edge of many companies. It is critical for companies to get and maintain profitable customers. For the purpose of sustaining this competitive edge, companies should manage the Customer Information (CI) more effectively The major goais of this research are 1) to identify the activities of the Customer Information Management (CIM) and the factors influencing on the CIM, 2) to show the relationship between the proficiency of the CIM and the CI Quality, and 3) to verify the mediating effects of the proficiency of the CIM Process between the influencing factors and the CI Quality. An empirical study was undertaken to test the hypotheses with data from 65 companies. Multiple regression analysis and ANOVA were employed to test the hypotheses. We found that 1) there are 6 activities in the CIM process and 5 factors affecting the CIM, 2) the proficiency of the CIM process is closely related to the CI Quality, and 3) the proficiency of the CIM process plays the mediator between the influencing factors and the CI Quality.

• The Journal of the Acoustical Society of Korea
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• v.14 no.1
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• pp.5-12
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• 1995

In the case where the set of training vectors constitute clusters, the codevectors of the codebook which is used to compression for speech and images in the vector quantization are regarded as the central vectors of the clusters constituted by given training vectors. In this work, we consider the distribution of Euclidean distance obtaining in the process of searching for the minimum distance between vectors, and propose the method searching for the proper number of and the central vectors of clusters. And then, the proposed method shows more than the about 4[dB] SNR than the LBG algorithm and the competitive learning algorithm

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.432-437
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• 1997

In this paper, Fuzzy ART which is one of the competitive learing neural networks is applied to machine-part cell formation problem. A large matrix and varios types of machine-part incidence matrices, especially including bottle-neck machines, bottle-neck parts, parts shared by several cells, and machines shared by several cells are used to test the performannce of Fuzzy ART neural network as a cell formation algorithm. The result shows Fuzzy ART neral network can be efficiently applied to machine-part cell formation problem which are large, and/or have much imperfection as exceptions, bottle-neck machines, and bottle-neck parts.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.178-186
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• 2003

Recently, many researchers have been interested in biometric systems such as fingerprint, handwriting, key-stroke patterns and human iris. From the viewpoint of reliability and robustness, iris recognition is the most attractive biometric system. Moreover, the iris recognition system is a comfortable biometric system, since the video image of an eye can be taken at a distance. In this paper, we discuss human iris recognition, which is based on accurate iris localization, robust feature extraction, and Neural Network classification. The iris region is accurately localized in the eye image using a multiresolution active snake model. For the feature representation, the localized iris image is decomposed using wavelet transform based on dyadic Haar wavelet. Experimental results show the usefulness of wavelet transform in comparison to conventional Gabor transform. In addition, we present a new method for setting initial weight vectors in competitive learning. The proposed initialization method yields better accuracy than the conventional method.

• Journal of Information Technology Services
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• v.14 no.1
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• pp.115-128
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• 2015

As the life expectancy of human beings gets longer and our society changes into highly competitive arena, the implementation of online adult learning is growing, and therefore the learners in self-regulated scaffolding learning environments is becoming an important topic. This study is to investigate the main effects of scaffolding and self-regulation and the interaction effect on discussion participation and comprehension in online learning environments. To do this, ninety-nine adults taking online learning courses with the open university in Korea were investigated. Adult learners were divided into one of the four groups (no scaffolding, conceptual, strategic, and conceptual and strategic scaffoldings). Regarding self-regulation, learners were divided into two groups (low and high self-regulated) based on the mean score of subjective report of self-regulated learning. The results are as follows : First, 'strategic scaffolding' is more effective than 'conceptual scaffolding' in discussion participation (F=2.772, p < .05) and comprehension test (F=7.156, p < .05). Second, high self-regulated learners more actively participate than low self-regulated learners in discussion (F=6.230, p < .05), and achieve higher scores (F=4.863, p < .05). Third, there is no interaction effect between scaffolding strategies and the level of self-regulation. The theoretical and practical implications of these findings are discussed.

• The Journal of Korean Association of Computer Education
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• v.10 no.2
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• pp.9-16
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• 2007

The purpose of this research is to suggest effective teaching and learning method suitable for IT-gifted primary school students based on their learning style. This study investigated the means of identifying IT-gifted students by comparing IT-gifted's learning style with average student's. Grasha-Reichmann Student Learning Style Inventory was used, which was proved to identify gifted IT students with 66.45% accuracy. As a result, The learning style of IT-gifted was determined as independent, competitive, participant Therefore, self-directed learning methods seem to be suitable for IT-gifted. IT-gifted also need to have more opportunities to participate in learning activities and discussion with their peers.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.97-104
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• 2015

An 'associative cube', a class of auto-associative memories, is revisited here, in which training data and hidden orthogonal basis functions such as wavelet packets or Fourier kernels, are combined in the weight cube. This weight cube has hidden units in its depth, represented by a three dimensional cubic structure. We develop an unsupervised incremental learning mechanism based upon the adaptive least squares method. Training data are mapped into orthogonal basis vectors in a least-squares sense by updating the weights which minimize an energy function. Therefore, a prescribed orthogonal kernel is incrementally assigned to an incoming data. Next, we show how a decoding procedure finds the closest one with a competitive network in the hidden layer. As noisy test data are applied to an associative cube, the nearest one among the original training data are restored in an optimal sense. The simulation results confirm robustness of associative cubes even if test data are heavily distorted by various types of noise.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.3
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• pp.522-538
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• 2013

Content based image retrieval has become an increasingly important research topic for its wide application. It is highly challenging when facing to large-scale database with large variance. The retrieval systems rely on a key component, the predefined or learned similarity measures over images. We note that, the similarity measures can be potential improved if the data distribution information is exploited using a more sophisticated way. In this paper, we propose a similarity measure learning approach for image retrieval. The similarity measure, so called Fisher kernel, is derived from the probabilistic distribution of images and is the function over observed data, hidden variable and model parameters, where the hidden variables encode high level information which are powerful in discrimination and are failed to be exploited in previous methods. We further propose a discriminative learning method for the similarity measure, i.e., encouraging the learned similarity to take a large value for a pair of images with the same label and to take a small value for a pair of images with distinct labels. The learned similarity measure, fully exploiting the data distribution, is well adapted to dataset and would improve the retrieval system. We evaluate the proposed method on Corel-1000, Corel5k, Caltech101 and MIRFlickr 25,000 databases. The results show the competitive performance of the proposed method.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.2099-2106
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• 2018

This study presents a novel approach of discriminative feature vectors based on manifold learning using nonlinear dimension reduction (DR) technique to improve loss function, and combine with the Adversarial examples to regularize the object function for image classification. The traditional convolutional neural networks (CNN) with many new regularization approach has been successfully used for image classification tasks, and it achieved good results, hence it costs a lot of Calculated spacing and timing. Significantly, distrinct from traditional CNN, we discriminate the feature vectors for objects without empirically-tuned parameter, these Discriminative features intend to remain the lower-dimensional relationship corresponding high-dimension manifold after projecting the image feature vectors from high-dimension to lower-dimension, and we optimize the constrains of the preserving local features based on manifold, which narrow the mapped feature information from the same class and push different class away. Using Adversarial examples, improved loss function with additional regularization term intends to boost the Robustness and generalization of neural network. experimental results indicate that the approach based on discriminative feature of manifold learning is not only valid, but also more efficient in image classification tasks. Furthermore, the proposed approach achieves competitive classification performances for three benchmark datasets : MNIST, CIFAR-10, SVHN.

• The Journal of Information Systems
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• v.32 no.4
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• pp.229-245
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• 2023

Purpose This study aims to understand the inclinations of young job seekers towards "hidden champions" - small but competitive companies that are emerging as potential solutions to the growing disparity between youth-targeted job vacancies and job seekers. We utilize machine learning techniques to discern the appeal of these hidden champions. Design/methodology/approach We examined the characteristics of small and medium-sized enterprises using data sourced from the Ministry of Employment and Labor and Youth Worknet. By comparing the efficacy of five machine learning classification models (i.e., Logistic Regression, Random Forest Classifier, Gradient Boosting Classifier, LGBM Classifier, and XGB Classifier), we discovered that the predictive model utilizing the LGBM Classifier yielded the most consistent performance. Findings Our analysis of the relative significance of preference determinants revealed that industry type, geographical location, and employee count are pivotal factors influencing preference. Drawing from these insights, we propose targeted strategic interventions for policymakers, hidden champions, and young job seekers.