• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.200-206
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• 2021

Fraud in e-commerce transaction increased in the last decade especially with the increasing number of online stores and the lockdown that forced more people to pay for services and groceries online using their credit card. Several machine learning methods were proposed to detect fraudulent transaction. Neural networks showed promising results, but it has some few drawbacks that can be overcome using optimization methods. There are two categories of learning optimization methods, first-order methods which utilizes gradient information to construct the next training iteration whereas, and second-order methods which derivatives use Hessian to calculate the iteration based on the optimization trajectory. There also some training refinements procedures that aims to potentially enhance the original accuracy while possibly reduce the model size. This paper investigate the performance of several NN models in detecting fraud in e-commerce transaction. The backpropagation model which is classified as first learning algorithm achieved the best accuracy 96% among all the models.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.11-20
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• 2024

Among the generative models in Artificial Intelligence (AI), especially Generative Adversarial Network (GAN) has been successful in various applications such as image processing, density estimation, and style transfer. While the GAN models including Conditional GAN (CGAN), CycleGAN, BigGAN, have been extended and improved, researchers face challenges in real-world applications in specific domains such as disaster simulation, healthcare, and urban planning due to data scarcity and unstable learning causing Image distortion. This paper proposes a new progressive learning methodology called Progressive Step Training (PST) based on the Auxiliary Classifier GAN (ACGAN) that discriminates class labels, leveraging the progressive learning approach of the Progressive Growing of GAN (PGGAN). The PST model achieves 70.82% faster stabilization, 51.3% lower standard deviation, stable convergence of loss values in the later high resolution stages, and a 94.6% faster loss reduction compared to conventional methods.

• Proceedings of the KAN Conference
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• 2004.11a
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• pp.57-63
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• 2004

• Journal of the Korea Convergence Society
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• v.9 no.5
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• pp.21-26
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• 2018

In this study, HRD-NET (http://hrd.go.kr), a vocational and training integrated computer network operated by the Ministry of Employment and Labor, is used to confirm whether job training information required by job seekers is being provided smoothly The question bulletin board was extracted using 'R' program which is optimized for big data technique. Therefore, the effectiveness, appropriateness, visualization, frequency analysis and association analysis of the vocational training system were conducted through this, The results of the study are as follows. First, the issue of vocational training card, video viewing, certificate issue, registration error, Second, management and processing procedures of learning cards for tomorrow 's learning cards are complicated and difficult. In addition, it was analyzed that the training cost system and the refund structure differentiated according to the training occupation, the process, and the training institution in the course of the training. Based on this paper, we will study not only the training system of the Ministry of Employment and Labor but also the improvement of the various training computer system of the government department through the analysis of big data.

• Journal of the Korea Convergence Society
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• v.10 no.9
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• pp.167-172
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• 2019

The purpose of this study was to investigate the effect of computer based cognitive rehabilitation program on the generative naming. Twenty - one patients were assigned to the CoTras program and eight were treated with traditional face - to - face language rehabilitation such as paper and table activities. The experimental group and the control group performed sequential language recall memory training, association memory recall training, language categorization memory training, and language integrated memory training for 12 weeks. The Welch's robust ANCOVA showed significant differences in mean fluency and MMSE-K changes (p<0.05). On the other hand, phonemic fluency increased significantly after 12 weeks of treatment compared to baseline in both experimental and control groups, but there was no statistically significant difference between treatment groups. The results of this study suggest that the computer based cognitive rehabilitation program may be more effective in improving the semantic fluency than the conventional cognitive-linguistic rehabilitation.

• Proceedings of the Korean Society Of Semiconductor Equipment Technology
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• 2003.12a
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• pp.169-176
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• 2003

Telematic and new programming technologies support the increasing demand of education and training leading to the delivery of computer based learining systems open to distance and continuing education. Using LabVIEW, we designed and implemented an interactive learning environment for practice on electronics measurement methodologies. The environment provides remote access to real and simulated instrumentation and guided experiments on basic circuits. The environment is applied to the education and training on electronics for engineers in the field of semiconductor industry.

• International Journal of Computer Science & Network Security
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• v.21 no.12spc
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• pp.526-538
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• 2021

Machine and deep learning-based models are emerging techniques that are being used to address prediction problems in biomedical data analysis. DNA sequence prediction is a critical problem that has attracted a great deal of attention in the biomedical domain. Machine and deep learning-based models have been shown to provide more accurate results when compared to conventional regression-based models. The prediction of the gene sequence that leads to cancerous diseases, such as prostate cancer, is crucial. Identifying the most important features in a gene sequence is a challenging task. Extracting the components of the gene sequence that can provide an insight into the types of mutation in the gene is of great importance as it will lead to effective drug design and the promotion of the new concept of personalised medicine. In this work, we extracted the exons in the prostate gene sequences that were used in the experiment. We built a Deep Neural Network (DNN) and Bi-directional Long-Short Term Memory (Bi-LSTM) model using a k-mer encoding for the DNA sequence and one-hot encoding for the class label. The models were evaluated using different classification metrics. Our experimental results show that DNN model prediction offers a training accuracy of 99 percent and validation accuracy of 96 percent. The bi-LSTM model also has a training accuracy of 95 percent and validation accuracy of 91 percent.

• The KIPS Transactions:PartB
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• v.13B no.6 s.109
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• pp.615-624
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• 2006

The objective of this paper is to implement parallel multi-layer ANN(Artificial Neural Network) simulator based on the mobile agent system which is executed in parallel in the virtual parallel distributed computing environment. The Multi-Layer Neural Network is classified by training session, training data layer, node, md weight in the parallelization-level. In this study, We have developed and evaluated the simulator with which it is feasible to parallel the ANN in the training session and training data parallelization because these have relatively few network traffic. In this results, we have verified that the performance of parallelization is high about 3.3 times in the training session and training data. The great significance of this paper is that the performance of ANN's execution on virtual parallel computer is similar to that of ANN's execution on existing super-computer. Therefore, we think that the virtual parallel computer can be considerably helpful in developing the neural network because it decreases the training time which needs extra-time.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.116-124
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• 2022

Information and communication technologies used in the social sphere are born due to the development of computer technologies. The main task of the distance learning process in higher education institutions is not to provide information, but to teach how to obtain and use it. The purpose of the article: to identify innovative approaches in the training of specialists in higher education institutions in the context of distance learning. Various innovative approaches to organizing the work of students of higher educational institutions in the context of distance learning are considered. Based on the conducted research, it is concluded that each of the approaches described by us outlines the study of the phenomenon of professional training of a specialist in the condition of distance learning. All the described approaches significantly contribute to the improvement of professional training of specialists, encourage students to self-improvement, professional development and enrich their professional competence in modern conditions. The emergence and spread of innovative technologies means not only a change in the activity itself and its inherent means and mechanisms of its implementation, but also a significant restructuring of goals, value orientations, specific knowledge, skills and abilities. Therefore, the current stage of the development of civilization, scientific and technological progress requires the emergence of such specialists who would have broad humanitarian thinking, would have good psychological training, would be able to build professional activities according to laws that take into account the relationship between economic productivity and creativity, as well as the desire of the individual for constant renewal, self-realization. Only such qualities will help you master the specifics of innovative technologies well. We see the prospects in the study of innovative approaches to training specialists in higher education institutions in the condition of distance learning in foreign countries.

• Journal of Korea Multimedia Society
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• v.14 no.12
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• pp.1517-1527
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• 2011

This paper presents a motion capture based rehabilitation training system for a lower-limb paretic patient. The system evaluates the rehabilitation status of the patient by using the bend posture of the knees and the weight balance of the body. The posture of both legs is captured with a single camera using the planar mirror. The weight distribution is obtained by the Wii Balance Board. Self-occlusion problem in the tracking of the legs is resolved by using k-nearest neighbor based clustering with body symmetry and local-linearity of the posture data. To do this, we present data normalization and its symmetric property in the normalized vector space.