• Journal of Digital Convergence
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• v.13 no.8
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• pp.289-294
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• 2015

This paper proposes an autonomous machine learning method applicable to the BCI(Brain-Computer Interface) is based on the self-organizing Kohonen method, one of the exemplary method of unsupervised learning. In addition we propose control method of learning region and self machine learning rule using an interactive function. The learning region control and machine learning was used to control the side effects caused by interaction function that is based on the self-organizing Kohonen method. After determining the winner neuron, we decided to adjust the connection weights based on the learning rules, and learning region is gradually decreased as the number of learning is increased by the learning. So we proposed the autonomous machine learning to reach to the network equilibrium state by reducing the flow toward the input to weights of output layer neurons.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.39-48
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• 2023

Breast cancer screening makes extensive utilization of mammography. Even so, there has been a lot of debate with regards to this application's starting age as well as screening interval. The deep learning technique of transfer learning is employed for transferring the knowledge learnt from the source tasks to the target tasks. For the resolution of real-world problems, deep neural networks have demonstrated superior performance in comparison with the standard machine learning algorithms. The architecture of the deep neural networks has to be defined by taking into account the problem domain knowledge. Normally, this technique will consume a lot of time as well as computational resources. This work evaluated the efficacy of the deep learning neural network like Visual Geometry Group Network (VGG Net) Residual Network (Res Net), as well as inception network for classifying the mammograms. This work proposed optimization of ResNet with Teaching Learning Based Optimization (TLBO) algorithm's in order to predict breast cancers by means of mammogram images. The proposed TLBO-ResNet, an optimized ResNet with faster convergence ability when compared with other evolutionary methods for mammogram classification.

• Journal of information and communication convergence engineering
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• v.19 no.1
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• pp.1-7
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• 2021

Reinforcement learning is an area of machine learning that studies how an intelligent agent takes actions in a given environment to maximize the cumulative reward. In this paper, we propose a new MAC protocol based on the Q-learning technique of reinforcement learning to improve the performance of the IEEE 802.11 wireless LAN CSMA/CA MAC protocol. Furthermore, the operation of each access point (AP) and station is proposed. The AP adjusts the value of the contention window (CW), which is the range for determining the backoff number of the station, according to the wireless traffic load. The station improves the performance by selecting an optimal backoff number with the lowest packet collision rate and the highest transmission success rate through Q-learning within the CW value transmitted from the AP. The result of the performance evaluation through computer simulations showed that the proposed scheme has a higher throughput than that of the existing CSMA/CA scheme.

• The Journal of Korean Association of Computer Education
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• v.22 no.2
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• pp.39-50
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• 2019

We designed and applied team project based learning for effective computer programming education and analyzed the effect on learning effect. Throughout simplified traditional theories and practices, teamed up with random lottery, divided role & responsibility, and conducted problem solving projects in a competitive way for a given task. When after completion of the course, we conducted questionnaires on learners in order to grasp the influence factors on the learning effect. As a result of the structural equation model analysis, it was shown that Team Project had a direct effect on the learning effect. The learning effect based on the relationships among the factors derived through exploratory factor analysis. Based on this analysis, we propose a more effective computer programming education way.

• Current Optics and Photonics
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• v.8 no.3
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• pp.270-281
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• 2024

Information-hiding technology is introduced into an optical ghost imaging encryption scheme, which can greatly improve the security of the encryption scheme. However, in the current mainstream research on camouflage ghost imaging encryption, information hiding techniques such as digital watermarking can only hide 1/4 resolution information of a cover image, and most secret images are simple binary images. In this paper, we propose an equal-resolution image-hiding encryption scheme based on deep learning and computational ghost imaging. With the equal-resolution image steganography network based on deep learning (ERIS-Net), we can realize the hiding and extraction of equal-resolution natural images and increase the amount of encrypted information from 25% to 100% when transmitting the same size of secret data. To the best of our knowledge, this paper combines image steganography based on deep learning with optical ghost imaging encryption method for the first time. With deep learning experiments and simulation, the feasibility, security, robustness, and high encryption capacity of this scheme are verified, and a new idea for optical ghost imaging encryption is proposed.

• Journal of the Korea Society of Computer and Information
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• v.22 no.12
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• pp.49-54
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• 2017

Pathfinding for pedestrians provided by various navigation programs is based on a shortest path search algorithm. There is no big difference in their guide results, which makes the path quality more important. Multiple criteria should be included in the search cost to calculate the path quality, which is called a multi-criteria pathfinding. In this paper we propose a user adaptive pathfinding algorithm in which the cost function for a multi-criteria pathfinding is defined as a weighted sum of multiple criteria and the weights are learned automatically by Perceptron learning. Weight learning is implemented in two ways: short-term weight learning that reflects weight changes in real time as the user moves and long-term weight learning that updates the weights by the average value of the entire path after completing the movement. We use the weight update method with momentum for long-term weight learning, so that learning speed is improved and the learned weight can be stabilized. The proposed method is implemented as an app and is applied to various movement situations. The results show that customized pathfinding based on user preference can be obtained.

• The Journal of the Korea Contents Association
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• v.5 no.1
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• pp.1-8
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• 2005

E-learning is the application of e-business technology and services to teaching and learning. It use of new multimedia technologies and Internet to improved the qualify of learning by facilitating access to remote resources and services. In this paper, we show a computer-based test system, which is carefully designed and implemented. The system consists of a contents delivery mechanism, computer-adaptive test algorithm, and review engine. In this papepr, we describe what are points to be considered when design and implementing a computer-based test system. In addition, this paper shows how to control the bias value for computer-adaptive algorithm using real data.

• Educational Technology International
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• v.15 no.1
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• pp.27-48
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• 2014

The purpose of the present research was to examine the relationship between the using goals of computer and self-regulated learning ability on the gender difference. To accomplish this goal, we have analyzed the data of Korea Children and Youth Panel Survey III which is nationally collected from primary school students, currently on the 6th grade in South Korea. 2,219 samples were used in the study excluding missing samples. The participants were 1167 males (49.5%) and 1052 females (50.5%). The mean age was 13.94 years (SD=.25). As results, female students spent more time on using computer than male students did: (1) the male students' time spent on Playing game was significantly larger than that of female students, but (2) on the rest seven using goals of computer including e-Learning/Information retrieval for learning, the female students spent significantly more time than the male students did. Also, in terms of the self-regulated learning ability, using computer for e-Learning/Information retrieval for learning itself gave significantly positive effects on both male and female students' self-regulated learning ability. On the other hand, Playing game gave significantly negative effects on both. Based on the results, some strategies were suggested on the proper use of computer for learning.

• English Language & Literature Teaching
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• v.10 no.2
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• pp.133-158
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• 2004

The purpose of the present study was to scrutinize computer games that can motivate elementary school students through their interactive "edutainment" effects. The types of elements in computer games that students find interesting as learning media and their impact were studied. The current status of Korean computer games, issues related to learning English, and methods to stimulate the motivation and interest in learning by elementary school students were explored. A computer game model for efficiently teaching English to elementary school students through a connection between computer games and education was suggested. In this model, overall games were designed with the focus on the integration of curriculum and content subjects related to learning activities. For games not to be biased toward entertainment and to have systemized learning steps, the games are composed of an introduction, presentation, practice, production and evaluation, in that order. The model suggested by this plan and composition make it possible to approach learning efficiently with entertaining games based on a systematic learning curriculum. As shown above, developing the model of educational computer games can be seen as an opportunity, which can provide amusement and interests and a broad learning experience as an additional learning method.

• Journal of the Korea Society of Computer and Information
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• v.12 no.1 s.45
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• pp.83-88
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• 2007

The goal of this research was to investigate the effect the computer game-based learning has on learning achievement in computer education for middle school students. To achieve this goal 74 middle schoolers were allocated into the experiment group(34 students) with the educational computer games class and the control group(34 students) with the traditional face-to-face class. After identifying the homogeneity of two groups through the pre-test, the experiment was carried out. As a result, the mean difference between the experiment group and the control group was statistically significant. That is, learning achievement of middle schoolers utilizing the computer games was higher than that of the face-to-face class. It is suggested that the game factors should be considered in designing the computer education.