• The Journal of the Korea Contents Association
• /
• v.14 no.6
• /
• pp.453-465
• /
• 2014

Although fairly descent amount of research outcomes has been generated on problem-based learning(PBL) with regard to science and medical subjects, relatively less outcome has been generated on social science and management subjects. Therefore this study attempts to exam effect of problem-based learning in food service entrepreneurship subject on improving problem solving skills. After literature review, for constructing for this research frame, each class schedules were formed by hybrid-PEL and traditional lecture style respectively then problems, solution, and evaluation process were generated for hybrid-PBL. Through the result, there was hardly any differences occurred before and after traditional lecture approach on students' problem solving skills. There was, however, certain differences appeared on students' skills after hybrid-PBL approach. Through the study, hybrid-PBL learning can be an effective teaching method in social science and management subject for improving students' problem solving skill.

• International Journal of Computer Science & Network Security
• /
• v.22 no.10
• /
• pp.73-82
• /
• 2022

Early detection continues to be the mainstay of breast cancer control as well as the improvement of its treatment. Even so, the absence of cancer symptoms at the onset has early detection quite challenging. Therefore, various researchers continue to focus on cancer as a topic of health to try and make improvements from the perspectives of diagnosis, prevention, and treatment. This research's chief goal is development of a system with deep learning for classification of the breast cancer as non-malignant and malignant using mammogram images. The following two distinct approaches: the first one with the utilization of patches of the Region of Interest (ROI), and the second one with the utilization of the overall images is used. The proposed system is composed of the following two distinct stages: the pre-processing stage and the Convolution Neural Network (CNN) building stage. Of late, the use of meta-heuristic optimization algorithms has accomplished a lot of progress in resolving these problems. Teaching-Learning Based Optimization algorithm (TIBO) meta-heuristic was originally employed for resolving problems of continuous optimization. This work has offered the proposals of novel methods for training the Residual Network (ResNet) as well as the CNN based on the TLBO and the Genetic Algorithm (GA). The classification of breast cancer can be enhanced with direct application of the hybrid TLBO- GA. For this hybrid algorithm, the TLBO, i.e., a core component, will combine the following three distinct operators of the GA: coding, crossover, and mutation. In the TLBO, there is a representation of the optimization solutions as students. On the other hand, the hybrid TLBO-GA will have further division of the students as follows: the top students, the ordinary students, and the poor students. The experiments demonstrated that the proposed hybrid TLBO-GA is more effective than TLBO and GA.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.49 no.9
• /
• pp.370-378
• /
• 2012

This paper proposes a method of constructing of hybrid web-based smart learning system to operable in a variety of mobile devices. To do this, the proposed system is developed a learning system with standardized and enhanced functions. In the proposed method, API specifications based on the standard functionality of smart learning system are created. And then, by building the API provider on a legacy system an organic linkage between the legacy system and the smart learning system is guaranteed. A standard API method is applied to data integration between the PC-based learning system and the smart learning system. The smart learning system interacts with legacy learning systems though Json/XML data forms via the https protocol. As a result, the legacy system using the proposed method dose not require major modifications and changes for a smart learning service.

• Journal of Korea Multimedia Society
• /
• v.18 no.3
• /
• pp.359-367
• /
• 2015

In this paper, we propose intelligent fire learning and detection system using hybrid visual attention mechanism of human. Proposed fire learning system generates leaned data by learning process of fire and smoke images. The features used as learning feature are selected among many features which are extracted based on bottom-up visual attention mechanism of human, and these features are modified as learned data by calculating average and standard variation of them. Proposed fire detection system uses learned data which is generated in fire learning system and features of input image to detect fire.

• Journal of Information Technology Applications and Management
• /
• v.9 no.2
• /
• pp.1-16
• /
• 2002

Recently, neural network methods have been studied to find out more valuable information in data bases. But the supervised learning methods of neural networks have an overfitting problem, which leads to errors of target patterns. And the unsupervised learning methods can distort important information in the process of regularizing data. Thus they can't efficiently classify data, To solve the problems, this paper introduces a hybrid neural networks HACAB(Hybrid Algorithm combining a Competition learning model And BP Algorithm) combining a competition learning model and 8P algorithm. HACAB is designed for cases which there is no target patterns. HACAB makes target patterns by adopting a competition learning model and classifies input patterns using the target patterns by BP algorithm. HACAB is evaluated with random input patterns and Iris data In cases of no target patterns, HACAB can classify data more effectively than BP algorithm does.

• Nuclear Engineering and Technology
• /
• v.55 no.8
• /
• pp.2854-2863
• /
• 2023

Objective: To present a hybrid approach that incorporates a constrained beam-hardening estimator (CBHE) and deep learning (DL)-based post-refinement for metal artifact reduction in dental cone-beam computed tomography (CBCT). Methods: Constrained beam-hardening estimator (CBHE) is derived from a polychromatic X-ray attenuation model with respect to X-ray transmission length, which calculates associated parameters numerically. Deep-learning-based post-refinement with an artifact disentanglement network (ADN) is performed to mitigate the remaining dark shading regions around a metal. Artifact disentanglement network (ADN) supports an unsupervised learning approach, in which no paired CBCT images are required. The network consists of an encoder that separates artifacts and content and a decoder for the content. Additionally, ADN with data normalization replaces metal regions with values from bone or soft tissue regions. Finally, the metal regions obtained from the CBHE are blended into reconstructed images. The proposed approach is systematically assessed using a dental phantom with two types of metal objects for qualitative and quantitative comparisons. Results: The proposed hybrid scheme provides improved image quality in areas surrounding the metal while preserving native structures. Conclusion: This study may significantly improve the detection of areas of interest in many dentomaxillofacial applications.

• The Journal of the Korea Contents Association
• /
• v.14 no.6
• /
• pp.513-528
• /
• 2014

The landscape of Christian higher education is changing. Students once spent most of their time in a traditional classroom with a professor, but now they take online and hybrid courses (face to face and online). Some students complete their entire degree in a fully online program. Nearly every type of college in the United States offers online courses. Online learning has clearly moved from a fad to a fixture, and nowhere is that more apparent than at one of the largest universities in the country. As the demand for online course and programs increase, teachers and administrators in Christian universities and colleges face new challenges. Even though some teachers and administrators still believe online education is inferior to traditional face-to-face learning, we found no statistically significant differences in standard measures of learning outcomes between students in the traditional classes and students in the hybrid-online format classes. In this situation, since online education will develop continuously, Christian universities should utilize it variously through complete understanding and research about it predicting the future of online education style.

• Proceedings of the IEEK Conference
• /
• 1998.10a
• /
• pp.1281-1284
• /
• 1998

This paper proposes an efficient time series prediction of the nonlinear dynamical discrete-time systems using multilayer neural networks of a hybrid learning algorithm. The proposed learning algorithm is a hybrid backpropagation algorithm based on the steepest descent for high-speed optimization and the dynamic tunneling for global optimization. The proposed algorithm has been applied to the y00 samples of 700 sequences to predict the next 100 samples. The simulation results shows that the proposed algorithm has better performances of the convergence and the prediction, in comparision with that using backpropagation algorithm based on the gradient descent for multilayer neural network.

• Journal of IKEEE
• /
• v.23 no.4
• /
• pp.1150-1156
• /
• 2019

This paper explores a model-free value-based approach for solving survival gridworld problem. Survival gridworld problem opens up a challenge involving taking risks to gain better rewards. Classic value-based approach in model-free reinforcement learning assumes minimal risk decisions. The proposed method involves a hybrid on-policy and off-policy updates to experience roll-outs using a modified Q-based update equation that introduces a parametric linear rectifier and motivational discount. The significance of this approach is it allows model-free training of agents that take into account risk factors and motivated exploration to gain better path decisions. Experimentations suggest that the proposed method achieved better exploration and path selection resulting to higher episode scores than classic off-policy and on-policy Q-based updates.

• Proceedings of the KIEE Conference
• /
• 1998.07b
• /
• pp.578-580
• /
• 1998

The paper presents an approach to the structure identification based on the CART (Classification And Regression Tree) algorithm and to the parameter identification by hybrid learning method in neuro-fuzzy system. By using the CART algorithm, the proposed method can roughly estimate the numbers of membership function and fuzzy rule using the centers of decision regions. Then the parameter identification is carried out by the hybrid learning scheme using BP (Back-propagation) and RLSE (Recursive Least Square Estimation) from the numerical data. Finally, we will show it's usefulness for fuzzy modeling to truck backer upper control.