• Journal of Digital Contents Society
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• v.10 no.4
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• pp.659-667
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• 2009

ADL SCORM(Sharable Content Object Reference Model) has been widely accepted as a global reference model for standardizing e-learning technology, and SCORM 2004 4th Edition, a stable version of SCORM, gives content developers the efficient way to build interoperable and reusable e-learning contents. Recently, a number of research efforts have been taken to build on-line SCORM contents based on some traditional training or learning styles. However, they have lacked for supporting more sophisticated learning style such as game based learning, and especially they do not consider employing the specific components of SCORM model for developing game based learning contents in practice. In this work, we elicit some SCORM data elements that is useful for representing game run-time data, and apply those elements to SCORM sequencing of game based learning SCOs(Sharable Content Objects). We thus present the whole procedure of developing SCORM game based learning contents with a sample contents.

• Journal of Yeungnam Medical Science
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• v.36 no.2
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• pp.78-84
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• 2019

Medical education research subjects are incredibly diverse and have changed over time. This work in particular aims to compare and analyze research trends in medical education through the words used in the titles of these research papers. Academic Medicine (the journal of the Association of American Medical Colleges), Medical Teacher (the journal of the Association of Medical Education in Europe), the Korean Journal of Medical Education (KJME), and Korean Medical Education Review (KMER) were selected and analyzed for the purposes of this research. From 2009 to 2018, Academic Medicine and Medical Teacher published approximately 10 to 20 times more papers than the KJME and KMER. Frequently used words in these titles include "medical," "student," "education," and "learning." The words "clinical" and "learning" were used relatively often (7.80% to 13.66%) in Korean journals and Medical Teacher, but Academic Medicine used these phrases relatively less often (6.47% and 4.41%, respectively). Concern with such various topics as problem-based learning, team-based learning, program evaluations, burnout, e-learning, and digital indicates that Medical Teacher seems to primarily deal with teaching and learning methodologies, and Academic Medicine handles all aspects of medical education. The KJME and KMER did not cover all subjects, as they publish smaller papers. However, it is anticipated that research on new subjects, such as artificial intelligence in medical education, will occur in the near future.

• The Journal of Korean Academic Society of Nursing Education
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• v.26 no.1
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• pp.36-46
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• 2020

Purpose: This study was conducted to develop and implement an education program based on the Whole Brain Model for novice nurses, and to evaluate its effects on work performance, interpersonal skills and self-efficacy. Methods: A pretest-posttest quasi-experimental design was used with an experimental group (n=20) and a control group (n=21). The experimental group participated in an education program based on the Whole Brain Model for seven sessions over 4 weeks. An independent t-test, χ²-test, and Mann-Whitney U test were performed to analyze the data. Results: There were statistically significant differences in work performance (p=.015), interpersonal skills (p=.014) and self-efficacy (p=.021) between the experimental and the control group. Conclusion: This program was an effective learning strategy to enhance nursing competence for novice nurses. The novice nurses who participated this program were able to reflect deeply on themselves, improve interpersonal skills, and induce whole-brain integrated thinking in learning how to solve the problems caused by changes in patient conditions that can be experienced in clinical practice. Therefore, this program can be recommended for regular continuing education for novice nurses.

• Health Policy and Management
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• v.18 no.4
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• pp.111-124
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• 2008

New ways of work-shift and learning programs, which were based on the concept of 'performance improvement through people', have been introduced to healthcare organizations. I analyzed the performance of the changes and the performance differences. Data were collected through interview and survey. I discussed that modification of management practices which were developed in manufacturing organizations is important for successful implementation in healthcare organizations.

• The Journal of Information Technology and Database
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• v.4 no.1
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• pp.65-77
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• 1997

This paper describes and effective teaching approach to an undergraduate database course. This research draws on practical experience based on the hands-on practice approach which leads students to develop a database application utilizing various tools. Students not only learn concepts, methodologies and tools of database technology in class and through online multimedia learning aids, but also practice how to integrate them through collaborative team projects. The course employs collaborative learning approach and multimedia and internet technologies. Students are encouraged to work collaboratively on assignments and projects and to learn independently through online multimedia learning aids.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.977.1-1000
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• 1993

This paper presents a fuzzy neural network, called the fuzzy counterpropagation network, that structures its inputs and generates its outputs in a manner based on counterpropagation networks. The fuzzy counterpropagation network is developed by incorporating the concept of fuzzy clustering into the hidden layer responses. Three learning algorithms are introduced for use with the proposed network. Simulations demonstrate that fuzzy counterpropagation networks with the proposed learning algorithms work well on approximating bipolar and continuous functions.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.247-253
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• 2021

The outbreak of the deadly virus COVID-19 is said to infect 17.3Cr people around the globe since 2019. This outbreak is continuously affecting a lot of new people till this day and, most of it is said to under control. However, vaccines introduced around the world can help mitigate the risk of the virus. Apart from medical professionals, prediction models are also said to combinedly help predict the risk of infection based on given datasets. This paper is based on publication of a machine learning approach using regression models to predict the output based on dataset which have indictors grouped based on active, tested, recovered and critical cases along with regions and cities covering most of it from Dubai. Hence, the active cases are tested based on the other indicators and other attributes. The coefficient of the determination (r²) is 0.96, which is considered promising. This model can be used as an frame work, among others, to predict the resources related to the dangerous outbreak.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3099-3120
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• 2023

The present fast-moving era brings a serious stress issue that affects elders and youngsters. Everyone has undergone stress factors at least once in their lifetime. Stress is more among youngsters as they are new to the working environment. whereas the stress factors for elders affect the individual and overall performance in an organization. Electroencephalogram (EEG) based stress level classification is one of the widely used methodologies for stress detection. However, the signal processing methods evolved so far have limitations as most of the stress classification models compute the stress level in a predefined environment to detect individual stress factors. Specifically, machine learning based stress classification models requires additional algorithm for feature extraction which increases the computation cost. Also due to the limited feature learning characteristics of machine learning algorithms, the classification performance reduces and inaccurate sometimes. It is evident from numerous research works that deep learning models outperforms machine learning techniques. Thus, to classify all the emotions based on stress level in this research work a hybrid deep learning algorithm is presented. Compared to conventional deep learning models, hybrid models outperforms in feature handing. Better feature extraction and selection can be made through deep learning models. Adding machine learning classifiers in deep learning architecture will enhance the classification performances. Thus, a hybrid convolutional neural network model was presented which extracts the features using CNN and classifies them through machine learning support vector machine. Simulation analysis of benchmark datasets demonstrates the proposed model performances. Finally, existing methods are comparatively analyzed to demonstrate the better performance of the proposed model as a result of the proposed hybrid combination.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1715-1723
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• 2018

This work investigates the performance of fully complex- valued radial basis function network(FC-RBF) and complex extreme learning machine (CELM) based neural approaches for classification of power quality disturbances. This work engages the use of S-Transform to extract the features relating to single and combined power quality disturbances. The performance of the classifiers are compared with their real valued counterparts namely extreme learning machine(ELM) and support vector machine(SVM) in terms of convergence and classification ability. The results signify the suitability of complex valued classifiers for power quality disturbance classification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.4255-4269
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• 2019

This paper proposes an image generation method using a Multi Discriminator Generative Adversarial Net (MDGAN) as a next generation 2D game sprite creation technique. The proposed GAN is an Autoencoder-based model that receives three areas of information-color, shape, and animation, and combines them into new images. This model consists of two encoders that extract color and shape from each image, and a decoder that takes all the values of each encoder and generates an animated image. We also suggest an image processing technique during the learning process to remove the noise of the generated images. The resulting images show that 2D sprites in games can be generated by independently learning the three image attributes of shape, color, and animation. The proposed system can increase the productivity of massive 2D image modification work during the game development process. The experimental results demonstrate that our MDGAN can be used for 2D image sprite generation and modification work with little manual cost.