• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.278-286
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• 2021

The aim of this paper is to examine the importance of skills development in the process of employee performance. As part of this research, we will seek to determine the nature and extent of skills development impact in improving employee performance. This research project is one of the new themes that some researchers have started in recent years. The novelty of this theme is the inclusion of the skill development factor. This factor is likely to have a positive impact on employee motivation and performance. Some of the factors already known to have an impact on employee performance, such as motivation, career development, training, and experience, will be adopted. It is assumed that the results of this research will have a positive impact on employee performance and employee retention.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.25-32
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• 2022

The current research aims to identify the effectiveness of a training program based on digital stories to develop writing skills for students with learning difficulties. The research sample consisted of (12) students with learning difficulties in the fifth and sixth grades, who were chosen intentionally. The results showed the effectiveness of the program and the maintenance of this improvement over time as results showed that there were statistically significant differences at the level (α = 0.05) between the two measurements, before and after, in favor of the post-measurement. The results also showed that there were no statistically significant differences at the level (α = 0.05) between the post and follow-up measurements on the writing skills scale. This indicates the long-term impact of the program. The researchers recommend the need to expand educational programs' adoption of digital stories to develop the skills of students with learning difficulties.

• International Journal of Computer Science & Network Security
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• v.23 no.2
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• pp.152-163
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• 2023

This research aimed at investigating Islamic Studies teachers' attitudes towards utilizing virtual learning environment in distance teaching among primary stage pupils. It also aimed at determining the statistical differences among variables due to sex, educational qualification, number of years of experience, and training sessions. This research adopted the descriptive approach. The sample consisted of male and female primary teachers of Islamic Studies (N=250) in governmental schools in Taif. The questionnaire was used as a main research tool. It included (20) items. Results showed that Islamic Studies teachers' attitudes towards utilizing virtual learning environment in distance teaching among primary stage pupils were ranked to a medium degree. There was a statistically significant difference among primary Islamic Studies teachers' attitudes due to sex variable. It was recommended to adopt more training sessions and seminars for adopting the idea of utilizing virtual learning environments among Islamic Studies teachers at boys' and girls' school in Mecca through emphasizing its significance and benefits in Teaching.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.627-628
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• 2021

본 논문은 제조 산업 현장에서의 업무 능력 향상을 위한 XR 교육훈련 콘텐츠의 가변적 시나리오를 제안한다. 가변적 시나리오를 적용한 XR 교육훈련 콘텐츠는 교육훈련 관리자가 자유롭게 시나리오를 가감할 수 있어 동일한 콘텐츠 내에서 다양한 시나리오로 콘텐츠를 구성할 수 있는 특징을 가진다. 이는 기존 하나의 시나리오로 반복되는 교육훈련의 한계를 해결할 수 있으며, 현장에서 발생할 수 있는 돌발적 상황 및 변화되는 업무 프로세스를 효과적으로 진행할 수 있을 것이다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.327-328
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• 2021

본 논문은 2014년 세월호 사고 이후 해양재난사고에 대한 국민들의 경각심이 고조되었으며 해양레저인구증가로 인한 해양사고가 증가되는 상황에서 사고예방 및 해양안전문화 고취를 위한 해양안전교육센터 구축하고자 수요추정을 수행하였다. 특히, 우리나라 대부분의 해양안전체험관과 유사한 체험관들은 대부분 전라, 경기지역에 분포하고 있어 지역 편향을 해소하는 차원에서 동남권 안전센터 구축이 필요한 상황이며 해양레저 및 해양산업의 중심이 영남권에 위치하고 있기 때문에 안전교육센터의 구축의 정책적인 타당성이 있다. 본 논문에서는 해양안전교육센터 수요추정을 중력모형을 활용하여 수행하였으며 사회경제적인 타당성의 근거로서 활용이 가능할 것으로 기대된다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.697-698
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• 2023

최근 딥 러닝 분야의 기술이 발달하면서 Chat GPT, Google Bard와 같은 자연어 처리 기술이 확대되고 있고 이미지 객체를 분석하는 CLIP, BLIP와 같은 기술도 발전되고 있다. 그러나 전시회와 같은 예술 분야는 딥 러닝 기술 기반의 이미지 데이터 활용이 제한적이다. 본 논문은 전시회장에서의 그림 내부의 객체 데이터를 분석하기 위해 이미지 객체 분석 기술을 사용하고 자연어 처리 기반으로 관람객이 특정 그림에 대한 질문을 입력하면 해당 그림을 식별하는 방법을 제시한다. 이를 통해 관람객이 원하는 그림을 선별하여 관람할 수 있도록 한다.

• ETRI Journal
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• v.45 no.6
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• pp.1007-1021
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• 2023

Stroke is the leading cause of permanent disability in adults, and it can cause permanent brain damage. According to the World Health Organization, 795 000 Americans experience a new or recurrent stroke each year. Early detection of medical disorders, for example, strokes, can minimize the disabling effects. Thus, in this paper, we consider various risk factors that contribute to the occurrence of stoke and machine learning algorithms, for example, the decision tree, random forest, and naive Bayes algorithms, on patient characteristics survey data to achieve high prediction accuracy. We also consider the semisupervised self-training technique to predict the risk of stroke. We then consider the near-miss undersampling technique, which can select only instances in larger classes with the smaller class instances. Experimental results demonstrate that the proposed method obtains an accuracy of approximately 98.83% at low cost, which is significantly higher and more reliable compared with the compared techniques.

• Geomechanics and Engineering
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• v.37 no.4
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• pp.307-321
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• 2024

This paper delves into the critical assessment of predicting sidewall displacement in underground caverns through the application of nine distinct machine learning techniques. The accurate prediction of sidewall displacement is essential for ensuring the structural safety and stability of underground caverns, which are prone to various geological challenges. The dataset utilized in this study comprises a total of 310 data points, each containing 13 relevant parameters extracted from 10 underground cavern projects located in Iran and other regions. To facilitate a comprehensive evaluation, the dataset is evenly divided into training and testing subset. The study employs a diverse array of machine learning models, including recurrent neural network, back-propagation neural network, K-nearest neighbors, normalized and ordinary radial basis function, support vector machine, weight estimation, feed-forward stepwise regression, and fuzzy inference system. These models are leveraged to develop predictive models that can accurately forecast sidewall displacement in underground caverns. The training phase involves utilizing 80% of the dataset (248 data points) to train the models, while the remaining 20% (62 data points) are used for testing and validation purposes. The findings of the study highlight the back-propagation neural network (BPNN) model as the most effective in providing accurate predictions. The BPNN model demonstrates a remarkably high correlation coefficient (R2 = 0.99) and a low error rate (RMSE = 4.27E-05), indicating its superior performance in predicting sidewall displacement in underground caverns. This research contributes valuable insights into the application of machine learning techniques for enhancing the safety and stability of underground structures.

• International journal of advanced smart convergence
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• v.6 no.2
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• pp.9-15
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• 2017

According to a recent survey, most elementary and secondary school students nationwide are stressed out by their academic records. Furthermore most of high school students in Korea have to study under the great duress. Some of them who can't overcome the academic stress finalize their life by suiciding. A study has found that it is one of the leading causes of stimulating the thought of committing suicide in Korean high school students. So it is necessary to reduce the high school student's suicide rate. Main content of this research is to implement a personalized music recommendation system. Music therapy can help the student deal with the stress, anxiety and depression problems. Proposed system works as a therapist. The music choice and duration of the music is adjusted based on the student's current emotion recognized automatically from EEG. If the happy emotion is not induced by the current music, the system would automatically switch to another one until he or she feel happy. Proposed system is personalized brain music treatment that is making a brain training application running on smart phone or pad. That overcomes the critical problems of time and space constraints of existing brain training program. By using this brain training program, student can manage the stress easily without the help of expert.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.171-178
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• 2015

In these days, the practical training education is getting highlighted in IT curriculum. This study is for the Cloud computing based Virtual Desktop Service Plan of IT education and its efficient operation and management plan. With the implementation of a virtual lab environment system, the training environment which is customized by the curriculum is able to be provided. Also in the case of the limited system, the curriculum is able to be provided for each subject in advance. Therefore if the Cloud Training (or Practicing) Portal system for the multiple cyber universities is implemented according to this study's estimated scale and operation managing policies, the virtual training education service system could be provided in more efficient and more effective ways.