• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.368-374
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• 2022

Children are by nature curious and enthusiastic about learning and love to explore and search for everything they see around them, but as a result of this exploration they may sometimes be exposed to dangerous situations ranging from falls to poisoning and suffocation. That is why when supporting a child's natural desire to explore the world and supporting his awareness of dangerous situations and good handling of them, helps him build a conscious scientific mind and enhance his curiosity in the natural world. It is not easy to imagine a difficult situation in which we or one of our family is in danger, unable to help ourselves or to help them in time, due to our complete ignorance of the rules of first aid. Hence the importance of learning first aid not only for the child but for the community and the world at large. "Hakeem" is an Arabic E-health educational application that aims to teach children from the age of six to eleven years first aid, in our belief that the seed of renaissance lies in the care and education of children, and the lack of Arabic content that aims to teach children first aid skills. The idea is to create a scenario in which the child is responsible for saving the person who will be in a dangerous situation using Augmented Reality (AR) technology, to increase engagement and interaction and provides a rich user experience, and according to the child's performance, he will get reward points. The game will have several levels: Beginner, Intermediate, and Hakeem, and based on the player's points he will get a title and move to the next level, and when he reaches the end, he will get the certificate.

• Asia pacific journal of information systems
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• v.16 no.4
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• pp.179-202
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• 2006

The objective of this study is to identify the impacts of communication reinforcement on performance of learning in Web-PBL. Communication reinforcement is defined as the combination of information sharing and co-construction. As factors facilitating communication reinforcement, we propose learner's characteristics, task characteristics, and group characteristics. Learner's characteristics are collaboration-orientation, openness, holistic approach, and online community-orientation which reflects e-learning environment. Collaboration-oriented tasks as group projects were developed and given to groups with 5-6 members. The group characteristics are categorized into 'horizontal' and 'vertical', according to the patterns of communication between a group leader and members. To verify empirically the proposed research model, an experimental design was performed to learners who took on-line and off-line courses with group projects. We found important results as follows; First, field dependence has positive impacts on information sharing, and online community-orientation has positive impacts on co-construction. These results correspond with prior studies on relationship between field dependence and collaborative learning. Second, collaboration-oriented task directly impacts on information sharing, and indirectly affects co-construction, This result implicates that information sharing is pre-requisite of co-construction. Third, 'horizontal' was identified as a factor giving positive effects on information sharing and co-construction. This result implies that horizontal communication is very important to facilitate communication reinforcement.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.3
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• pp.522-538
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• 2013

Content based image retrieval has become an increasingly important research topic for its wide application. It is highly challenging when facing to large-scale database with large variance. The retrieval systems rely on a key component, the predefined or learned similarity measures over images. We note that, the similarity measures can be potential improved if the data distribution information is exploited using a more sophisticated way. In this paper, we propose a similarity measure learning approach for image retrieval. The similarity measure, so called Fisher kernel, is derived from the probabilistic distribution of images and is the function over observed data, hidden variable and model parameters, where the hidden variables encode high level information which are powerful in discrimination and are failed to be exploited in previous methods. We further propose a discriminative learning method for the similarity measure, i.e., encouraging the learned similarity to take a large value for a pair of images with the same label and to take a small value for a pair of images with distinct labels. The learned similarity measure, fully exploiting the data distribution, is well adapted to dataset and would improve the retrieval system. We evaluate the proposed method on Corel-1000, Corel5k, Caltech101 and MIRFlickr 25,000 databases. The results show the competitive performance of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.4
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• pp.49-60
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• 1996

The objective of this paper is to develop an adaptive learning method for fuzzy hypercubes using a neural network. An intelligent control system is proposed by exploiting only the merits of a fuzzy logic controller and a neural network, assuming that we can modify in real time the consequential parts of the rulebase with adaptive learning, and that initial fuzzy control rules are established in a temporarily stable region. We choose the structure of fuzzy hypercubes for the fuzzy controller, and utilize the Perceptron learning rule in order to upda1.e the fuzzy control ru1c:s on-line with the output errors. As a result, the effectiveness and the robustness of this intelligent controller are shown with application of the proposed adaptive fuzzy-neuro controller to control of the cart-pole system.

• The Journal of Information Systems
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• v.32 no.4
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• pp.229-245
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• 2023

Purpose This study aims to understand the inclinations of young job seekers towards "hidden champions" - small but competitive companies that are emerging as potential solutions to the growing disparity between youth-targeted job vacancies and job seekers. We utilize machine learning techniques to discern the appeal of these hidden champions. Design/methodology/approach We examined the characteristics of small and medium-sized enterprises using data sourced from the Ministry of Employment and Labor and Youth Worknet. By comparing the efficacy of five machine learning classification models (i.e., Logistic Regression, Random Forest Classifier, Gradient Boosting Classifier, LGBM Classifier, and XGB Classifier), we discovered that the predictive model utilizing the LGBM Classifier yielded the most consistent performance. Findings Our analysis of the relative significance of preference determinants revealed that industry type, geographical location, and employee count are pivotal factors influencing preference. Drawing from these insights, we propose targeted strategic interventions for policymakers, hidden champions, and young job seekers.

• Asia pacific journal of information systems
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• v.33 no.2
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• pp.282-297
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• 2023

One of the emerging trends in the marketing field is digital video marketing. Online videos offer rich content typically containing more information than any other type of content (e.g., audible or textual content). Accordingly, previous researchers have examined factors influencing videos' popularity. However, few studies have examined what causes a video to remain popular. Some videos achieve continuous, ongoing popularity, while others fade out quickly. For practitioners, videos at the recommendation slots may serve as strong communication channels, as many potential consumers are exposed to such videos. So,this study will provide practitioners important advice regarding how to choose videos that will survive as long-lasting favorites, allowing them to advertise in a cost-effective manner. Using deep learning techniques, this study extracts text from videos and measured the videos' tones, including factual and emotional tones. Additionally, we measure the aesthetic score by analyzing the thumbnail images in the data. We then empirically show that the cognitive features of a video, such as the tone of a message and the aesthetic assessment of a thumbnail image, play an important role in determining videos' long-term popularity. We believe that this is the first study of its kind to examine new factors that aid in ensuring a video remains popular using both deep learning and econometric methodologies.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1472-1479
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• 2024

Cyber-Physical Energy Systems (CPESs) integrate cyber and hardware components to ensure a reliable and safe physical power production and supply. Renewable Energy Sources (RESs) add uncertainty to energy demand that can be dealt with flexible operation (e.g., load-following) of CPES; at the same time, scenarios that could result in severe consequences due to both component stochastic failures and aging of the cyber system of CPES (commonly overlooked) must be accounted for Operation & Maintenance (O&M) planning. In this paper, we make use of Deep Reinforcement Learning (DRL) to search for the optimal O&M strategy that, not only considers the actual system hardware components health conditions and their Remaining Useful Life (RUL), but also the possible accident scenarios caused by the failures and the aging of the hardware and the cyber components, respectively. The novelty of the work lies in embedding the cyber aging model into the CPES model of production planning and failure process; this model is used to help the RL agent, trained with Proximal Policy Optimization (PPO) and Imitation Learning (IL), finding the proper rejuvenation timing for the cyber system accounting for the uncertainty of the cyber system aging process. An application is provided, with regards to the Advanced Lead-cooled Fast Reactor European Demonstrator (ALFRED).

• The Journal of Information Systems
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• v.30 no.3
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• pp.65-91
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• 2021

Purpose In this study, we investigated the impact of perceived risk and switching costs on switching intention to cloud service based on PPM (Pull-Push-Mooring) model. Design/methodology/approach We focused on revealing the switching factors of the switching intention to the cloud services. The switching factors to the cloud services were defined as perceived risk consisting of performance risk, economic risk, and security risk, and switching costs consisting of financial and learning costs. On the PPM model, we defined the pull factors consisting of perceived usefulness and perceived ease of use, and the push factor as satisfaction of the legacy system, and the mooring factor as policy supports. Findings The results of this study as follows; (1) Among the perceived risk factors, performance risk has a negative effect on the ease of use of pull factors, and finally it was found to affect the switching intention to the cloud services. Therefore, cloud service providers need to improve trust in cloud services, service timeliness, and linkage to the legacy systems. And it was found that economic risk and security risk among the perceived risk factors did not affect the switching intention to the cloud services. (2) Of the perceived risk factors, financial cost and learning cost did not affect the satisfaction of the legacy system, which is a push factor. It indicates that the respondents are positively considering switching to cloud service in the future, despite the fact that the respondents are satisfied with the use of the legacy system and are aware of the switching cost to cloud service. (3) Policy support was found to improve the switching intention to cloud services by alleviating the financial and learning costs required for cloud service switching.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.271-271
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• 2007

본 내용은 첨단 정보통신 기술을 이용하여 강의자 중심의 단방향 교육(Teaching) 환경을 학생 중심의 양방향 교육(Learning) 환경으로 개선하여 보다 창의적인 인재를 양성할 수 있는 교육환경 구축에 관한 내용이다. 우리나라를 포함한 OECD 국가들은 ICT활용 수업에 대한 필요성을 공감하고 교단 선진화를 위한 연구 및 지원을 다양하게 진행하고 있다. 학생들에게 지급하는 교과서를 인쇄매체 대신에 메모리 스틱, CD-ROM 및 인터넷을 통한 전자 매체로 대체하는 방안 등이 그 예이다. 따라서 학생들이 강의실에서 멀티미디어를 이용해 강의를 듣고, 과제를 풀며 정리된 내용을 발표하고 토론할 수 있는 양방향 수업환경이 요구된다. 그러나 컴퓨터를 활용한 수업을 진행할 때의 문제점이 강의내용을 학생들에게 효율적으로 전달하기가 어렵고, 학생들의 컴퓨터를 통제할 수 없기 때문에 수업을 이탈하는 경우가 발생되는 등 교육에 역효과가 초래된다. 본 내용에서 소개하는 양방향 수업진행 장비(드림랩)는 강의자가 학생들의 컴퓨터 모니터, 키보드 및 마우스를 자유로이 통제할 수 있어서 강의자의 화면과 음성을 실시간으로 선명하게 학생들에게 전달하고, 학생들의 내용을 모니터하고 제어할 수 있으며, 개인지도 및 수준별 그룹지도가 가능하다. 또한 강의자에게 개인적으로 질문을 할 수 있고, 학생들의 내용을 자신의 자리에서 전체 학생들에게 발표할 수도 있다. 드림랩은 순수 하드웨어로 구성되어 컴퓨터 기종이나 운영체제에 영향을 받지 않으며, 컴퓨터 자원과 네트워크 자원을 사용하지 않기 때문에 컴퓨터나 네트워크의 성능을 저하시키지 않는다. 또한 사용법이 간단하고 유지관리가 쉬운 장점 등이 있다. 따라서 컴퓨터를 활용한 수업진행이 원활하여 다양한 과목에 활용 가능하고, 학생들의 자발적인 수업 참여로 강의 중심 교육에서 자기 주도적 수업환경(T2L, Teaching to Learning)으로 자연스럽게 전환되어 교육의 질적 향상과 함께 창의적인 인재를 양성할 수 있을 것으로 기대된다.

• KIISE Transactions on Computing Practices
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• v.23 no.7
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• pp.440-445
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• 2017

Due to the advance of e-commerce systems, the number of people using online shopping and products has significantly increased. Therefore, the need for an accurate recommendation system is becoming increasingly more important. Recurrent neural network is a deep-learning algorithm that utilizes sequential information in training. In this paper, an evaluation is performed on the application of recurrent neural networks to recommendation systems. We evaluated three recurrent algorithms (RNN, LSTM and GRU) and three optimal algorithms(Adagrad, RMSProp and Adam) which are commonly used. In the experiments, we used the TensorFlow open source library produced by Google and e-commerce session data from RecSys Challenge 2015. The results using the optimal hyperparameters found in this study are compared with those of RecSys Challenge 2015 participants.