• Journal of Intelligence and Information Systems
• /
• v.26 no.4
• /
• pp.27-65
• /
• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Journal of the Korea Convergence Society
• /
• v.12 no.9
• /
• pp.169-178
• /
• 2021

It is the post-corona era that we will soon face. It is time to achieve the original purpose of job training for health and welfare personnel operated by the Korea Human Resource Development Institute for Health and Welfare and to innovate change to maintain educational consistency. This study reviewed literatures to find alternatives for efficient and effective curriculum operation by integrating contents of health and welfare job education. Through this, we decided to check the possibility of building an open platform and suggest it as a sufficient alternative. It is expected that the establishment of the open platform for job education in the health and welfare sector will enable the education accessibility and the management of the learning management system of the subjects. Above all, it will contribute to the duplication of the education experts and the efficiency of the budget.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.11 no.3
• /
• pp.1-9
• /
• 2015

In this paper, we implemented the computer vision platform design with MEAN Stack through Raspberry PI 2 model which is an open source platform. we experimented the face recognition, temperature and humidity sensor data logging with WiFi communication under Raspberry Pi 2 model. Especially we directly made the shape of platform with 3D printing design. In this paper, we used the face recognition algorithm with OpenCV software through haarcascade feature extraction machine learning algorithm, and extended the functionality of wireless communication function ability with Bluetooth technology for the purpose of making Android Mobile devices interface. And therefore we implemented the functions of the vision platform for identifying the face recognition characteristics of scanning with PI camera with gathering the temperature and humidity sensor data under IoT environment. and made the vision platform with 3D printing technology. Especially we used MongoDB for developing the performance of vision platform because the MongoDB is more akin to working with objects in a programming language than what we know of as a database. Afterwards, we would enhance the performance of vision platform for clouding functionalities.

• Journal of Korean Society for Quality Management
• /
• v.48 no.4
• /
• pp.581-596
• /
• 2020

Purpose: purpose of this study was to propose the web application platform which can be to detect and discriminate various diseases and pest of tomato plant based on the large amount of disease image data observed in the facility or the open field. Methods: The deep learning algorithms uesed at the web applivation platform are consisted as the combining form of Faster R-CNN with the pre-trained convolution neural network (CNN) models such as SSD_mobilenet v1, Inception v2, Resnet50 and Resnet101 models. To evaluate the superiority of the newly proposed web application platform, we collected 850 images of four diseases such as Bacterial cankers, Late blight, Leaf miners, and Powdery mildew that occur the most frequent in tomato plants. Of these, 750 were used to learn the algorithm, and the remaining 100 images were used to evaluate the algorithm. Results: From the experiments, the deep learning algorithm combining Faster R-CNN with SSD_mobilnet v1, Inception v2, Resnet50, and Restnet101 showed detection accuracy of 31.0%, 87.7%, 84.4%, and 90.8% respectively. Finally, we constructed a web application platform that can detect and discriminate various tomato deseases using best deep learning algorithm. If farmers uploaded image captured by their digital cameras such as smart phone camera or DSLR (Digital Single Lens Reflex) camera, then they can receive an information for detection, identification and disease control about captured tomato disease through the proposed web application platform. Conclusion: Incheon Port needs to act actively paying.

• Journal of Distribution Science
• /
• v.16 no.7
• /
• pp.67-75
• /
• 2018

Purpose - In the 21st century, as information technology advances alongside the emergence of the 4th generation, industrial age, industrial environment has become individualized and customized. It is important to hire good quality employees for good service in the industry. The e-learning market is growing every year. Although e-learning companies are finding better quality employees in e-learning, it is not easy to find it. Companies also spend a lot of time and cost to find employee. On the employees side, they want to get a job freely when they want, but they cannot find their job easily. Furthermore, the labor market environment is changing fast. In the 4th generation, industrial age, employers require to find manpower whenever they need and want at little cost. So of their own accord, we have considered the necessity of management of human resources for employees and employers in e-learning. The purpose of this study is to propose a human cloud platform framework for enabling an efficient management of human resources in e-learning industry. Research design, data, and methodology - To pinpoint the items of a human cloud platform framework, the study was initiated according to the following process. First, items of competency relating to e-learning instructional designer was analyzed. Second, based on the items of information from this analysis, selection and validity verification took place with 5 e-learning specialists group. Third, the opinion of experts who were in charge of hiring in e-learning companies were collated with the questionnaire. Lastly, the human cloud platform framework was proposed based on opinion results. Results - The framework was comprised of 7 domains and 27 items in order to develop the human cloud platform for e-learning instructional designer. The analysis results showed that the most highly considered item were 'skill (4.60)' that employee already have the capability. Following this (in order) were 'project type (4.56)', 'work competency (4.56)', and 'strength area of instructional design (4.52)'. Conclusions - The 27 items in the human cloud platform framework were suggested in this study. Following this, we can consider to develop the human cloud platform for finding a job and hiring e-learning instructional designer easily. For successful platform operation, we need to consider reliability between employer and employee. In addition, we need quality assurance system based on operation has public confidence.

• International Journal of Computer Science & Network Security
• /
• v.22 no.12
• /
• pp.160-170
• /
• 2022

COVID pandemic has reshaped the world as it has been known to us and the education system is one of the most affected by it. Due to social distancing, quarantines and isolations have made it impossible for the knowledge transition to the masses using conventional methods. For cope with pandemic, the only other way available for some of the fortunate countries is the use of E-learning having somewhat the same traditional teaching method. This paper is concerned with the study of the preparedness of the learning system in some Sudanese universities due to the impact of the COVID-19 pandemic. Critical analysis has been performed to evaluate the current developing scenario, usage of the facilities available in open-source platforms, and the interaction of the universities folks with e-learning systems. The impact of such measures has been thoroughly investigated in this paper for Sudan which is already deprived of a proper education system. The investigation shows that the interact of the staff and the students with the system was acceptable where more than 85% of those enrolled to the system were interact properly and efficiently. The lecturers conducted through the platform were attended with more than 75% of the students. We also found that most of the lecturer were avoid to exam students by utilize the platform; where only 45% of the uploaded courses were conducted exams over Moodle platform. As Moodle is an open source and still need to be improved to be used for high examination credibility.

• Knowledge Management Research
• /
• v.18 no.2
• /
• pp.85-102
• /
• 2017

With rapid development of information and communication technologies, open collaboration can be eased through the Internet. Open source software, as a representative area of open collaboration, is developed and adopted to various fields. In this research, based on organizational learning theory, we examine the impacts of exploration and exploitation on innovation performance in open source software development projects. We define knowledge exploration as a number of developers from outside organization and knowledge exploitation as the ratio of member of an organization who participated in an open source software project managed by the organization. For analysis, we collect data of 4794 projects from github which is a representative open source software development platform using Web crawler developed by Python. As a result, we find that excessive exploration has curvilinear (invers U-shape) relationship on project performance. On the other hand, exploitation with enough external developers will positively impact on project performance.

• Journal of Advanced Technology Convergence
• /
• v.1 no.2
• /
• pp.01-09
• /
• 2022

Recently, safety issues in companies and public institutions are no longer a task that can be postponed, and when a major safety accident occurs, not only direct financial loss, but also indirect loss of social trust in the company and public institution is greatly increased. In particular, in the case of a fatal accident, the damage is even more serious. Accordingly, as companies and public institutions expand their investments in industrial safety education and prevention, open AI learning model creation technology that enables safety management services without being affected by user behavior in industrial sites where high-risk situations exist, edge terminals System development using inter-AI collaboration technology, cloud-edge terminal linkage technology, multi-modal risk situation determination technology, and AI model learning support technology is underway. In particular, with the development and spread of artificial intelligence technology, research to apply the technology to safety issues is becoming active. Therefore, in this paper, an open cloud platform design method that can support AI model learning for high-risk site safety management is presented.

• Journal of Digital Convergence
• /
• v.13 no.3
• /
• pp.31-36
• /
• 2015

The Many universities and educational institutions have focused on shifting education paradigm into smart learning using high-tech devices and internet as the level of technology has growing rapidly in every society. Especially, cyber universities and open universities in Korea are trying to develop educational network system and infrastructure corresponding to new convergence technology environment. Therefore, the purpose of this study is to introduce clouded based education system in order to suggest an effective way of using new educational learning system. This study shows the case of Hangout learning system used in K University in Korea to suggest a new educational learning model for real-time lecture and cloud based service platform for improving educational learning environment.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.9
• /
• pp.35-43
• /
• 2021

This study aims to investigate factors affecting eBook acceptance and learning outcomes among students experiencing online distance learning. As conventional textbooks are now switched into eBooks, the effects of contextual factors including lecturer, student computer competency, content and design of the course, access ability, infrastructure, and university support on eBook acceptance and learning outcome needs to be evaluated. The sample of this study is represented by students at the Universiti Teknologi MARA, City Campus Melaka, undertaking 'strategic management course'. Non-probability random sampling was selected as the sampling technique and a purposive sampling method was chosen to select the samples. The samples comprised 171 students randomly selected through Google Form. The questionnaire data was analyzed by using PLS-SEM. The results indicated that these factors contributed 62.3% variations in the eBook acceptance and 67.1% variations in the learning outcomes. The strongest factor affecting both dependent variables was content and design of course. Managerial implication suggested that the content for all courses taught through the eBook platform needs to be revisited and improved in accordance with the mode of online deliverance. Tutorial on how to navigate the eBook platform is important to all users as this would enhance acceptance and produce better learning outcomes among students.