• Physical Therapy Rehabilitation Science
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• v.11 no.2
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• pp.224-234
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• 2022

Objective: The purpose of this study was to identify the practitioner and organizational characteristics that either detracted from or encouraged the use of evidence-based practice (EBP) by physical therapists. Design: A cross-sectional survey study Methods: Participants were 260 physical therapists currently practicing in South Korea. They completed a questionnaire designed to determine attitudes, beliefs, interest, self-efficacy and barriers to EBP, as well as demographic information about themselves and their practice settings. Logistic regression was used to examine relationships between socio-demographic and work environment characteristics and each practitioner factor. Results: Respondents agreed that the use of evidence in practice was necessary. Although 80% of them agreed that research findings are useful, 71% felt that a divide exists between research and practice. In terms of confidence in their skills, the ability to interpret results of statistical procedures ranked lowest. Despite internet access at work for 63% of respondents, only 14% were given protected work time to search and appraise the literature. Only 2% of respondents stated that their organization had a written requirement to use current evidence in their practice. The primary barrier to implementing EBP was a reported lack of time. Conclusions: In conclusion, most physical therapists stated they had a positive attitude toward EBP and were interested in learning or improving the skills necessary for implementation. Most recognized a need to increase the use of evidence in their daily practice, but a lack of ability to understand the results of research represents a significant barrier to implementing EBP.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.149-156
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• 2023

Joint research on software, electronic engineering, computer engineering, and financial engineering and the use of ICT knowledge through network formation play an important role in strengthening science and technology-based innovation capabilities and facilitating the development and production process of products using new technologies. For the purpose of this study, I would like to strategically propose ICT specialized education in the 4th industrial revolution. To this end, the ICT specialization model, ICT specialization strategy analysis, and ICT specialization operation and effect were explored to establish ICT specialization strategies centered on software, electronic engineering, computer engineering, and financial engineering in the era of super-intelligence, hyper-connected, and hyper-convergence. Secondly, a roadmap for detailed promotion tasks related to efficient ICT characterization based on core strategies, detailed promotion tasks, and programs was proposed, focusing on talent related to ICT characterization. Thirdly, we would like to propose a reorganization of the academic structure and organization related to ICT characterization. Finally, we would like to propose the establishment of a future-oriented education system related to ICT specialization based on the advanced education and research environment.

• Annual Conference of KIPS
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• 2022.05a
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• pp.385-387
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• 2022

With the tremendous rise in popularity of the Internet and technological advancements, many news keeps generating every day from multiple sources. As a result, the information (News) on the network has been highly increasing. The critical problem is that the volume of articles or news content can be overloaded for the readers. Therefore, the people interested in reading news might find it difficult to decide which content they should choose. Recommendation systems have been known as filtering systems that assist people and give a list of suggestions based on their preferences. This paper studies a personalized news recommendation system to help users find the right, relevant content and suggest news that readers might be interested in. The proposed system aims to build a hybrid system that combines collaborative filtering with content-based filtering to make a system more effective and solve a cold-start problem. Twitter social media data will analyze and build a user's profile. Based on users' tweets, we can know users' interests and recommend personalized news articles that users would share on Twitter.

• Journal of IKEEE
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• v.22 no.1
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• pp.53-60
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• 2018

Smart farm dissemination by continuously developing IoT is one of the best solution for decreasing labor in Korea farming area because of ageing. For this reason, the number of Smart farm in Korea is being increased. The Smart farm can control farming environment such as temperature for human. Specially, The important thing is controlling proper temperature for farming. In order to control the temperature, legacy smart farms are usually using pans or air conditioners which can control the temperature. However, those devices result in increasing production cost because the electric power consumption is high. For this reason, we propose a smart farm which can predict the proper temperature after an hour by using Deep learning to minimize the electric power consumption by controlling window instead of pans or air conditioners. We can see the 83% of electric power saving by means of the proposed smart farm.

• Korean Journal of Cognitive Science
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• v.10 no.1
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• pp.1-11
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• 1999

Agent-based systems technology has generated lots of excitement in these years because of its promise as a new paradigm for conceptualizing. designing. and l implementing software systems Especially, there has been many researches for multi agent system because of the characteristics that it fits to the distributed and open Internet environments. In a multiagent system. agents must cooperate with each other through a Coordination procedure. when the conflicts between agents arise. where those are caused b by the point that each action acts for a purpose separately without coordination. But P previous researches for coordination methods in multi agent system have a deficiency that they can not solve correctly the cooperation problem between agents which have different goals in dynamic environment. In this paper. we solve the cooperation problem of multiagent that has multiple goals in a dynamic environment. with an automatic cooperative coordination model using I reinforcement learning. We will show the two pursuit problems that we extend a traditional problem in multi agent systems area for modeling the restriction in the multiple goals in a dynamic environment. and we have verified the validity of the proposed model with an experiment.

• Journal of The Korean Association of Information Education
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• v.12 no.1
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• pp.49-56
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• 2008

It can be said that the feature of elementary education on Society studies is localization of all curriculum. Among them, the social curriculum of 3rd grade emphasizes on character of local and living experiences. Also the Society studies of 7th curriculum reform focused on new types of localization materials and way of practical using because of development on internet and computer communication, To correspond this need, this paper suggests regional learning system based on the web. which is designed for social studies of 3rd grade student to study Pyeongtaek-City, in Gyeonggi-do. Also, current school field education materials which is elementary 3rd grade localization supporting text can be used as well as it can be provided through the web educational system to support the needed localization materials for the students.

• International journal of advanced smart convergence
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• v.8 no.1
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• pp.24-34
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• 2019

In this research, a practical deep learning framework to differentiate the lesions and nodules in breast acquired with ultrasound imaging has been proposed. 7408 ultrasound breast images of 5151 patient cases were collected. All cases were biopsy proven and lesions were semi-automatically segmented. To compensate for the shift caused in the segmentation, the boundaries of each lesion were drawn using Fully Convolutional Networks(FCN) segmentation method based on the radiologist's specified point. The data set consists of 4254 benign and 3154 malignant lesions. In 7408 ultrasound breast images, the number of training images is 6579, and the number of test images is 829. The margin between the boundary of each lesion and the boundary of the image itself varied for training image augmentation. The training images were augmented by varying the margin between the boundary of each lesion and the boundary of the image itself. The images were processed through histogram equalization, image cropping, and margin augmentation. The networks trained on the data with augmentation and the data without augmentation all had AUC over 0.95. The network exhibited about 90% accuracy, 0.86 sensitivity and 0.95 specificity. Although the proposed framework still requires to point to the location of the target ROI with the help of radiologists, the result of the suggested framework showed promising results. It supports human radiologist to give successful performance and helps to create a fluent diagnostic workflow that meets the fundamental purpose of CADx.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.272-282
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• 2022

Between 2014 and 2019, the US lost more than 2.1 billion USD to phishing attacks, according to the FBI's Internet Crime Complaint Center, and COVID-19 scam complaints totaled more than 1,200. Phishing attacks reflect these awful effects. Phishing websites (PWs) detection appear in the literature. Previous methods included maintaining a centralized blacklist that is manually updated, but newly created pseudonyms cannot be detected. Several recent studies utilized supervised machine learning (SML) algorithms and schemes to manipulate the PWs detection problem. URL extraction-based algorithms and schemes. These studies demonstrate that some classification algorithms are more effective on different data sets. However, for the phishing site detection problem, no widely known classifier has been developed. This study is aimed at identifying the features and schemes of SML that work best in the face of PWs across all publicly available phishing data sets. The Scikit Learn library has eight widely used classification algorithms configured for assessment on the public phishing datasets. Eight was tested. Later, classification algorithms were used to measure accuracy on three different datasets for statistically significant differences, along with the Welch t-test. Assemblies and neural networks outclass classical algorithms in this study. On three publicly accessible phishing datasets, eight traditional SML algorithms were evaluated, and the results were calculated in terms of classification accuracy and classifier ranking as shown in tables 4 and 8. Eventually, on severely unbalanced datasets, classifiers that obtained higher than 99.0 percent classification accuracy. Finally, the results show that this could also be adapted and outperforms conventional techniques with good precision.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.7
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• pp.949-955
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• 2022

Deep learning-based text summarization models are not free from datasets. For example, a summarization model trained with a news summarization dataset is not good at summarizing other types of texts such as internet posts and papers. In this study, we define this phenomenon as Data Bias Problem (DBP) and propose two training methods for solving it. The first is the 'proper nouns masking' that masks proper nouns. The second is the 'length variation' that randomly inflates or deflates the length of text. As a result, experiments show that our methods are efficient for solving DBP. In addition, we analyze the results of the experiments and present future development directions. Our contributions are as follows: (1) We discovered DBP and defined it for the first time. (2) We proposed two efficient training methods and conducted actual experiments. (3) Our methods can be applied to all summarization models and are easy to implement, so highly practical.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.173-178
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• 2022

Recently, various computing technologies such as artificial intelligence, big data, and IoT are developing. In particular, artificial intelligence-based deepfake technology is being used in various fields such as the content and medical industry. Deepfake technology is a combination of deep learning and fake, and is a technology that synthesizes a person's face or body through deep learning, which is a core technology of AI, to imitate accents and voices. This paper uses deepfake technology to study the creation of virtual characters through the synthesis of animation models and real person photos. Through this, it is possible to minimize various cost losses occurring in the animation production process and support writers' work. In addition, as deepfake open source spreads on the Internet, many problems emerge, and crimes that abuse deepfake technology are prevalent. Through this study, we propose a new perspective on this technology by applying the deepfake technology to children's material rather than adult material.