• Archives of Plastic Surgery
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• v.49 no.5
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• pp.668-675
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• 2022

Background Educational resources on the internet are extensively used to obtainmedical information. YouTube is the most accessed video platform containing information to enhance the learning experience of medical professionals. This study systematically analyzed the educational value of microsurgery-related videos on this platform. Methods A systematic review was conducted on YouTube from April 18 to May 18, 2020, using the following terms: "microsurgery," "microsurgical," "microsurgical anastomosis," "free flap," and "free tissue transfer." The search was limited to the first 100 videos, and two independent reviewers screened for eligible entries and analyzed their educational value using validated scales, including a modified version of the DISCERN score (M-DISCERN), Journal of the American Medical Association (JAMAS) benchmark criteria, and the Global Quality Score (GQS). Evaluation of video popularity was also assessed with the video power index (VPI). Results Of 356 retrieved videos, 75 (21%) were considered eligible. The educational quality of videos was highly variable, and the mean global scores for the M-DISCERN, JAMAS, and GQS for our sample were consistent with medium to low quality. Conclusions A limited number of videos on YouTube for microsurgical education have high-educational quality. The majority scored low on the utilized criteria. Peer-reviewed resources seem to be a more reliable resource. Although the potential of YouTube should not be disregarded, videos should be carefully appraised before being usedas an educational resource.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.27 no.1
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• pp.69-75
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• 2023

Small communication devices used in the Internet of Things are vulnerable to various hacking because they do not apply advanced encryption techniques due to their low memory capacity or slow computation speed. In order to increase the authentication reliability of small-sized transmitters operating in 433MHz band, we introduce an RF fingerprint and adopt a convolutional neural network (CNN) as a classification algorithm. The preamble signal transmitted by each transmitter are extracted and collected using software-defined-radio to constitute a training data set, which is used for training the CNN. We tested identification of 20 transmitters in four different scenarios and obtained high identification accuracy. In particular, the accuracy of 95.8% and 92.6% was obtained, respectively in the scenario where the test was performed at a location different from the transmitter's location at the time of collecting training data, and in the scenario where the transmitter moves at walking speed.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.185-196
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• 2022

The Internet of Things (IoT) has become more and more widespread in recent years, thus attackers are placing greater emphasis on IoT environments. The IoT connects a large number of smart devices via wired and wireless networks that incorporate sensors or actuators in order to produce and share meaningful information. Attackers employed IoT devices as bots to assault the target server; however, because of their resource limitations, these devices are easily infected with IoT malware. The Distributed Denial of Service (DDoS) is one of the many security problems that might arise in an IoT context. DDOS attempt involves flooding a target server with irrelevant requests in an effort to disrupt it fully or partially. This worst practice blocks the legitimate user requests from being processed. We explored an intelligent intrusion detection system (IIDS) using a particular sort of machine learning, such as Artificial Neural Networks, (ANN) in order to handle and mitigate this type of cyber-attacks. In this research paper Feed-Forward Neural Network (FNN) is tested for detecting the DDOS attacks using a modified version of the KDD Cup 99 dataset. The aim of this paper is to determine the performance of the most effective and efficient Back-propagation algorithms among several algorithms and check the potential capability of ANN- based network model as a classifier to counteract the cyber-attacks in IoT environments. We have found that except Gradient Descent with Momentum Algorithm, the success rate obtained by the other three optimized and effective Back- Propagation algorithms is above 99.00%. The experimental findings showed that the accuracy rate of the proposed method using ANN is satisfactory.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.535-538
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• 2022

For Plug & Play of IoT devices, we develop a module that recognizes the type of USB, which is a typical wired interface of IoT devices, through image recognition. In order to drive an IoT device, a driver for communication and device hardware is required. The wired interface for connecting to the IoT device is recognized by using the image obtained through the camera of smartphone shooting to recognize the corresponding communication interface. For USB, which is a most popular wired interface, types of USB are classified through artificial neural network-based machine learning. In order to secure sufficient data set of artificial neural networks, USB images are collected through the Internet, and additional image data sets are secured through image processing. In addition to the convolution neural networks, recognizers are implemented with various deep artificial neural networks, and their performance is compared and evaluated.

• Journal of Creative Information Culture
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• v.5 no.3
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• pp.245-253
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• 2019

To evaluate the achievements of the Solar School Project that has been implemented in twelve African countries since 2013, a case study was implemented in Kenya and in Uganda to investigate networking activities, student accessibility to computers, the frequency of student computer use, the extent to which teaching quality was improved by the enhanced accessibility to ICT-based teaching and learning practices. The results showed the followings. First, Solar Schools have significantly improved the rates of enrollment, transferring, and school attendance. Second, Solar Schools have organized local and invitational training programs to build the capacities of teachers. Third, Solar Schools have facilitated change in neighboring schools and local communities. Fourth, the participants are required to have a clear vision, take ownership of the project, and make a commitment to continuing their individual efforts toward empowerment.

• International journal of advanced smart convergence
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• v.12 no.1
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• pp.18-30
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• 2023

We compared empirically the forecast accuracies of the LSTM model, and the ARIMA model. ARIMA model used auto.arima function. Data used in the model is 100 days. We compared with the forecast results for 50 days. We collected the stock closing prices of the top 4 companies by market capitalization in Korea such as "Samsung Electronics", and "LG Energy", "SK Hynix", "Samsung Bio". The collection period is from June 17, 2022, to January 20, 2023. The paired t-test is used to compare the accuracy of forecasts by the two methods because conditions are same. The null hypothesis that the accuracy of the two methods for the four stock closing prices were the same were rejected at the significance level of 5%. Graphs and boxplots confirmed the results of the hypothesis tests. The accuracies of ARIMA are higher than those of LSTM for four cases. For closing stock price of Samsung Electronics, the mean difference of error between ARIMA and LSTM is -370.11, which is 0.618% of the average of the closing stock price. For closing stock price of LG Energy, the mean difference is -4143.298 which is 0.809% of the average of the closing stock price. For closing stock price of SK Hynix, the mean difference is -830.7269 which is 1.00% of the average of the closing stock price. For closing stock price of Samsung Bio, the mean difference is -4143.298 which is 0.809% of the average of the closing stock price. The auto.arima function was used to find the ARIMA model, but other methods are worth considering in future studies. And more efforts are needed to find parameters that provide an optimal model in LSTM.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.284-286
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• 2021

With the continuous development of the Internet and digital, platforms are emerging to store large amounts of media data and provide customized services to individuals through online. Companies that provide these services recommend movies that suit their personal tastes to promote media consumption. Each company is doing a lot of research on various algorithms to recommend media that users prefer. Movies are divided into genres such as action, melodrama, horror, and drama, and the film's audio (music, sound effect, voice) is an important production element that makes up the film. In this research, based on movie trailers, we extract audio for each genre, check the commonalities of audio for each genre, distinguish movie genres through supervised learning of artificial intelligence, and propose a utilization method for generating metadata in the future.

• Korean Educational Research Journal
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• v.43 no.1
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• pp.29-57
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• 2022

This study was conducted to determine the effects of adolescents' media literacy, self-esteem, and cognitive related to on critical media understanding ability using data from the Korea Media Panel Survey 2020. As a result of the study, PC literacy and smart device literacy influenced adolescents' critical media literacy. The mediating effect of cognitive needs between Internet literacy and critical media understanding ability using PCs and smart devices was significant, and the mediating effect of self-esteem between email literacy using PCs and smart devices and critical media understanding ability was significant. Therefore, to improve adolescents' critical media understanding ability, it is necessary to educate them to use PCs and smart devices as tools for interaction. To this end, it is desirable for information and technology subjects to focus on media literacy to increase cognitive needs, and for other subjects, cooperative learning using media is desirable.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.434-442
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• 2023

The relationship between acute kidney injury (AKI) prediction and nephrotoxic drugs, or drugs that adversely affect kidney function, is one that has yet to be explored in the critical care setting. One contributing factor to this gap in research is the limited investigation of drug modalities in the intensive care unit (ICU) context, due to the challenges of processing prescription data into the corresponding drug representations and a lack in the comprehensive understanding of these drug representations. This study addresses this gap by proposing a novel approach that leverages patient prescription data as a modality to improve existing models for AKI prediction. We base our research on Electronic Health Record (EHR) data, extracting the relevant patient prescription information and converting it into the selected drug representation for our research, the extended-connectivity fingerprint (ECFP). Furthermore, we adopt a unique multimodal approach, developing machine learning models and 1D Convolutional Neural Networks (CNN) applied to clinical drug representations, establishing a procedure which has not been used by any previous studies predicting AKI. The findings showcase a notable improvement in AKI prediction through the integration of drug embeddings and other patient cohort features. By using drug features represented as ECFP molecular fingerprints along with common cohort features such as demographics and lab test values, we achieved a considerable improvement in model performance for the AKI prediction task over the baseline model which does not include the drug representations as features, indicating that our distinct approach enhances existing baseline techniques and highlights the relevance of drug data in predicting AKI in the ICU setting.

• Advanced Industrial SCIence
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• v.3 no.1
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• pp.30-36
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• 2024

Recently, the Internet of Things (IoT), which has been applied to various industrial fields, is constantly evolving in the process of automation and digitization. However, in the network where IoT devices are built, research on IoT critical information-related data sharing, personal information protection, and data integrity among intermediate nodes is still being actively studied. In this study, we propose a blockchain-based IoT critical information management technique that is easy to implement without burdening the intermediate node in the network environment where IoT is built. The proposed technique allocates a random value of a random size to the IoT critical information arriving at the intermediate node and manages it to become a decentralized P2P blockchain. In addition, the proposed technique makes it easier to manage IoT critical data by creating licenses such as time limit and device limitation according to the weight condition of IoT critical information. Performance evaluation and proposed techniques have improved delay time and processing time by 7.6% and 10.1% on average compared to existing techniques.