• Journal of The Korean Association of Information Education
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• v.4 no.1
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• pp.1-11
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• 2000

In this paper, What is called WBI teaching model, is designed by the principle of teaching guidance which is a new research according to changes of educational media such as a computer. It's planned to take the business department in a commercial high school as an example so as to master learning suitable to learners individual characteristics with focusing on man to man education. The education by means of WBI makes learners have motivations and achievement as well as it makes possible to learn by each student's level and even further to study by individuals. Especially, even though the presentations of problems and learning contents like the established WBI are basic in this survey, I designed and embodied for the interaction between learner and instructor and focused on the showing of accomplishment at each field.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.342-344
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• 2018

This paper shows the possibility to substitute statistical methods such as mean imputation, correlation coefficient analysis, graph correlation analysis for the proposed algorithm, and replace statistician for processing various abnormal data measured in the water treatment process with it. In addition, this study aims to model a data-filtering system based on a recent fractile pattern and a deep learning-based LSTM algorithm in order to improve the reliability and validation of the algorithm, using the open-sourced libraries such as KERAS, THEANO, TENSORFLOW, etc.

• Annual Conference of KIPS
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• 2012.04a
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• pp.1294-1297
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• 2012

기존의 이러닝에서는 교수자가 강의동영상을 통하여 주로 단방향으로 지식을 전달하였다. 이런 문제점을 해결하기 위해 본 연구에서논 실시간 상호작용이 가능한 이러닝시스템인 ECube을 제안하고 구현하였다. 교수자에게는 학습자와 실시간 소통을 위한 실시간 강의기능, 전문가의 도움없이 미디어 제작과 편집이 가능한 동영상삼 저작도구인 EMC(Effective Media Contents) 솔루션을 제공한다. ECube 시스템 안의 EMC 솔루션만으로도 자막, 이미지, 퀴즈, 비디오를 합쳐 통합된 콘텐츠의 제작이 가능하다. 학습자에게는 실시간 강의를 수강하는 동안에 발표수업에 참여할 수 있는 기능을 지원하고 자신의 학습에 관한 학습계획부터 학습성과까지의 내용을 문서화할 수 있는 기능을 제공한다. 이 기능을 활용하여 학습자는 과목별 포트폴리오 작성이 가능하여 자기주도적 학습을 수행할 수 있는 학습환경을 제공한다.

• Journal of Broadcast Engineering
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• v.26 no.5
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• pp.566-574
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• 2021

The arc is one of factors causing electrical fires. Over past decades, various researches have been carried out to detect arc occurrences. Even though frequency analysis, wavelet and statistical features have been used, arc detection performance is degraded due to diverse arc waveforms. On the contray, Deep neural network (DNN) direcly utilizes raw data without feature extraction, based on end-to-end learning. However, a disadvantage of the DNN is processing complexity, posing the difficulty of being migrated into a termnial device. To solve this, this paper proposes an arc detection method using a logistic regression that is one of simple machine learning methods.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.317-323
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• 2021

Social media is increasingly becoming a part of our daily life for communicating each other. There are various tools and applications for communication and therefore, identity theft is a common issue among users of such application. A new style of identity theft occurs when cybercriminals break into WhatsApp account, pretend as real friends and demand money or blackmail emotionally. In order to prevent from such issues, data mining can be used for text classification (TC) in analysis authorship attribution (AA) to recognize original sender of the message. Arabic is one of the most spoken languages around the world with different variants. In this research, we built a machine learning model for mining and analyzing the Arabic messages to identify the author of the messages in Saudi dialect. Many points would be addressed regarding authorship attribution mining and analysis: collect Arabic messages in the Saudi dialect, filtration of the messages' tokens. The classification would use a cross-validation technique and different machine-learning algorithms (Naïve Baye, Support Vector Machine). Results of average accuracy for Naïve Baye and Support Vector Machine have been presented and suggestions for future work have been presented.

• Journal of Broadcast Engineering
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• v.24 no.4
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• pp.564-572
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• 2019

With the development of deep learning, the performance of facial landmark detection methods has been greatly improved. The heat map regression method, which is a representative facial landmark detection method, is widely used as an efficient and robust method. However, the landmark coordinates cannot be directly obtained through a single network, and the accuracy is reduced in determining the landmark coordinates from the heat map. To solve these problems, we propose to combine integral regression with the existing heat map regression method. Through experiments using various datasets, we show that the proposed integral regression network significantly improves the performance of facial landmark detection.

• Journal of Broadcast Engineering
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• v.24 no.3
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• pp.430-439
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• 2019

The CNN (convolutional neural network) based single degradation restoration method shows outstanding performance yet is tailored on solving a specific degradation type. In this paper, we present an algorithm of multi-degradation classification and restoration. We utilize the CNN based algorithm for solving image degradation classification problem using pre-trained Inception-v3 network. In addition, we use the existing CNN based algorithms for solving particular image degradation problems. We identity the restoration order of multi-degraded images empirically and compare with the non-reference image quality assessment score based on CNN. We use the restoration order to implement the algorithm. The experimental results show that the proposed algorithm can solve multi-degradation problem.

• Journal of Broadcast Engineering
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• v.24 no.2
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• pp.243-250
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• 2019

In this paper, we propose a system for processing and analyzing drone image information which can be applied variously in disasters-security situation. The proposed system stores the images acquired from the drones in the server, and performs image processing and analysis according to various scenarios. According to each mission, deep-learning method is used to construct an image analysis system in the images acquired by the drone. Experiments confirm that it can be applied to traffic volume measurement, suspect and vehicle tracking, survivor identification and maritime missions.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.79-86
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• 2021

Alzheimer's is a chronic progressive disease which exhibits varied symptoms and behavioural traits from person to person. The deterioration in cognitive abilities is more noticeable through their Activities and Instrumental Activities of Daily Living rather than biological markers. This information discussed in social media communities was collected and features were extracted by using the proposed fuzzy logic based algorithm to address the uncertainties and imprecision in the data reported. The data thus obtained is used to train machine learning models in order to predict the longevity of the patients. Models built on features extracted using the proposed algorithm performs better than models trained on full set of features. Important findings are discussed and Support Vector Regressor with RBF kernel is identified as the best performing model in predicting the longevity of Alzheimer's patients. The results would prove to be of high value for healthcare practitioners and palliative care providers to design interventions that can alleviate the trauma faced by patients and caregivers due to chronic diseases.

• The Journal of Industrial Distribution & Business
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• v.13 no.6
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• pp.9-18
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• 2022

Purpose: The government around the world is still highlighting the effect of the new variant of Covid-19. The government continues to make efforts to restore the economy through several programs, one of them is National Economic Recovery. This program is expected to increase public and investor confidence in handling Covid-19. This study aims to capture public sentiment on the economic growth rate in Indonesia, especially during the third wave of the omicron variant of the covid-19 virus, that is at the time in the fourth quarter of 2021. Research design, data, and methodology: The approach used in this research is to collect crowdsourcing data from twitter, in the range of 1st to 10th October 2021. The analysis is done by building model using Deep Learning Neural Network method. Results: The result of the sentiment analysis is that most of the tweets have a neutral sentiment on the Economic Growth discussion. Several central figures who discussed were Minister of Coordinating for the Economy of Indonesia, Minister of State-Owned Enterprises. Conclusions: Data from social media can be used by the government to capture public responses, especially public sentiment regarding economic growth. This can be used by policy makers, for example entrepreneurs to anticipate economic movements under certain conditions.