• Journal of information and communication convergence engineering
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• 제12권4호
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• pp.252-256
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• 2014

Traditional block cipher Advanced Encryption Standard (AES) is widely used in the field of network security, but it has high overhead on each operation. In the 15th international workshop on information security applications, a novel lightweight and low-power encryption algorithm named low-power encryption algorithm (LEA) was released. This algorithm has certain useful features for hardware and software implementations, that is, simple addition, rotation, exclusive-or (ARX) operations, non-Substitute-BOX architecture, and 32-bit word size. In this study, we further improve the LEA encryptions for cloud computing. The Web-based implementations include JavaScript and assembly codes. Unlike normal implementation, JavaScript does not support unsigned integer and rotation operations; therefore, we present several techniques for resolving this issue. Furthermore, the proposed method yields a speed-optimized result and shows high performance enhancements. Each implementation is tested using various Web browsers, such as Google Chrome, Internet Explorer, and Mozilla Firefox, and on various devices including personal computers and mobile devices. These results extend the use of LEA encryption to any circumstance.

• Journal of information and communication convergence engineering
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• 제15권3호
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• pp.170-174
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• 2017

Nowadays, people share and discuss scientific papers on social media such as the Web 2.0, big data, online forums, blogs, Twitter, Facebook and scholar community, etc. In addition to a variety of metrics such as numbers of citation, download, recommendation, etc., paper review text is also one of the effective resources for the study of scientific impact. The social media tools improve the research process: recording a series online scholarly behaviors. This paper aims to research the huge amount of paper reviews which have generated in the social media platforms to explore the implicit information about research papers. We implemented and shown the result of text mining on review texts using R language. And we found that Zika virus was the research hotspot and association research methods were widely used in 2016. We also mined the news review about one paper and derived the public opinion.

• 대한안전경영과학회지
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• 제16권4호
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• pp.21-29
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• 2014

In this study, we recognize that the interest about construction safety and disaster increases, and check the status of current research about construction safety. And we obtain the direction of future researches and implications for development. For this study, we analyzed the published researches in the last 11 years about construction safety, and addressed annual trends and research topics for a lot of construction safety research. And the future prospects of construction safety research were analyzed with the latest news articles.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2015년도 추계학술대회
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• pp.1025-1026
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• 2015

With the development of sensor, wireless mobile communication, embedded system and cloud computing, the technologies of Internet of Things have been widely used in real word. Connecting wireless sensor networks with traditional communication networks or Internet, IoT gateway plays an important role in IoT applications. It facilitates the integration of wireless sensor networks and mobile communication networks or Internet. The IoT gateway is a key component in IoT application systems. But It has lot of security issues. We analyzed the issues of security and privacy.

• 대한안전경영과학회지
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• 제21권3호
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• pp.11-16
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• 2019

The latest issue is the smart factory. In order to implement this smart factory, the most fundamental element is to establish product specifications for factors affecting the product, obtain useful data to analyzed and predicted, and maintain safety. But most manufacturers have many errors. Therefore, the purpose of this study is to verify factors of product through statistical techniques and to study the process control and safety.

• 보건행정학회지
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• 제29권2호
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• pp.138-145
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• 2019

Background: The purpose of this study is supposed to figure out the keyword network from 2009 to 2018 with social network analysis and provide the research data that can help the Korea government's policy making on smoking cessation. Methods: First, frequency analysis on the keyword was performed. After, in this study, I applied three classic centrality measures (degree centrality, betweenness centrality, and eigenvector centrality) with R 3.5.1. Moreover, I visualized the results as the word cloud and keyword network. Results: As a result of network analysis, 'smoking' and 'smoking cessation' were key words with high frequency, high degree centrality, and betweenness centrality. As a result of looking at trends in keyword, many study had been done on the keyword 'secondhand smoke' and 'adolescent' from 2009 to 2013, and 'cigarette graphic warning' and 'electronic cigarette' from 2014 to 2018. Conclusion: This study contributes to understand trends on smoking cessation study and seek further study with the keyword network analysis.

• Journal of Information Processing Systems
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• 제15권5호
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• pp.1029-1035
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• 2019

In recent years, artificial intelligence (AI) services have become one of the most essential parts to extend human capabilities in various fields such as face recognition for security, weather prediction, and so on. Various learning algorithms for existing AI services are utilized, such as classification, regression, and deep learning, to increase accuracy and efficiency for humans. Nonetheless, these services face many challenges such as fake news spread on social media, stock selection, and volatility delay in stock prediction systems and inaccurate movie-based recommendation systems. In this paper, various algorithms are presented to mitigate these issues in different systems and services. Convolutional neural network algorithms are used for detecting fake news in Korean language with a Word-Embedded model. It is based on k-clique and data mining and increased accuracy in personalized recommendation-based services stock selection and volatility delay in stock prediction. Other algorithms like multi-level fusion processing address problems of lack of real-time database.

• International Journal of Advanced Culture Technology
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• 제11권4호
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• pp.279-285
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• 2023

This study provides academic implications by considering trends of domestic research regarding therapy for Mental disorder schizophrenia and psychosocial. For the analysis of this study, text mining with the use of R program and social network analysis method have been used and 65 papers have been collected The result of this study is as follows. First, collected data were visualized through analysis of keywords by using word cloud method. Second, keywords such as intervention, schizophrenia, research, patients, program, effect, society, mind, ability, function were recorded with highest frequency resulted from keyword frequency analysis. Third, LDA (latent Dirichlet allocation) topic modeling result showed that classified into 3 keywords: patient, subjects, intervention of psychosocial, efficacy of interventions. Fourth, the social network analysis results derived connectivity, closeness centrality, betweennes centrality. In conclusion, this study presents significant results as it provided basic rehabilitation data for schizophrenia and psychosocial therapy through new research methods by analyzing with big data method by proposing the results through visualization from seeking research trends of schizophrenia and psychosocial therapy through text mining and social network analysis.

• 한국컴퓨터정보학회:학술대회논문집
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• 한국컴퓨터정보학회 2024년도 제69차 동계학술대회논문집 32권1호
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• pp.175-177
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• 2024

OTT(Over-The-Top) 플랫폼의 부상은 미디어 콘텐츠 소비 방식을 혁명적으로 변화시키고 있다. 본 논문은 Netflix, Amazon Prime Video, Disney+, Hulu 등 주요 OTT 플랫폼에 등록된 영화들을 IMDb 평점과 러닝타임, Rotten Tomatoes 지수를 중심으로 분석한다. 이를 통해 현재의 영화 시장 트렌드와 소비자 선택, 시장 전략에 중요한 정보를 제공하려 한다. 분석 결과, 플랫폼별로 제공하는 영화의 품질과 러닝타임이 다양하며, 소비자들이 선호하는 영화 테마를 시각적으로 파악할 수 있는 워드 클라우드를 포함한다. 이러한 결과는 OTT 플랫폼의 전략적 콘텐츠 제공과 소비자 행동 이해에 기여할 수 있는 중요한 통찰력을 제공한다.

• 한국가정과교육학회지
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• 제30권4호
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• pp.93-112
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• 2018

제1차부터 2009개정 교육과정까지 교육과정의 변천에 따른 식생활 영역의 변화를 살펴보기 위하여 핵심 교육 내용, 내용 특성, 식생활 영역의 비중을 분석하였다. 텍스트 마이닝 기법을 이용한 워드 크라우드 분석을 통하여 가정교과서 40권에 기술된 식생활 영역의 내용으로부터 핵심단어를 추출하였고, 식생활 영역의 비중은 전체 가정교과서에서 식생활 영역에 해당하는 면수의 비율로 계산하였다. 전 교육과정에 걸쳐 가장 많이 등장하는 주요 핵심단어는 '식품'·'음식'·'식사'였다. 제1, 2차 교육과정에서는 식재료와 조미료, 식품에 함유된 '비타민', '단백질' 등의 영양소가 주요 핵심단어로 추출되었고, 제3차 교육과정부터 '영양'·'섭취'·'필요' 제6차 교육 과정에서 '분량', 제7차 교육과정부터 '식단'·'청소년' 등이 핵심단어로 등장했다. 식생활 영역이 가정교과에서 차지하는 비중은 평균 24.3%였으며, 제6차(29.7%) 및 제7차(31.8%) 교육과정에서 가장 높았고 2009개정 교육과정에서는 15.2%로 크게 감소했다. 아울러 식생활 영역의 구성을 살펴보면 제2차와 3차 교육과정에서 10개를 상회하던 식생활영역의 중영역의 수가 2009개정 교육과정에서는 중영역이 없어지고 소영역 2개로 축소되었다. 1차부터 2009개정 교육과정까지 식생활영역의 교육내용은 점진적으로 구체화되고 시대적 요구와 변화를 반영하고 있었다. 다만 2009개정 교육과정에서 식생활 영역의 비중이 대폭 축소되면서 교과서에 관련내용에 대한 정보와 설명이 부족하여 식생활 교육에 대한 질적 저하가 우려된다.