• Title/Summary/Keyword: BIG4

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A Study on Change in Perception of Community Service and Demand Prediction based on Big Data

  • Chun-Ok, Jang
    • International Journal of Advanced Culture Technology
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    • v.10 no.4
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    • pp.230-237
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    • 2022
  • The Community Social Service Investment project started as a state subsidy project in 2007 and has grown very rapidly in quantitative terms in a short period of time. It is a bottom-up project that discovers the welfare needs of people and plans and provides services suitable for them. The purpose of this study is to analyze using big data to determine the social response to local community service investment projects. For this, data was collected and analyzed by crawling with a specific keyword of community service investment project on Google and Naver sites. As for the analysis contents, monthly search volume, related keywords, monthly search volume, search rate by age, and gender search rate were conducted. As a result, 10 items were found as related keywords in Google, and 3 items were found in Naver. The overall results of Google and Naver sites were slightly different, but they increased and decreased at almost the same time. Therefore, it can be seen that the community service investment project continues to attract users' interest.

A Study on Machine Learning model for detection of DoS Attack (IP카메라의 DoS 공격 탐지 머신러닝 모델에 대한 연구)

  • Jung, Woong-Kyo;Kim, Dong-Young;Kwak, Byung Il
    • Annual Conference of KIPS
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    • 2022.11a
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    • pp.709-711
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    • 2022
  • ICT 기술의 빠른 발전과 함께 Internet of Things (IoT) 환경에서의 Internet Protocol (IP) 카메라의 사용률이 증가하면서, IP 카메라에 대한 개인정보 이슈와 제품의 보안성 검토 관련 소비자의 개인정보 유출 우려가 증가하고 있다. 본 논문에서는, IP 카메라에 대한 4개 종류의 Denial of Service (DoS) 공격을 통해 IP 카메라 이상 반응을 확인했다. 또한, 이 과정에서 수집한 공격 패킷 데이터를 기반으로, DoS 공격을 탐지하는 간단한 피쳐 구성과 머신러닝 모델을 제안하였다. 최종적으로, DoS 공격을 통해 실제 IP 카메라에 대한 가용성 테스트를 수행하였으며 머신러닝 알고리즘 4개 Decision Tree, Random Forest, Multilayer Perceptron, SVM에서의 DoS 공격 탐지 성능을 비교하였다.

IT Jobs in the Era of Digital Transformation: Big Data Analytics

  • Ho Lee;Jaewon Choi
    • Asia pacific journal of information systems
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    • v.29 no.4
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    • pp.717-730
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    • 2019
  • The era of digital transformation (or the fourth industrial revolution) has been triggered by the rapid development of software (SW) technologies. In this era, several studies suspected rapid changes in job structures occurring around the world. Thus, there is a growing need for acquiring the skill sets required for the future. However, there are no specific studies on how existing jobs are changing. To cope with this ambiguity of job changes, this paper aims to investigate how the current job structure is changing in response to digital transformation. To identify the dynamic nature of job change over time, we conducted an analysis based on job posting data. As a result, nine job occupations and fifteen jobs were found.

해외 동향 - 원전의 빅 데이터(Big Data) 활용

  • 한국원자력산업회의
    • Nuclear industry
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    • v.35 no.4
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    • pp.63-64
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    • 2015
  • 국제원자력안전연구소(WINS : World Institute for Nuclear Security)가 내놓은 최신 보고서는 원전 분야에서도 빅 데이터 기반의 통합적 데이터 관리와 분석의 도입을 진지하게 고려할 것을 권고하고 있다.

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Personalized Diet in the Era of the 4th Industrial Revolution (4차 산업혁명 시대 맞춤형 식이)

  • Soo-Hyun Park;Jae-Ho Park
    • Journal of the Korean Society of Food Culture
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    • v.38 no.4
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    • pp.185-190
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    • 2023
  • This paper elucidates the novel direction of food research in the era of the 4th Industrial Revolution characterized by personalized approaches. Since conventional approaches for identifying novel food materials for health benefits are expensive and time-consuming, there is a need to shift towards AI-based approaches which offer more efficient and cost-effective methods, thus accelerating progress in the field of food science. However, relevant research papers in this field present several challenges such as regional and ethnic differences and lack of standardized data. To tackle this problem, our study proposes to address the issues by acquiring and normalizing food and biological big data. In addition, the paper demonstrates the association between heath status and biological big data such as metabolome, epigenome, and microbiome for personalized healthcare. Through the integration of food-health-bio data with AI technologies, we propose solutions for personalized healthcare that are both effective and validated.

Animal Infectious Diseases Prevention through Big Data and Deep Learning (빅데이터와 딥러닝을 활용한 동물 감염병 확산 차단)

  • Kim, Sung Hyun;Choi, Joon Ki;Kim, Jae Seok;Jang, Ah Reum;Lee, Jae Ho;Cha, Kyung Jin;Lee, Sang Won
    • Journal of Intelligence and Information Systems
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    • v.24 no.4
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    • pp.137-154
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    • 2018
  • Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.

Study of Re-writing "A Tale of the Conquest over a Big Enemy from an Underground Nation" - Focusing on picture book narrative (지하국대적퇴치설화를 활용한 새로쓰기 연구 - 그림책 서사를 중심으로)

  • Kim, Hwa-Lim;Kim, Hanil
    • Smart Media Journal
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    • v.6 no.4
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    • pp.88-93
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    • 2017
  • The story of "A Tale of the Conquest over a Big Enemy from an Underground Nation" is a story that is distributed all over the world. This familiar narrative structure can be accepted without great objection even in places with different cultures. In case of storytelling with the elements extracted from the narrative according to the previous research, storytelling of the picture book was carried out using the element and structure of the narrative. One of the ways to content the story is called a re-writing. In this paper, we divided the components of picture book into literary subjects, plots, characters, and backgrounds. "A Tale of the Conquest over a Big Enemy from an Underground Nation" is analyzed in the same way. And presented storytelling of the picture book.