• Journal of Digital Contents Society
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• v.14 no.4
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• pp.447-454
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• 2013

As the surveillance CCTVs are increasing every year, big data image processing for the CCTV image data has become a hot issue. In this paper, we propose a Hadoop-based big data image processing technique to recognize a vehicle number from a large amount of automatic number plate images taken from CCTVs. We also implement the vehicle route tracking system that displays the moving path of the searched vehicle on Google Maps with the related information together. In order to evaluate the performance we compare and analysis the vehicle number recognition time for a lot of CCTV image data in Hadoop and the single PC environment.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.225-230
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• 2016

Recently, Web has arisen large amount of data that to the development of the network and the Internet. In order to process it appeared that Big Data technology. Big Data technologies have been studied aiming a multifaceted and accurate analysis using existing regular data and a variety of data social data. But social data does not have the expertise and objectivity. And such manipulation and concealment and distortion of information have been raised troubling. Thus, this paper proposes for trust big data platform and will be described in detail. The big data platform proposed in this paper consists of data refiner, Data Analyzer, co-truster, visualizer, searcher, etc.

• Journal of Korea Multimedia Society
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• v.23 no.1
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• pp.50-57
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• 2020

The work that detection and elimination to disease and pest have important in agricultural field because it is directly related to the production of the crops, early detection and treatment of the disease insects. Image classification technology based on traditional computer vision have not been applied in part such as disease and pest because that is falling a accuracy to extraction and classification of feature. In this paper, we proposed model that determine to disease and pest of shiitake based on deep-CNN which have high image recognition performance than exist study. For performance evaluation, we compare evaluation with Alexnet to a proposed deep learning evaluation model. We were compared a proposed model with test data and extend test data. The result, we were confirmed that the proposed model had high performance than Alexnet which approximately 48% and 72% such as test data, approximately 62% and 81% such as extend test data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1570-1594
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• 2017

Social network services have recently changed from environments for simply building connections among users to open platforms for generating and sharing various forms of information. Existing user reputation computation methods are inadequate for determining the trust in users on social media where explicit ratings are rare, because they determine the trust in users based on user profile, explicit relations, and explicit ratings. To solve this limitation of previous research, we propose a user reputation computation method suitable for the social media environment by incorporating implicit as well as explicit ratings. Reliable user reputation is estimated by identifying malicious information raters, modifying explicit ratings, and applying them to user reputation scores. The proposed method incorporates implicit ratings into user reputation estimation by differentiating positive and negative implicit ratings. Moreover, the method generates user reputation scores for individual categories to determine a given user's expertise, and incorporates the number of users who participated in rating to determine a given user's influence. This allows reputation scores to be generated also for users who have received no explicit ratings, and, thereby, is more suitable for social media. In addition, based on the user reputation scores, malicious information providers can be identified.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.5
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• pp.217-221
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• 2017

Proactive prevention is important for crime. Past crimes have focused on coping after death and punishing them. But with Big Data technology, crime can be prevented spontaneously. Big data can predict the behavior of criminals or potential criminals. This article discusses how to build a big data system for crime prevention. Specifically, it deals with the way to combine unstructured data of big data with basic form data, and as a result, designs crime prevention system. Through this study, it is expected that the possibility of using big data for crime prevention is described through fingerprints, and it is expected to help crime prevention program and research in future.

• Journal of Service Research and Studies
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• v.5 no.1
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• pp.57-70
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• 2015

This paper develop curnel based high speed packet filtering imbedded gateway and firewall using cloud database. This study develop equipment include of predict function through bigdata analysis using cloud system. This equipment include intrusion prevention for network attack, and include system security function of L7 switch based contents. This study can improve security level of little company and general family. This study can pioneer a new market. This study can develop high perfomance switch and replacement of existing security equipment. This study proposed new next generation algorithm for constuction of high performance system from low specifications.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.282-285
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• 2018

The purpose of this study is to investigate the effect of personal factors and community factors on the quality of life based on the presence of chronic diseases based on the Big Data Platform. The research methodology was the matching of the 2017 Community Health Survey data and the National Statistical Office data to the health center units. In the study, The higher the age, the higher the education level, the higher the monthly household income, the economic activity, the spouse, the higher the quality of life. In the case of community factors, the lower the population density, the lower the elderly population ratio, the more doctors engaged in medical institutions, the higher the financial independence, the higher the quality of life.

• Annual Conference of KIPS
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• 2018.10a
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• pp.428-429
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• 2018

본 논문에서는 다기관 의료영상 분석 방법 및 시스템을 제안한다. 다기관 연구에 참여하는 기관에게 분석 가이드 및 분석 프로그램을 제공하여 표준화된 영상분석 연구를 지원하고자 한다. 이를 위해 동일한 프로토콜로 표준화된 영상을 획득 및 분석하고 결과를 공유하는 분산형 연구방법을 제시한다. 제안하는 시스템은 개인정보보호법 및 보안문제가 강조되고 있는 의료현장에 적합한 시스템으로 다양한 다기관 의료 빅데이터 분석 연구에 활용될 것으로 기대된다.

• Korean Journal of Artificial Intelligence
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• v.9 no.2
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• pp.15-21
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• 2021

This paper conducted a feasibility study by applying an Internet of Things and Artificial intelligence-based management system for the elderly living alone in an aging society. The number of single-person families over the age of 50 is expected to increase, and problems such as health, safety, and loneliness may occur due to aging. Therefore, by establishing an IoT-based care system for the elderly living alone, a stable service was developed through securing a rapid response system for the elderly living alone and automatically reporting 119. The participants of the demonstration test were subjects under the jurisdiction of the "Seongnam Senior Complex," and the data collection rate between the IoT sensor and the emergency safety gateway was high. During the demonstration period, as a result of evaluating the satisfaction of the IoT-based care system for the elderly living alone, 90 points were achieved. We are currently in the COVID-19 situation. Therefore, the number of elderly living alone is continuously increasing, and the number of people who cannot benefit from care services will continue to occur. Also, even if the COVID-19 situation is over, the epidemic will happen again. So the care system is essential. The elderly care system developed in this way will provide safety management services based on artificial intelligence-based activity pattern analysis, improving the quality of in-house safety services.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.4
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• pp.781-786
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• 2019

We should give a realistic value on the large amounts of relevant data obtained from these studies to achieve effective objectives of the disease study which is dealing with various vital phenomenon today. In this paper, the proposed MKSV algorithm is estimated by optimal probability distribution, and the input pattern is determined. After classifying it into data mining, it is possible to obtain efficient computational quantity and recognition rate. MKSV algorithm is useful for studying the relationship between disease and gene in the present society by simulating the probabilistic flow of gene data and showing fast and effective performance improvement to classify data through the data mining process of big data.