• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권12호
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• pp.3960-3975
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• 2022

To analyze and compare the most influencing factors on cloud computing adoption (CCA) in the healthcare organization, a systematic review and meta-analyses of studies was performed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Cochrane collaboration recommendations. A search of PubMed, ScienceDirect, Springer, Wiley Online, and Taylor & Francis Online digital libraries (From inception to January 19, 2022) was performed. A total of 17 studies met the defined studies' inclusion and exclusion criteria. Statistical significance difference favoring most influencing factors on CCA were (MD 0.76, 95% CI -1.48 - 3.01, p <0.00001, I² = 90%), (MD 1.40, 95% CI -4.76 - 7.55, p < 0.00007, I² = 97%) (MD 0.17, 95% CI -2.69 - 3.03, p<0.00001, I² = 96%) for technology vs. organizational, technology vs. environmental and business vs. human factors, respectively. Organizational and environmental factors had greater impacts on CCA compared with technological factors. Moreover, business factors were more influential than the human factors.

• 인터넷정보학회논문지
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• 제20권1호
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• pp.39-46
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• 2019

This paper investigates the importance of various environmental factors that have a strong influence on strawberry yields grown in greenhouse using the pattern recognition methods. The environmental factors influencing the production of strawberries were six factors such as average inside temperature, average inside humidity, average $CO_2$ level, average soil temperature, cumulative solar radiation, and average illumination. The results of analyzing the observed data using Dynamic Time Warping (DTW) showed that the most significant factor influencing the strawberry production was average soil temperature, average inside humidity, and cumulative solar radiation. Second, the results of analyzing the observed data using Multidimensional Scaling (MDS) showed that the most influential factors on the strawberry yields, such as average $CO_2$ level, average inside humidity, and average illumination were differently given for each farms. However, these results are based on the distance in 3D space and can be deduced from the fact that there is not a large difference between these distances. Therefore, in order to increase the harvest of strawberries cultivated in the farms, it is necessary to manage the environmental factors such as thoroughly controlling the humidity and maintaining the concentration of $CO_2$ constantly by ventilation of the greenhouse.

• Journal of the Korean Data and Information Science Society
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• 제16권3호
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• pp.529-538
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• 2005

Data mining is the method to find useful information for large amounts of data in database. It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are association rules, decision tree, clustering, neural network and so on. Association rule mining searches for interesting relationships among items in a riven large data set. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary quality measures for association rule, support and confidence and lift. We analyze Gyeongnam social indicator survey data using association rule technique for environmental information discovery. We can use to environmental preservation and environmental improvement by association rule outputs.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2005년도 춘계학술대회
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• pp.185-192
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• 2005

There are many data mining techniques such as association rule, decision tree, neural network analysis, clustering, genetic algorithm, bayesian network, memory-based reasoning, etc. We analyze 2003 Gyeongnam social indicator survey data using k-means clustering technique for environmental information. Clustering is the process of grouping the data into clusters so that objects within a cluster have high similarity in comparison to one another. In this paper, we used k-means clustering of several clustering techniques. The k-means clustering is classified as a partitional clustering method. We can apply k-means clustering outputs to environmental preservation and environmental improvement.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2004년도 추계학술대회
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• pp.115-124
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• 2004

Data mining is the method to find useful information for large amounts of data in database. It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are association rules, decision tree, clustering, neural network and so on. Association rule mining searches for interesting relationships among items in a given large data set. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary quality measures for association rule, support and confidence and lift. We analyze Gyeongnam social indicator survey data using association rule technique for environmental information discovery. We can use to environmental preservation and environmental improvement by association rule outputs.

• 한국환경생물학회:학술대회논문집
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• 한국환경생물학회 2001년도 학술대회
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• pp.134-134
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• 2001

• 한국생물공학회:학술대회논문집
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• 한국생물공학회 2005년도 생물공학의 동향(XVII)
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• pp.446-446
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• 2005

• 디지털산업정보학회논문지
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• 제17권4호
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• pp.133-146
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• 2021

In the field of food and drug, the role of the Ministry of Food and Drug Safety is becoming more important for national and public safety as well as national development and improvement of public welfare. Food and drug statistics are being used to determine the priorities and directions of policy for the promotion of public health and the development of the health industry. This study focuses on statistics from the MFDS. Through the analysis of the MFDS's statistics, the current status of the MFDS's production statistics was identified, and the survey of utilization and satisfaction of the MFDS's statistics was conducted on food and drug experts who actually use the statistics of the MFDS. In order to identify problems of the MFDS statistics, environmental factors affecting the MFDS statistics were derived, and the priorities of improvement tasks for its statistics were identified using AHP and IPA. In addition, the current situation of the statistical system, which serve as the basic coordinate for the establishment and execution of domestic food and drug policies, was identified and implications were provided.

• 대한환경공학회지
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• 제33권1호
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• pp.1-8
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• 2011

본 연구는 도시환경개선계획의 일환으로서 강우유출수 제어설비의 크기 결정과 관련된 복합 설계기법을 제안하고 있다. 제안된 복합설계기법의 목적은 도시 개발이전의 수문순환상태를 복원시키는 것에 있다. 먼저, 연속적인 강우기록으로부터 개개의 강우사상을 분리하기 위해 IETD를 결정한다. 그 다음에 NRCS-CN 방법을 적용하여 모든 강우사상에 대한 직접유출량과 침투량을 산정한다. 직접유출량과 침투량의 장기간 통계치는 개발이전, 개발이후, 개발이후 유수지 설계, 그리고 개발이후 제안된 복합설계의 경우에 대하여 각각 분석된다. 개발이전의 직접유출량과 침투량을 재현하기 위해서 유전자 알고리즘을 적용하여 유수지 및 침투기반 저류지의 크기가 산정된다. 제안된 복합설계기법은 자연 상태의 직접유출량과 침투량의 통계치를 재현하는데 매우 효과적인 것이 보여진다.

• 응용통계연구
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• 제33권5호
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• pp.603-614
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• 2020

본 연구는 이미지 데이터에 대한 예측 모형으로 뛰어난 성능을 보여온 convolutional neural networks (CNN) 모형을 이용하여 서울 아파트 가격의 예측과 서울 각 지역 아파트들의 가격결정요인들을 연구한다. 이를 위해 강, 녹지, 고도와 같은 자연환경요인, 버스정류장, 지하철역, 상권, 학교 등과 같은 기반시설요소, 일자리수, 범죄율 등의 사회경제요소들을 설명변수로 고려하고, CNN 모형이 이미지 데이터에 좋은 성능을 보여온 것을 기반으로 이 설명변수들의 값들을 CNN 모형 입력층으로써 이미지 채널의 픽셀값과 같은 역할을 하도록 변환하여 아파트 가격의 예측과 가격결정요인에 대한 해석을 시도한다. 덧붙여 본 연구에서 사용된 CNN 모형은 자연환경요인과 기반시설요인 변수들을 각 아파트를 중심으로 하는 각 입력층의 채널에 이진의 이미지로 표현함으로써 각 아파트의 공간적인 특성을 고려할 수 있다.