• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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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.

• Journal of Internet Computing and Services
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• v.20 no.1
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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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• v.16 no.3
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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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• 2005.04a
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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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• 2004.10a
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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.

• Proceedings of the Korea Society of Environmental Biology Conference
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• 2001.12a
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• pp.134-134
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• 2001

• 한국생물공학회:학술대회논문집
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• 2005.10a
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• pp.446-446
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• 2005

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.4
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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.

• Journal of Korean Society of Environmental Engineers
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• v.33 no.1
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• pp.1-8
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• 2011

This article proposes a hybrid approach involved in determining the size of stormwater control facilities as part of a very large scale urban retrofit project. The objective of the proposed hybrid approach is to restore the pre-development hydrologic cycle. Firstly, an appropriate IETD is determined to isolate single storm events from the continuous rainfall record. Then, using the NRCS-CN method, direct runoff and infiltration volume are computed for every storm events. Long-term statistics of direct runoff and infiltration volume are analyzed in each case of pre-development, post development, post development with detention only, and post-development with the proposed hybrid approach. In order to preserve long-term statistics of direct runoff and infiltration volume in the case of pre-development, the size of detention and infiltration-based retention are estimated using the genetic algorithm. The result shows that the proposed hybrid approach is very useful for restoring statistics of natural direct runoff and infiltration volume.

• The Korean Journal of Applied Statistics
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• v.33 no.5
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• pp.603-614
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• 2020

This study focuses on the prediction and factors of apartment prices in Seoul using a convolutional neural networks (CNN) model that has shown excellent performance as a predictive model of image data. To do this, we consider natural environmental factors, infrastructure factors, and social economic factors of the apartments as input variables of the CNN model. The natural environmental factors include rivers, green areas, and altitudes of apartments. The infrastructure factors have bus stops, subway stations, commercial districts, schools, and the social economic factors are the number of jobs and criminal rates, etc. We predict apartment prices and interpret the factors for the prices by converting the values of these input variables to play the same role as pixel values of image channels for the input layer in the CNN model. In addition, the CNN model used in this study takes into account the spatial characteristics of each apartment by describing the natural environmental and infrastructure factors variables as binary images centered on each apartment in each input layer.