• Journal of Preventive Medicine and Public Health
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• v.44 no.5
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• pp.201-209
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• 2011

Objectives: This study examined the use of health impact assessment (HIA) as a tool for intersectoral collaboration using the case of an HIA project conducted in Gwang Myeong City, Korea. Methods: A typical procedure for rapid HIA was used. In the screening step, the Aegi-Neung Waterside Park Plan was chosen as the target of the HIA. In the scoping step, the specific methods and tools to assess potential health impacts were chosen. A participatory workshop was held in the assessment step. Various interest groups, including the Department of Parks and Greenspace, the Department of Culture and Sports, the Department of Environment and Cleansing, civil societies, and residents, discussed previously reviewed literature on the potential health impacts of the Aegi-Neung Waterside Park Plan. Results: Potential health impacts and inequality issues were elicited from the workshop, and measures to maximize positive health impacts and minimize negative health impacts were recommended. The priorities among the recommendations were decided by voting. A report on the HIA was submitted to the Department of Parks and Greenspace for their consideration. Conclusions: Although this study examined only one case, it shows the potential usefulness of HIA as a tool for enhancing intersectoral collaboration. Some strategies to formally implement HIA are discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3962-3980
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• 2019

To deal with single sample face recognition, this paper presents a patch based semi-supervised linear regression (PSLR) algorithm, which draws facial variation information from unlabeled samples. Each facial image is divided into overlapped patches, and a regression model with mapping matrix will be constructed on each patch. Then, we adjust these matrices by mapping unlabeled patches to $[1,1,{\cdots},1]^T$. The solutions of all the mapping matrices are integrated into an overall objective function, which uses ${\ell}_{2,1}$-norm minimization constraints to improve discrimination ability of mapping matrices and reduce the impact of noise. After mapping matrices are computed, we adopt majority-voting strategy to classify the probe samples. To further learn the discrimination information between probe samples and obtain more robust mapping matrices, we also propose a multistage PSLR (MPSLR) algorithm, which iteratively updates the training dataset by adding those reliably labeled probe samples into it. The effectiveness of our approaches is evaluated using three public facial databases. Experimental results prove that our approaches are robust to illumination, expression and occlusion.

• ETRI Journal
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• v.41 no.4
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• pp.494-505
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• 2019

In this paper, we propose a new framework for detecting the unauthorized dumping of garbage in real-world surveillance camera. Although several action/behavior recognition methods have been investigated, these studies are hardly applicable to real-world scenarios because they are mainly focused on well-refined datasets. Because the dumping actions in the real-world take a variety of forms, building a new method to disclose the actions instead of exploiting previous approaches is a better strategy. We detected the dumping action by the change in relation between a person and the object being held by them. To find the person-held object of indefinite form, we used a background subtraction algorithm and human joint estimation. The person-held object was then tracked and the relation model between the joints and objects was built. Finally, the dumping action was detected through the voting-based decision module. In the experiments, we show the effectiveness of the proposed method by testing on real-world videos containing various dumping actions. In addition, the proposed framework is implemented in a real-time monitoring system through a fast online algorithm.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.32 no.3
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• pp.201-212
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• 2014

This study is to clarify the contents and marketing strategy of Kikurakuen held throughout 3 years at Taisho Park in modern Busan. Kikurakuen consists of three programs. One is Chrysanthemum Dolls that are dolls with scenery to represent a scene of Japanese Kabuki drama or Japanese historic scenes using Chrysanthemum flowers. To make Chrysanthemum Dolls, the Busan daily news invited Japanese Chrysanthemum Doll virtuoso. And Chrysanthemum flower bed which consists of Large-flowered chrysanthemum, sag chrysanthemum and Bonsai, which were producted from Busan and suburban and chrysanthemum of individual exhibitions. And the third is Kabuki Drama by Japanese Geisha and Kukeuk by Korean Geisha who trained throughout one month by profesional Kabuki actors from Japan and profesional actors from Dongrae. Marketing strategy of Kikurakuen is to report in a newspaper articles every days while helded Kikurakuen, finest geisha selection contest by voting of visitors and gifts from the Busan daily news and stores. It ended Kikurakuen only three times. This study is life history of modern park which may provide to understand the role and function of the urban park.

• Journal of KIISE:Software and Applications
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• v.29 no.3
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• pp.192-201
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• 2002

Automated text classification is considered as an important method to manage and process a huge amount of documents in digital forms that are widespread and continuously increasing. Recently, text classification has been addressed with machine learning technologies such as k-nearest neighbor, decision tree, support vector machine and neural networks. However, only few investigations in text classification are studied on real problems but on well-organized text corpus, and do not show their usefulness. This paper proposes and analyzes text classification methods for a real application, email document classification task. First, we propose a combining method of multiple neural networks that improves the performance through the combinations with maximum and neural networks. Second, we present another strategy of combining multiple machine learning classifiers. Voting, Borda count and neural networks improve the overall classification performance. Experimental results show the usefulness of the proposed methods for a real application domain, yielding more than 90% precision rates.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.101-106
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• 2023

Chronic illnesses are among the most common serious problems affecting human health. Early diagnosis of chronic diseases can assist to avoid or mitigate their consequences, potentially decreasing mortality rates. Using machine learning algorithms to identify risk factors is an exciting strategy. The issue with existing feature selection approaches is that each method provides a distinct set of properties that affect model correctness, and present methods cannot perform well on huge multidimensional datasets. We would like to introduce a novel model that contains a feature selection approach that selects optimal characteristics from big multidimensional data sets to provide reliable predictions of chronic illnesses without sacrificing data uniqueness.[1] To ensure the success of our proposed model, we employed balanced classes by employing hybrid balanced class sampling methods on the original dataset, as well as methods for data pre-processing and data transformation, to provide credible data for the training model. We ran and assessed our model on datasets with binary and multivalued classifications. We have used multiple datasets (Parkinson, arrythmia, breast cancer, kidney, diabetes). Suitable features are selected by using the Hybrid feature model consists of Lassocv, decision tree, random forest, gradient boosting,Adaboost, stochastic gradient descent and done voting of attributes which are common output from these methods.Accuracy of original dataset before applying framework is recorded and evaluated against reduced data set of attributes accuracy. The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy on multi valued class datasets than on binary class attributes.[1]