• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.135-155
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• 2024

Urban public health monitoring in smart cities focuses on the control of conditions and health challenges in urban environments. Considering the rapid spread of diseases and pandemics, it is important for health authorities to trace people carrying the virus. In smart cities, this tracing must be interoperable and intelligent, especially in indoor surfaces characterized by small distances between people. Therefore, to fight pandemics, it is necessary to start with the already-existing digital equipment of the Internet of Things, such as connected objects and smartphones. In this study, the developed system is employed to provide a social IoT network and suggest a strategy which allows reliable traceability without threatening the privacy of users. This IoT-based system allows respecting the social distance between persons sharing public services in smart cities without applying smartphone applications or severe confinement. It also permits a return to normal life in case of viral pandemic and ensures the much-desired balance between economy and health. The present study analyses previous proposed social distance systems then, unlike these studies, suggests an intelligent and distributed IoT based strategy for positioning students. Two scenarios of static and dynamic optimization-based placement of Bluetooth Low Energy devices are proposed and an experimental study shows the contribution and complementarity of the introduced contact tracing strategy with the applications on smartphones.

• Journal of Environmental Health Sciences
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• v.50 no.4
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• pp.291-301
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• 2024

Background: Based on rapid industrial development, environmental pollution has emerged as a social problem and exposure to environmental diseases is increasing. The number of patients suffering environmental diseases in Daejeon Metropolitan City is also steadily increasing, and the prevalence of atopic dermatitis there is the highest in the country. Objectives: In order to minimize exposure to harmful factors for the prevention and management of environmental diseases, an environmental disease management system suitable for the environmental characteristics of each region is needed. Basic preliminary research should be conducted to identify environmental hazards in Daejeon Metropolitan City and establish a management system. Methods: Among the households (about 50 people) participating in the 2022 Indoor Environment Remote Measurement (IoT) program, households (children aged 5 or older and adults) with insufficient results for indoor air quality measurement and symptoms related to environmental diseases were selected. The subjects were tested for living conditions, blood tests, biomarker analysis (immunoglobulin E, Eosinophil count, histamine) and multiple allergy antigen tests (MAST, 93 types). Results: Participants were 53.7% female and 46.3% male, and the average age showed an even age distribution. IgE and eosinophil count were positively correlated, and significant results were found for atopic dermatitis and IgE (p<0.05). Typical risk factors observed in the survey was the amount of indoor ventilation, chemical exposure, heredity, house dust mites, fungi, and food. Conclusions: The purpose of this study was to help establish a regional management system for environmental diseases, research and diagnosis of environmental diseases. This study is meaningful in that it is a study with customized consulting suitable for the environment of Daejeon Metropolitan City. If the limitations are addressed and continuous research is conducted, it will be helpful for the management, diagnosis, and research of environmental diseases.

• Journal of Information Technology Applications and Management
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• v.23 no.4
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• pp.55-70
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• 2016

Recently, development of indoor positioning system and IoT such as beacon makes it possible to collect and analyze each customer's shopping behavior in off-line shopping malls. In this study, we propose a realtime brand recommendation scheme based on each customer's brand visiting history for off-line shopping mall with indoor positioning system. The proposed scheme, which apply collaborative filtering to off-line shopping mall, is composed of training and apply process. The training process is designed to make the base brand network (BBN) using historical transaction data. Then, the scheme yields recommended brands for shopping customers based on their behaviors and BBN in the apply process. In order to verify the performance of the proposed scheme, simulation was conducted using purchase history data from a department store in Korea. Then, the results was compared to the previous scheme. Experimental results showd that the proposed scheme performs brand recommendation effectively in off-line shopping mall.