[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124242-en":3,"doc-seo-124242-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124242,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","A Hybrid Machine Learning and Fuzzy Inference Approach with UAV for Indoor Virus Contamination Risk","With the COVID-19 pandemic, wearing medical face masks became a widely adopted preventive routine, especially in enclosed environments where indoor transmission risk can remain elevated. This study analyzes indoor virus transmission risk linked to mask-wearing styles using an integrated pipeline combining Machine Learning and a Fuzzy Inference System. Images captured by a UAV enable contactless, mobile mask status estimation, while ambient temperature and mask-wearing ratio are incorporated into the fuzzy model to produce risk estimates. Results support decision makers in identifying and implementing measures to reduce spread indoors.","International Journal of Engineering and Technology, Vol. 15, No. 3, August 2023  \nA Hybrid Machine Learning and Fuzzy Inference Approach with UAV for Indoor Virus Contamination Risk  \nEsra Çakır*, Furkan Erdi, Emre Demircioğlu, and Mehmet Ali Taş  \nAbstract—With the impact of the Covid-19 pandemic in 2020, major established health rituals were forced to transform. The most well-known of these is the medical mask, which is widely used and required to be worn in designated areas. Although pandemic regulations have been relaxed recently, health authorities agree that wearing masks, especially in closed areas, is a life-saving measure. Proper use of face masks is one of the most effective, easy and inexpensive actions to prevent the rapid spread of viruses indoors. By examining the use of masks inclosed areas, the risk of transmission of the virus can be analyzed, and the measures can be determined correctly. Taking advantage of up-to-date technological equipment and approaches are important tools for making these determinations accurately and easily. In this study, the risk of indoor virus transmission from mask wearing styles is analyzed with an integrated method that includes Machine Learning (ML) and Fuzzy Inference System (FIS) approach. In order to achieve this, images taken from the camera of the Unmanned Aerial Vehicle (UAV), which is one of the current technologies suitable for contactless, mobile operations, were used. While determining the mask wearing status with the help of machine learning over the images, the ambient temperature and themask wearing ratio gave the risk results with the fuzzy inference system. The results are intended to guide decision makers in identifying and implementing measures to reduce and prevent the spread of the virus indoors.  \nIndex Terms—Covid-19, fuzzy inference system; indoor locations, machine learning, mask detection, Python, risk analysis, UAV, virus contamination  \nI. INTRODUCTION  \nSince the declaration of the pandemic, more than six and a half million people have died from diseases caused by the COVID-19 virus [1] . Thanks to increased vaccination and the improved immunity of survivors, daily death rates have been lower recently than when the pandemic peaked [2]. However, it is essential to be vigilant, as some diseases such as seasonal influenza and diseases caused by the Covid-19 virus show similar symptoms [3] . Moreover, variants of the virus that emerge over time continue to threaten public health [4, 5] . These reveal that medical face masks, one of the first and most successful measures applied in the pandemic, are shown to be a part of daily life for many years to come [6] . Although the high rate of wearing a mask is promising, wearing themask properly is also an issue that should be noticed [7] . Compliance with the instructions for use recommended by  \nManuscript received April 21, 2023; revised May 25, 2023; accepted June 17, 2023.  \nE. Çakır, F. Erdi, and E. Demircioğlu are with the Department of Industrial Engineering, Galatasaray University, Çırağan Cad. No.36, 34349 Beşiktaş/ İstanbul, Turkey.  \nM. A. Taş is with the Department of Industrial Engineering, Turkish-German University, 34820 Beykoz, İstanbul, Turkey.  \n*Correspondence: [ecakir@gsu.edu.tr](ecakir@gsu.edu.tr) (E.C.)  \nthe World Health Organization (WHO) is of vital importance in protecting the person and those around him from the spread of the virus [8] . It can be useful to monitor mask detection and the way they are worn indoors, which are environments that are conducive to the spread of the virus by nature [9] . Unmanned Aerial Vehicles (UAVs) (also called drones) are modern technological tools that can be used for contactless and mobile detection [10] . Photographing and temperature measurement, which are remarkable features offered by UAVs, can be used to obtain data for investigate environment. Thus, the data of unmanned aerial vehicles are suitable for analyzing the risk level.  \nIn the literature","cbCaigmj5tEzB8KU","https://ap.wps.com/l/cbCaigmj5tEzB8KU","pdf",2533189,1,6,"English","en",105,"# Introduction\n## Background on pandemic risk and mask compliance\n## Prior work on mask detection and UAV-based monitoring\n# Proposed integrated ML + FIS approach\n## UAV image processing for mask status\n## Fuzzy inference using temperature and mask ratio\n# Expected decision support\n## Guiding indoor mitigation measures","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study evaluates indoor virus contamination/transmission risk related to mask wearing in enclosed areas.\"},{\"question\":\"How does the proposed method estimate mask-wearing status and risk?\",\"answer\":\"UAV camera images are processed with machine learning to determine mask status, and ambient temperature plus mask-wearing ratio are fed into a fuzzy inference system to compute risk.\"},{\"question\":\"Why use a hybrid ML and fuzzy inference approach?\",\"answer\":\"Machine learning supports image-based mask detection, while fuzzy inference models handle uncertainty and incorporate transition values to improve risk-oriented decision making.\"}]","A Hybrid Machine Learning and Fuzzy Inference Approach with UAV for Indoor Virus Contamination Risk | 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problem does the study address?","Question",{"text":75,"@type":76},"The study evaluates indoor virus contamination/transmission risk related to mask wearing in enclosed areas.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method estimate mask-wearing status and risk?",{"text":80,"@type":76},"UAV camera images are processed with machine learning to determine mask status, and ambient temperature plus mask-wearing ratio are fed into a fuzzy inference system to compute risk.",{"name":82,"@type":73,"acceptedAnswer":83},"Why use a hybrid ML and fuzzy inference approach?",{"text":84,"@type":76},"Machine learning supports image-based mask detection, while fuzzy inference models handle uncertainty and incorporate transition values to improve risk-oriented decision 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