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المؤلفChamola, Vinay
المؤلفHassija, Vikas
المؤلفGupta, Sakshi
المؤلفGoyal, Adit
المؤلفGuizani, Mohsen
المؤلفSikdar, Biplab
تاريخ الإتاحة2022-10-29T11:32:38Z
تاريخ النشر2020-12-15
الاقتباسChamola, V., Hassija, V., Gupta, S., Goyal, A., Guizani, M., & Sikdar, B. (2020). Disaster and pandemic management using machine learning: a survey. IEEE Internet of Things Journal, 8(21), 16047-16071.‏
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85098758305&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/35561
الملخصThis article provides a literature review of state-of-the-art machine learning (ML) algorithms for disaster and pandemic management. Most nations are concerned about disasters and pandemics, which, in general, are highly unlikely events. To date, various technologies, such as IoT, object sensing, UAV, 5G, and cellular networks, smartphone-based system, and satellite-based systems have been used for disaster and pandemic management. ML algorithms can handle multidimensional, large volumes of data that occur naturally in environments related to disaster and pandemic management and are particularly well suited for important related tasks, such as recognition and classification. ML algorithms are useful for predicting disasters and assisting in disaster management tasks, such as determining crowd evacuation routes, analyzing social media posts, and handling the post-disaster situation. ML algorithms also find great application in pandemic management scenarios, such as predicting pandemics, monitoring pandemic spread, disease diagnosis, etc. This article first presents a tutorial on ML algorithms. It then presents a detailed review of several ML algorithms and how we can combine these algorithms with other technologies to address disaster and pandemic management. It also discusses various challenges, open issues and, directions for future research.
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعCrowd evacuation
disaster management
healthcare
machine learning (ML)
pandemic management
social distancing
العنوانDisaster and Pandemic Management Using Machine Learning: A Survey
النوعOther
الصفحات16047-16071
رقم العدد21
رقم المجلد8


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