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dc.contributor.authorGill, Harsuminder Kaur-
dc.contributor.authorSehgal, Vivek Kumar-
dc.contributor.authorVerma, Anil Kumar-
dc.description.abstractNovel Coronavirus (COVID-19) has become one of the deadliest pandemics that has affected almost all the nations in the world. Lockdown and systematic re-opening of shopping malls, offices, etc. is still one of the major weapons against this virus. However, the government and medical agencies take long time to reopen the places due to risks involved in this deadly virus. The delay to reopen places has resulted in sharp decline in the growth of economy. In this paper a current context aware framework is proposed which uses multiple inputs for a specific region to decide whether to open it or not. The proposed framework used series of deep neural network models to generate recommendations specific to a particular region. Most of the inputs are real-time and readily available with the government. The main aim is to develop framework which can be used in any kind of pandemic even in small region to easily contain it. However, it has been tested using opensource data available for COVID- 19. Data was crawled from web for 22 districts of Haryana state of India. Experimental result proved the efficiency of proposed framework.en_US
dc.publisherJaypee University of Information Technology, Solan, H.P.en_US
dc.subjectNeural networken_US
dc.subjectContext awareen_US
dc.titleCASE‑CF: Context Aware Smart Epidemic Control Frameworken_US
Appears in Collections:Journal Articles

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