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ξboost: An AI-based Data Analytics Scheme for COVID-19 Prediction and Economy Boosting
IEEE Internet of Things Journal ; 2020.
Article in English | Scopus | ID: covidwho-1015475
ABSTRACT
The coronavirus (COVID-19) outbreak has a significant impact on people's lives, occupations, businesses, and economies globally. The world economic market is experiencing a big shift and the share market has observed crashes day-by-day. Even, the Indian economy has witnessed a slowdown in the current pandemic, and recovery of it is quite difficult. The restrictions and restrain strategies (e.g., lockdown and social distancing) introduced by the government leave many professions and facilities in a dormant state, catalyzing economy downfall. It necessitates to improve economy along with control strategies of COVID-19, which is a challenging task. To handle the above-mentioned issues, this paper proposes a novel economy-boosting scheme, i.e., ξboost, which is a fusion of Artificial Intelligence (AI) and Big Data analytic (BDA) integrated with the Internet of Things (IoT)-based data communication. Here, a Bidirectional Long-Short Term Memory (LSTM) model is anticipated for early prediction of total positive cases as well as the economy. Then, it calculates an optimal subsegment of days, in which trade and commerce related restrictions could be reduced to control a sharp decline in the economy. Next, a Spark-based Pre and Post Unlock (PPU) analytics is carried out on the rise of COVID-19 cases to validate the intensity of testing in the country and deciding economy-boosting activities. Then, the ξboost scheme is evaluated based on various factors such as prediction accuracy and others while comparing to existing approaches. It facilitates healthy and profitable smart cities by the means to control pandemic with subsequent economy rise. IEEE

Full text: Available Collection: Databases of international organizations Database: Scopus Document Type: Article Type of study: Health economic evaluation / Prognostic study / Risk factors Language: English Journal: IEEE Internet of Things Journal Clinical aspect: Prediction Year: 2020

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Full text: Available Collection: Databases of international organizations Database: Scopus Document Type: Article Type of study: Health economic evaluation / Prognostic study / Risk factors Language: English Journal: IEEE Internet of Things Journal Clinical aspect: Prediction Year: 2020
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