id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-292835-zzc1a7id	Otoom, Mwaffaq	An IoT-based Framework for Early Identification and Monitoring of COVID-19 Cases	2020-08-15		.txt	text/plain	5253	328	58	The proposed system would employ an Internet of Things (IoTs) framework to collect real-time symptom data from users to early identify suspected coronaviruses cases, to monitor the treatment response of those who have already recovered from the virus, and to understand the nature of the virus by collecting and analyzing relevant data. To quickly identify potential coronaviruses cases from this real-time symptom data, this work proposes eight machine learning algorithms, namely Support Vector Machine (SVM), Neural Network, Naïve Bayes, K-Nearest Neighbor (K-NN), Decision Table, Decision Stump, OneR, and ZeroR. Based on these results we believe that real-time symptom data would allow these five algorithms to provide effective and accurate identification of potential cases of COVID-19, and the framework would then document the treatment response for each patient who has contracted the virus. The proposed framework consists of five main components: (1) real-time symptom data collection (using wearable devices), (2) treatment and outcome records from quarantine/isolation centers, (3) a data analysis center that uses machine learning algorithms, (4) healthcare physicians, and (5) a cloud infrastructure.	./cache/cord-292835-zzc1a7id.txt	./txt/cord-292835-zzc1a7id.txt