Predicting Influenza Outbreak using Constrained Static and dynamic Feature

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DC Field	Value	Language
dc.contributor.author	Dofadar, S.
dc.contributor.author	Venkatesan, M.
dc.date.accessioned	2020-03-30T10:22:49Z	-
dc.date.available	2020-03-30T10:22:49Z	-
dc.date.issued	2018
dc.identifier.citation	Proceedings of the 2018 International Conference on Current Trends towards Converging Technologies, ICCTCT 2018, 2018, Vol., , pp.-	en_US
dc.identifier.uri	http://idr.nitk.ac.in/jspui/handle/123456789/8824	-
dc.description.abstract	Twitter is a free social networking and micro-blogging service that gives the opportunity to write and read each others tweet to its 330 million users all over the world with a limitation of 280 characters in each tweet. As a result, Twitter can provide a huge amount of data regarding what is happening at a particular time in all over the world. One of those is epidemic event detection and prediction from the twitter data. In this study, the use of Twitter data to detect influenza outbreak is examined. The result from this experiment shows that estimate of influenza outbreak can be derived from twitter correctly combining constrained supervised and unsupervised features and then using a prediction model. � 2018 IEEE.	en_US
dc.title	Predicting Influenza Outbreak using Constrained Static and dynamic Feature	en_US
dc.type	Book chapter	en_US
Appears in Collections:	2. Conference Papers

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