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Please use this identifier to cite or link to this item: https://ir.iimcal.ac.in:8443/jspui/handle/123456789/1654
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dc.contributor.authorChaudhuri, Neha
dc.contributor.authorBose, Indranil
dc.date.accessioned2021-08-26T06:23:42Z-
dc.date.available2021-08-26T06:23:42Z-
dc.date.issued2020
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85097983839&doi=10.1145%2f3369740.3372729&partnerID=40&md5=90e601a76136ac3ac0f44ec98ac65557
dc.identifier.urihttps://ir.iimcal.ac.in:8443/jspui/handle/123456789/1654-
dc.descriptionNeha Chaudhuri, Management Information Systems, Indian Institute of Management, Calcutta, India; Indranil Bose, Management Information Systems, Indian Institute of Management, Calcutta, India
dc.descriptionDOI - 10.1145/3369740.3372729
dc.description.abstractThis study identifies a novel source of data, i.e. images from smart urban infrastructures, that would be helpful in effective disaster management decision-making. For this purpose, we collected images from disaster-hit environments of Central Mexico (2017 earthquake). Also, this study utilizes deep learning convolutional neural network to analyze this novel dataset and evaluates the model effectiveness and technical viability during crisis scenarios. TensorFlow was utilized for the image classification task. The findings have important significance for effective disaster response.
dc.publisherSCOPUS
dc.publisherACM International Conference Proceeding Series
dc.publisherAssociation for Computing Machinery
dc.relation.ispartofseriesPart F165625
dc.subjectConvolutional neural networks
dc.subjectdeep learning
dc.subjectdisaster management
dc.subjectimage classification
dc.subjectsmart urban environments
dc.titleApplication of Image Data Analytics for Immediate Disaster Response
dc.typeConference Paper
Appears in Collections:Management Information Systems

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