A framework of genetic algorithm-based CNN on multi-access edge computing for automated detection of COVID-19 Hassan, MR, Ismail, WN, Chowdhury, A, Hossain, S, Huda, Shamsul and Hassan, MM 2022, A framework of genetic algorithm-based CNN on multi-access edge computing for automated detection of COVID-19, Journal of Supercomputing, pp. 1-25, doi: 10.1007/s11227-021-04222-4. Attached Files Name Description MIMEType Size Downloads Title A framework of genetic algorithm-based CNN on multi-access edge computing for automated detection of COVID-19 Author(s) Hassan, MR Ismail, WN Chowdhury, A Hossain, S Huda, Shamsul orcid.org/0000-0001-7848-0508 Hassan, MM Journal name Journal of Supercomputing Start page 1 End page 25 Total pages 25 Publisher Springer Place of publication Berlin, Germany Publication date 2022 ISSN 0920-8542 1573-0484 Keyword(s) Classification CNN Computer Science Computer Science, Hardware & Architecture Computer Science, Theory & Methods CONVOLUTIONAL NEURAL-NETWORK COVID-19 Engineering Engineering, Electrical & Electronic Genetic Algorithm Multi-access edge Science & Technology Technology Language eng DOI 10.1007/s11227-021-04222-4 Indigenous content off Field of Research 0803 Computer Software 0805 Distributed Computing HERDC Research category C1 Refereed article in a scholarly journal Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30162290 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Open Access Collection Related Links Link Description Link to full-text (open access)   Connect to published version Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Mon, 07 Feb 2022, 07:14:34 EST Mon, 07 Feb 2022, 07:25:45 EST Mon, 07 Feb 2022, 07:36:36 EST Tue, 08 Feb 2022, 13:02:36 EST Tue, 08 Feb 2022, 14:11:06 EST Sat, 26 Feb 2022, 01:30:21 EST Fri, 18 Mar 2022, 06:28:37 EST Fri, 18 Mar 2022, 15:45:58 EST Fri, 18 Mar 2022, 15:49:22 EST Mon, 21 Mar 2022, 09:17:39 EST Mon, 21 Mar 2022, 09:35:16 EST Fri, 08 Apr 2022, 14:35:51 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 36 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Tue, 08 Feb 2022, 13:02:36 EST