Openly accessible

Hot-spot zone detection to tackle COVID19 spread by fusing the traditional machine learning and deep learning approaches of computer vision

Khan, M Z, Khan, M U G, Saba, T, Razzak, Muhammad Imran, Rehman, A and Bahaj, S A 2021, Hot-spot zone detection to tackle COVID19 spread by fusing the traditional machine learning and deep learning approaches of computer vision, IEEE Access, vol. 9, pp. 1-11, doi: 10.1109/access.2021.3094720.

Attached Files
Name Description MIMEType Size Downloads

Title Hot-spot zone detection to tackle COVID19 spread by fusing the traditional machine learning and deep learning approaches of computer vision
Author(s) Khan, M Z
Khan, M U G
Saba, T
Razzak, Muhammad ImranORCID iD for Razzak, Muhammad Imran orcid.org/0000-0002-3930-6600
Rehman, A
Bahaj, S A
Journal name IEEE Access
Volume number 9
Start page 1
End page 11
Total pages 11
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Publication date 2021
ISSN 2169-3536
Keyword(s) Science & Technology
Technology
Computer Science, Information Systems
Engineering, Electrical & Electronic
Telecommunications
Computer Science
Engineering
COVID-19
Computer vision
Feature extraction
Object detection
Diseases
Task analysis
Filtering algorithms
Convolution neural network
person detection
hotspot zone
fine-tuning
Language eng
DOI 10.1109/access.2021.3094720
Indigenous content off
Field of Research 08 Information and Computing Sciences
09 Engineering
10 Technology
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30153521

Connect to link resolver
 
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
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 27 Abstract Views, 1 File Downloads  -  Detailed Statistics
Created: Tue, 13 Jul 2021, 20:39:24 EST

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.