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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 11, 2020.
Abstract: Coronavirus is a pandemic disease spreading from human-to-human rapidly all over the world. This virus is origin from common cold to severe disease such as MERS-CoV and SARS-CoV. Initially it was identified in China, December 2019. The main aim of this research is used to identify the COVID-19 transmission risks assessment from human-to-human within a cluster. The agent-based weighted clustering approach is used to identify the corona virus infected people rapidly within a cluster. In the weighted clustered approach, the normal agents are consisted as susceptible node and the corona virus infected people are considered as malicious node. The Cluster Head (CH) is elected based upon some weighting factors and the trust value is evaluated for all the agents within the cluster. The cluster head were periodically transfers the malicious node information to all other nodes within the cluster. Finally, the agent-based weighted clustering machine learning model approach is used to identify the number of corona virus infected people within the cluster.
P. Vidya Sagar, T. Pavan Kumar, G. Krishna Chaitanya and Moparthi Nageswara Rao, “COVID-19 Transmission Risks Assessment using Agent-Based Weighted Clustering Approach” International Journal of Advanced Computer Science and Applications(IJACSA), 11(11), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0111167
@article{Sagar2020,
title = {COVID-19 Transmission Risks Assessment using Agent-Based Weighted Clustering Approach},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0111167},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0111167},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {11},
author = {P. Vidya Sagar and T. Pavan Kumar and G. Krishna Chaitanya and Moparthi Nageswara Rao}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.