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10.1007/s42979-020-00290-0 http://scihub22266oqcxt.onion/10.1007/s42979-020-00290-0 33063052!7427696!33063052     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       SN+Comput+Sci 2020 ; 1 (5): 271 Nephropedia Template TP {{tp|p=33063052|t=2020. A Smartphone Enabled Approach to Manage COVID-19 Lockdown and Economic Crisis |pdf=|usr=}}{{33063052}} gab.com Text A Smartphone Enabled Approach to Manage COVID-19 Lockdown and Economic Crisis JO: SN Comput Sci http://www.kidney.de/pget.php?&tw=1&pmid=33063052 #FOAvip The emergence of novel COVID-19 causes an over-load in health system and high mortality rate. The key priority is to contain the epidemic and prevent the infection rate. In this context, many countries are now in some degree of lockdown to ensure extreme social distancing of entire population and hence slowing down the epidemic spread. Furthermore, authorities use case quarantine strategy and manual second/third contact-tracing to contain the COVID-19 disease. However, manual contact-tracing is time-consuming and labor-intensive task which tremendously over-load public health systems. In this paper, we developed a smartphone-based approach to automatically and widely trace the contacts for confirmed COVID-19 cases. Particularly, contact-tracing approach creates a list of individuals in the vicinity and notifying contacts or officials of confirmed COVID-19 cases. This approach is not only providing awareness to individuals they are in the proximity to the infected area, but also tracks the incidental contacts that the COVID-19 carrier might not recall. Thereafter, we developed a dashboard to provide a plan for policymakers on how lockdown/mass quarantine can be safely lifted, and hence tackling the economic crisis. The dashboard used to predict the level of lockdown area based on collected positions and distance measurements of the registered users in the vicinity. The prediction model uses k-means algorithm as an unsupervised machine learning technique for lockdown management. Twit Text FOAVip A Smartphone Enabled Approach to Manage COVID-19 Lockdown and Economic Crisis ????SN Comput Sci http://www.kidney.de/pget.php?&tw=1&pmid=33063052 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33063052 #FOAvip English Wikipedia <ref>{{Cite pmid|33063052}}</ref> A Smartphone Enabled Approach to Manage COVID-19 Lockdown and Economic Crisis #MMPMID33063052 Maghdid HS; Ghafoor KZ SN Comput Sci 2020[]; 1 (5): 271 PMID33063052show ga The emergence of novel COVID-19 causes an over-load in health system and high mortality rate. The key priority is to contain the epidemic and prevent the infection rate. In this context, many countries are now in some degree of lockdown to ensure extreme social distancing of entire population and hence slowing down the epidemic spread. Furthermore, authorities use case quarantine strategy and manual second/third contact-tracing to contain the COVID-19 disease. However, manual contact-tracing is time-consuming and labor-intensive task which tremendously over-load public health systems. In this paper, we developed a smartphone-based approach to automatically and widely trace the contacts for confirmed COVID-19 cases. Particularly, contact-tracing approach creates a list of individuals in the vicinity and notifying contacts or officials of confirmed COVID-19 cases. This approach is not only providing awareness to individuals they are in the proximity to the infected area, but also tracks the incidental contacts that the COVID-19 carrier might not recall. Thereafter, we developed a dashboard to provide a plan for policymakers on how lockdown/mass quarantine can be safely lifted, and hence tackling the economic crisis. The dashboard used to predict the level of lockdown area based on collected positions and distance measurements of the registered users in the vicinity. The prediction model uses k-means algorithm as an unsupervised machine learning technique for lockdown management. äA Smartphone Enabled Approach to Manage COVID-19 Lockdown and Economic(epmc).pdf DeepDyve Pubget Overpricing