Hybrid deep learning model based smart IOT based monitoring system for Covid-19
Liping Yu,
M.M. Vijay,
J. Sunil,
V.G. Anisha Gnana Vincy,
Vediyappan Govindan,
M. Ijaz Khan,
Shahid Ali,
Nissren Tamam,
Barno Sayfutdinovna Abdullaeva
Affiliations
Liping Yu
School of Computer Science and Technology, Shandong Technology and Business University, Yantai, 264005, China
M.M. Vijay
SCAD College of Engineering and Technology, Tirunelveli, India
J. Sunil
Department of Computer Science and Engineering, Annai Vailankanni College of Engineering, Kanyakumari, India
V.G. Anisha Gnana Vincy
Department of Computer Science and Engineering, Danish Ahmed College of Engineering, India
Vediyappan Govindan
Department of Mathematics, Hindustan Institute of Technology and Science (Deemed to be University), Padur, Kelambakkam, 603103, India
M. Ijaz Khan
Department of Mechanical Engineering, Lebanese American University, Kraytem, Beirut, 1102-2801, Lebanon; Corresponding author. Lebanese American University, Lebanon.
Shahid Ali
School of Electronics Engineering Peking University, Beijing, China
Nissren Tamam
Department of Physics, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
Barno Sayfutdinovna Abdullaeva
Pedagogical Sciences, Vice-Rector for Scientific Affairs, Tashkent State Pedagogical University, Tashkent, Uzbekistan
Recently, COVID-19 becomes a hot topic and explicitly made people follow social distancing and quarantine practices all over the world. Meanwhile, it is arduous to visit medical professionals intermittently by the patients for fear of spreading the disease. This IoT-based healthcare monitoring system is utilized by many professionals, can be accessed remotely, and provides treatment accordingly. In context with this, we designed an IoT-based healthcare monitoring system that sophisticatedly measures and monitors the parameters of patients such as oxygen level, blood pressure, temperature, and heart rate. This system can be widely used in rural areas that are linked to the nearest city hospitals to monitor the patients. The collected data from the monitoring system are stored in the cloud-based data storage and for the classification our approach proposes an innovative Recurrent Convolutional Neural Network (RCNN) based Puzzle optimization algorithm (PO). Based on the outcome further treatments are made with the assistance of physicians. Experimental analyses are made and analyzed the performance with state-of-art works. The availability of more data storage capacity in the cloud can make physicians access the previous data effortlessly.
