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AI-assisted Emergency Healthcare using Vehicular Network and Support Vector Machine.
Senapati, Biswa Ranjan; Khilar, Pabitra Mohan; Dash, Tirtharaj; Swain, Rakesh Ranjan.
  • Senapati BR; Bhubaneswar, India Present Address: Department of CSE, ITER, Siksha 'O' Anusandhan (Deemed to be University).
  • Khilar PM; Bhubaneswar, India Present Address: Department of CSE, ITER, Siksha 'O' Anusandhan (Deemed to be University).
  • Dash T; Rourkela, 769008 India Department of Computer Science & Engineering, National Institute of Technology.
  • Swain RR; K.K. Birla Goa Campus, Goa, 403726 India Department of Computer Science and Information Systems, Birla Institute of Technology and Science Pilani.
Wirel Pers Commun ; 130(3): 1929-1962, 2023.
Article in English | MEDLINE | ID: covidwho-2280498
ABSTRACT
The COVID-19 pandemic has created an emergency across the globe. The number of corona positive and death cases is still rising worldwide. All countries' governments are taking various steps to control the infection of COVID-19. One step to control the coronavirus's spreading is to quarantine. But the number of active cases at the quarantine center is increasing daily. Also, the doctors, nurses, and paramedical staff providing service to the people at the quarantine center are getting infected. This demands the automatic and regular monitoring of people at the quarantine center. This paper proposed a novel and automated method for monitoring people at the quarantine center in two phases. These are the health data transmission phase and health data analysis phase. The health data transmission phase proposed a geographic-based routing that involves components like Network-in-box, Roadside-unit, and vehicles. An effective route is determined using route value to transmit data from the quarantine center to the observation center. The route value depends on the factors such as density, shortest path, delay, vehicular data carrying delay, and attenuation. The performance metrics considered for this phase are E2E delay, number of network gaps, and packet delivery ratio, and the proposed work performs better than the existing routing like geographic source routing, anchor-based street traffic aware routing, Peripheral node based GEographic DIstance Routing . The analysis of health data is done at the observation center. In the health data analysis phase, the health data is classified into multi-class using a support vector machine. There are four categories of health data normal, low-risk, medium-risk, and high-risk. The parameters used to measure the performance of this phase are precision, recall, accuracy, and F-1 score. The overall testing accuracy is found to be 96.8%, demonstrating strong potential for our technique to be adopted in practice.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Observational study / Prognostic study Language: English Journal: Wirel Pers Commun Year: 2023 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Observational study / Prognostic study Language: English Journal: Wirel Pers Commun Year: 2023 Document Type: Article