Intelligent Mobile Coronavirus Recognition Centre Based on IEEE 802.15.4

Authors

  • Abdulkareem Alzahrani Faculty of Computer Science & Information Technology, Albaha University, Saudi Arabia,
  • Khattab M Ali Alheeti Computer Networking Systems Dept., College of Computer Sciences & Information Technology, University of Anbar, Ramadi, Iraq
  • Samer Salah Thabit Almamoon university college, Baghdad, Iraq
  • Duaa Al_Dosary College of Computer Sciences & Information Technology, University of Anbar Ramadi, Iraq,
  • Muzhir Shaban Al-Ani University of Human Development, College of Science and Technology, Department of Information Technology Sulaymaniyah, Iraq

DOI:

https://doi.org/10.3991/ijim.v15i16.24193

Keywords:

Artificial intelligence, COVID-19, Decision tree algorithm, Detection system.

Abstract


The novel coronavirus (COVID-19) has become widespread around the world. It started in Wuhan, China, and has since spread rapidly among people living in other countries. Hence, the World Health Organization has considered COVID-19 as a pandemic that threatens millions of people’s lives. Due to the high number of infected people, many hospitals have been facing critical issues in providing the required medical services. For instance, some clinical centers have been unable to provide one of the most important medical services, namely blood tests to determine whether an individual is infected with COVID-19. Therefore, it is important to present an alternative diagnosis option to prevent the further spread of COVID-19. In this paper, a proposed intelligent detection communication system (IDCS) is configured for distributed mobile clinical centers to control the pandemic. In addition, the intelligent system is integrated with the Zigbee communication protocol to build a mobile COVID-19 detection system. The proposed system was trained on X-ray COVID-19 lung images used to identify infected people. The Zigbee protocol and decision tree algorithm were used to design the IDCS. The results of the proposed system show high accuracy 94.69% and accept results according to the performance measurements.

Author Biographies

Abdulkareem Alzahrani, Faculty of Computer Science & Information Technology, Albaha University, Saudi Arabia,

Faculty of Computer Science & Information Technology, Albaha University, Saudi Arabia,

Khattab M Ali Alheeti, Computer Networking Systems Dept., College of Computer Sciences & Information Technology, University of Anbar, Ramadi, Iraq

Computer Networking Systems Dept., College of Computer Sciences &

Information Technology, University of Anbar, Ramadi, Iraq

Samer Salah Thabit, Almamoon university college, Baghdad, Iraq

Almamoon university college, Baghdad, Iraq

Duaa Al_Dosary, College of Computer Sciences & Information Technology, University of Anbar Ramadi, Iraq,

College of Computer Sciences & Information Technology, University of Anbar

Ramadi, Iraq,

Muzhir Shaban Al-Ani, University of Human Development, College of Science and Technology, Department of Information Technology Sulaymaniyah, Iraq

University of Human Development, College of Science and Technology,

Department of Information Technology Sulaymaniyah, Iraq

Downloads

Published

2021-08-23

How to Cite

Alzahrani, A., M Ali Alheeti, K., Salah Thabit, S., Al_Dosary, D., & Shaban Al-Ani, M. (2021). Intelligent Mobile Coronavirus Recognition Centre Based on IEEE 802.15.4. International Journal of Interactive Mobile Technologies (iJIM), 15(16), pp. 4–15. https://doi.org/10.3991/ijim.v15i16.24193

Issue

Section

Papers