Intelligent Mobile Coronavirus Recognition Centre Based on IEEE 802.15.4

• Main Authors: Abdulkareem Alzahrani, Khattab M Ali Alheeti, Samer Salah Thabit, Duaa Al_Dosary, Muzhir Shaban Al-Ani
• Format: Article
• Language: English
• Published: International Association of Online Engineering (IAOE) 2021-08-01
• Series: International Journal of Interactive Mobile Technologies
• Subjects: artificial intelligence, covid-19, decision tree algorithm, detection system.
• Online Access: https://online-journals.org/index.php/i-jim/article/view/24193

<p class="0abstract">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.</p>