TY - JOUR
T1 - Enhancement of COVID-19 Detection by Unravelling its Structure and Selecting the Optimal Attributes
AU - Andreas, Andreou
AU - Mavromoustakis, Constandinos X.
AU - Mastorakis, George
AU - Batalla, Jordi Mongay
AU - Sahalos, John N.
AU - Pallis, Evangelos
AU - Markakis, Evangelos
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - According to the current unprecedented pandemic, we realise that we cannot respond to every contagion novel virus as fast as possible, either by vaccination or medication. Therefore, it is paramount for the sustainable development of antiviral urban ecosystems to promote early detection, control, and prevention of an outbreak. The structure of an antivirus-based multi-generational smart-city framework could be crucial to a post-COVID-19 urban environment. Humanitarian efforts in the pandemic's framework deployed novel technological solutions based on the Internet of Things (IoT), Machine Learning, Cloud Computing and Artificial Intelligence (AI). We aim to contribute by improving real-time detection using data mining in collaboration with machine learning techniques through our research work. Initially, for detection, we propose an innovative system that could detect in real-time virus propagation based on the density of the airborne COVID-19 molecules-the proposal based on the detection through the isothermal amplification RT-Lamp [1]. We also propose real-time detection by spark-induced plasma spectroscopy during the internal airborne transmission process [17]. The novelty of this research work, called characteristic subset selection, is based on identifying irrelevant data. By deducting the unrelated information dimension, machine learning algorithms would operate more efficiently. Therefore, it optimises data mining and classification in high-dimensional medical data analysis, particularly in effectively detecting COVID-19. It can play an essential role in providing timely detection with critical attributes and high accuracy. We elaborate the teaching-learning method optimisation to achieve the optimal set of features for the detection.
AB - According to the current unprecedented pandemic, we realise that we cannot respond to every contagion novel virus as fast as possible, either by vaccination or medication. Therefore, it is paramount for the sustainable development of antiviral urban ecosystems to promote early detection, control, and prevention of an outbreak. The structure of an antivirus-based multi-generational smart-city framework could be crucial to a post-COVID-19 urban environment. Humanitarian efforts in the pandemic's framework deployed novel technological solutions based on the Internet of Things (IoT), Machine Learning, Cloud Computing and Artificial Intelligence (AI). We aim to contribute by improving real-time detection using data mining in collaboration with machine learning techniques through our research work. Initially, for detection, we propose an innovative system that could detect in real-time virus propagation based on the density of the airborne COVID-19 molecules-the proposal based on the detection through the isothermal amplification RT-Lamp [1]. We also propose real-time detection by spark-induced plasma spectroscopy during the internal airborne transmission process [17]. The novelty of this research work, called characteristic subset selection, is based on identifying irrelevant data. By deducting the unrelated information dimension, machine learning algorithms would operate more efficiently. Therefore, it optimises data mining and classification in high-dimensional medical data analysis, particularly in effectively detecting COVID-19. It can play an essential role in providing timely detection with critical attributes and high accuracy. We elaborate the teaching-learning method optimisation to achieve the optimal set of features for the detection.
KW - coronavirus
KW - covid-19
KW - data mining
KW - machine learning
KW - pandemic
KW - teaching-learning
UR - http://www.scopus.com/inward/record.url?scp=85184578364&partnerID=8YFLogxK
U2 - 10.1109/GLOBECOM46510.2021.9685980
DO - 10.1109/GLOBECOM46510.2021.9685980
M3 - Conference article
AN - SCOPUS:85184578364
SN - 2334-0983
JO - Proceedings - IEEE Global Communications Conference, GLOBECOM
JF - Proceedings - IEEE Global Communications Conference, GLOBECOM
T2 - 2021 IEEE Global Communications Conference, GLOBECOM 2021
Y2 - 7 December 2021 through 11 December 2021
ER -