TY - JOUR
AU - Hossain, M. Shamim
AU - Guizani, Nadra
AU - Rayes, Ammar
AU - Leung, Victor C. M.
AU - Wang, Honggang
AU - Wang, Cheng-Xiang
PY - 2021
DA - May
TI - Guest Editorial: AI-Enabled Networking Technologies for Tackling Epidemic Diseases
JO - IEEE Network
EP - 13
VL - 35
IS - 3
DO - https://doi.org/10.1109/MNET.2021.9454555
AB - With the outbreak of the coronavirus COVID-19 pandemic, the whole world has been facing the greatest challenge of a global health crisis. This crisis puts a heavy burden on the network community with regards to unprecedented challenges such as massive network data traffic and resource optimization. The next-generation networking (NGN) technologies (5G, B5G, and the upcoming 6G) driven by artificial intelligence (AI) and machine learning (ML) has the potential to address these challenges by providing powerful computational processing, ultra-massive machine-type communications with ultra-low latency along with a very high bitrate. The AI algorithms/techniques have huge potential for handling the massive volume of pandemic data, predicting the live pandemic crisis and initiating new research directions to have better network insights to tackle serious threats that effect the global community.
PB - IEEE
UR - https://researchportal.hw.ac.uk/en/publications/7d799959-050d-4030-8c13-64a910b41895
KW - Coronavirus (COVID-19)
KW - Digital health and technology
ER