Predicting COVID-19 using lioness optimization algorithm and graph convolution network-Reference-Cited by-同舟云学术

Predicting COVID-19 using lioness optimization algorithm and graph convolution network

Published:2023-01-09 Issue:9 Volume:27 Page:5437-5501
ISSN:1432-7643
Container-title:Soft Computing
language:en
Short-container-title:Soft Comput

Author:

Li Dong,Ren Xiaofei,Su Yunze

Funder

Scientific Research Program Funded by Shaanxi Provincial Education Department

Postgraduate Innovation Fund Project of Xi'an University of Posts and Telecommunications

Publisher

Springer Science and Business Media LLC

Subject

Geometry and Topology,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s00500-022-07778-2.pdf

Reference72 articles.

1. Alessa A, Faezipour M (2018) A review of influenza detection and prediction through social networking sites. Theor Biol Med Model. https://doi.org/10.1186/s12976-017-0074-5

2. Arthur CK, Temeng VA, Ziggah YY (2020) Novel approach to predicting blast-induced ground vibration using Gaussian process regression. Eng Comput 36:29–42. https://doi.org/10.1007/s00366-018-0686-3

3. Askari Q, Saeed M, Younas I (2020) Heap-based optimizer inspired by corporate rank hierarchy for global optimization. Expert Syst Appl 161:113702. https://doi.org/10.1016/j.eswa.2020.113702

4. Aslan IH, Demir M, Wise MM et al (2022) Modeling COVID-19: forecasting and analyzing the dynamics of the outbreaks in Hubei and Turkey. Math Methods Appl Sci 45(10):6481–6494. https://doi.org/10.1002/mma.8181

5. Bi J, Wang XM, Hu YY et al (2021) A method for dynamic risk assessment and prediction of public health emergencies based on an improved SEIR model: novel coronavirus COVID-19 in ten European countries. J Geo-Inform Sci 23(02):259–273

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Advances in the Development of Representation Learning and Its Innovations against COVID-19;COVID;2023-09-13