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Applications of artificial intelligence in battling against covid-19: A literature review
M Tayarani - Chaos, Solitons and Fractals, 2020 - researchprofiles.herts.ac.uk
Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2
(SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of …
(SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of …
Data sources and approaches for building occupancy profiles at the urban scale–A review
Buildings' occupant profiles at the urban scale play an important role in various applications
like Urban Building Energy Modeling (UBEM) and assessing energy consumption patterns …
like Urban Building Energy Modeling (UBEM) and assessing energy consumption patterns …
Attentive gated graph sequence neural network-based time-series information fusion for financial trading
With the advances in financial technology (FinTech) in recent years, the finance industry has
attempted to enhance the efficiency of their services through technology. The financial …
attempted to enhance the efficiency of their services through technology. The financial …
Machine learning applications for COVID-19: a state-of-the-art review
The COVID-19 pandemic has galvanized the machine learning community to create new
solutions that can help in the fight against the virus. The body of literature related to …
solutions that can help in the fight against the virus. The body of literature related to …
An algorithm to build synthetic temporal contact networks based on close-proximity interactions data
Small populations (eg, hospitals, schools or workplaces) are characterised by high contact
heterogeneity and stochasticity affecting pathogen transmission dynamics. Empirical …
heterogeneity and stochasticity affecting pathogen transmission dynamics. Empirical …
Spatio-temporal-frequency graph attention convolutional network for aircraft recognition based on heterogeneous radar network
This article proposes a knowledge-and data-driven graph neural network-based
collaboration learning model for reliable aircraft recognition in a heterogeneous radar …
collaboration learning model for reliable aircraft recognition in a heterogeneous radar …
Forecasting infections with spatio-temporal graph neural networks: a case study of the Dutch SARS-CoV-2 spread
VM Croft, SCJL van Iersel, C Della Santina - Frontiers in Physics, 2023 - frontiersin.org
The spread of an epidemic over a population is influenced by a multitude of factors having
both spatial and temporal nature, which are hard to completely capture using first principle …
both spatial and temporal nature, which are hard to completely capture using first principle …
Leveraging deep-learning and unconventional data for real-time surveillance, forecasting, and early warning of respiratory pathogens outbreak
Background Controlling re-emerging outbreaks such as COVID-19 is a critical concern to
global health. Disease forecasting solutions are extremely beneficial to public health …
global health. Disease forecasting solutions are extremely beneficial to public health …
Spatiotemporal modeling of multivariate signals with graph neural networks and structured state space models
Multivariate signals are prevalent in various domains, such as healthcare, transportation
systems, and space sciences. Modeling spatiotemporal dependencies in multivariate …
systems, and space sciences. Modeling spatiotemporal dependencies in multivariate …
Health crowd sensing and computing: from crowdsourced digital health footprints to population health intelligence
J Wang, L Chen, X Wang - Mobile Crowdsourcing: From Theory to Practice, 2023 - Springer
Population health monitoring and modelling is important and fundamental for public health
operations for the control and intervention of Non-Communicable Diseases (NCD) …
operations for the control and intervention of Non-Communicable Diseases (NCD) …