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11th International Conference on Computer and Knowledge Engineering
Artificial Intelligence applications addressing different aspects of the Covid-19 crisis and key technological solutions for future epidemics control
Authors :
Nadia Khalili
1
Hojatollah Hamidi
2
1- Department of Industrial Engineering, K. N. Toosi University of Technology
2- Group of Information Technology, Department of Industrial Engineering K. N. Toosi University of Technology
Keywords :
Artificial intelligence, Covid-19, Machine learning, pandemics, Data
Abstract :
Artificial intelligence (AI) development and the application of its technology in human life is continuing at an astounding rate. Among the variety of examples of AI being used for a variety of tasks, its contribution to epidemic control has recently captured a great deal of interest at the time of the recent Covid-19 pandemic, a crisis which occurred due to the Coronavirus spread. As the entire world has been concerning over urgent efforts addressing the damaging effects since the outbreak, Artificial Intelligence emerged as a great possible solution. This study is aimed at examining how artificial intelligence gets us ready to combat and control COVID-19 and other future pandemics, as this is unlikely to be the last of the epidemics. Moreover, several key technological solutions are outlined that could help combat future pandemics and make us prepared. As a result, technology development can be promoted to overcome any possible unexpected subsequent crisis.
Papers List
List of archived papers
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Synthetic Trajectory Sharing Indoors under Privacy Constraints
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A Deep Reinforcement Learning Approach Combining Technical and Fundamental Analyses with a Large Language Model for Stock Trading
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Adaptive Hybrid TRCA–CORRCA algorithm for enhanced accuracy in SSVEP-based brain-computer interfaces
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Deep Learning-Driven Beamforming Optimization for High-Performance 5G Planar Antenna Arrays
Rahman Mohammadi - Seyed Reza Razavi Pour
Averting Mode Collapse for Generative Zero-Shot Learning
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A Robust Network for Embedded Traffic Sign Recognation.
Omid Nejati Manzari - Shahriar Baradaran Shokouhi
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