Machine learning and prediction of infectious diseases: a systematic review
OE Santangelo, V Gentile, S Pizzo, D Giordano… - Machine Learning and …, 2023 - mdpi.com
The aim of the study is to show whether it is possible to predict infectious disease outbreaks
early, by using machine learning. This study was carried out following the guidelines of the …
early, by using machine learning. This study was carried out following the guidelines of the …
Artificial intelligence and internet of things (AI-IoT) technologies in response to COVID-19 pandemic: A systematic review
The origin of the COVID-19 pandemic has given overture to redirection, as well as
innovation to many digital technologies. Even after the progression of vaccination efforts …
innovation to many digital technologies. Even after the progression of vaccination efforts …
Deep learning for time series forecasting: Advances and open problems
A time series is a sequence of time-ordered data, and it is generally used to describe how a
phenomenon evolves over time. Time series forecasting, estimating future values of time …
phenomenon evolves over time. Time series forecasting, estimating future values of time …
Applications and challenges of federated learning paradigm in the big data era with special emphasis on COVID-19
A Majeed, X Zhang, SO Hwang - Big Data and Cognitive Computing, 2022 - mdpi.com
Federated learning (FL) is one of the leading paradigms of modern times with higher privacy
guarantees than any other digital solution. Since its inception in 2016, FL has been …
guarantees than any other digital solution. Since its inception in 2016, FL has been …
On the adoption of modern technologies to fight the COVID-19 pandemic: a technical synthesis of latest developments
A Majeed, X Zhang - COVID, 2023 - mdpi.com
In the ongoing COVID-19 pandemic, digital technologies have played a vital role to minimize
the spread of COVID-19, and to control its pitfalls for the general public. Without such …
the spread of COVID-19, and to control its pitfalls for the general public. Without such …
FairTL: A Transfer Learning Approach for Bias Mitigation in Deep Generative Models
CTH Teo, M Abdollahzadeh… - IEEE Journal of Selected …, 2024 - ieeexplore.ieee.org
This work studies fair generative models. We reveal and quantify the biases in state-of-the-
art (SOTA) GANs wrt different sensitive attributes. To address the biases, our main …
art (SOTA) GANs wrt different sensitive attributes. To address the biases, our main …
A comprehensive review of artificial intelligence in prevention and treatment of COVID-19 pandemic
The unprecedented outbreak of the Corona Virus Disease 2019 (COVID-19) pandemic has
seriously affected numerous countries in the world from various aspects such as education …
seriously affected numerous countries in the world from various aspects such as education …
Joint representation learning with generative adversarial imputation network for improved classification of longitudinal data
Generative adversarial networks (GANs) have demonstrated their effectiveness in
generating temporal data to fill in missing values, enhancing the classification performance …
generating temporal data to fill in missing values, enhancing the classification performance …
Using epidemic modeling, machine learning and control feedback strategy for policy management of COVID-19
Coronavirus disease (COVID-19) is one of the world's most challenging pandemics,
affecting people around the world to a great extent. Previous studies investigating the …
affecting people around the world to a great extent. Previous studies investigating the …
MPSTAN: Metapopulation-Based Spatio–Temporal Attention Network for Epidemic Forecasting
Accurate epidemic forecasting plays a vital role for governments to develop effective
prevention measures for suppressing epidemics. Most of the present spatio–temporal …
prevention measures for suppressing epidemics. Most of the present spatio–temporal …