Covid 19
COVID-19 research continues to explore the pandemic's multifaceted impact, focusing on accurate prediction of disease severity and mortality, effective diagnosis and treatment strategies, and understanding the spread of misinformation. Current research leverages machine learning, particularly deep learning models like convolutional neural networks and large language models, to analyze diverse data sources including chest X-rays, blood test parameters, and social media posts. These efforts aim to improve clinical decision-making, enhance public health interventions, and ultimately mitigate the long-term consequences of the pandemic.
Papers
Additional Look into GAN-based Augmentation for Deep Learning COVID-19 Image Classification
Oleksandr Fedoruk, Konrad Klimaszewski, Aleksander Ogonowski, Michał Kruk
Interpreting Time Series Transformer Models and Sensitivity Analysis of Population Age Groups to COVID-19 Infections
Md Khairul Islam, Tyler Valentine, Timothy Joowon Sue, Ayush Karmacharya, Luke Neil Benham, Zhengguang Wang, Kingsley Kim, Judy Fox
Open-source data pipeline for street-view images: a case study on community mobility during COVID-19 pandemic
Matthew Martell, Nick Terry, Ribhu Sengupta, Chris Salazar, Nicole A. Errett, Scott B. Miles, Joseph Wartman, Youngjun Choe
Secure Federated Learning Approaches to Diagnosing COVID-19
Rittika Adhikari, Christopher Settles
UCE-FID: Using Large Unlabeled, Medium Crowdsourced-Labeled, and Small Expert-Labeled Tweets for Foodborne Illness Detection
Ruofan Hu, Dongyu Zhang, Dandan Tao, Huayi Zhang, Hao Feng, Elke Rundensteiner
Exploring a Hybrid Deep Learning Framework to Automatically Discover Topic and Sentiment in COVID-19 Tweets
Khandaker Tayef Shahriar, Iqbal H. Sarker