COVID-19
BIMCV-COVID19+ dataset is a large dataset with chest X-ray images CXR (CR, DX) and computed tomography (CT) imaging of COVID-19 patients along with their radiographic findings, pathologies, polymerase chain reaction (PCR), immunoglobulin G (IgG) and immunoglobulin M (IgM) diagnostic antibody tests and radiographic reports from Medical Imaging Databank in Valencian Region Medical Image Bank (BIMCV).
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Supplementary data and source code for vaccine allocation study
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This dataset (MegaGeoCOV Extended), which is an extended version of MegaGeoCOV, was introduced in this paper: A Twitter narrative of the COVID-19 pandemic in Australia (the paper will appear in proceedings of the 20th ISCRAM conference, Omaha, Nebraska, USA May 2023). Please refer to the paper for more details (e.g., keywords and hashtags used, descriptive statistics, etc.).
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124 Views
BIMCV-COVID19+ dataset is a large dataset with chest X-ray images CXR (CR, DX) and computed tomography (CT) imaging of COVID-19 patients along with their radiographic findings, pathologies, polymerase chain reaction (PCR), immunoglobulin G (IgG) and immunoglobulin M (IgM) diagnostic antibody tests and radiographic reports from Medical Imaging Databank in Valencian Region Medical Image Bank (BIMCV).
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In order to explore the influencing factors of college students' learning intention on online teaching video during the pandemic, this article uses empirical research on college students from four aspects including performance expectancy, effort expectancy, social influence and facilitating conditions.
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BillionCOV is a global billion-scale English-language COVID-19 tweets dataset with more than 1.4 billion tweets originating from 240 countries and territories between October 2019 and April 2022. This dataset has been curated by hydrating the 2 billion tweets present in COV19Tweets.
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This is a research about a method that I studied to treat virus COVID-19 or any other viruses by modifying and tricking the virus .
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The problem of effective disposal of the trash generated by people has rightfully attracted major interest from various sections of society in recent times. Recently, deep learning solutions have been proposed to design automated mechanisms to segregate waste. However, most datasets used for this purpose are not adequate. In this paper, we introduce a new dataset, TrashBox, containing 17,785 images across seven different classes, including medical and e-waste classes which are not included in any other existing dataset.
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