COVID-19

In Indian sub-continent COVID-19 second wave started in early March 2021 and its effect was more lethal than the first wave, the confirmed cases and the death rate was higher than in the first wave. Unlike the national lockdown in 2020, this year different states have started imposing lockdown like restrictions spanning April-June 2021. This paper investigates the sentiments of the people using twitter messages during early period of the second wave. Two-weeks data is manually annotated and several machine learning models were built.
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This dataset is in support of my Research paper 'Detection of Pancreatic,Ovarian & Prostate Tumor, Cancer and Treatment by Ablation'.Due to computer crash, all work, datasets and old papers lost. Re-work may be submitted.
For Machine design, pls refer, open-access page 'Data and Designs of B-Machines'
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Depressive/Non-depressive tweets between December 2019 and December 2020 originated largely from India and parts of Indian subcontinent. Sentiment Scores alloted using text blob. Tweets are extracted specifically keeping in mind the top 250 most frequently used negative lexicons and positive lexicons accesed using SentiWord and various research publications.
Tweet Amount : 1.4 Lakhs
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The CoVID19-FNIR dataset contains news stories related to CoVID-19 pandemic fact-checked by expert fact-checkers. CoVID19-FNIR is a CoVID-19-specific dataset consisting of fact-checked fake news scraped from Poynter and true news from the verified Twitter handles of news publishers. The data samples were collected from India, The United States of America, and European regions and consist of online posts from social media platforms between February 2020 to June 2020. The dataset went through prepossessing steps that include removing special characters and non-vital information.
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Reverse transcription-polymerase chain reaction (RT-PCR) is currently the gold standard in COVID-19 diagnosis. It can, however, take days to provide the diagnosis, and false negative rate is relatively high. Imaging, in particular chest computed tomography (CT), can assist with diagnosis and assessment of this disease. Nevertheless, it is shown that standard dose CT scan gives significant radiation burden to patients, especially those in need of multiple scans.
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The dataset consists of two classes: COVID-19 cases and Healthy cases
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We delicately designed, collected and labelled a realistic audio dataset containing recordings of patients with respiratory diseases, named the Corp Dataset. 168 hours of recordings with 9969 coughs from 42 different patients are included. The dataset is published online on the MARI Lab website (https://mari.tongji.edu.cn/info/1012/1030.htm).
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This datasets contains Xrays of positive COVID-19 and Pneumonia patients.
For the COVID-19 class, three sources were used in this work, BIMCV-COVID-19+ (Spain), COVID-19- AR (USA) and V2-COV19-NII (Germany).
The pneumonia class data came from 3 sources: (i) the National Institute of Health (NIH) dataset, (ii) Chexpert dataset and (iii) Padchest dataset.
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Lung segmentation is essential in developing AI-assisted diagnosis methods. Here is the result of lung segmentation using morphological operation, and it has been used in our study. It contains 7053 CT slices in .jpg format. And the original dataset can be seen via the Kaggle link https://www.kaggle.com/hgunraj/covidxct
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The dataset collects the results of a survey of 325 respondents. Each respondent is asked to design a route from an origin to a destination taking into account the following considerations:
- The route should avoid crowds to avoid getting COVID-19.
- They should take into account the context provided: day, time, month, holiday period.
A total of 10 scenarios located in the city of Ciudad Real were designed.
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