ABSTRACT
Due to their need to be connected to the rest of the world, people started to use social networks extensively to share their feelings and be informed, especially during the Covid-19 pandemic and its lockdown. The tremendous growth of content in social media increased the frequency of researchers' work on natural language understanding, text classification, and information retrieval. Unfortunately, not all languages have benefited equally from this interest. Arabic is an example of such languages. The main reason behind this gap is the limited number of datasets that addressed Covid-19-related topics. To this aim, we performed the first-of-its-kind systematic review that covered, to the best of our knowledge, the most Arabic Covid-19 datasets freely available or access granted upon request. This paper presents these 15 datasets alongside their features and the type of analysis conducted. The general concern of the authors is to direct researchers to reliable and freely available datasets that advance the progress of Arabic Covid-19-related studies. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Due to their need to be connected to the rest of the world, people started to use social networks extensively to share their feelings and be informed, especially during the Covid-19 pandemic and its lockdown. The tremendous growth of content in social media increased the frequency of researchers' work on natural language understanding, text classification, and information retrieval. Unfortunately, not all languages have benefited equally from this interest. Arabic is an example of such languages. The main reason behind this gap is the limited number of datasets that addressed Covid-19-related topics. To this aim, we performed the first-of-its-kind systematic review that covered, to the best of our knowledge, the most Arabic Covid-19 datasets freely available or access granted upon request. This paper presents these 15 datasets alongside their features and the type of analysis conducted. The general concern of the authors is to direct researchers to reliable and freely available datasets that advance the progress of Arabic Covid-19-related studies. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The COVID-19 Open Research Dataset (CORD-19) is a growing resource of scientific papers on COVID-19 and related historical coronavirus research. CORD-19 is designed to facilitate the development of text mining and information retrieval systems over its rich collection of metadata and structured full text papers. Since its release, CORD-19 has been downloaded over 200K times and has served as the basis of many COVID-19 text mining and discovery systems. In this article, we describe the mechanics of dataset construction, highlighting challenges and key design decisions, provide an overview of how CORD-19 has been used, and describe several shared tasks built around the dataset. We hope this resource will continue to bring together the computing community, biomedical experts, and policy makers in the search for effective treatments and management policies for COVID-19. © ACL 2020.All right reserved.
ABSTRACT
As we see coronavirus is the very dangerous diseases and to identify this diseases in one's body is also not as easy. So during identification of diseases there are many false positive cases we see that person does not have corona and still the prediction comes true and also in some cases, it happens that person has corona but it does not get detected (false negative case). So due to this problem, we here come up with the two approaches and make comparison between these two approaches and decide which one is better to analyze the diseases in the body. We are using CNN to scan chest X-ray dataset and ML algorithms for tabular dataset as it contains many text information too. So in this project, we explain in detail, what is CNN, what is ML, how to implement CNN and ML algorithms on particular dataset, what output we will get as a comparison. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
As we see coronavirus is the very dangerous diseases and to identify this diseases in one’s body is also not as easy. So during identification of diseases there are many false positive cases we see that person does not have corona and still the prediction comes true and also in some cases, it happens that person has corona but it does not get detected (false negative case). So due to this problem, we here come up with the two approaches and make comparison between these two approaches and decide which one is better to analyze the diseases in the body. We are using CNN to scan chest X-ray dataset and ML algorithms for tabular dataset as it contains many text information too. So in this project, we explain in detail, what is CNN, what is ML, how to implement CNN and ML algorithms on particular dataset, what output we will get as a comparison. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.