Regret, Delete, (Do Not) Repeat: An Analysis of Self-Cleaning Practices on Twitter After the Outbreak of the COVID-19 Pandemic

During the outbreak of the COVID-19 pandemic, many people shared their symptoms across Online Social Networks (OSNs) like Twitter, hoping for others' advice or moral support. Prior studies have shown that those who disclose health-related information across OSNs often tend to regret it and delete their publications afterwards. Hence, deleted posts containing sensitive data can be seen as manifestations of online regrets. In this work, we present an analysis of deleted content on Twitter during the outbreak of the COVID-19 pandemic. For this, we collected more than 3.67 million tweets describing COVID-19 symptoms (e.g., fever, cough, and fatigue) posted between January and April 2020. We observed that around 24% of the tweets containing personal pronouns were deleted either by their authors or by the platform after one year. As a practical application of the resulting dataset, we explored its suitability for the automatic classification of regrettable content on Twitter. © 2023 Owner/Author.

COVID-LiteNet: A lightweight CNN based network for COVID-19 detection using X-ray images

To restrict the virus's transmission in the pandemic and lessen the strain on the healthcare industry, computer-assisted diagnostics for the accurate and speedy diagnosis of coronavirus illness (COVID-19) has become a prerequisite. Compared to other types of imaging and detection, chest X-ray imaging (CXR) provides several advantages. Healthcare practitioners may profit from any technology instrument providing quick and accurate COVID-19 infection detection. COVID-LiteNet is a technique suggested in this paper that combines white balance with Contrast Limited Adaptive Histogram Equalization (CLAHE) and a convolutional neural network (CNN). White balance is employed as an image pre-processing step in this approach, followed by CLAHE, to improve the visibility of CXR images, and CNN is trained using sparse categorical cross-entropy for image classification tasks and gives the smaller parameters file size, i.e., 2.24 MB. The suggested COVID-LiteNet technique produced better results than vanilla CNN with no pre-processing. The proposed approach outperformed several state-of-the-art methods with a binary classification accuracy of 98.44 percent and a multi-class classification accuracy of 97.50 percent. COVID-LiteNet, the suggested technique, outperformed the competition on various performance parameters. COVID-LiteNet may help radiologists discover COVID-19 patients from CXR pictures by providing thorough model interpretations, cutting diagnostic time significantly. © 2023 IEEE.

Detection of Covid-19 using Mask R-CNN

Deep learning has been widely used to analyze radiographic pictures such as chest scans. These radiographic pictures include a wealth of information, including patterns and cluster-like formations, which aid in the discovery and conformance of COVID-19-like pandemics. The COVID-19 pandemic is wreaking havoc on global well-being and public health. Until present, more than 27 million confirmed cases have been recorded globally. Due to the increasing number of confirmed cases and issues with COVID-19 variants, fast and accurate categorization of healthy and infected individuals is critical for COVID-19 management and treatment. In medical image analysis and classification, artificial intelligence (AI) approaches in general, and region-based convolutional neural networks (CNNs) in particular, have yielded promising results. In this study, a deep Mask R-CNN architecture based on chest image classification is suggested for the diagnosis of COVID-19. An effective and reliable Mask R-CNN classification was difficult due to a lack of sufficient size and high-quality chest image datasets. These complications are addressed with Mask Region-based convolutional neural networks (R-CNNs) as a framework for detecting COVID-19 patients from chest pictures using an open-source dataset. First, the model was evaluated using 100 photos from the original processed dataset, and it was found to be accurate. The model was then validated against an independent dataset of COVID-19 X-ray pictures. The suggested model outperformed all other models in general and specifically when tested using an independent testing set. © 2023 IEEE.

Analysis of the Impacts of COVID-19 using Deep Convolutional Neural Network

COVID-19 is the transmittable disease that emerged as a recent epidemic and threatened the lives of various people. The emerged pandemic initiated a change in the people's routine and impacted a serious financial crisis. This initiated a necessity for developing a deeper insight of the COVID-19 disease and multiple researches are performed based on the COVID-19 epidemic, which possess the challenges of basic analysis of information about the disease, lack of data, lack of knowledge about the parameters that cause disease and to overcome this a deep COVID-19 analysis epidemic via the deep CNN classifier is accomplished in the research. The impact of the disease is examined based on the gender, age group, symptoms and outbreak of the disease. This analysis provides comprehensive information about the disease and helps in making the preventive measures, which will greatly reduce the impacts of the disease. The accomplishment of deep CNN instinctively analyzes the essential features needed for the classification that helps in reducing the effort and time of the individuals. The performance is analyzed with the metrics specificity, accuracy and sensitivity, which obtained values of 0.48 %, 0.27 %, 2.82 % corresponding to and 2.88 %, 1.5 %, 0.36% considering training percentage, which is more efficient. © 2023 IEEE.

Determinants of the Ability to Identify Fake News Among Seniors in Portugal: A Proposal

The COVID-19 pandemic boosted the production and circulation of false information, especially online, leading the World Health Organization to classify this phenomenon as an infodemic, i.e., a misinformation epidemic. In addition to this, the growing aging of the population is a reality not only in Portugal, but throughout the world. The Internet, and in particular social networks, can be an important contribution to the well-being of the elderly, reducing their social isolation. However, it makes them even more susceptible to the consumption of false information. Considering the increasing contact with fake news, it is important to evaluate the determinants of the ability of the elderly to identify fake news. In this article we present a research proposal with a quantitative methodology, based on a hypothetical-deductive process, supported by a self-administered online questionnaire survey for data collection, to meet this objective. © 2022 Associacao Portuguesa de Sistemas de Informacao. All rights reserved.

Machine Learning Model to Study the Genetic Susceptibility of Patients with Sever Coronavirus Symptoms

COVID 19 is constantly changing properties because of its contagious as an urgent global challenge, and there are no vaccines or effective drugs. Smart model used to measure and prevent the spread of COVID 19 continues to provide health care services is an urgent need. Previous methods to identify severe symptoms of coronavirus in the early stages, but they have failed to predict the symptoms of coronavirus in an accurate way and also take more time. To overcome these issues the effective severe coronavirus symptoms techniques are proposed. Initially, Gradient Conventional Recursive Neural Classifier based classification and Linear Discriminant Genetic Algorithm used feature selection, mutation, and cross-analysis of features of coronary symptoms. These methods are used to select optimized features and selected features, and then classified by neural network. This Gradient Conventional Recursive Neural Classifier selects features based on the correlation between features that reduce irrelevant features involved in the identification process of coronary symptoms. Gradient Conventional Recursive Neural Classifier based on each function, helping to maximize the correlation between the prediction accuracy of coronavirus symptoms. For this reason, it has always been recommended in an effort to increase the accuracy and reliability of diagnostics to use machine learning to design different classification models. © 2023 IEEE.