A Comprehensive Analysis of Approaches for Sentiment Analysis Using Twitter Data on COVID-19 Vaccines

Sentiment Analysis has paved routes for opinion analysis of masses over unrestricted territorial limits. With the advent and growth of social media like Twitter, Facebook, WhatsApp, Snapchat in today’s world, stakeholders and the public often takes to ex-pressing their opinion on them and drawing conclusions. While these social media data are extremely informative and well connected, the major challenge lies in incorporating efficient Text Classification strategies which not only overcomes the unstructured and humongous nature of data but also generates correct polarity of opinions (i.e. positive, negative, and neutral) . This paper is a thorough effort to provide a brief study about various approaches to SA including Machine Learning, Lexicon Based, and Automatic Approaches. The paper also highlights the comparison of positive, negative, and neu-tral tweets of the Sputnik V, Moderna, and Covaxin vaccines used for preventive and emergency use of COVID-19 disease

A Review on the use of Artificial Intelligence Techniques in Brain MRI Analysis

Over the past 20 years, the global research going on in Artificial Intelligence in applica-tions in medication is a venue internationally, for medical trade and creating an ener-getic research community. The Artificial Intelligence in Medicine magazine has posted a massive amount. This paper gives an overview of the history of AI applications in brain MRI analysis to research its effect at the wider studies discipline and perceive de-manding situations for its destiny. Analysis of numerous articles to create a taxono-my of research subject matters and results was done. The article is classed which might be posted between 2000 and 2018 with this taxonomy. Analyzed articles have excessive citations. Efforts are useful in figuring out popular studies works in AI primarily based on mind MRI analysis throughout specific issues. The biomedical prognosis was ruled by way of knowledge engineering research in its first decade, whilst gadget mastering, and records mining prevailed thereafter. Together these two topics have contributed a lot to the latest medical domain

Facebook an Anti-Stereotyping Tool: A Case Study

Facebook, the most popular social media (SM) platform has penetrated every nook and corner of the world. SM is now treated as the ‘fifth Estate’, other than legislative, executive, judiciary, and mainstream media. The power of SM as a critique is widely acknowledged. Establishments are finding it difficult to deal with it at times. Due to its ease of usage and relative anonymity, the general public finds it very convenient to put across their viewpoints, even if it’s against the establishment. Some establishments at times are at loggerheads with champions of freedom of speech including civil rights activists. SM has been used for propaganda, marketing, and awareness campaigns. In this paper, we are proposing to use this powerful tool towards social change. Through a case study, a detailed process is being proposed for using social media particularly Facebook as an anti-stereotyping tool. The response to an online survey, the outcome of opinion min-ing, and the enthusiastic response to our case study by the targeted audience validate our hypothesis that Facebook can be effectively utilized as an anti-stereotyping tool

Deep learning and knowledge graph for image/video captioning: A review of datasets, evaluation metrics, and methods

Abstract Generating an image/video caption has always been a fundamental problem of Artificial Intelligence, which is usually performed using the potential of Deep Learning Methods, Computer Vision, Knowledge Graphs, and Natural Language Processing (NLP). The significant task of image/video captioning is to describe visual content in terms of natural language. Due to a semantic gap, this presents a massive problem in understanding and explaining images or videos syntactically and semantically. The current systems need somewhere to fill the gap between low‐level and high‐level features while mapping. Therefore, to tackle this problem, there is a need to describe the latest research and methods to overcome difficulties and to propose effective solutions. This work thoroughly analyses and investigates the most related methods (deep learning and knowledge graph‐based approaches), benchmark datasets, and evaluation metrics with their benefits and limitations. Here we have also reviewed the state‐of‐the‐art methods related to image/video captioning and their applications in the current scenario. Finally, we provide thorough information on existing research with comparisons of results on benchmark datasets. We have also mentioned the existing challenges and future direction of research

Increased Levels of Autoantibodies against ROS-Modified Proteins in Depressed Individuals with Decrease in Antibodies against SARS-CoV-2 Antigen (S1-RBD)

Coronavirus 2019 (COVID-19) disease management is highly dependent on the immune status of the infected individual. An increase in the incidence of depression has been observed during the ongoing COVID-19 pandemic. Autoantibodies against in vitro reactive oxygen species (ROS) modified BSA and Lys as well as antibodies against receptor binding domain subunit S1 (S1-RBD) (S1-RBD-Abs) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were estimated using direct binding and competition ELISA. Serum samples were also tested for fasting blood glucose (FBG), malondialdehyde (MDA), carbonyl content (CC), interferon-gamma (IFN-γ) and tumor necrosis factor-alpha (TNF-α). Significant structural changes were observed in ROS modified BSA and Lys. Female depressed subjects who were also smokers (F-D-S) showed the highest levels of oxidative stress (MDA and CC levels). Similarly, increased levels of autoantibodies against ROS modified proteins were detected in F-D-S subjects, in males who were depressed and in smokers (M-D-S) compared to the other subjects from the rest of the groups. However, contrary to this observation, levels of S1-RBD-Abs were found to be lowest in the F-D-S and M-D-S groups. During the pandemic, large numbers of individuals have experienced depression, which may induce excessive oxidative stress, causing modifications in circulatory proteins. Thus, the formation of neo-antigens is induced, which lead to the generation of autoantibodies. The concomitant effect of increased autoantibodies with elevated levels of IFN-γ and TNF-α possibly tilt the immune balance toward autoantibody generation rather than the formation of S1-RBD-Abs. Thus, it is important to identify individuals who are at risk of depression to determine immune status and facilitate the better management of COVID-19