Privacy Concerned on Contact-Tracing Application during COVID-19

The COVID-19 pandemic has greatly affected humanity by destabilizing the world economy through strain on hospital systems and deaths. Medical personnel is working around the clock to establish vaccines. On the other hand, technology contributes to the fight against the virus by tracking COVID-19 infections. Many digital contact tracking smartphone applications have been created to address this epidemic successfully. However, the applications lack transparency, raising worries about their privacy. Contact tracing has been employed to stop the spread of the disease. When battling the coronavirus epidemic, computerized contact tracking has quickly emerged as an essential tool. Therefore, the research conducted in this paper focuses on the challenges of tracking applications to analyze the perspective view of privacy issues. Besides, the paper proposes policies for data privacy to aid in making the tracking applications more effective and successful. © 2022 IEEE.

Question Classification Using Universal Sentence Encoder and Deep Contextualized Transformer

One of the most vital steps in automatic Question Answer systems is Question classification. The Question classification is also known as Answer type classification, identification, or prediction. The precise and accurate identification of answer types can lead to the elimination of irrelevant candidate answers from the pool of answers available for the question. High accuracy of Question Classification phase means highly accurate answer for the given question. This paper proposes an approach, named Question Sentence Embedding(QSE), for question classification by utilizing semantic features. Extracting a large number of features does not solve the problem every time. Our proposed approach simplifies the feature extraction stage by not extracting features such as named entities which are present in fewer questions because of their short length and features such as hypernyms and hyponyms of a word which requires WordNet extension and hence makes the system more external sources dependent. We encourage the use of Universal Sentence Embedding with Transformer Encoder for obtaining sentence level embedding vector of fixed size and then calculate semantic similarity among these vectors to classify questions in their predefined categories. As it is the time of the Global pandemic COVID-19 and people are more curious to ask questions about COVID. So, our experimental dataset is a publicly available COVID-Q dataset. The acquired result highlights an accuracy of 69% on COVID questions. The approach outperforms the baseline method manifesting the efficacy of the QSE method. © 2021 IEEE.

A bibliometric analysis on the influence of social media during the COVID-19 pandemic

There is limited literature that discusses the trends of social media during the COVID-19 pandemic. This bibliometric analysis aims to evaluate the global research growth on "social media and COVID-19" by analysing related publications in the Scopus database. The objectives are to examine the research trends related to social media and COVID-19 as well as details of co-authorship, leading institutions and countries, top scholars, and leading author keywords. The study used the VOS Viewer 1.6.11 software to analyse the collected bibliographic data and visualise the global research trends using bibliometric maps. A total of 1, 994 journal articles from the Scopus database published in 2020-2021 was analysed. The leading countries in social media and COVID-19 research are United States and United Kingdom. Among the 15 leading universities, four can be found in the world's top-40 university ranking. Among the social media platforms, Twitter was found to have the most linkages with social media, which suggests that Twitter is the most frequently used platform to disseminate pandemic information. Based on the author keywords analysis, "older adults" and "health policy" are potential areas of concern that future research could fruitfully explore. This paper has significant implications for healthcare academicians, organisations, and policymakers in understanding the global trends of "social media and COVID-19".

Epidemiology of epidemics in India

In the recent wake of the novel coronavirus outbreak, countries around the world have suffered greatly in terms of economy, health, and loss of lives. To date (5 July 2021), there have been more than 184,562,051 cases, with 3,993,319 deaths and around 168,907,181 recoveries. Not a single country has been spared;India included with 30.5 million cases, 402,000 deaths and 29.7 million recoveries. India is a vast country that has undergone and survived the onslaught of many viruses and epidemics. From the Black Death, Blue Death, Severe Acute Respiratory Syndrome (SARS), dengue and chikungunya fever, meningococcal disease, Japanese encephalitis (JE), avian influenza, Nipah virus and now, COVID-19, India has seen and experienced it all. The country saw the first spike of COVID-19 cases at the beginning of March 2020 with 50 cases recorded daily and by the end of March, the lockdown which was imposed by the Prime Minister, Narendra Modi had been extended five times as the country awaits the number of cases to reach its peak, and then hopefully, a plateau. This paper looks at the string of major pandemics and viruses that have hit this country, the global alerts and responses, the impacts and number of lives lost to all these catastrophes and the rise of pandemic-related misinformation.

Review and evaluation of heart rate monitoring based vital signs, a case Study: Covid-19 Pandemic

The monitoring of vital signs is essential in the clinical setting, including temperature, respiratory, and heart rates. Remote monitoring devices, systems, and services are emerging as vital signs monitoring must be performed daily. Different types of sensors can be used to monitor breathing patterns and frequency. However, the respiratory rate remains the least measured vital sign in many scenarios due to the intrusiveness of most of the sensors adopted. This is not the case with Covid-19, which directly infects the respiratory system. In this paper, we present a state of the art on the different applications that monitor temperature, heart rate, and restorative rate, as well as the evaluation of an algorithm that extracts the heart rate from an RGB camera. © 2021 IEEE.

Monitoring of physiological signs and their impact on the Covid-19 pandemic: Review

Recently, monitoring of physiological signs such as heart rate and respiratory rate is very important, especially when we are talking about pandemics like Covid-19. In this paper we present a state of the art on the different techniques used for heart rate and respiratory rate extraction. These techniques presented will be based on image processing, were traditional sensor-based techniques creating a lot of problem at the contact level between patient and doctor. For this reason, we focus on non-contact techniques to avoid these problems. Generally, the literature review shows that non-contact monitoring techniques are based on RGB, thermal and multispectral cameras, the choice between these different cameras depends on the application that will be used. For example, thermal cameras are dedicated to the prediction of respiratory rate and temperature, while RGB and multispectral cameras are used for heart rate. © The Authors, published by EDP Sciences, 2021.