Forecasting COVID-19 Pandemic using Prophet, LSTM, hybrid GRU-LSTM, CNN-LSTM, Bi-LSTM and Stacked-LSTM for India

The COVID-19 Pandemic has been around for four years and remains a health concern for everyone. Although things are somewhat returning to normal, increased incidence of COVID-19 cases in some regions of the world (such as China, Japan, France, South Korea, etc.) has bred worry and anxiety in world, including India. The scientific community, which includes governmental organizations and healthcare facilities, was eager to learn how the COVID-19 Pandemic would develop. The current work makes an attempt to address this question by employing cutting-edge machine learning and Deep Learning algorithms to anticipate the daily incidence of COVID-19 for India over the course of the next six months. For the purpose famous timeseries algorithms were implemented including LSTM, Bi-Directional LSTM and Stacked LSTM and Prophet. Owing to success of hybrid algorithms in specific problem domains- the present study also focuses on such algorithms like GRU-LSTM, CNN-LSTM and LSTM with Attention. All these models have been trained on timeseries dataset of COVID-19 for India and performance metrics are recorded. Of all the models, the simplistic algorithms have performed better than complex and hybrid ones. Owing to this best result was obtained with Prophet, Bidirectional LSTM and Vanilla LSTM. The forecast reveals flat nature of COVID-19 case load for India in future six months. . © 2023 IEEE.

Deep CNN and LSTM Architecture-Based Approach for COVID-19 Detection

The severe acute respiratory syndrome(SARS-CoV2) led to a pandemic of respiratory disease, namely COVID19. The disease has scaled worldwide and has become a global health concern. Unfortunately, the pandemic not just cost several individuals their lives but also, resulted in many people losing their jobs and life savings. In times like these, ordinary people become fearful of their resources in a world that gives its best resources to the wealthiest beings. Following the pandemic, the world suffered greatly and survival was rather difficult. As a result, numerous analytical techniques were developed to address this issue, with the key one being the discovery that the efficacy of clinically tested vaccines is actually quite poor. When researchers and medical professionals were unable to find a cure, radiologists and engineers created techniques to detect infected chests with the help of X-rays. Our proposed solution involves a CNN + LSTM model which has secured an accuracy of 98% compared to 95% of the trusted VGG-16 architecture. Our model's area under the curve (AUC) scores reached 99.458% while using RMSprop. A crucial feature of image processing till depth is accessible through scanning features from the layers of images using CNN. Our model uses 5 convolution blocks to detect the features. The coordination of activator functions, learning rates, and flattening has enabled accurate in-point predictions. With merely X-rays, models like ours ensure that anyone can easily detect covid-19. The best results obtained were at a learning rate =0.01 with RMSprop and Adam functions. The model has good fortune in detecting any other lung disease which occurs in the near future, as our data collectively rounds up to 4.5 gigabytes of data providing higher precision. © 2023 IEEE.

Covid19 Disease Assessment Using CNN Architecture

Recently, the COVID-19 pandemic has emerged as one of the world's most critical public health concerns. One of the biggest problems in the present COVID-19 outbreak is the difficulty of accurately separating COVID-19 cases from non-COVID-19 cases at an affordable price and in the initial stages. Besides the use of antigen Rapid Test Kit (RTK) and Reverse Transcription Polymerase Chain Reaction (RT-PCR), chest x-rays (CXR) can also be used to identify COVID-19 patients. Unfortunately, manual checks may produce inaccurate results, delay treatment or even be fatal. Because of differences in perception and experience, the manual method can be chaotic and imprecise. Technology has progressed to the point where we can solve this problem by training a Deep Learning (DL) model to distinguish the normal and COVID-19 X-rays. In this work, we choose the Convolutional Neural Network (CNN) as our DL model and train it using Kaggle datasets that include both COVID-19 and normal CXR data. The developed CNN model is then deployed on the website after going through a training and validation process. The website layout is straightforward to navigate. A CXR can be uploaded and a prediction made with minimal effort from the patient. The website assists in determining whether they have been exposed to COVID-19 or not. © 2023 IEEE.

The social representation and social action effect of critical issues: autonomic system and self-report measures

By combining words and images that impact emotions and generate empathetic storytelling, advertising (ADV) has evolved into a form of communication for promoting consumer awareness, positive social change, and ADV-related decisional processes, even on topics of high-social relevance such as crisis communication. This study explored consumers' emotional and cognitive responses to crisis-related ADVs using implicit (autonomic) and explicit (self-report) measurements. Nineteen participants watched twelve high-impact social communications about Covid-19, personal health, safety, and prosociality, while autonomic and self-report data were collected. Personal health, safety, and prosociality had higher skin conductance than Covid-19 stimuli, indicating higher arousal and engagement. Personal health reported lower heart rate variability values than Covid-19, suggesting greater emotional reactions for personal health topics, but also lesser mental load for Covid-19 stimuli. Self-report results confirmed autonomic findings. In conclusion, communications about personal health, safety, and prosociality generate higher emotional impact and allow for effective storytelling that facilitates viewer identification, developing a high level of empathy.

The Drivers and Inhibitors of COVID-19 Vaccinations: A Descriptive Approach

The SARS-COV-2/ COVID-19 pandemic is a global challenge affecting hundreds of millions globally. The COVID-19 pandemic that began in Wuhan in China in 2019 has continued to pose a health concern and economic meltdown across the globe. Globally, numerous vaccines have been successfully rolled out against many vaccine-preventable diseases at all stages of human development. Despite the number of approved SARS-COV-2 vaccines, the seeming success in the global rollout, and the inoculation of billions globally, COVID-19 vaccination interventions still need improvement. However, for total control and eradication, there is a need to review the campaign methodologies to identify the drivers and inhibitors of COVID-19 vaccinations to promote booster uptake. Questions concerning the acceptability of the covid-19 vaccination were posed to respondents using a convenience sample method. This study contributes to the African vaccination literature and descriptively shows the drivers and inhibitors of COVID-19 vaccination. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Unlearning in crisis: Forces of change in unlearning

The study of unlearning continues to be important, not only due to the relevance of the concept itself, but in light of current strong, unforeseen forces, knowledge change opportunities have been created beyond our prediction. A knowledge exchange is often needed to revise processes, use new technologies, or due to forces that stem from catastrophic situations. Examples include economic, such as in business failures or the recent public health concerns from the COVID-19 pandemic. Building from new insights using the typological model from Rushmer and Davies (2004), deep unlearning may the end result of catastrophic forces of change. First, deep unlearning occurs with striking events, or yield change that adds anxiety, psychological, or technological upset. Second, inherent in many catastrophic changes are rapid interruptions in the trajectory of "previous” actions and unique processes toward recovery where knowledge base may be forever altered. We address the following question: "Is Rushmer and Davies' deep unlearning typology exhibited during catastrophic situations?” This theoretical paper examines the concept of deep unlearning, the process of replacement or lack of use of a belief, action, or process in a context of an emergency situation where little is currently known. What type of agent for change would be needed? Will unintended consequences not be identified by individuals and organizations;what may be the cost to future learning skills when deep unlearning of current tasks occurs? Third, some insights and directions for future research are presented. © 2022 IEEE Computer Society. All rights reserved.

Effectiveness of hand sanitizers in the prevention of COVID-19 and related public health concerns: A review

The COVID-19 pandemic poses a severe threat to public health, resulting in high levels of mortality and morbidity. In response, there has been a significant usage of hand sanitizers in homes, public places, and healthcare systems. In the global panic, the market has a variety of products, and there are serious concerns about the safety and the potential of hand sanitization as the blue bullet for COVID-19. Therefore, this article presents a critical review of types of hand sanitizers available on the market, their active ingredients coupled with their mode of action in the wake of antiviral efficacies. In addition, the adoption of a culture of hand sanitization by society could raise the demand for hand sanitizers for an extended period. The continuous use of hand sanitizers might pose some safety concerns. Consequently, the review articulates potential dangers associated with hand sanitizer used to equip suppliers and manufacturers with knowledge on the safety of different ingredients and formulations, hence safeguarding the final users.Copyright © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.

Dynamic Computer-Aided Process Control with Computer Vision for Industry 4.0

Because of health concerns and factory operational scale backs during the recent COVID-19 pandemic, we now need factory automation more than ever to maintain our productivity. However, most of our factories cannot operate remotely, and none can function without considerable human input and oversight. Trying to automate our factory highlights gaps in our technology, as it seems far behind our expectations, needs, and vision. Thus, this paper aims to fill this gap by showing how we have developed practical methodologies and applied technology to enhance legacy factories and their equipment. Specifically, we present the ORiON Production Interface (OPI) unit to run the factory as a smart networked edge device for virtually any machine or process. We have also implemented various computer vision algorithms in the OPI unit to detect errors autonomously, make decentralized decisions, and even control the quality. Although Industry 4.0 is a known concept to equip our factory to see, understand, and predict, we know that many machines today are closed source and cannot even communicate, let alone join a network. This research provides a workable solution to realize Industry 4.0 truly in existing factories with legacy equipment. Experimental results show that this system has a variety of applications, including process monitoring, part positioning, broken tool detection, etc. This novel intelligent networked system can enable our factories to be more innovative and responsive. It also allows for remote operations that can be unattended or lightly tended—a trend needed for the future. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Deep Learning Based Face Mask Detection in Religious Mass Gathering During COVID-19 Pandemic

Notwithstanding the religious intention of billions of devotees, the religious mass gathering increased major public health concerns since it likely became a huge super spreading event for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Most attendees ignored preventive measures, namely maintaining physical distance, practising hand hygiene, and wearing facemasks. Wearing a face mask in public areas protects people from spreading COVID-19. Artificial intelligence (AI) based on deep learning (DL) and machine learning (ML) could assist in fighting covid-19 in several ways. This study introduces a new deep learning-based Face Mask Detection in Religious Mass Gathering (DLFMD-RMG) technique during the COVID-19 pandemic. The DLFMD-RMG technique focuses mainly on detecting face masks in a religious mass gathering. To accomplish this, the presented DLFMD-RMG technique undergoes two pre-processing levels: Bilateral Filtering (BF) and Contrast Enhancement. For face detection, the DLFMD-RMG technique uses YOLOv5 with a ResNet-50 detector. In addition, the face detection performance can be improved by the seeker optimization algorithm (SOA) for tuning the hyperparameter of the ResNet-50 module, showing the novelty of the work. At last, the faces with and without masks are classified using the Fuzzy Neural Network (FNN) model. The stimulation study of the DLFMD-RMG algorithm is examined on a benchmark dataset. The results highlighted the remarkable performance of the DLFMD-RMG model algorithm in other recent approaches. © 2023 CRL Publishing. All rights reserved.

Effect of the COVID-19 on Perceptions of Health, Anticipated Need for Health Services, and Cost of Health Care.

OBJECTIVE: This article investigates how perceived vulnerability to the coronavirus disease 2019 (COVID-19) pandemic at its early stages is associated with people's perception of their health, the need for health-care services, and expenses related to addressing the COVID-19 impact on their health. METHODS: The results are based on the analysis of surveys that were distributed among members of 26 random Facebook groups in April-May, 2020. Perceived COVID-19 pandemic related stress and health concerns were examined by using the analysis of variance (ANOVA) test. RESULTS: Among 315 respondents, 64% have experienced COVID-19 related stress and identified anxiety, headache, insomnia, and weight gain as their primary health concerns. The ANOVA test revealed that females are more impacted by the COVID-19 stress than males. Around 40% of respondents believed that the COVID-19 would lead to an increase in the cost of health services, and 20% of respondents anticipated that the COVID-19 pandemic would increase their need for health services. CONCLUSIONS: Learning about how people perceive the COVID-19 pandemic impact on their health, particularly in the pandemic's early stages can allow health professionals to develop targeted interventions that can influence pandemic preventative behaviors among different population groups. This study can help understand use patterns and mitigate financial barriers that could interfere with patients' care-seeking behavior.

The monkeypox diagnosis, treatments and prevention: A review.

The world is currently dealing with a second viral outbreak, monkeypox, which has the potential to become an epidemic after the COVID-19 pandemic. People who reside in or close to forest might be exposed indirectly or at a low level, resulting in subclinical disease. However, the disease has lately emerged in shipped African wild mice in the United States. Smallpox can cause similar signs and symptoms to monkeypox, such as malaise, fever, flu-like signs, headache, distinctive rash, and back pain. Because Smallpox has been eliminated, similar symptoms in a monkeypox endemic zone should be treated cautiously. Monkeypox is transmitted to humans primarily via interaction with diseased animals. Infection through inoculation via interaction with skin or scratches and mucosal lesions on the animals is conceivable significantly once the skin barrier is disrupted by scratches, bites, or other disturbances or trauma. Even though it is clinically unclear from other pox-like infections, laboratory diagnosis is essential. There is no approved treatment for human monkeypox virus infection, however, smallpox vaccination can defend counter to the disease. Human sensitivity to monkeypox virus infection has grown after mass vaccination was discontinued in the 1980s. Infection may be prevented by reducing interaction with sick patients or animals and reducing respiratory exposure among people who are infected.

The Role of Economic Stress, Health Concerns, and Institutional Trust in Supporting Public Protests against COVID-19 Lockdown Measures in Denmark.

During the current COVID-19 pandemic, most governments around the world have adopted strict COVID-19 lockdown measures. In Denmark, mainly from January to March 2021, an anonymous protest group called Men in Black organized demonstrations against the Danish COVID-19 lockdown measures in the three major cities in Denmark. Based on an online survey that we carried out in March 2021 in the Danish population aged 16 years and above (n = 2692), we analyze the individual-level factors behind supporting these demonstrations. Based on ordered logit regressions, the results show that being Muslim and being self-employed (business owner) was positively related to supporting the demonstrations, and that age and living in a city municipality was negatively related to supporting the demonstrations. Based on structural equation modeling (SEM), the results showed that the municipal COVID-19 incidence rate mediates the effect of living in a city municipality, that institutional trust mediates the effect of being Muslim, and that COVID-19 health concerns and institutional trust mediate the effect of age. Overall, economic stress among business owners, health concerns, and institutional trust were found to be the main predictors of supporting the demonstrations against the COVID-19 lockdown measures in Denmark.

Gaming for Better Psychological Health: A Solution Based on the FPGA Zynq 7000

Anxiety and depression are the two most prevalent mental health problems throughout the globe. They may present either suddenly or persistently, with a broad range of symptoms, many of which are often asymptomatic. This need is a direct outcome of the mental illness-related economic and healthcare insurance service burden. The expansion of the COVID-19 pandemic and the resulting increase in the incidence of mental health concerns have both contributed to the rising need for mental health care. In response to these demands, a substantial amount of research is examining alternatives to the usual methods used to treat mental health issues. According to research, digital games include cognitive benefits such as attention management, cognitive flexibility, and information processing. This study dissects and analyses the game 'Space Invaders' in terms of its design and implementation. The game has enormous potential as a resource for the mitigation of some mental health issues in lieu of or in addition to established therapeutic therapies. The resource is inexpensive, readily accessible, globally accessible, beneficial, and not connected with shame. © 2022 IEEE.

Determination of the Morphological Characteristics of Eight Types of Protective Face Masks and Respirators

Respiratory infections have arisen as a public health concern. The regulation of COVID-19 is based on knowledge of its transmission mechanism. Masks and respirators act as a physical barrier against respiratory droplets that enter through the nose and mouth, as well as droplets spat by sick persons. Textile masks (including 'do-it-yourself'), surgical (medical) masks and respirators are the three basic types of personal protection devices, covering the human face. The purpose of our work is to give a study on the morphological features of masks and respirators, which are widely accessible in Bulgarian shops and pharmacies, revealing their structure and differences between them. The results will be further used for the assessment of heat and mass transfer abilities of the masks/respirators, which are largely preconditioned by the masks' morphology. © 2022 IEEE.

Mobile payment service quality: a new approach for continuance intention

PurposeThis paper assesses the continuance intention (CI) for mobile-based payment (M-payment) services following the COVID-19 pandemic by combining the self-efficacy construct with the electronic service quality model.Design/methodology/approachThis exploratory, cross-sectional research employs qualitative and quantitative research methods;specifically, a questionnaire and interviews. A total of 403 Jordanian participants completed valid questionnaires. Mediation and moderation evaluations assessed the M-payment service quality (MPSQ), self-efficacy and health concerns (HC) to determine CI.FindingsThe results verify the significance of MPSQ and self-efficacy in developing CI and show the mediating influence of self-efficacy between MPSQ and CI. Moreover, HC negatively impact the self-efficacy/CI link.Practical implicationsThis research benefits M-payment service providers seeking to secure customer loyalty via improved M-payment services. The behavioral intention investigation will provide rich information about potential customers' CI and illuminate areas for development.Originality/valueThis research makes an original contribution to the existing M-payment literature by investigating the impact of customers' perception of service quality on their CI to utilize M-payment services, balanced with self-efficacy and HC.

Consequences of the Early Phase of the COVID-19 Pandemic for Home-Healthcare Recipients in Norway: A Nursing Perspective.

Municipal home-healthcare services are becoming increasingly important as growing numbers of people are receiving healthcare services in their home. The COVID-19 pandemic represented a challenge for this group, both in terms of care providers being restricted in performing their duties and care receivers declining services for fear of being infected. Furthermore, preparedness plans were not always in place. The purpose of this study is to investigate the consequences for recipients of home healthcare in Norway of the actual level of COVID-19 infection spread in the local population, as observed by licensed nurses working in home-healthcare services. Approximately 2100 nurses answered the survey. The most common adverse consequences for home-healthcare recipients were increased isolation and loneliness, increased health concerns, and the loss of respite care services. An increased burden for relatives/next of kin and fewer physical meetings with service providers were frequently observed and reported as well. This study shows that there were more adverse consequences for service users in municipalities with higher levels of contagion than in those with lower levels of contagion. This indicates that the municipalities adapted measures to the local rate of contagion, in line with local municipal preparedness strategies.

iMask: An IoT-based Intelligent Mask to Identify and Track COVID-19 Suspects

COVID-19 has become a global health concern, and wearing masks is a key measure to curb COVID-19 from rapidly spreading. While COVID-19 patients can be accurately determined using Rapid Antigen and PCR tests, these tests are costly, time-consuming, invasive, and uncomfortable. Further, they should be performed in a specialized environment despite showing the COVID-19 symptoms such as fever, cough, rapid heart rate, shortness of breath, and low blood oxygen saturation level. To this end, this study aims to automatically identify, and track the COVID-19 suspects in real-time by embedding smart sensors to face masks. The mask was developed to gather the data related to five major symptoms of COVID-19: body temperature, cough, heart rate, breathing pattern, and blood oxygen level. Data collected using smart sensors were used to identify and track COVID-19 suspects using Deep Neural Networks, the Internet of Things (IoT), and Artificial Intelligence (AI). Yielded results showed the proposed mask can identify COVID-19 suspects 92% accurately. © 2022 IEEE.

Comparative Analysis of Face Mask Detection Models

With an ongoing episode of Covid, the world health security and precaution need reformation and a new approach to be dealt with. The health concerns of the individual is a topic of utmost importance for every nation fighting the pandemic. With limited healthcare staff and the large public to look after, the assistance of Computer vision and AI is needed. Social distancing is a very effective way of containing the spread of a pandemic. Social distancing becomes difficult when dealing with a number of subjects like at gateways of offices, Airports, and many other sectors that have significant footfall in a day. In this paper we have tried to compare the different models for the recognition of mask on the face, for doing so we have used Real world masked face dataset (RMFD) (Iqbal et al, Renewable power for sustainable growth, Springer Nature, Berlin, LNEE, 2020) and Kaggle (Tomar et al, Machine learning, advances in computing, renewable energy and communication, vol 768. Springer Nature, Berlin, LNEE, 2020) dataset. At first we gather the images where face have actual mask on it and also augmented the image with editing the image of unmasked face with mask so that model can learn very details of the image and result will come more accurate and clean. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Integrating “Impacts of Covid-19 pandemic on air quality” in STEM courses and internships for undergraduate students

Air pollution is a global public health concern and has led to millions of premature deaths worldwide. In overpopulated cities, particulate matter such PM2.5, nitrogen dioxide (NO2), and ozone (O3) in the troposphere have deleterious effects on human health leading to cardiovascular and respiratory diseases. The students in two undergraduate courses (Principles of Biology for Honors students and Ecology) and one graduate course (Teaching STEM at the K-12 schools) at the University of Maryland Eastern Shore;and summer-exchange undergraduate engineering interns learnt about the positive and negative effects of Covid-19 pandemic on air quality for some of the selected overpopulated cities in the world that witnessed lockdowns from March 2020 through spring 2021. The STEM students as well as the interns had the opportunity to learn how to analyze the real-time and historical air quality data from the Environmental Protection's Agency's centralized data system, AirNow, as well as from the Air Quality Open Data Platform (https://aqicn.org/data-platform/covid19) Worldwide Covid-19 dataset. For the above-mentioned courses, the materials pertaining to Covid-19 and air quality were taught in the form of modules (two for each course) with lectures;discussions and class debates;video materials;simulations with real-time data;and a project centering on that theme. The engineering students who worked as summer interns worked on analyzing data from five of the major cities in the world. Besides analyzing the effects of the pandemic on PM2.5, NO2, and O3 in the selected populated cities, the students also studied whether any correlations existed among the air quality parameters or not. The students' learning outcomes included honing content knowledge in atmospheric chemistry and physics of particulate matter;environmental sciences and engineering;public health and policies;research skills with respect to data analysis and problem-solving;as well as presentation and writing skills. The students and interns in the courses and internships also addressed and debated on the various issues of sustainability, which encompasses social, environmental, economic considerations along with policies. The crisis of the pandemic on climate change is dependent on the policies of the governments towards which directions the economies need to head. When the governments prioritize to shift from fossil fuels to cleaner energy such as wind, solar, geothermal, biofuels, then the mitigation efforts of climate change could come to fruition. It is anticipated that with more ongoing collaborations across disciplines, the authors will be able to permanently integrate these diverse components in other STEM courses such as Statistics for Engineers, Big Data Analytics, and enhance multidisciplinary learning for all majors. This integration of research findings in STEM courses is a reflection of the KDB (Know, Do, Be) framework, as the interns and the students honed their skills not only in content knowledge through inquiry, but felt responsible in taking action towards mitigation efforts of climate change. © American Society for Engineering Education, 2022

Consumers in Limbo : How the COVID-19 Pandemic Has Changed Local Food Consumption in Tehran

The present paper investigates the impacts of the COVID-19 pandemic on consumer behavior related to local food products (LFPs). The study relies on the interpretation of in-depth interviews (N = 26) conducted through phone calls in Tehran, Iran, between September 23 to October 27, 2020. The results reveal substantial changes in buying behavior and food habits. Our findings present four behavioral categories with different underlying motivational factors: (1) ceased consumption, (2) reduced consumption, (3) unchanged consumption, and (4) increased consumption of LFPs. The results show that reduced accessibility during the lockdowns inhibited some respondents from acquiring the products they wanted. Moreover, health concerns due to distrust of food safety made some consumers hesitant about local food consumption. Our findings enhance understanding of how and why pandemics like COVID-19 may affect food habits and, consequently, attitudes and behaviors toward local food consumption. As consumption is constrained by time and place, the study contributes by bringing a localized perspective into consumers’ understanding of “local” products and the effects of the COVID-19 pandemic in Tehran.