Fusion-Based Segmentation and Classification of Novel Coronavirus or Pneumonia from Chest X-ray Images Using Machine Learning Techniques

The deadfall widespread of coronavirus (SARS-Co V-2) disease has trembled every part of the earth and has significant disruption to health support systems in different countries. In spite of such existing difficulties and disagreements for testing the coronavirus disease, an advanced and low-cost technique is required to classify the disease. For the sense of reason, supervised machine learning (ML) along with image processing has turned out as a strong technique to detect coronavirus from human chest X-rays. In this work, the different methodologies to identify coronavirus (SARS-CoV-2) are discussed. It is essential to expand a fully automatic detection system to restrict the carrying of the virus load through contact. Various deep learning structures are present to detect the SARS-CoV-2 virus such as ResNet50, Inception-ResNet-v2, AlexNet, Vgg19, etc. A dataset of 10,040 samples has been used in which the count of SARS-CoV-2, pneumonia and normal images are 2143, 3674, and 4223 respectively. The model designed by fusion of neural network and HOG transform had an accuracy of 98.81% and a sensitivity of 98.65%. © 2022 IEEE.

Automatic Boot Spray Machine: A Preventive Measure Against Corona Virus

Corona Virus (COVID-19) has already done havoc in the world. More than six hundred million people suffered from this virus and six million people are dead amongst them in the world. In Bangladesh, two million people have tested positive and approximately 30 thousand people are dead. SARS-CoV-2 virus causes this infectious corona disease. When an infected individual sneezes, coughs, speaks, or breathes, the virus is disseminated from their mouth or nose. One can also be infected by touching contaminated surface and spreads more in indoor environment. So, it has taught us the necessity of washing and sanitizing in our daily affairs. Automatic boot spray machine is a very practical and useful instrument to fight against the corona virus. This contact free spray machine helps to sanitize the boot without getting in contact with the surface and ultimately helps to prevent the spread of corona virus. The purpose of this essay is to create a boot sprayer that automatically releases soapy water. At a distance of meters, an obstruction sensor is employed to identify the heat and presence of the boot. A p-n-p transistor is used to operate the machine. This machine works perfectly at a distance of 2-30cm. It also provides good sensitivity percentage. This setup is user convenient and it saves money and power. © 2023 IEEE.

Big Data Analytics Based Recommender System for Tele-Communication Industry

In recent times, the amount of data sent and received through wireless networks has grown quickly. Smartphones and the growth of Internet access around the world are two big reasons for this volume. Due to the current state of global health, which is mostly caused by Covid-19, telecommunications companies have a great chance to find new ways to make money by using Big Data Analytics (BDA) solutions. This is because data traffic has gone up. After all, more customers are using telecommunications services. As most of the world's data is now made by smartphones and sent through the telecom network, telecom operators are facing an information explosion that makes it harder to make decisions based on the data they need to predict how people will act. This problem was solved by making a system that sorts through information and makes suggestions based on how people have behaved in the past. Content-based filtering, collaborative filtering, and a hybrid approach are the three main ways that recommender systems filter data to solve the problem of too much data and give users relevant recommendations based on their interests and the data that is being created in real-time. Distance algorithms like Cosine, Euclidean, Manhattan, and Minkowski are at the heart of the suggested recommender system, which aims to research and design an effective recommendation strategy. The suggested model suggests different telecom packages to meet the needs of users to increase revenue per subscriber and get consumers, telecom providers, and corporations to sign long-term contracts. © 2022 IEEE.

A Case of Cortical Blindness Secondary to Pres

Blindness brought on by an intact anterior visual pathway but due to bilateral occipital lobe involvement is known as cortical blindness. Compared to partial blindness, it is less frequent. The posterior reversible encephalopathy disease has a well-documented history of reversible cortical visual blindness (PRES). The neurological condition PRES is characterised by reversible subcortical vasogenic brain oedema and sudden onset neurological symptoms. This illness can be reversed with prompt diagnosis and treatment. It frequently occurs in conjunction with disorders like eclampsia, cancer, kidney disease, hypertension, and hypertension. This case involves a male teenager who was infected with COVID and later developed PRES. The presence of Anton's blindness complicated PRES. Utilizing radiological investigation, an early diagnosis was obtained, and once therapy was started, all symptoms disappeared. Copyright © 2023 Ubiquity Press. All rights reserved.

An Analytical Approach to Predict the COVID-19 Death Rate in Bangladesh Utilizing Multiple Regression and SEIR Model

The epidemic of COVID-19 has turned out to be a huge fear for the world. There is currently no advisable drug or cure available to treat this condition. According to WHO statistics, COVID-19 has become a progressive lung illness that is spread by respiratory droplets and other forms of contact. According to WHO, there is still no treatment or defensive plan that has risen till the period to encounter the COVID- 19 pandemic that was arisen in China in late 2019. The purpose of our study is to predict the COVID-19 situation by analyzing the death rate, recovery rate, and susceptibility rate with the help of the regression model and SEIR model. Two analytical models (SEIR and Regression) have been used. Our analysis has shown the prediction of the COVID-19 death rate in Bangladesh with the help of a Regression and SEIR model. We have analyzed the instances per million, number of death rates per million from the SEIR and Regression results and compared them with the real-time result. We have used a valid data set of Bangladesh, collected from the Institute of Epidemiology, Disease Control and Research (ICR) from 18 March 2020 to July 18, 2021. Our experimental result shows promising performance. Examples and descriptions are provided to explain the technique. © 2021 IEEE.

A Hybrid Approach to Find COVID-19 Related Lung Infection Utilizing 2-Bit Image Processing

This study describes the deployment of an image processing approach for finding COVID-19 affected lungs. Medical scans are useful in diagnosing illnesses and determining if organs are working normally. Medical image processing is an ongoing research subject in where numerous ways are used to help diagnosis, as well as different image processing techniques that may be used. Picture processing was used in this work, which includes image pretreatment, histogram leveling, smothering, eroding, and dilation. The usage of 2-bit picture is selected since this characteristic is well-known and there are several resources accessible. The Open CV library, which includes a plethora of image processing functions, is likewise free to use. Our experiment has shown how COVID-19 affected lung disorders can easily be identified with the help of a 2-bit image segmentation technique. The plan comprises (1) using a deep robust acquisition access to portion proper regions of interest from bleak medical examination image sizes of 903 total, (2) using a propagative neural network to improve contrast, sharpness, and illuminance of image contents, and (3) from the beginning to the conclusion, a regression strategy plan was used to accomplish medical picture categorization by material design in deep neural networks.

A Spotlight on Novel Coronavirus (COVID - 19) with Various Types of Strains

Deadly COVID-19 viruses have raised a pandemic situation in the year 2019, causing serious and contagious respiratory infections in humans. SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) is the main causative agent for this disease outbreak. The pandemic created a critical impact on the global economy. The emergence of SARS-CoV-2 in late 2019 was followed by a period of relative evolutionary stasis that lasted about 11 months. Since, late 2020, SARS-CoV-2 evolution has been characterized by the emergence of sets of mutations. This resulted so far, in over 2.7 million deaths and near about 122 million infection cases. Most mutations in the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) genome are either deleterious and swiftly purged or relatively neutral. As far as the concern is the variants it impacts the virus characteristics, including antigenicity and transmissibility in response to the modification of the human immune profile. In recent days, COVID-19 affected cases are rapidly increasing and it became difficult to inhibit this virus as they are continuously mutated in the host cell forming various new strains like B.1.1.7, B.1.351, P.1, P.2, B.1.1.529, etc. These monitoring, surveillance of variation, and sequencing efforts within the SARS-CoV-2 genome enabled the rapid identification of the first some of Variants of Concern (VOCs) in late 2020, where genome changes became the most observable impact on virus biology and disease transmission. In this review article, we tried to focus and spot the light on the genetic diversification of various strains, their nature, similarities and dissimilarities, mechanism of action, and the prophylactic interventions which could prevent this life-threatening disease in the long run.

Rapid increase of myeloid-derived suppressor cells and prolonged innate immune dysfunctions in patients with COVID-19

Background: PMN and monocytic myeloid-derived suppressor cells (PMN-MDSCs, M-MDSCs) are immunosuppressive cells rising during infections. Aim: To characterize the dynamic of MDSCs in relation with immune parameters in COVID-19 patients followed for 3 months. Methods: 56 SARS-CoV-2 infected adult patients hospitalized at CHUV were included. Blood was obtained at inclusion and 3 months later in 21 patients, and from 10 healthy controls. Blood was stimulated with TLR ligands. Leukocyte populations and cytokines were analyzed by flow cytometry, mass cytometry, multiplex bead assay and ELISA. Results: At hospital admission, PMN-MDSCs and M-MDSCs were increased 2-4-fold in COVID-19 patients (P <0.05). PMN-MDSCs and M-MDSCs counts were higher in severe than in moderate COVID-19 patients (P <0.005). PMN-MDSCs and M-MDSCs correlated positively with EGF and HGF (P <0.05). M-MDSCs correlated positively with IL-1β, IL-7, PDGF and VEGF (P <0.05). In whole blood stimulated with TLR ligands, the proportion of TNF and IL-6- producing monocytes and DCs were reduced in patients. After 32 months, MDSCs were back to normal levels, while the production of cytokines by blood, monocytes and DCs was still largely affected. Conclusions: PMN-MDSCs and M-MDSCs were elevated and correlated with disease severity in patients analyzed at hospitalization. Innate immune blood responses were impaired in patients, which persisted for up to 3 months. Our results suggest that COVID-19 induces rapid and long-standing innate immune dysregulation.

Blockchain-Based COVID-19 Detection Framework Using Federated Deep Learning

With the increase of COVID-19 instances worldwide, a reliable method for diagnosing COVID-19 cases is needed. The major issue in detecting COVID-19 clients is a lack of diagnostic techniques that are both reliable and affordable. Due to the virus’s rapid dissemination, medical professionals are having difficulties finding positive cases. The second real-life issue is sharing data across clinics worldwide but keeping in mind the organizations’ privacy concerns. Developing a collaborative approach and protecting personal information are two important issues while creating a global classifier. This article offers a system that uses Ethereum - based federated learning to gather a modest quantity of data from many sources and train a global deep learning model. The data is authenticated using blockchain technology, and federated learning trained the system worldwide while maintaining the institution’s anonymity. The suggested structure may make use of current data to enhance diseases recognition. Our findings show that our method is more effective in detecting COVID-19 participants. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Can neglegence toward COVID-19 symptoms initiate third wave of COVID-19 in India

Background: The second wave of Covid-19 had a huge number of asymptomatic, false negative (indeterminant) and symptomatic untested cases (query Covid). Objective: The aim is to understand the dynamics among these groups to know their impact on the spread of the diseases. Methods: In a prospective online survey we collected data using snowball sampling method via social media, from in and around Kolkata with the help of Google forms. The data included Covid related symptoms, evaluation, and behavior related to treatment during first and second wave of the disease. The discrepancies and duplicities were first excluded, and 989 respondents' data were statistically analyzed using SPSSver26. Results: The percentage of RT-PCR confirmed symptomatic and asymptomatic Covid cases were 21.84% (n=216) and 2.12% (n=21) respectively. Symptomatic but unconfirmed cases (query Covid) were 17.18% (n=170) and symptomatic false-negative cases (indeterminant) were 93 (9.40%). Rest 489 (49.44%) did not have any symptoms or never tested positive. The analysis revealed the reasons for doing RT-PCR test include a) less symptoms severity (47.06%), b)considering test unnecessary (22.94%),c) home collection unavailability(14.71%) and d)longer waiting time for results(8.82%). According to regression analysis, compared to confirmed Covid symptomatic group, only 47% [OR: 0.13(0.57-0.30) p<0.0001] of query covid patients consulted doctor for test or treatment and 21% [OR:9.55 (1.97-46.16), p<0.001] of indeterminant cases took medicine based on advice of friends/ relatives. Conclusion: There is a high percentage of untested (query Covid) and probable false negative cases (indeterminant) likely going unreported. The reasons for poor testing and seeking medical attention inadequately needs to be addressed and further investigated.

Impact of environmental and socio-economic stressors leading to unequal distribution of COVID-19 incidences in the state of Louisiana

Louisiana (LA) ranks fifth in the United States in cancer mortality rate. LA's infamous “cancer alley” is a well evidenced region near the southeast part of the Mississippi river surrounding the petrochemical hub of the state. LA has also experienced a high COVID-19 death rate and incidences compared to other states during the recent pandemic. In this study we analyzed publicly available datasets related to health and socio-economic parameters in LA to determine the factors triggering high incidences and deaths caused by COVID-19. Correlation analysis was performed to find the impact of different parameters on the outcome of COVID-19. Our analysis showed higher COVID-19 incidences in the parishes which are in and around the “cancer alley” with a correlation of r = 0.9. Interestingly, results also indicated a strong correlation (r = 0.9) between the death rates caused by asbestos toxicity to COVID-19 caused death rate. Furthermore, we found that office-administration related employment has a positive correlation to COVID-19 incidences in the “cancer alley.” However, we also found both white and black races are equally affected by the COVID-19 pandemic in the “cancer alley” region. In conclusion, our analysis strongly suggests that inhabiting “cancer alley” could significantly enhance the chances of getting affected by SARS-CoV-2 virus compared to other regions in LA. © 2022 Wiley Periodicals LLC.

Pediatric appendicitis in the time of the COVID-19 pandemic: A retrospective chart review.

Aim: Conduct a time trend analysis that describes 2 groups of patients admitted to a large tertiary children's hospital that presented with appendicitis and determine if there was an increase in complicated appendicitis when compared between 2 time periods before and during the early coronavirus disease 2019 (COVID-19) pandemic of 2020. Methods: We conducted a retrospective analysis of all children presenting to a single-center site with appendicitis between March 23 and August 31, 2020, in the Central Texas region. We compared 507 patients presenting with appendicitis from the non-COVID-19 era in 2019 with n = 249 to patients presenting during the COVID time period with n = 258. All patients with appendicitis within those time periods were reviewed with analysis of various characteristics in regard to presentation, diagnosis of uncomplicated versus complicated appendicitis, and management outcomes. Results: There were no significant demographic differences or change in the number of appendicitis cases noted between the 2 time periods of comparison. There was no significant difference in rates of complicated appendicitis or presentation time following symptom onset between the 2 eras. There was no significant difference in intraoperative or postoperative complications. There was a statistically significant increase in the use of computed tomography (CT) scans (P-value = 0.004) with patients 1.81 times more likely to have a CT scan in the pandemic era after adjusting for patient-level factors. The effect of severe acute respiratory syndrome coronavirus 2 status on outcomes was not part of the data analysis. Conclusion: Our study is the largest to date examining appendicitis complications in the era of COVID. In the time of the COVID-19 pandemic, we found no delay in presentation in children presenting to the emergency department and no increase in complicated appendicitis. We did identify an increase in the use of CT scans for definitive diagnosis of appendicitis noted in the pandemic era. Although COVID-19 status was not studied, the finding of increased CT use for a definitive diagnosis of appendicitis was a distinctive finding of this study showing a change in practice in pediatric emergency medicine.

Utilizing Smart City Cyber-physical Infrastructure for Tracking and Monitoring Pandemics like COVID-19 with the ICCC as the Nerve Centre

Managing the ongoing COVID-19 (aka Coronavirus) pandemic has presented both challenges and new opportunities for urban local body administrators. With the Indian government's Smart City mission taking firm roots in some of the Indian cities, the authors share their learnings and experiences of how a Smart City Integrated Command and Control Centre (ICCC) can be extended to become the nerve centre of pandemic-related operations and management, leveraging the Smart City IoT infrastructure such as surveillance cameras for monitoring and enforcement. The authors are of the opinion that the lessons learned and experiences gained from these cities are extremely valuable and can easily be replicated in other cities in a relatively short time period, thus providing a standard and uniform method across the nation for handling epidemics in the future. © 2020 ACM.

A Comprehensive Analysis of Sustainable IoT Infrastructure in the Post-COVID-19 Era

The recent worldwide outbreak Corona Virus Disease 2019 (COVID-19) had an impact on our daily lifestyle enormously. The enterprises have reconsidered their work practices to cope up with the new scenario and a severe impact has been observed in different sectors including manufacturing, shipping and distribution of products. Different Internet of Things (IoT) infrastructures allowed us to obtain some better solutions throughout the decade and it also became one of the most researched areas due to the convergence with other emerging technologies like artificial intelligence, cloud computing, etc. In the pandemic era also, IoT has proved its efficiency in different online platforms like e-medicine, e-learning, online shopping, etc. On the other hand, the global trend is moving towards sustainability and hence sustainable solutions in different sectors are in high demand. Though IoT helps to reduce energy consumption in different applications, still environmental concern regarding IoT components has been raised recently since the modern electronics and IoT devices are often difficult to recycle. In this chapter, sustainability of different IoT infrastructures will be reviewed and their applicability in appropriate domains will be analysed for the post-COVID-19 era. Different challenges will be attempted to be identified for future redress satisfying the customer requirement and a possible future direction will be provided based upon the current research findings. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Patterns of HRCT findings and its clinical correlation in COVID-19 patients of a tertiary care hospital in Assam

Background: Covid-19, which is usually transmitted from droplet exposure, proved out to be a novel disease of human race. It is still unclear whether the new coronavirus can transmit in any other ways, but it can be detected in nasal swabs, sputum, respiratory secretions, blood, stool, and other samples. Early detection and prompt intervention play a vital role in reducing the number and severity of cases.