Sentiment Analysis on COVID-19 Vaccination in Bangladesh

The COVID-19 pandemic has caused a shocking loss of life on a worldwide scale and influenced every sector of Bangladesh very badly. The simplest method for preventing infectious diseases is vaccination. Bangladeshi netizens discuss their opinions, feelings, and experiences associated with the COVID-19 vaccination program on social media platforms. The purpose of this research is to conduct a sentiment analysis of the vaccination campaign, and for this purpose, the reactions of Bangladeshi netizens on social media to the vaccination program were collected. The dataset was manually labelled into two categories: positive and negative. Then process the dataset using Natural Language Processing (NLP). The processed data is then classified using various machine learning algorithms using N-gram as a feature extraction method. The recall, precision, f1-score, and accuracy of various algorithms are all measured. The experiment results show that 61% of the reviews indicate the positive aspects of the vaccination program, while 39% are negative. For unigram, bigram, and trigram, the very best accuracy was achieved by Logistic Regression (LR) at 80.70%, 79.45%, and 78.65%. © 2022 IEEE.

Convolutional Neural Networks for Detection of COVID-19 from Chest X-Rays

The coronavirus (COVID-19) pandemic was rapid in its outbreak, and the contagion of the virus led to an extensive loss of life globally. This study aims to propose an efficient and reliable means to differentiate between chest x-rays indicating COVID-19 and other lung conditions. The proposed methodology involved combining deep learning techniques such as data augmentation, CLAHE image normalization, and transfer learning with eight pre-trained networks. The highest performing networks for binary, 3-class (normal vs. COVID-19 vs. viral pneumonia) and 4-class classifications (normal vs. COVID-19 vs. lung opacity vs. viral pneumonia) were MobileNetV2, InceptionResNetV2, and MobileNetV2, achieving accuracies of 97.5%, 96.69%, and 92.39%, respectively. These results outperformed many state-of-the-art methods conducted to address the challenges relating to the detection of COVID-19 from chest x-rays. The method proposed can serve as a basis for a computer-aided diagnosis (CAD) system to ensure that patients receive timely and necessary care for their respective illnesses. Copyright © 2022, IGI Global.

A Survey on Diagnosing Pulmonary Diseases

Due to the increase in world population, a lot of research is being done in the medical sciences. Pandemics and epidemics have multiple outbreaks in many regions of the world. In order to solve the issue, creative probing is being used. Most of the illnesses in the group are obstructive and may result in a loss of life. Heart and lung conditions make up a large portion of the obstructive illnesses in this group. More than 5 lakh people die each year from lung illnesses, generally known as pulmonary disorders, with an equal proportion of men and women affected. Each disease has unique symptoms that are connected to it in the fields of medicine and healthcare. There are several new tests that are being developed to identify each of the dangerous diseases that are on the rise. This results from the necessity for quick illness prediction. This paper examines numerous studies and experiments carried out over a variety of timelines and approaches selected by various experiments, carefully examining the benefits and drawbacks of the approaches in order to construct an appropriate model for the cause. It focuses on the study of diagnosing pulmonary disorders and making the user's task easy in understanding the scanned images obtained. © 2023 IEEE.

COVID-19 Growth Curve Forecasting for India Using Deep Learning Techniques

Due to sudden evolution and spread of COVID-19, the entire community in the globe is at risk. The covid has affected the health and economy and caused loss of life. In India, due to social economic factors, several thousands of people are infected, and India is seen as one of the top countries seriously impacted by the pandemic. Despite of having a modern medical instruments, drugs, and technical technology, it is very difficult to contain the spread of virus and save people from risk. Healthcare system and government personnel need to get an insight of covid outbreaks in the near future to decide on stepping up the healthcare facilities, to take necessary actions and to implement prevention policies to minimize the spread. In order to help the government, this study aims to build model a forecast COVID-19 model to foretell growth curve by predicting number of confirmed cases. Three variant models based on long short-term memory (LSTM) were built on the Indian COVID-19 dataset and are compared using the root mean squared error (RMSE) and mean absolute percentage error (MAPE). The findings have revealed that the proposed stacked LSTM model outperforms the other proposed LSTM variants and is suitable for forecasting COVID-19 progress in India. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

The ELF Tribe: Redefining Primary Education in a Post-COVID Era

Coronavirus brought the entire world to a grinding halt, and it has still not recovered from the vandalization caused. Along with the dramatic loss of lives worldwide, the situation also proved to be disruptive in every sector of the economy. Education system also faced immense turbulence. It has impacted the younger children the most as the effectiveness of online classes is highly doubtful among them. Even the teachers are not able to deliver classes to their best ability. Keeping in mind the present circumstances and the return to state of normalcy, ‘The ELF Tribe' is designed as an educational tool for both teachers and students. There is substantial evidence in science to prove that kids learn better with hands-on experience but nothing can replace a good teacher. This application assists them in non-teaching tasks which, when blended with traditional schooling, has the potential to redefine meaningful schooling. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Diagnosis of COVID-19 Using Low-Energy IoT-Enabled System

The entire world is currently bustling battling the danger of the innovative coronavirus. Though losses of life are mounting sending stun waves to the open medicinal services authorities and foundations of different types everywhere throughout the world, worries over close to home cleanliness and actions like public isolation are persistently existent specified to break spreading of the disease. Novel and inventive methods for restricting the infection in its path are generally being talked about through technological loops. This is the manner by which a wide range of touchless technological answers for sense the day by day needs of individuals are getting center. In this specific situation, the job of versatile applications and the Internet of Things (IoT) are likewise going to have a significant effect. At this juncture through the extent of this support, we will clarify a portion of the significant techniques with IoT, and versatile applications can assume a positive job in halting the spread of the COVID-19 disease. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Business Intelligence Solution for Covid-19 Pandemic

The COVID-19 pandemic, first detected in December 2019, spread drastically globally within a short period. Such a pandemic has caused enormous loss of lives and health complications and adversely affected the world economies. Effective tracking of the spread of the disease around the globe and predicting when the next wave will occur has become critical in measures geared towards mitigating COVID-19. This paper explores different ways of utilizing analytics and business intelligence tools and solutions to understand the spread of COVID-19 around the globe and predict the number of new COVID-19 cases likely to be recorded. Microsoft Power BI is used to visualize COVID-19 data simply and intuitively in different ways for health decision-makers and concerned parties to easily understand the spread of COVID-19 through various visualizations and dashboards. We also utilize predictive analytics capabilities in Microsoft Power BI to predict the number of COVID-19 cases likely to be recorded in the next few months. The obtained results showed that COVID-19 cases increased over time, particularly in crowded countries. In addition, the results proved that the death rate is reducing with time even though the cases number is increasing. © 2022 IEEE.

Multi-Criteria Recommender System for Determining Risk Level of Covid-19

The coronavirus pandemic is a global disease outbreak causing countless loss of lives and also threatening the economic, social, religious, education, and other key sectors of nations. This highly infectious virus continues to spread rapidly and therefore, the need to develop innovative strategies and policies to curb the growing effects becomes very crucial. One significant approach is the introduction of lockdown measures, although this instrument is not completely dependable, due to possible adverse effects on societal activities. Prior to deployment, a number of criteria are taken into account, including demographic conditions, healthcare options, and Covid-19 case data. Depending on the influencing factors, a lockdown decision is typically made by assessing the different danger levels of a certain place. Consequently, this research propose a multi-criteria recommender system to determine the worth and risk of various regions, based on several constraints and databases. The model, which utilized the analytical network process (ANP) to discover interconnectedness and feedback, also included the weighting technique. In this study carried out in 27 districts and cities in West Java, Indonesia, 15% of the selected locations were categorized as high-risk levels. Meanwhile, 63% and 22% were associated with medium and low risk, respectively. © 2022 IEEE.

The ELF Tribe: Redefining Primary Education in a Post-COVID Era

Coronavirus brought the entire world to a grinding halt, and it has still not recovered from the vandalization caused. Along with the dramatic loss of lives worldwide, the situation also proved to be disruptive in every sector of the economy. Education system also faced immense turbulence. It has impacted the younger children the most as the effectiveness of online classes is highly doubtful among them. Even the teachers are not able to deliver classes to their best ability. Keeping in mind the present circumstances and the return to state of normalcy, ‘The ELF Tribe’ is designed as an educational tool for both teachers and students. There is substantial evidence in science to prove that kids learn better with hands-on experience but nothing can replace a good teacher. This application assists them in non-teaching tasks which, when blended with traditional schooling, has the potential to redefine meaningful schooling. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Novel Deep Learning Based Healthcare Model for COVID-19 Pandemic Stress Analysis

Coronavirus (COVID-19) has impacted nearly every person across the globe either in terms of losses of life or as of lockdown. The current coronavirus (COVID-19) pandemic is a rare/special situation where people can express their feelings on Internet-based social networks. Social media is emerging as the biggest platform in recent years where people spend most of their time expressing themselves and their emotions. This research is based on gathering data from Twitter and analyzing the behavior of the people during the COVID-19 lockdown. The research is based on the logic expressed by people in this perspective and emotions for the suffering of COVID-19 and lockdown. In this research, we have used a Long Short-Term Memory (LSTM) network model with Convolutional Neural Network using Keras python deep-learning library to determine whether social media platform users are depressed in terms of positive, negative, or neutral emotional out bust based on their Twitter posts. The results showed that the model has 88.14% accuracy (representation of the correct prediction over the test dataset) after 10 epochs which most tweets showed had neutral polarity. The evaluation shows interesting results in positive (1), negative (-1), and neutral (0) emotions through different visualization. © 2022 Tech Science Press. All rights reserved.

Covid-19 Vaccination Concerns and its way out in Rustic Zones

The world is experiencing Covid-19. As the pace of rate of Covid infection 2019 (COVID-19) is quickly expanding in various pieces of world, a dependable conjecture for the aggregate affirmed cases and the quantity of passing can be useful for policymakers in settling on the choices for using accessible assets in the country. The widespread of Covid 19 spoilage the world, with the highest loss of lives in US. To reduce the number of Covid-19 affected population, Vaccine are available in public domain. Some Covid 19 Vaccines are currently in human trials. For the effective result of Covid 19 Vaccine, it must be accepted by maximum number of population. A survey was conducted to analyze the health effect of the vaccine in different category of people. Information was collected such as demographic data (age, sex, gender, marital status), mental condition of people before vaccination, tobacco/smoking, alcohol consumption, people suffering from any prior disease, labour group, people taking precaution medicine after vaccination, prepare for second dose of vaccination. Using these given information we have applied machine learning algorithms to predict if the individual will take the second dose of Covid-19 vaccine or not. © 2022 IEEE.

Overview of surface plasmon resonance optical sensors for Covid-19 (SARS-CoV-2) detection

The SARS-CoV-2 i.e., the novel severe acute respiratory syndrome corona virus;has caused massive loss of life. Mitigating this pandemic requires rapid inexpensive technologies for testing COVID-19. Optical sensors can be used to detect the Covid-19 virus by the surface Plasmon resonance phenomenon. Surface plasmon resonance sensors have good sensitivity, response times, fine resolution, and limits of detection. This paper, provides a brief overview on the COVID-19 effects, currently used testing technology, and potential of surface plasmon resonance optical sensors use for detecting this virus. © 2021 Institute of Physics Publishing. All rights reserved.

Loss of life expectancy due to respiratory infectious diseases: findings from the global burden of disease study in 195 countries and territories 1990-2017.

Understanding of the patterns of and changes in mortality from respiratory infectious diseases (RID) and its contribution to loss of life expectancy (LE) is inadequate in the existing literature. With rapid sociodemographic changes globally, and the current COVID-19 pandemic, it is timely to revisit the disease burden of RID. Using the approaches of life table and cause-eliminated life table based on data from the Global Burden of Disease Study (GBD), the study analyses loss of LE due to RID in 195 countries/territories and its changes during the period 1990-2017. Results indicate that loss of LE due to RID stood at 1.29 years globally in 2017 globally and varied widely by age, gender, and geographic location, with men, elderly people, and populations in middle/low income countries/territories suffering a disproportionately high loss of LE due to RID. Additionally, loss of LE due to RID decreased remarkably by 0.97 years globally during the period 1990-2017 but increased slightly among populations older than 70 years and in many high income countries/territories. Results suggest that RID still pose a severe threat for population and public health, and that amid dramatic sociodemographic changes globally, the disease burden of RID may resurge. The study presents the first examination of the life-shortening effect of RID at the global and country/territory levels, providing new understanding of the changing disease burden of RID and shedding light on the potential consequences of the current COVID-19 pandemic.

A CNN-based CADx Model for Pneumonia Detection from Chest Radiographs with Web Application

-Pneumonia is a bacterial infection-caused life-threatening respiratory disease. About 15% all over the world kid's loss of life is triggered via pneumonia. A new virus called COVID-19 (in which) most important indications are pneumonia. Computer-aided diagnostic (CADx) methods have been studied for decades for the diagnosis of chest X-ray images based on lung diseases. For visual recognition, these tools assess the image properties derived from CNN. CNN filters a photo to acquire information from the chest X-ray. Throughout this study, we consider the performance of a customized CNN model used as feature extractors by the way of a variety of classifiers to distinguish the unusual and pneumonic chest X-Rays. Statistical findings point out that our CADx model can assist in the evaluation of clinical images as well. The user can insert their chest radiograph to the web app and find out their pneumonia condition, whether it is present or not present. Our proposed identification method's accuracy is 94% which is very high compared with other states of artwork. © 2021 IEEE.

Distribution Transformer Aging Due to COVID-19 Shelter-in-Place

Shelter-in-place orders induced by COVID-19 prevention may age distribution transformers numerous years in just several months. The following manuscript summarizes an analysis performed to identify distribution transformers' susceptibility to extreme aging and failure due to load profiles influenced by COVID-19 shelter-in-place orders. The results suggest only transformers serving peak demands greater than their rating may be susceptible to "sooner-than-expected" failure. The results also indicate transformers may only be susceptible if shelter-in-place orders continue longer than 6 months in warm climates. A 50 kVA liquid immersed transformer that originally served loads 120% of their ratings can age up to 4 years if the order was enforced longer than 6 months. For comparison, a transformer serving the historical load profile with a peak demand of 130% loading only ages 0.7 years in the six-month period. This same transformer ages 4.8 years if influenced by shelter-in-place orders. Therefore, utilities may see a higher number of transformer failures over the next several years due aging associated with one or multiple shelter-in-place orders.