AI-based wavelet and stacked deep learning architecture for detecting coronavirus (COVID-19) from chest X-ray images.

A novel coronavirus (COVID-19), belonging to a family of severe acute respiratory syndrome coronavirus 2 (SARs-CoV-2), was identified in Wuhan city, Hubei, China, in November 2019. The disease had already infected more than 681.529665 million people as of March 13, 2023. Hence, early detection and diagnosis of COVID-19 are essential. For this purpose, radiologists use medical images such as X-ray and computed tomography (CT) images for the diagnosis of COVID-19. It is very difficult for researchers to help radiologists to do automatic diagnoses by using traditional image processing methods. Therefore, a novel artificial intelligence (AI)-based deep learning model to detect COVID-19 from chest X-ray images is proposed. The proposed work uses a wavelet and stacked deep learning architecture (ResNet50, VGG19, Xception, and DarkNet19) named WavStaCovNet-19 to detect COVID-19 from chest X-ray images automatically. The proposed work has been tested on two publicly available datasets and achieved an accuracy of 94.24% and 96.10% on 4 classes and 3 classes, respectively. From the experimental results, we believe that the proposed work can surely be useful in the healthcare domain to detect COVID-19 with less time and cost, and with higher accuracy.

A study on virtual engagement of women employees during COVID pandemic

Women worked in double shifts before pandemic to manage both work and their home responsibilities [5]. This was doubled during the Covid pandemic that they need to manage their house hold responsibilities, online classes for their kids along with their regular office work. Because of this most of the women are working round the clock during this pandemic. Women getting support from their family are managing everything, those who are not getting the support from their family members are thinking to quit their jobs [4]. This study tries to understand the virtual engagement of women employees during the Covid-19 pandemic. The questionnaire was framed and collected from 65 women employees who are taking their job through work from home such as IT sectors and educational institutions. The Frequency Distribution and Chi-square analysis are the sampling technique used for the study. The study has shown that there is a significant relationship between time management to accomplish the goal and getting colleagues support to complete the task assigned.

A study on c-reactive protein and liver function tests in laboratory rt-pcr positive covid-19 patients in a tertiary care centre – a retrospective study

Background: COVID-19 pandemic has been one of the greatest challenges to the global healthcare system. Although the respiratory system is the main target of SARS-CoV-2 infection;other organs, exposure to the viral infection might also be a concern for CVID-19 affected patients especially the cardiovascular system and liver. Objective: To know the status of C-reactive protein (CRP)and Liver Function Tests (LFT) in Covid-19 positive patients before initiating any treatment in a tertiary care hospital. Methods: Age and sex-matched 40 cases were taken for the study who were hospitalized and COVID-19 infection had been confirmed by real-time RT PCR for COVID-19. Patients with a previous history of liver illness, renal disorders, chronic inflammatory conditions, malignancy and autoimmune disorders were excluded from the study. Results: Almost all the liver enzymes were higher than the normal levels as seen in aspartate transaminase (35%), alanine transaminase (22.5%), alkaline phosphatase (20%), and gamma-glutamyl transaminase (35%). And whenever the protein, especially albumin was low there was an increased value of CRP and correspondingly with increased total and direct bilirubin levels. Conclusion: In our study liver function test was altered even before starting any treatment for SARS-CoV-2 indicates that LFT can be a tool to assess multiorgan involvement whenever the patient is going for complication or cytokine storm by doing serial measurements of liver function. ©@IJCRR.

Eco-friendly vaccination: Tackling an unforeseen adverse effect.

The beginning of 2021 was marked by COVID-19 vaccination campaigns worldwide. The pace of production has been accelerated, in order to meet global needs and achieve the desired levels of immunization of the general population against COVID-19 within the year. Several debatable aspects of this endeavor, from logistics to health promotion have been addressed so far. However, the environmental repercussions of plastic syringes used for massive COVID-19 vaccinations are yet to be discussed. This article delves into the impact of the increasing medical waste, associated with massive COVID-19 vaccination on the environment, citing the practices followed and its possible solutions. The increasing production of nonbiodegradable materials is inevitably going to affect the world we live in. .Moreover, this article highlights the importance of developing sustainable methods of vaccination and disposal, providing examples and evidence based recommendations. Along with educating the unaware proportion of the population, there is a need to develop sustainable and recyclable products for a better tomorrow.

Response of ambient BC concentration across the Indian region to the nation-wide lockdown: results from the ARFINET measurements of ISRO-GBP

In this study, we assess the response of ambient aerosol black carbon (BC) mass concentrations and spectral absorption properties across Indian mainland during the nation-wide lockdown (LD) in connection with the Coronavirus Disease 19 (COVID-19) pandemic. The LD had brought near to total cut-off of emissions from industrial, traffic (road, railways, marine and air) and energy sectors, though the domestic emissions remained fairly unaltered. This provided a unique opportunity to delineate the impact of fossil fuel combustion sources on atmospheric BC characteristics. In this context, the primary data of BC measured at the national network of aerosol observatories (ARFINET) under ISRO-GBP are examined to assess the response to the seizure of emissions over distinct geographic parts of the country. Results indicate that average BC concentrations over the Indian mainland are curbed down significantly (10–40%) from prelockdown observations during the first and most intense phase of lockdown. This decline is significant with respect to the long-term (2015–2019) averaged (climatological mean) values. The drop in BC is most pronounced over the Indo-Gangetic Plain (>60%) and north-eastern India (>30%) during the second phase of lockdown, while significant reduction is seen during LD1 (16–60%) over central and peninsular Indian as well as Himalayan and sub-Himalayan regions. Despite such a large reduction, the absolute magnitude of BC remained higher over the IGP and north-eastern sites compared to other parts of India. Notably, the spectral absorption index of aerosols changed very little over most of the locations, indicating the still persisting contribution of fossil-fuel emissions over most of the locations. [ABSTRACT FROM AUTHOR] Copyright of Current Science (00113891) is the property of Indian Academy of Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Data based analysis, modeling and forecasting of novel coronavirus in infected regions using extreme learning machine algorithm

In current development in science and technology, Machine learning algorithms play an essential role for prediction, classification, data analysis and data visualization. With this efficient algorithm, we can solve many real-world problems in all domains like education, healthcare, banking, geographical analysis, etc., in the current scenario;much research work is going on with the new virus's infection called the corona. This Corona virus is a comprehensive unit of virus this cause illness in humans or animals, now in East Asian countries, this virus affected more people. In India, the first case was found in January month, originated from China. The entire world is focusing on the disease, and day by day, the infection and death rate is increasing. In this, we intended to focus on the spread of this deadly disease and to demonstrate which countries are the most affected by doing statistical analysis. On December 2019, As of 10 February 2020, China reported overall of 40,235 cases 909 deaths, evoking local and foreign terror. Here we provide estimates of the major epidemiological parameters, Based on the epidemiological data available to the public for Hubei, China, 11 January to 10 February 2020. In particular, we give an estimate Fatality and case recovery rates, along with their 90 per cent confidence levels as the epidemic progresses. For this work implementation, Extreme Learning Machine algorithm used. ELM is a feed-forward network and its learning rate also fast when compare to normal neural network. No need to provide any weights and bias values. This algorithm will give a promising result with the best accuracy. © 2020 IOP Conference Series: Materials Science and Engineering.