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1.
An Interdisciplinary Approach in the Post-COVID-19 Pandemic Era ; : 1-290, 2022.
Article in English | Scopus | ID: covidwho-2101095

ABSTRACT

COVID-19 is not only a medical science issue, but it is also a critical issue for other experts such as social scientists, economists, technologists, psychiatrists, statisticians, sociologists, policymakers, politicians, and administrators, among others. Therefore, it is important to make collective efforts to deal with this pandemic. Interdisciplinary research is one of the best ways to achieve this. Interdisciplinary research is capable of bridging traditional divides between disciplines and also combines research excellence with relevant impact. Interdisciplinary research should be treated as policy research. The quality of the interdisciplinary research structure not only provides new ideas and areas of research, but also flexibility and expanded possibilities for traditional disciplines. This manuscript will likely inspire researchers and policymakers to further their interdisciplinary research on the coronavirus pandemic. In the present book, authors from diverse backgrounds have expressed their views on this specific problem. They have contributed their ideas on how the pandemic has affected every aspect of human life, including education, economics, social life, finance, information technology, etc. © 2022 by Nova Science Publishers, Inc. All rights reserved.

2.
An Interdisciplinary Approach in the Post-COVID-19 Pandemic Era ; : 197-206, 2022.
Article in English | Scopus | ID: covidwho-2092866

ABSTRACT

There is a lot of change in the learning of the students after the pandemic COVID-19. To study the resulting impact on their learning is the main aim of this article. To review this, a dataset of the various students is created and subsequently processed and visualized. The data is undergone to the various classification techniques using machine learning. It is observed after the analysis that the support vector machine (SVM) method is best in terms of the classification accuracy while random forest (RF) method is best in terms of the classification sensitivity. © 2022 Nova Science Publishers, Inc..

3.
An Interdisciplinary Approach in the Post-COVID-19 Pandemic Era ; : 33-45, 2022.
Article in English | Scopus | ID: covidwho-2092150

ABSTRACT

COVID-19 is a disease that belongs to the SARS COV-2 family and is highly infectious. Complete lockdown is the most common strategy employed by affected countries. Increased COVID-19 instances have affected people's health and daily lives and various sectors of the country. This chapter aims to explain the impact of COVID-19 on the various sectors. We examined ten various sectors in this study: financial institutions, agricultural and supply chains, tourism and hospitality, FMCG, Pharmaceuticals, Education, Railways, Delhi rail services, Power, and Telecom. © 2022 Nova Science Publishers, Inc..

4.
Lecture Notes in Computational Vision and Biomechanics ; 37:185-192, 2023.
Article in English | Scopus | ID: covidwho-1971588

ABSTRACT

The goal of this research is to see how well is a fast primary screening method for COVID-19 that relies only on cough sounds collected from 2200 clinically verified samples utilizing the laboratory molecular testing performs (1100 Covid-19 positive and 1100 Covid-19 negative). The clinical labels were applied to the results, and severity of the samples may be judged based on quantitative RT-PCR (qRT-PCR), cycle threshold, and patient lymphocyte counts. The fast spread of the COVID-19 virus poses a significant danger of serious pulmonary disease, and it also causes the most heinous harm to humanity. As a result, a quick and clear disease classification model to distinguish between normal and COVID-19 infected individuals is critical. In this article, we describe the various machine learning and other models that have been used to predict COVID-19 patients. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

5.
21st International Conference on Intelligent Systems Design and Applications (ISDA) ; 418:933-943, 2021.
Article in English | Web of Science | ID: covidwho-1866602

ABSTRACT

As per World Health Organization, COVID-19 is causing even the most important health systems across the countries under considerable strain. The advanced recognition of COVID 19 will result into decreasing the stress of a lot of health systems. Much similar to the customary usage of Chest X-Rays for detecting different pathologies, COVID-19 can also be detected using X-Ray of patients that indicates a very critical function in the diagnosis of SARS Covid-19. With rampant growth in the area of Deep Learning (DL) as well as Machine Learning (ML), it is much easier to design the framework that can detect COVID-19 infection easily. This paper proposes deep learning-based detection process by incorporating the concept of Transfer Learning for the classification of this pandemic using X-ray images of chest. This non-invasive and early-prediction of the corona virus by observing the X-rays of chest can subsequently be utilized to estimate the expansion of COVID-19 in the patients. This study got a maximum of 97% classifiers' accuracy using ResNet based model. This method can be utilized to upscale the effectiveness of the screening process.

6.
21st International Conference on Intelligent Systems Design and Applications, ISDA 2021 ; 418 LNNS:933-943, 2022.
Article in English | Scopus | ID: covidwho-1787720

ABSTRACT

As per World Health Organization, COVID-19 is causing even the most important health systems across the countries under considerable strain. The advanced recognition of COVID 19 will result into decreasing the stress of a lot of health systems. Much similar to the customary usage of Chest X-Rays for detecting different pathologies, COVID-19 can also be detected using X-Ray of patients that indicates a very critical function in the diagnosis of SARS Covid-19. With rampant growth in the area of Deep Learning (DL) as well as Machine Learning (ML), it is much easier to design the framework that can detect COVID-19 infection easily. This paper proposes deep learning-based detection process by incorporating the concept of Transfer Learning for the classification of this pandemic using X-ray images of chest. This non-invasive and early-prediction of the corona virus by observing the X-rays of chest can subsequently be utilized to estimate the expansion of COVID-19 in the patients. This study got a maximum of 97% classifiers’ accuracy using ResNet based model. This method can be utilized to upscale the effectiveness of the screening process. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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