Evaluating the Impact of COVID-19 on Society, Environment, Economy, and Education

Kumar, Vijay; Alshazly, Hammam; Idris, Sahar Ahmed; Bourouis, Sami

doi:10.3390/su132413642

Cited by 68 publications

(77 citation statements)

References 84 publications

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“…However, one of the major issues arising during the pandemic is the use and disposal of a large amount of medical waste from personal protective equipment(s) (PPE) such as masks, gloves, face shields, etc. Kumar et al (2021) discussed the impact of COVID-19 on society, environment, economy, education as well as globalization. Specifically, the influence of the pandemic on air pollution, water, as well as wildlife is discussed.…”

Section: Literature Reviewmentioning

confidence: 99%

Changes in water use and wastewater generation influenced by the COVID-19 pandemic: A case study of China

Jia

Shahzad

Klemeš

et al. 2022

Journal of Environmental Management

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Section: Literature Reviewmentioning

confidence: 99%

Changes in water use and wastewater generation influenced by the COVID-19 pandemic: A case study of China

Jia

Shahzad

Klemeš

et al. 2022

Journal of Environmental Management

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“…In severe cases, this illness can cause organ failure and breathing difficulties. Aside from the medical consequences, the disease had a massive economic and environmental impact on the world ( 4 , 5 ).…”

Section: Introductionmentioning

confidence: 99%

COVID-19 classification using chest X-ray images: A framework of CNN-LSTM and improved max value moth flame optimization

Hamza

Khan

Wang

et al. 2022

Front. Public Health

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Coronavirus disease 2019 (COVID-19) is a highly contagious disease that has claimed the lives of millions of people worldwide in the last 2 years. Because of the disease's rapid spread, it is critical to diagnose it at an early stage in order to reduce the rate of spread. The images of the lungs are used to diagnose this infection. In the last 2 years, many studies have been introduced to help with the diagnosis of COVID-19 from chest X-Ray images. Because all researchers are looking for a quick method to diagnose this virus, deep learning-based computer controlled techniques are more suitable as a second opinion for radiologists. In this article, we look at the issue of multisource fusion and redundant features. We proposed a CNN-LSTM and improved max value features optimization framework for COVID-19 classification to address these issues. The original images are acquired and the contrast is increased using a combination of filtering algorithms in the proposed architecture. The dataset is then augmented to increase its size, which is then used to train two deep learning networks called Modified EfficientNet B0 and CNN-LSTM. Both networks are built from scratch and extract information from the deep layers. Following the extraction of features, the serial based maximum value fusion technique is proposed to combine the best information of both deep models. However, a few redundant information is also noted; therefore, an improved max value based moth flame optimization algorithm is proposed. Through this algorithm, the best features are selected and finally classified through machine learning classifiers. The experimental process was conducted on three publically available datasets and achieved improved accuracy than the existing techniques. Moreover, the classifiers based comparison is also conducted and the cubic support vector machine gives better accuracy.

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“…In December 2019, an epidemic of a coronavirus disease (COVID-19) was reported, and due to its rapid spread in the entire world, the World Health Organization (WHO) indicated it as a pandemic [ 1 ]. This pandemic has wreaked havoc on the world, affecting all aspects of life including economy, education, society, and environment [ 2 ]. The timely diagnosis of infected patients is essential both for infection control and care of the patient [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Ensemble Deep Learning and Internet of Things‐Based Automated COVID‐19 Diagnosis Framework

Kini

Reddy²,

Kaur

et al. 2022

Contrast Media & Molecular Imaging

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Coronavirus disease (COVID-19) is a viral infection caused by SARS-CoV-2. The modalities such as computed tomography (CT) have been successfully utilized for the early stage diagnosis of COVID-19 infected patients. Recently, many researchers have utilized deep learning models for the automated screening of COVID-19 suspected cases. An ensemble deep learning and Internet of Things (IoT) based framework is proposed for screening of COVID-19 suspected cases. Three well-known pretrained deep learning models are ensembled. The medical IoT devices are utilized to collect the CT scans, and automated diagnoses are performed on IoT servers. The proposed framework is compared with thirteen competitive models over a four-class dataset. Experimental results reveal that the proposed ensembled deep learning model yielded 98.98% accuracy. Moreover, the model outperforms all competitive models in terms of other performance metrics achieving 98.56% precision, 98.58% recall, 98.75% F-score, and 98.57% AUC. Therefore, the proposed framework can improve the acceleration of COVID-19 diagnosis.

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Evaluating the Impact of COVID-19 on Society, Environment, Economy, and Education

Cited by 68 publications

References 84 publications

Changes in water use and wastewater generation influenced by the COVID-19 pandemic: A case study of China

Changes in water use and wastewater generation influenced by the COVID-19 pandemic: A case study of China

COVID-19 classification using chest X-ray images: A framework of CNN-LSTM and improved max value moth flame optimization

Ensemble Deep Learning and Internet of Things‐Based Automated COVID‐19 Diagnosis Framework

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