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1.
Computers & Electrical Engineering ; 93:107277, 2021.
Article in English | ScienceDirect | ID: covidwho-1275234

ABSTRACT

The drastic impact of COVID-19 pandemic is visible in all aspects of our lives including education. With a distinctive rise in e-learning, teaching methods are being undertaken remotely on digital platforms due to COVID-19. To reduce the effect of this pandemic on the education sector, most of the educational institutions are already conducting online classes. However, to make these digital learning sessions interactive and comparable to the traditional offline classrooms, it is essential to ensure that students are properly engaged during online classes. In this paper, we have presented novel deep learning based algorithms that monitor the student’s emotions in real-time such as anger, disgust, fear, happiness, sadness, and surprise. This is done by the proposed novel state-of-the-art algorithms which compute the Mean Engagement Score (MES) by analyzing the obtained results from facial landmark detection, emotional recognition and the weights from a survey conducted on students over an hour-long class. The proposed automated approach will certainly help educational institutions in achieving an improved and innovative digital learning method.

2.
Indian Journal of Community Health ; 33(1):3-8, 2021.
Article in English | Web of Science | ID: covidwho-1257676

ABSTRACT

Introduction: Following the pandemic, screening suspected individuals on a large scale is imperative to curtail the spread of the disease to a large extent. The walk-in kiosk is an ideal example of an innovation that is time and labour efficient and safe to use. Methodology and review of literature: Embase, Google Scholar, and PubMed were used to extract scholarly articles about the subject published worldwide. The Walk-in kiosk concept was an idea taken from the biosafety chamber used in advanced microbiology laboratories. Results: This ergonomic design enabled the HCW to perform better without bending forward or reaching out for the oropharyngeal or nasopharyngeal swabs. It avoids a great deal of inconvenience for both HCW and the patient.

3.
International Journal of Pharma and Bio Sciences ; 11(3):P56-P62, 2020.
Article in English | EMBASE | ID: covidwho-845952

ABSTRACT

Currently, the outbreak of the novel human respiratory coronavirus, also popularly known as COVID-19, has sought the attention of the scientific community across the world and stresses on the need for new therapeutic alternatives in order to cease the global health crisis and fight the pandemic. The situation, therefore, calls out for new researchcentred on targeting the pathogen. A number of studies reveal the potential of different chemical moieties that could possibly act against the virus. In our work, we report the semi-empirical based 3D-QSAR 3D-quantitaive structure and activity relationship/QSAR studies of 3 series of compounds viz. Ethacrynic Acid Derivatives (E1-E3), Isatin (2,3-oxindole) Inhibitors (I1-I7) and Flavonoid and Biflavonoid Derivatives (F1-F7) on the basis of their reported activities against SARS Co-V. The studies are carried out on Hyperchem 8.0 version software using AM1 and PM3 methods. Selected QSAR/3D-QSAR equations including different physical parameters of these series are reported.

4.
Journal of Indian Association for Child and Adolescent Mental Health ; 16(3):194-198, 2020.
Article in English | EMBASE | ID: covidwho-718266
5.
Chaos Solitons Fractals ; 140: 110190, 2020 Nov.
Article in English | MEDLINE | ID: covidwho-696427

ABSTRACT

The world is suffering from an existential global health crisis known as the COVID-19 pandemic. Countries like India, Bangladesh, and other developing countries are still having a slow pace in the detection of COVID-19 cases. Therefore, there is an urgent need for fast detection with clear visualization of infection is required using which a suspected patient of COVID-19 could be saved. In the recent technological advancements, the fusion of deep learning classifiers and medical images provides more promising results corresponding to traditional RT-PCR testing while making detection and predictions about COVID-19 cases with increased accuracy. In this paper, we have proposed a deep transfer learning algorithm that accelerates the detection of COVID-19 cases by using X-ray and CT-Scan images of the chest. It is because, in COVID-19, initial screening of chest X-ray (CXR) may provide significant information in the detection of suspected COVID-19 cases. We have considered three datasets known as 1) COVID-chest X-ray, 2) SARS-COV-2 CT-scan, and 3) Chest X-Ray Images (Pneumonia). In the obtained results, the proposed deep learning model can detect the COVID-19 positive cases in  ≤  2 seconds which is faster than RT-PCR tests currently being used for detection of COVID-19 cases. We have also established a relationship between COVID-19 patients along with the Pneumonia patients which explores the pattern between Pneumonia and COVID-19 radiology images. In all the experiments, we have used the Grad-CAM based color visualization approach in order to clearly interpretate the detection of radiology images and taking further course of action.

6.
Chaos Solitons Fractals ; 138: 109944, 2020 Sep.
Article in English | MEDLINE | ID: covidwho-401363

ABSTRACT

Presently, COVID-19 has posed a serious threat to researchers, scientists, health professionals, and administrations around the globe from its detection to its treatment. The whole world is witnessing a lockdown like situation because of COVID-19 pandemic. Persistent efforts are being made by the researchers to obtain the possible solutions to control this pandemic in their respective areas. One of the most common and effective methods applied by the researchers is the use of CT-Scans and X-rays to analyze the images of lungs for COVID-19. However, it requires several radiology specialists and time to manually inspect each report which is one of the challenging tasks in a pandemic. In this paper, we have proposed a deep learning neural network-based method nCOVnet, an alternative fast screening method that can be used for detecting the COVID-19 by analyzing the X-rays of patients which will look for visual indicators found in the chest radiography imaging of COVID-19 patients.

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