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1.
Malaysian Journal of Medicine and Health Sciences ; 18:72-82, 2022.
Article in English | Scopus | ID: covidwho-2146716

ABSTRACT

Introduction: Online teaching, learning, and evaluation are inevitable to ensure the continuity of medical education delivery throughout the COVID-19 Pandemic. Based on medical lecturers' experiences during the COVID-19 movement control order (MCO), this study looked into the problems of implementing online teaching, learning, and assessment. Methods: During the COVID-19 MCO, a hermeneutic phenomenology study was conducted using reflective written exercises to explore the challenges faced by medical lecturers. The medical lecturers were given online open-ended questions via a Google form to help them reflect on their previous experiences. The reflective written comments were analysed by ATLAS.ti. Thematic analysis was performed for coding and categorizing the reflective comments into meaningful codes, categories, and themes. Results: A total of 29 medical lecturers responded to the open-ended reflective questions. They were 16 females, and 13 males representing four main medical specialties: basic science (n=10), medical-based (n=9), surgical-based (n=5), and laboratory-based (n=5). The thematic analysis identified five themes of challenges faced by medical lecturers during the pandemic that include ICT facility and support, lecturers' receptivity, online students' engagement, online assessment, and online teaching. Conclusion: This study emphasised the common obstacles faced by medical lecturers during the COVID-19 MCO in order to maintain the continuity of medical education delivery. Students, lecturers, curriculum, ICT facility, and technical assistance were all part of the issues. Several proposals for charting ways to improve medical education delivery during the epidemic were explored. © 2022 UPM Press. All rights reserved.

2.
6th International Conference on Inventive Systems and Control, ICISC 2022 ; 436:775-788, 2022.
Article in English | Scopus | ID: covidwho-2014003

ABSTRACT

This study is divided into risk factor analysis (RFA) and proposed system architecture (PSA). The light gradient boosting machine (LightGBM) algorithm in the RFA will work with the PSA to predict the risk factors. The results, efficacy, and performance will be validated via a ROC-AUC curve. Therefore, a system usability scale (SUS) procedure will be implemented to increase the performance. If the SUS score reaches 85–99 and 100 thresholds, it will be classified as appropriate for use and robust. The prediction score thresholds will be 0–100. If the score is below 25, it will be classified as normal, 26–50 as moderate, 51–70 risk, and 71–100 as severe. Due to a shortage of experienced staff and intelligent technology, it is becoming progressively difficult to reduce COVID-19 fatality rates. In this research, a lightweight mobile application has been suggested from which the significant patterns and factors can be recognised. Furthermore, it will assist both doctors and patients become aware of COVID-19 risk factors and take the required steps to mitigate them. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

3.
7th IEEE International conference for Convergence in Technology, I2CT 2022 ; 2022.
Article in English | Scopus | ID: covidwho-1992601

ABSTRACT

In this study, the Traditional Convolution Neural Network (TCNN) and state-of-the-art approaches were applied to the datasets of Chest X-ray and CT scan imaging modalities and trained them concurrently. The TCNN's performance for detecting COVID-19 infected tissues was determined through a comparison examination using state-of-the-art approaches. The accuracy of the models has been improved by lowering the model's losses and overfitting. Finally, the training data size has been enhanced utilizing various picture augmentation methods such as flip-up-down, flip-down-left-right, and so on. VGG19 and InceptionV3 were tested in this work, and accuracy scores of 97 percent (X-ray images) and 96 percent (CT-scan images) were obtained. The model's loss functions, Precision, Recall, and F1-Score, were extracted and interpreted in the study. We examined the researchers' modified DL models and discovered that they were 65 percent accurate on X-ray data and 62 percent accurate on CT scan images. Experiments have demonstrated that when the number of sample images rises, the VGG19 and InceptionV3 perform well. © 2022 IEEE.

4.
Computers, Materials and Continua ; 72(1):2033-2053, 2022.
Article in English | Scopus | ID: covidwho-1732655

ABSTRACT

On the edge of the worldwide public health crisis, the COVID-19 disease has become a serious headache for its destructive nature on humanity worldwide. Wearing a facial mask can be an effective possible solution to mitigate the spreading of the virus and reduce the death rate. Thus, wearing a face mask in public places such as shopping malls, hotels, restaurants, homes, and offices needs to be enforced. This research work comes up with a solution of mask surveillance system utilizing the mechanism of modern computations like Deep Learning (DL), Internet of things (IoT), and Blockchain. The absence or displacement of the mask will be identified with a raspberry pi, a camera module, and the operations of DL and Machine Learning (ML). The detected information will be sent to the cloud server with the mechanism of IoT for real-time data monitoring. The proposed model also includes a Blockchain-based architecture to secure the transactions of mask detection and create efficient data security,monitoring, and storage fromintruders. This research further includes an IoT-based mask detection scheme with signal bulbs, alarms, and notifications in the smartphone. To find the efficacy of the proposed method, a set of experiments has been enumerated and interpreted. This research work finds the highest accuracy of 99.95% in the detection and classification of facial masks. Some related experiments with IoT and Block-chain-based integration have also been performed and calculated the corresponding experimental data accordingly.ASystemUsability Scale (SUS) has been accomplished to check the satisfaction level of use and found the SUS score of 77%. Further, a comparison among existing solutions on three emergent technologies is included to track the significance of the proposed scheme. However, the proposed system can be an efficient mask surveillance system for COVID-19 and workable in real-time mask detection and classification. © 2022 Tech Science Press. All rights reserved.

5.
Asian Journal of Microbiology, Biotechnology and Environmental Sciences ; 22(3):497-503, 2020.
Article in English | EMBASE | ID: covidwho-1357796

ABSTRACT

Coronavirus disease 2019 (COVID-19) represents a significant and urgent threat to global health and is a respiratory tract infection caused by a newly emergent coronavirus, SARS-CoV-2 with mild to severe outcomes. Genetic sequencing of the virus suggests that SARS-CoV-2 is a beta coronavirus closely linked to the SARS virus. Recent studies have begun to reveal some fundamental aspects of the complicated host-HCoV interaction, multiplication, epidemiology, symptoms and possible treatments in detail. In this review, we recapitulate the current knowledge of types of signalling mechanisms and pathways opted by the virus during HCoV infection, with emphasis on genetics, epidemiology, routes of transmission, its comparison with other flus, statistical data of the confirmed cases, and precautions. The cross talk among the vaccinations is also discussed.

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