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Current Issues in Tourism ; 2023.
Article in English | Scopus | ID: covidwho-2293888
Novel AI and Data Science Advancements for Sustainability in the Era of COVID-19 ; : 1-20, 2022.
Article in English | Scopus | ID: covidwho-2035524
International Journal of Electrical and Electronics Research ; 10(2):111-116, 2022.
Article in English | Scopus | ID: covidwho-1904222
J Med Biol Eng ; 41(5): 678-689, 2021.
Article in English | MEDLINE | ID: covidwho-1392062


Purpose: In early 2020, the world is amid a significant pandemic due to the novel coronavirus disease outbreak, commonly called the COVID-19. Coronavirus is a lung infection disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 virus (SARS-CoV-2). Because of its high transmission rate, it is crucial to detect cases as soon as possible to effectively control the spread of this pandemic and treat patients in the early stages. RT-PCR-based kits are the current standard kits used for COVID-19 diagnosis, but these tests take much time despite their high precision. A faster automated diagnostic tool is required for the effective screening of COVID-19. Methods: In this study, a new semi-supervised feature learning technique is proposed to screen COVID-19 patients using chest CT scans. The model proposed in this study uses a three-step architecture, consisting of a convolutional autoencoder based unsupervised feature extractor, a multi-objective genetic algorithm (MOGA) based feature selector, and a Bagging Ensemble of support vector machines based binary classifier. The proposed architecture has been designed to provide precise and robust diagnostics for binary classification (COVID vs.nonCOVID). A dataset of 1252 COVID-19 CT scan images, collected from 60 patients, has been used to train and evaluate the model. Results: The best performing classifier within 127 ms per image achieved an accuracy of 98.79%, the precision of 98.47%, area under curve of 0.998, and an F1 score of 98.85% on 497 test images. The proposed model outperforms the current state of the art COVID-19 diagnostic techniques in terms of speed and accuracy. Conclusion: The experimental results prove the superiority of the proposed methodology in comparison to existing methods.The study also comprehensively compares various feature selection techniques and highlights the importance of feature selection in medical image data problems.

Int J Oral Maxillofac Surg ; 50(8): 989-993, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-997028


Surgical practice during the coronavirus disease 2019 (COVID-19) pandemic has changed significantly, without supporting data. With increasing experience, a dichotomy of practice is emerging, challenging existing consensus guidelines. One such practice is elective tracheostomy. Here, we share our initial experience of head and neck cancer surgery in a COVID-19 tertiary care centre, emphasizing the evolved protocol of perioperative care when compared to pre-COVID-19 times. This was a prospective study of 21 patients with head and neck cancers undergoing surgery during the COVID-19 pandemic, compared to 193 historical controls. Changes in anaesthesia, surgery, and operating room practices were evaluated. A strict protocol was followed. One patient tested positive for COVID-19 preoperatively. There was a significant increase in pre-induction tracheostomies (28.6% vs 6.7%, P=0.005), median hospital stay (10 vs 7 days, P=0.001), and postponements of surgery (57.1% vs 27.5%, P=0.01), along with a significant decrease in flap reconstructions (33.3% vs 59.6%, P=0.03). There was no mortality and no difference in postoperative morbidity. No healthcare personnel became symptomatic for COVID-19 during this period. Tracheostomy is safe during the COVID-19 pandemic and rates have increased. Despite increased rescheduling of surgeries and longer hospital stays, definitive cancer care surgery has not been deferred and maximum patient and healthcare worker safety has been ensured.

COVID-19 , Head and Neck Neoplasms , Head and Neck Neoplasms/surgery , Humans , Pandemics , Prospective Studies , SARS-CoV-2 , Tracheostomy