Street food nostalgia and COVID-19 perceptions on street food desire

With COVID-19 paralyzing street food businesses, street food vendors are trying to sustain their operations. The current study helps them by identifying the importance of five prominent stimuli viz. authenticity, quality, staff-service, ambience, and value for money in developing desire for street food in individuals in India. Furthermore, the study contributes by identifying the role of street food nostalgia (as a mediator) and perceived risk of COVID-19, age, and gender (as moderators) on the direct impact of each stimulus on the desire for street food. The study uses partial least squares path modelling to validate the hypotheses using SmartPLS. The findings are comparable to other developing Asian countries, as the proposed associations are validated with perceptual responses from three prominent cities and well-known street food destinations in India. The study showed the relative importance of the five-stimuli based on the stimulus-organism-response framework in developing a desire for street food. The findings suggest partial to complete mediation of street food nostalgia across the three samples. Lastly, the perceived risk of COVID-19 along with age and gender emerged as prominent moderators for many of the direct effects of stimuli on desires for street food. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Deep learning-based hybrid models for prediction of COVID-19 using chest X-ray

The ongoing COVID-19 virus infection has ended up being the biggest pandemic to hit mankind in the last century. It has infected in excess of 50 Million across the globe and has taken in excess of 1.5 Million lives. It has posed problems even to the best healthcare systems across the globe. The best way to reduce the spread and damage of COVID-19 is by early detection of the infection and quarantining the infected patients with necessary medical care. COVID-19 infection can be detected by a chest X-ray. With limited rapid COVID-19 testing kits, this approach of detection with the aid of deep learning can be adopted. The only problem being, the side effects of COVID-19 infection imitate those of conventional Pneumonia, which adds some complexity in utilizing the Chest X-rays for its prediction. In this investigation, we attempt to investigate four approaches i.e., Feature Ensemble, Feature Extraction, Layer Modification and weighted Max voting utilizing State of the Art pre-trained models to accurately identify between COVID-19 Pneumonia, Non-COVID-19 Pneumonia, and Healthy Chest X-ray images. Since very few images of patients with COVID-19 are publicly available, we utilized combinations of image processing and data augmentation methods to build more samples to improve the quality of predictions. Our best model i.e., Modified VGG-16, has achieved an accuracy of 99.5216%. More importantly, this model did not predict a False Negative Normal (i.e., infected case predicted as normal), making it the most attractive feature of the study. The establishment of such an approach will be useful to predict the outbreak early, which in turn can aid in controlling it effectively. © 2022 Elsevier Inc. All rights reserved.

Effective Cyber Security Using IoT to Prevent E-Threats and Hacking During Covid-19

This research work is conducted to make the analysis of digital technology is one of the most admired and effective technologies that has been applied in the global context for faster data management. Starting from business management to connectivity, everywhere the application of IoT and digital technology is undeniable. Besides the advancement of the data management, cyber security is also important to prevent the data stealing or accessing from the unauthorized data. In this context the IoT security technology focusing on the safeguarding the IoT devices connected with internet. Different technologies are taken under the consideration for developing the IoT based cyber security such as Device authentication, Secure on boarding, data encryption and creation of the bootstrap server. All of these technologies are effective to its ground for protecting the digital data. In order to prevent cyber threats and hacking activities like SQL injection, Phishing, and DoS, this research paper has proposed a newer technique of the encryption process by using the python codes and also shown the difference between typical conventional system and proposed system for understanding both the system in a better way. © 2022 by Dr. Santosh Kumar, Dr. Rajeev Yadav, Dr. Priyanka Kaushik, S B G Tilak Babu, Dr. Rajesh Kumar Dubey and Dr. Muthukumar Subramanian.

Multi-objective Genetic Algorithm Based Deep Learning Model for Automated COVID-19 Detection Using Medical Image Data.

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.

Increased tracheostomy rates in head and neck cancer surgery during the COVID-19 pandemic.

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.