ABSTRACT
While ICT is burgeoning in southeast Asia, online food delivery (OFD) picking upstream due to its concrete influence on the mob’s experience. The COVID-19 pandemic caused an unprecedented impact in most commerce including OFD due to the escalation of safety aspects. On account of the explosion of the pandemic and to prevent the spread of COVID-19, socio- and economic factors arise that likely turn OFD potential user’s attitudes and behavior. This report highlights a six-month-long online survey (n = 158) in Bangladesh that analyzes the fluctuation in OFD consumer tendency, identifying the polarization of potential purchasers during the COVID-19 and considering the safety, e-satisfaction, and e-trust. Besides, we also discussed the associations between these determinants that are responsible for polarizing the consumers into marginal groups during the pandemic in developing countries like Bangladesh and proposed some suggestions for OFD service providers based on our findings. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Since the onset of COVID-19, radiographic image analysis coupled with artificial intelligence (AI) has become popular due to insufficient RT-PCR test kits. In this paper, an automated AI-assisted COVID-19 diagnosis scheme is proposed utilizing the ensembling approach of multiple convolutional neural networks (CNNs). Two different strategies have been carried out for ensembling: A feature level fusionbased ensembling method and a decision level ensembling method. Several traditional CNN architectures are tested and finally in the ensembling operation, MobileNet, InceptionV3, DenseNet201, DenseNet121 and Xception are used. To handle the computational complexity of multiple networks, transfer learning strategy is incorporated through ImageNet pre-trained weight initialization. For feature-level ensembling scheme, global averages of the convolutional feature maps generated from multiple networks are aggregated and undergo through fully connected layers for combined optimization. Additionally, for decision level ensembling scheme, final prediction generated from multiple networks are converged into a single prediction by utilizing the maximum voting criterion. Both strategies perform better than any individual network. Outstanding performances have been achieved through extensive experimentation on a public database with 96% accuracy on 3-class (COVID-19/normal/pneumonia) diagnosis and 89.21% on 4-class (COVID-19/normal/viral pneumonia/bacterial pneumonia) diagnosis. © 2020 IEEE.
ABSTRACT
In December 2019, a novel corona virus SARS-CoV-2 causes atypical pneumonia now known as corona virus disease 2019 COVID-19 emerged in Wuhan, China and spread rapidly throughout the world.