Applied Enhanced Q-NAS for COVID-19 Detection in CT Images

The Deep Neural Networks are flexible and robust models that have gained attention from the machine learning community over the last decade. During the construction of a neural network, an expert can spend significant time designing a neural architecture with trial and error sessions. Because of the manual process, there is a greater interest in Neural Architecture Search (NAS), which is an automated method of architectural search in neural networks. Quantum-inspired evolutionary algorithms present propitious results regarding faster convergence when compared to other solutions with restricted search space and high computational costs. In this work, we enhance the Q-NAS model: a quantum-inspired algorithm to search for deep networks by assembling substructures. We present a new architecture that was designed automatically by the Q-NAS and applied to a case study for COVID-19 vs. healthy classification. For this classification, the Q-NAS algorithm was able to find a network architecture with only 1.23 M parameters that reached the accuracy of 99.44%, which overcame benchmark networks like Inception (GoogleLeNet), EfficientNet and VGG that were also tested in this work. The algorithm is publicly avaiable at https://github.com/julianoce/qnas. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Acceptance and Perception of Covid-19 Vaccination for Children

Covid-19 vaccine hesitancy and acceptance delay is an unprecedented challenge for concerned authorities. Existing studies lack the investigation about public vaccination acceptance, specifically for children. In this study, we surveyed the adult population in the UK to determine the diversity in public perception and acceptance of Covid-19 vaccination specifically for the children, among different sociodemographic groups. Statistical results and intelligent clustering outcomes indicate significant relationships between sociodemographic diversity and vaccination acceptance for children and their families. Acceptability for children is significantly dependent on ethnicity (p=3.7e-05), age group, and gender, where only 47% of participants show willingness towards children's vaccination. Primary dataset in this study, along with the experimental outcomes, might be useful for public awareness and policy makers towards better preparation for future epidemics as well as working globally to combat the ongoing Covid-19 variations while running effective vaccination campaigns in the identified sociodemographic groups. © 2022 IEEE.