Synergic Deep Learning for Smart Health Diagnosis of COVID-19 for Connected Living and Smart Cities

COVID-19 pandemic has led to a significant loss of global deaths, economical status, and so on. To prevent and control COVID-19, a range of smart, complex, spatially heterogeneous, control solutions, and strategies have been conducted. Earlier classification of 2019 novel coronavirus disease (COVID-19) is needed to cure and control the disease. It results in a requirement of secondary diagnosis models, since no precise automated toolkits exist. The latest finding attained using radiological imaging techniques highlighted that the images hold noticeable details regarding the COVID-19 virus. The application of recent artificial intelligence (AI) and deep learning (DL) approaches integrated to radiological images finds useful to accurately detect the disease. This article introduces a new synergic deep learning (SDL)-based smart health diagnosis of COVID-19 using Chest X-Ray Images. The SDL makes use of dual deep convolutional neural networks (DCNNs) and involves a mutual learning process from one another. Particularly, the representation of images learned by both DCNNs is provided as the input of a synergic network, which has a fully connected structure and predicts whether the pair of input images come under the identical class. Besides, the proposed SDL model involves a fuzzy bilateral filtering (FBF) model to pre-process the input image. The integration of FBL and SDL resulted in the effective classification of COVID-19. To investigate the classifier outcome of the SDL model, a detailed set of simulations takes place and ensures the effective performance of the FBF-SDL model over the compared methods.

Smart Health From the Hospital to the City

The COVID-19 pandemic has accelerated the transition of health systems from “hospital” to “city” thanks to smart devices aimed at health promotion through the deployment of ICT in urban contexts. Worldwide, the pandemic has triggered a rethinking of the healthcare system according to the prevention principle, characterized by the usage of advanced technology, which can widely re-orient markets and public action in the field of public and private health services. The chapter will consider the impacts of such innovations and the challenges for the regulatory framework, with a special focus on the Italian context. First, it outlines the main global and EU strategies for digital healthcare. Second, it focuses on the use of smart devices as tools of health prevention. Third, it highlights critical aspects regarding competition and privacy. Finally, the author reflects upon the centrality of the city as a crucial actor for urban health innovation and its upcoming regulatory challenges.

Role of Big Data in Internet of Things Networks

With recent advancement in cyber-physical systems and technological revolutions, internet of things is the focus of research in industry as well as in academia. IoT is not only a research and technological revolution but in fact a revolution in our daily life. It is considered a new era of smart lifestyle and has a deep impact on everyday errands. Its applications include but are not limited to smart home, smart transportation, smart health, smart security, and smart surveillance. A large number of devices connected in all these application networks generates an enormous amount of data. This leads to problems in data storage, efficient data processing, and intelligent data analytics. In this chapter, the authors discuss the role of big data and related challenges in IoT networks and various data analytics platforms, used for the IoT domain. In addition to this, they present and discuss the architectural model of big data in IoT along with various future research challenges. Afterward, they discuss smart health and smart transportation as a case study to supplement the presented architectural model.