ABSTRACT
Recent advancements in the Internet of Things (Io), 5G networks, and cloud computing (CC) have led to the development of Human-centric IoT (HIoT) applications that transform human physical monitoring based on machine monitoring. The HIoT systems find use in several applications such as smart cities, healthcare, transportation, etc. Besides, the HIoT system and explainable artificial intelligence (XAI) tools can be deployed in the healthcare sector for effective decision-making. The COVID-19 pandemic has become a global health issue that necessitates automated and effective diagnostic tools to detect the disease at the initial stage. This article presents a new quantum-inspired differential evolution with explainable artificial intelligence based COVID-19 Detection and Classification (QIDEXAI-CDC) model for HIoT systems. The QIDEXAI-CDC model aims to identify the occurrence of COVID-19 using the XAI tools on HIoT systems. The QIDEXAI-CDC model primarily uses bilateral filtering (BF) as a preprocessing tool to eradicate the noise. In addition, RetinaNet is applied for the generation of useful feature vectors from radiological images. For COVID-19 detection and classification, quantum-inspired differential evolution (QIDE) with kernel extreme learning machine (KELM) model is utilized. The utilization of the QIDE algorithm helps to appropriately choose the weight and bias values of the KELM model. In order to report the enhanced COVID-19 detection outcomes of the QIDEXAI-CDC model, a wide range of simulations was carried out. Extensive comparative studies reported the supremacy of the QIDEXAI-CDC model over the recent approaches. © 2023 Authors. All rights reserved.
ABSTRACT
Privacy and security are unavoidable challenges in the future of smart health services and systems. Several approaches for preserving privacy have been provided in the Internet of Health Things (IoHT) applications. However, with the emergence of COVID-19, the healthcare centers needed to track, collect, and share more critical data such as the location of those infected and monitor social distancing. Unfortunately, the traditional privacy-preserving approaches failed to deal effectively with emergency circumstances. In the proposed research, we introduce a Tokens Shuffling Approach (TSA) to preserve collected data's privacy, security, and reliability during the pandemic without the need to trust a third party or service providers. TSA depends on a smartphone application and the proposed protocol to collect and share data reliably and safely. TSA depends on a proposed algorithm for swapping the identities temporarily between cooperated users and then hiding the identities by employing fog nodes. The fog node manages the cooperation process between users in a specific area to improve the system's performance. Finally, TSA uses blockchain to save data reliability, ensure data integrity, and facilitate access. The results prove that TSA performed better than traditional approaches regarding data privacy and the performance level. Further, we noticed that it adapted better during emergency circumstances. Moreover, TSA did not affect the accuracy of the collected data or its related statistics. On the contrary, TSA will not affect the quality of primary healthcare services. © 2022 by the authors.