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Quantum Inspired Differential Evolution with Explainable Artificial Intelligence-Based COVID-19 Detection

Abdullah M. Basahel, Mohammad Yamin*

Department of Management Information Systems, Faculty of Economics and Administration, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

* Corresponding Author: Mohammad Yamin. Email: email

Computer Systems Science and Engineering 2023, 46(1), 209-224. https://doi.org/10.32604/csse.2023.034449

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.

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APA Style
Basahel, A.M., Yamin, M. (2023). Quantum inspired differential evolution with explainable artificial intelligence-based COVID-19 detection. Computer Systems Science and Engineering, 46(1), 209-224. https://doi.org/10.32604/csse.2023.034449
Vancouver Style
Basahel AM, Yamin M. Quantum inspired differential evolution with explainable artificial intelligence-based COVID-19 detection. Comput Syst Sci Eng. 2023;46(1):209-224 https://doi.org/10.32604/csse.2023.034449
IEEE Style
A.M. Basahel and M. Yamin, "Quantum Inspired Differential Evolution with Explainable Artificial Intelligence-Based COVID-19 Detection," Comput. Syst. Sci. Eng., vol. 46, no. 1, pp. 209-224. 2023. https://doi.org/10.32604/csse.2023.034449



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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