Analyzing and Battling The Emerging Variants Of Covid-19 Using Artificial Neural Network And Blockchain

R. U. Khan, A. Haq, S. Hussain, Shakir Ullah, Sultan Almakdi, Rajesh Kumar, H. H. Shah, Jianping Li
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引用次数: 3

Abstract

The fast expansion of the COVID-19 epidemic has revealed the shortcomings of current healthcare institutions in dealing with public emergency situations. One of the big reasons of Covid-19 spread is the lack of standard track and trace mechanisms in healthcare infrastructures. Furthermore, throughout the epidemic, the transmission of disinformation has accelerated, and existing platforms lacking capability of verifying the veracity of information, resulting to social unrest and illogical conduct. Therefore, building a track and trace system is critical to ensuring that data collected by the government and the public entities is accurate and dependable. It is obvious that implementing state-of-the-art predictive models like Artificial Neural Network and Blockchain-based traceable mechanisms can help to prevent the spreads of the new variants. In this paper, we proposed a Blockchain based traceable model to track and trace the infected cases so to help an effective planning to prevent the spread.
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利用人工神经网络和区块链分析和对抗新出现的Covid-19变体
新冠肺炎疫情的快速蔓延,暴露了当前医疗机构在应对突发公共事件方面的不足。Covid-19传播的一个重要原因是医疗基础设施缺乏标准的跟踪和追踪机制。此外,在整个疫情期间,虚假信息的传播速度加快,现有平台缺乏核实信息真实性的能力,导致社会动荡和不合逻辑的行为。因此,建立跟踪和追溯系统对于确保政府和公共实体收集的数据准确可靠至关重要。很明显,实施最先进的预测模型,如人工神经网络和基于区块链的可追溯机制,可以帮助防止新变种的传播。在本文中,我们提出了一种基于区块链的可追溯模型来跟踪和追踪感染病例,从而帮助有效规划防止传播。
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