论文标题
部分可观测时空混沌系统的无模型预测
Advancing the cybersecurity of the healthcare system with self-optimising and self-adaptative artificial intelligence (part 2)
论文作者
论文摘要
本文促进了有关教学和培训新的人工智能算法的知识，以保护，准备和适应医疗保健系统以应对未来的大流行系统。核心目标是开发一个由自主人工智能支持的概念医疗保健系统，该系统可以将边缘健康设备与实时数据一起使用。本文构建了两种情况，用于将网络安全和自主人工智能应用于（1）疾病X事件期间医疗保健系统失败的预测性网络风险分析（即未定义的未来大流行），以及（2）在未来的Pandemics期间对医疗生产和供应链的自我适应性预测。为了构建两种测试场景，本文使用Covid-19的案例合成算法数据，即，以优化和确保数字医疗保健系统，以期待疾病X。构建了测试方案，以应对物理上的挑战，并破坏与疫苗分布的复杂生产和供应链，并具有优化藻类的疫苗分布。
This article advances the knowledge on teaching and training new artificial intelligence algorithms, for securing, preparing, and adapting the healthcare system to cope with future pandemics. The core objective is to develop a concept healthcare system supported by autonomous artificial intelligence that can use edge health devices with real-time data. The article constructs two case scenarios for applying cybersecurity with autonomous artificial intelligence for (1) self-optimising predictive cyber risk analytics of failures in healthcare systems during a Disease X event (i.e., undefined future pandemic), and (2) self-adaptive forecasting of medical production and supply chain bottlenecks during future pandemics. To construct the two testing scenarios, the article uses the case of Covid-19 to synthesise data for the algorithms i.e., for optimising and securing digital healthcare systems in anticipation of disease X. The testing scenarios are built to tackle the logistical challenges and disruption of complex production and supply chains for vaccine distribution with optimisation algorithms.
