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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
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Author Spotlight: Automated Deep Brain Stimulation for Parkinson's Disease - Exploring the Possibilities and Challenges of Home Monitoring
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区块链和机器学习启发了安全的智能家居通信网络.

Subhita Menon1, Divya Anand1, Kavita2

  • 1School of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, India.

Sensors (Basel, Switzerland)
|July 14, 2023
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概括

本研究介绍了使用区块链和云数据评估的智能家居网络的安全学习引擎. 它提高了安全性和效率,优于现有的AI-IoT技术,为更好的用户生活方式提供了更好的性能.

关键词:
莱文伯格模型是一个模型.区块链区块链区块链区块链区块链达成共识的协议协议.龙算法 龙算法 龙算法智能合约是一个智能合约.

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科学领域:

  • 计算机科学 计算机科学
  • 网络安全 网络安全
  • 人工智能的人工智能

背景情况:

  • 通过物联网 (IoT) 互联的智能家居设备的扩散带来了重大的安全挑战.
  • 现有的安全措施很难有效地管理智能家居网络中复杂的数据流和潜在威胁.

研究的目的:

  • 为智能家居通信网络提出一种新的学习引擎,该引擎集成区块链和基于云的数据评估.
  • 提高智能家居通信系统的安全性,效率和决策能力.

主要方法:

  • 实现一个区块链层,用于安全的用户身份验证和网络分类账生成.
  • 利用基于云的数据评估层将智能家居数据分类为智能交易 (T),修改交易 (T) 和避免交易 (T).
  • 采用神经网络进行训练和分类,以改善区块链层的决策.

主要成果:

  • 与融合实时序列深度极端学习机器 (RTS-DELM),数据融合技术和AI-IoT技术相比,拟的学习引擎表现出卓越的性能.
  • 实现了较低的平均计算复杂性和较低的虚假身份验证率.
  • 提供了增强的电子信息工程和优化方案.

结论:

  • 开发的系统确保了安全高效的智能家居通信网络.
  • 区块链和人工智能驱动的数据分析的整合显著改善了网络安全和性能.
  • 改进后的系统通过安全高效的智能家居技术,有助于改善整体生活方式.