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相关概念视频

Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,

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相关实验视频

Updated: May 12, 2026

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
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混合最佳特征选择为基础的代深度卷积学习,用于COVID-19分类系统.

P Santosh Kumar Patra1, Biswajit Tripathy2

  • 1Research Scholar, Department of Computer Science and Engineering, Biju Patnaik University of Technology, Rourkela, Odisha, 769015, India.

Computers in biology and medicine
|August 22, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了最佳代COVID-19分类网络 (OICC-Net),用于使用物联网 (IoT) 数据准确检测COVID-19. 由人工智能驱动的OICC-Net在识别SARS-CoV-2方面实现了高精度,提高了早期诊断能力.

关键词:
黑寡妇优化优化 黑寡妇优化在COVID-19大流行中,物联网的物联网,就是物联网.代的深度卷积学习.机器学习优化优化粒子群集优化优化 粒子群集优化远程监控患者的监控.

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科学领域:

  • 医疗信息学 医疗信息学
  • 医疗保健中的人工智能
  • 计算生物学 计算生物学

背景情况:

  • 随着COVID-19的爆发,人们越来越需要快速,准确的诊断工具.
  • 物联网 (IoT) 设备产生了对医疗保健应用至关重要的大量数据集.
  • 传统的人工智能 (AI) 方法与物联网数据的复杂性和规模作斗争,以预测疾病.

研究的目的:

  • 利用物联网数据开发基于人工智能的系统,用于早期检测COVID-19.
  • 解决传统AI在分析复杂物联网数据集以预测疾病方面的局限性.
  • 实施和评估最佳代COVID-19分类网络 (OICC-Net) 以提高诊断准确度.

主要方法:

  • 使用预处理技术进行数据集规范化.
  • 使用随机森林注入粒子群基于黑寡妇优化 (RFI-PS-BWO) 算法进行特征提取,以识别特定疾病的模式.
  • 代深度卷积学习 (IDCL) 用于增强特征选择和缩小维度.
  • 使用一维卷积神经网络 (1D-CNN) 在大型COVID-19数据集上训练的分类.

主要成果:

  • RFI-PS-BWO算法成功提取了特定疾病的模式,将SARS-CoV-2与类似病毒区分开来.
  • IDCL方法改善了特征表示,并减少了数据维度.
  • 该OICC-Net实现了高性能指标:99.97%的F1得分,100%的灵敏度,100%的特异性,99.98%的精度和99.99%的回忆.
  • 与现有方法相比,拟议的OICC-Net显示出更高的准确性.

结论:

  • OICC-Net系统有效地利用AI和物联网数据进行准确的COVID-19检测.
  • RFI-PS-BWO,IDCL和1D-CNN的组合为疾病预测提供了一个强大的方法.
  • 这种人工智能驱动的方法显著提高了对COVID-19等传染病的早期检测和诊断能力.