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Issues And Trends In Healthcare Delivery System01:29

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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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Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
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Natural flora, body system defenses, and inflammation are natural barriers of the body against infectious agents regardless of previous exposure. Normal floras of the human body refer to the microbial population that colonizes the skin and mucous membranes.
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相关实验视频

Updated: Jul 26, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
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人工智能驱动的恶意软件检测框架用于物联网环境环境.

Shtwai Alsubai1, Ashit Kumar Dutta2, Abdullah M Alnajim3

  • 1Prince Sattam Bin Abdulaziz University, Al-Kharj, Kingdom of Saudi Arabia.

PeerJ. Computer science
|June 22, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种基于图像的恶意软件检测 (MD) 框架,用于物联网 (IoT) 安全. 这种新的方法在识别恶意软件方面实现了高精度,提高了物联网资源保护.

关键词:
卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.工业物联网工业物联网工业物联网物联网的物联网,就是物联网.机器学习 机器学习恶意软件检测 恶意软件检测

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科学领域:

  • 网络安全 网络安全
  • 计算机科学 计算机科学
  • 机器学习 机器学习

背景情况:

  • 物联网 (IoT) 环境需要强大的恶意软件检测 (MD) 框架来保护敏感数据.
  • 现有的MD框架面临着有效保护物联网资源免受不断变化的威胁的挑战.

研究的目的:

  • 开发和评估专门针对物联网环境的基于图像的创新恶意软件检测框架.
  • 为了提高物联网设备中的恶意软件分类的准确性和效率.

主要方法:

  • 恶意软件二进制文件使用图像转换和增强技术转换为RGB图像.
  • 你只看一次 (Yolo V7) 用于从恶意软件图像中提取功能.
  • 通过哈里斯·霍克斯对图像分类的优化,优化了DenseNet161模型.

主要成果:

  • 与现有的MD框架相比,拟议的框架表现出优越的性能.
  • 在物联网恶意软件数据集上实现了高精度 (98.65%) 和F1得分 (98.5%).
  • 在Virusshare数据集上也观察到出色的表现,准确率为97.3%,F1得分为96.63%.

结论:

  • 开发的基于图像的MD框架有效地保护物联网资源.
  • 该框架的高精度和强大的性能使其适合在物联网环境中部署.
  • 这种方法提供了一个有希望的解决方案,用于增强物联网安全对恶意软件威胁.