Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

125
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
125

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Optimising hyperparameters with a tree structured Parzen estimator to improve diabetes prediction.

Scientific reports·2025
Same author

Reprofiling lamivudine as an antibiofilm and anti-pathogenic agent against Pseudomonas aeruginosa.

AMB Express·2025
Same author

An improved biometric stress monitoring solution for working employees using heart rate variability data and Capsule Network model.

PloS one·2024
Same author

Correction: Novel ensemble learning approach with SVM-imputed ADASYN features for enhanced cervical cancer prediction.

PloS one·2024
Same author

Novel ensemble learning approach with SVM-imputed ADASYN features for enhanced cervical cancer prediction.

PloS one·2024
Same author

IoT based smart home automation using blockchain and deep learning models.

PeerJ. Computer science·2023

相关实验视频

Updated: Jun 28, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

741

一种基于深度学习的方法,用于预测COVID-19诊断.

Raafat M Munshi1, Mashael M Khayyat2, Sami Ben Slama3,4

  • 1Department of Medical Laboratory Technology (MLT) Faculty of Applied Medical Sciences, King Abdulaziz University, Rabigh, Saudi Arabia.

Heliyon
|April 10, 2024
PubMed
概括

这项研究使用ARIMA,数学建模和深度学习网络 (DQN) 预测沙特阿拉伯的COVID-19病例. 深度学习网络 (DQN) 与传统预测方法相比,显示出更高的准确性和效率.

关键词:
在阿里马,阿里马就是阿里马.人工智能的人工智能是人工智能.预测 预测 预测 预测机器学习是机器学习.数学模型是一个数学模型.时间序列时间序列

更多相关视频

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.4K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K

相关实验视频

Last Updated: Jun 28, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

741
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.4K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K

科学领域:

  • 流行病学 流行病学
  • 计算生物学 计算生物学
  • 数据科学数据科学数据科学

背景情况:

  • 准确预测COVID-19病例对于公共卫生干预至关重要.
  • 像ARIMA和数学建模这样的传统方法在复杂的流行病预测方面存在局限性.
  • 深度学习有可能提高疾病预测的准确性.

研究的目的:

  • 为了预测沙特阿拉伯的COVID-19确诊病例.
  • 为了比较ARIMA,数学建模和深度神经网络 (DQN) 的预测性能.
  • 确定最有效的COVID-19病例预测方法,以告知公共卫生战略.

主要方法:

  • 使用自回归集成移动平均线 (ARIMA) 模型.
  • 运用数学建模技术进行时间序列预测.
  • 应用深度神经网络 (DQN) 算法用于比较预测.
  • 数据涵盖了2020-2021年沙特阿拉伯,英国和突尼斯的COVID-19病例.

主要成果:

  • 深度神经网络 (DQN) 技术的性能优于传统的ARIMA和数学建模方法.
  • DQN在预测COVID-19病例数量方面表现出更高的效率和准确性.
  • 对比分析强调了深度学习在流行病预测中的优势.

结论:

  • 深度学习网络 (DQN) 是预测COVID-19病例的更可靠,更准确的方法.
  • 这些发现支持采用先进的计算方法为公共卫生做好准备.
  • 增强的预测能力可以大大帮助规划和执行有效的干预措施.