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相关概念视频

Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

73
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
73
Aggregates Classification01:29

Aggregates Classification

350
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
350
Classification of Signals01:30

Classification of Signals

556
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
556
Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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相关实验视频

Updated: Jul 26, 2025

An R-Based Landscape Validation of a Competing Risk Model
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一个基于多尺度卷积神经网络的模型,用于集群经济风险检测.

Yi Zhao1

  • 1School of Management, Wuhan University of Bioengineering, Wuhan, China.

PeerJ. Computer science
|June 22, 2023
PubMed
概括

这项研究引入了一种新型的多尺度卷积神经网络 (MCNN) 模型,用于在公共卫生事件后早期检测经济风险. 该MCNN模型准确预测金融风险异常,为应急响应提供关键时间.

科学领域:

  • 金融风险分析 金融风险分析
  • 公共卫生经济学 公共卫生经济学
  • 金融领域的人工智能

背景情况:

  • 随着COVID-19的流行,人们需要更好地预测与公共卫生危机相关的经济风险.
  • 目前用于检测金融风险异常的现有方法是有限的,并且通常只有在聚合后才能发现问题.

研究的目的:

  • 开发一种先进的模型,用于早期检测和预测聚合经济风险.
  • 提高金融风险异常检测的及时性和准确性.

主要方法:

  • 一个多尺度卷积神经网络 (MCNN) 已开发用于金融风险异常检测.
  • 经济风险统计,前体坐标,分布,距离,潜在能量和密度都被提取出来.
  • 基于粒子群优化的极端学习机器 (PSO-ELM) 用于预测建模.

主要成果:

  • 该MCNN模型在检测异常的综合经济风险方面表现出了很高的及时性.
  • 实现了97.68%的预测准确度,超过了现有的算法.
  • 该模型为实施紧急行动提供了额外的时间.

结论:

  • 拟议的MCNN模型为针对经济风险的早期预警系统提供了高效和准确的解决方案.
关键词:
聚合异常预测的预测.经济风险 经济风险在MCNN中,MCNN是MCNN.公共服务人员-ELMM风险检测 风险发现

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  • 这种方法显著提高了管理公共卫生事件的财务影响的能力.
  • 这些发现有助于在面对全球卫生危机时更有弹性的经济规划.