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相关概念视频

Survival Tree01:19

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Propagation of Uncertainty from Random Error00:59

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
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相关实验视频

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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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利用基于三重树种子算法的概率神经网络智能企业量化风险管理框架.

Iyad Katib1, Emad Albassam1, Sanaa A Sharaf1

  • 1Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.

Scientific reports
|September 27, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了用于智能企业系统的深度学习启用风险评估模型 (IMDLRA-SES) 改进的元启证. 这种新的方法在金融风险评估中实现了高准确度,增强了企业的决策.

关键词:
分类 分类 分类 分类.深度学习是一种深度学习.功能选择 功能选择财务决定 财务决定超听证学是一种超听证学.风险评估 风险评估 风险评估智能企业系统是一个智能企业系统.

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科学领域:

  • 人工智能的人工智能
  • 数据科学数据科学数据科学
  • 统计 统计 统计 统计
  • 企业风险管理 企业风险管理

背景情况:

  • 企业管理系统 (EMS) 对于长期的业务成功至关重要,它整合了人工智能,数据科学和统计.
  • 在EMS中进行有效的风险评估对于企业做出明智决策至关重要.
  • 人工智能,机器学习 (ML) 和深度学习 (DL) 的进步正在推动复杂风险评估模型的开发.

研究的目的:

  • 为智能企业系统 (SES) 介绍一个使用深度学习启用风险评估模型 (IMDLRA-SES) 的改进的元启证学.
  • 应用特征选择和深度学习来准确估计业务风险.
  • 展示应用概率和统计学在风险管理跨学科研究中的应用.

主要方法:

  • 数据预处理将原始金融数据转化为可用的格式.
  • 反对狮子群优化 (OLSO) 用于特征选择 (FS) 来识别最佳特征子集.
  • 三重树种子算法 (TTSA) 优化了概率神经网络 (PNN) 用于对金融风险进行分类.

主要成果:

  • 该IMDLRA-SES技术有效地使用特征选择和深度学习模型估计业务风险.
  • TTSA超参数优化显著提高PNN模型的分类效率.
  • 德国和澳大利亚信用数据集的实验评估显示,其性能优于现有方法.

结论:

  • IMDLRA-SES模型为智能企业系统中的金融风险评估提供了强大而准确的解决方案.
  • 集成先进的元启发学和深度学习为改善企业决策提供了强大的工具.
  • 该研究验证了应用概率和统计学在开发先进风险管理框架中的有效性.