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相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

45
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.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
45
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

4.0K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
4.0K
Decision Making: P-value Method01:09

Decision Making: P-value Method

5.3K
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...
5.3K
Heuristics01:21

Heuristics

81
Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
81
Aggregates Classification01:29

Aggregates Classification

306
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...
306
Reducing Line Loss01:18

Reducing Line Loss

149
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
149

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相关实验视频

Updated: Jun 12, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

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基于元启发式算法和CNN的智能会计优化方法

Yanrui Dong1

  • 1School of Accounting, Zhengzhou Vocational College of Finance and Taxation, Zhengzhou, Henan, China.

PeerJ. Computer science
|September 24, 2024
PubMed
概括

本研究介绍了一种使用卷积神经网络 (CNN) 和元启发算法的智能会计优化方法. 该方法增强会计审计和财务绩效评估,以改善企业会计实践.

科学领域:

  • 会计技术 会计技术
  • 金融领域的人工智能
  • 计算智能是一种计算智能.

背景情况:

  • 企业会计日益复杂,需要先进的解决方案.
  • 社会智能的发展推动了智能会计实践的采用.
  • 当前的会计系统需要提高效率和提高会计师的能力.

研究的目的:

  • 提出一个智能会计优化方法.
  • 增强企业会计运作和会计师的能力.
  • 为完善会计审计机制提供技术支持.

主要方法:

  • 增强的卷积神经网络 (CNN) 框架集成文档和凭证信息,用于多式模式的特征提取.
  • 一种用于评估会计质量的新方法,使用多模式会计特征来客观评估财务业绩.
  • 一种优化技术,将遗传算法与回火模型相结合,用于改进会计系统.

主要成果:

  • 拟议的方法实现了0.943.4的高精度.
  • 平均平均精度 (mAP) 分数达到0.812,表明性能强.
  • 实验结果验证了综合元启发和CNN方法的有效性.

结论:

关键词:
会计优化会计优化在美国,CNN是CNN.的元启发式算法.

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  • 开发的智能会计优化方法显著提高了会计审计.
  • 该方法提供客观的财务绩效评估和系统改进.
  • 这项研究为智能会计的未来提供了坚实的技术基础.