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Related Concept Videos

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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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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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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.
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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.
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Related Experiment Video

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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Intelligent accounting optimization method based on meta-heuristic algorithm and CNN.

Yanrui Dong1

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

Peerj. Computer Science
|September 24, 2024
PubMed
Summary

This study introduces an intelligent accounting optimization approach using convolutional neural networks (CNN) and meta-heuristic algorithms. The method enhances accounting audits and financial performance assessment for improved enterprise accounting practices.

Keywords:
Accounting optimizationCNNMeta-heuristic algorithm

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Area of Science:

  • Accounting Technology
  • Artificial Intelligence in Finance
  • Computational Intelligence

Background:

  • The increasing complexity of enterprise accounting necessitates advanced solutions.
  • Social intelligence evolution drives the adoption of intelligent accounting practices.
  • Current accounting systems require enhanced efficiency and improved accountant capabilities.

Purpose of the Study:

  • To propose an intelligent accounting optimization approach.
  • To enhance enterprise accounting operations and accountant capabilities.
  • To provide technological support for refining accounting audit mechanisms.

Main Methods:

  • Enhanced Convolutional Neural Network (CNN) framework integrating document and voucher information for multi-modal feature extraction.
  • A novel method for assessing accounting quality using multi-modal accounting features to objectively evaluate financial performance.
  • An optimization technique combining genetic algorithms with annealing models for accounting system improvement.

Main Results:

  • The proposed approach achieved a high accuracy of 0.943.
  • The mean average precision (mAP) score reached 0.812, indicating robust performance.
  • Experimental results validate the effectiveness of the integrated meta-heuristic and CNN approach.

Conclusions:

  • The developed intelligent accounting optimization method significantly enhances accounting audits.
  • The approach offers objective financial performance evaluation and system improvement.
  • This research provides a strong technological foundation for the future of intelligent accounting.