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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
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Deep Neural Networks for Image-Based Dietary Assessment
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A deep neural network-based smart error measurement method for fiscal accounting data.

Yutian Cai1, Ting Wang1, Shaohua Wang1

  • 1College of Accounting, Xijing University, Xi'an 710000, China.

Mathematical Biosciences and Engineering : MBE
|June 16, 2023
PubMed
Summary

This study introduces a deep neural network model for measuring fiscal and tax accounting errors, improving performance evaluation and reducing prediction costs. The model accurately monitors financial data trends and assesses economic growth contributions.

Keywords:
deep neural networkerror measurementintelligent computingsmart fiscal accounting

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Area of Science:

  • Accounting and Finance
  • Data Science
  • Economic Analysis

Background:

  • Fiscal accounting data errors can impact financial asset stability.
  • Accurate performance evaluation is crucial for financial institutions.
  • Existing methods for error prediction are costly and time-consuming.

Purpose of the Study:

  • To develop a deep neural network model for measuring fiscal and tax accounting data errors.
  • To enhance the evaluation of fiscal and tax performance.
  • To accurately monitor trends in urban finance and tax benchmark data.

Main Methods:

  • Utilized deep neural network theory to construct an error measurement model.
  • Applied the entropy method and deep neural networks to measure fiscal and tax performance.
  • Employed MATLAB programming for calculating contribution rates to economic growth.

Main Results:

  • The model accurately monitors the changing trend of fiscal and tax benchmark data errors.
  • The contribution rates of fiscal and tax accounting input, commodity/service expenditure, other capital expenditure, and capital construction expenditure to regional economic growth were quantified.
  • Demonstrated the model's ability to map relationships between variables effectively.

Conclusions:

  • The proposed deep neural network model offers a scientifically accurate and efficient method for fiscal and tax accounting error measurement.
  • The model aids in solving the high cost and delay issues associated with error prediction.
  • The study provides valuable insights into the impact of fiscal and tax accounting inputs on regional economic growth.