From Mass to Flow: Emerging Sepsis Diagnostics Based on Flow Cytometry Analysis of Neutrophils

Verdonk, Franck; Hsu, Joe L.; Gaudillière, Brice

doi:10.1164/rccm.202110-2291ed

Cited by 1 publication

(1 citation statement)

References 21 publications

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“…e learning process of the BP neural network is the process of constantly changing weights and thresholds to minimize the error value [15]. e specific steps of the improved AHP-BP neural network algorithm in the evaluation of circular economy are shown in Figure 2.…”

Section: Bp Neural Network Modelmentioning

confidence: 99%

Application of Neural Network Sample Training Algorithm in Regional Economic Management

Yan

2022

Mathematical Problems in Engineering

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In order to solve the limitations of the traditional method of determining expert weights in AHP and improve the level of the regional economy, this paper proposes a neural network algorithm based on AHP. This paper first calculates the interest correlation coefficient among experts, then obtains the comprehensive evaluation value of all experts according to the evaluation value matrix, and then determines the evaluation weight of each expert in the expert group. Finally, the comprehensive weight of each index is obtained by weighting the index weight obtained from the traditional AHP and the expert evaluation weight. Then, the evaluation value obtained by the improved AHP is used as a prior sample to train and test the BP neural network, and a classifier that can be popularized is obtained. The experimental results show that the overall evaluation value of circular economy of 9 construction enterprises is at a medium level, only 2 enterprises have an evaluation value of more than 50, and one enterprise has an evaluation value of only 35, indicating that the overall implementation of circular economy of these 9 construction enterprises is still at a low level, with great room for development and improvement. Conclusion. The economic management method based on a neural network algorithm can make the evaluation results more accurate and reliable and has high popularization and application value.

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Section: Bp Neural Network Modelmentioning

confidence: 99%

Application of Neural Network Sample Training Algorithm in Regional Economic Management

Yan

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

From Mass to Flow: Emerging Sepsis Diagnostics Based on Flow Cytometry Analysis of Neutrophils

Cited by 1 publication

References 21 publications

Application of Neural Network Sample Training Algorithm in Regional Economic Management

Application of Neural Network Sample Training Algorithm in Regional Economic Management

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