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Real-time task parameter selection method of accounting system based on multi-objective optimization and genetic algorithm.

Authors :
Qin, Rongjie
Shahbaz, Muhammad
Source :
PeerJ Computer Science; Apr2024, p1-22, 22p
Publication Year :
2024

Abstract

The progress of the digital economy has promoted the enterprise accounting system. To accelerate the update and evolution of accounting systems, we propose a parameter selection method based on multi-objective optimization and genetic algorithm. Firstly, this article proposes an accounting feature extraction method based on multimodal information embedding. The dual-branch structure and feature pyramid network are used to realize the feature extraction of the information involved in accounting. Then, this article proposes a multi-objective parameter selection method based on a parallel genetic algorithm. By embedding a genetic algorithm in the process of dual-branch model training, the model's ability to sense accounting information is improved. Finally, using the above two methods, an accounting system evaluation method upon recurrent Transformer is proposed to improve the financial situation of enterprises. Our experiments have proven that our approach attains a remarkable performance with an 87.6% F-value, 83.5% mAP value, and 83.4% accuracy. These results position our method at an advanced level globally, showcasing its capability to enhance accounting systems. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
23765992
Database :
Complementary Index
Journal :
PeerJ Computer Science
Publication Type :
Academic Journal
Accession number :
177325791
Full Text :
https://doi.org/10.7717/peerj-cs.1952