Qin Rongjie, Shahbaz Muhammad
Wuhan Technology and Business University, Wuhan, China.
Hubei Business Service Development Research Center, Wuhan, China.
PeerJ Comput Sci. 2024 Apr 11;10:e1952. doi: 10.7717/peerj-cs.1952. eCollection 2024.
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.
数字经济的发展推动了企业会计系统的进步。为加速会计系统的更新与演进，我们提出了一种基于多目标优化和遗传算法的参数选择方法。首先，本文提出了一种基于多模态信息嵌入的会计特征提取方法。利用双分支结构和特征金字塔网络实现会计相关信息的特征提取。然后，本文提出了一种基于并行遗传算法的多目标参数选择方法。通过在双分支模型训练过程中嵌入遗传算法，提高模型感知会计信息的能力。最后，利用上述两种方法，提出了一种基于循环Transformer的会计系统评估方法，以改善企业财务状况。我们的实验证明，我们的方法取得了显著的性能，F值为87.6%，平均精度均值（mAP）值为83.5%，准确率为83.4%。这些结果使我们的方法在全球处于先进水平，展示了其增强会计系统的能力。
