| Title |
Optimization Of Accounting Information System Based On Svm, Cnn, Rnn And Self-Encoder: A Study Of Technical Implementation And Performance Improvement |
| ID_Doc |
40608 |
| Authors |
Yang N. |
| Year |
2025 |
| Published |
Proceedings of SPIE - The International Society for Optical Engineering, 13682 |
| DOI |
http://dx.doi.org/10.1117/12.3075563 |
| Abstract |
This study focuses on the application and optimization of supervised learning, deep learning, and neural networks in accounting information systems in smart city construction and explores their effects in terms of technical implementation and performance improvement. The accounting information system uses advanced AI techniques. These include Support Vector Machines (SVM), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Autoencoder. The system becomes faster at processing data. It generates reports more efficiently. The error rate is reduced. Work efficiency is also enhanced. The case study shows that the application of these AI algorithms not only provides strong support for enterprise financial management but also contributes to the modernization of human resource planning and urban governance in smart cities. The research results are important. They offer a theoretical basis. They also provide a practical reference. This is for the intelligent transformation of accounting information systems. It is in the context of smart cities. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only. |
| Author Keywords |
accounting information system; deep learning; neural network; Supervised learning |
