Implementation of a dynamic planning algorithm in accounting information technology administration

Abstract

Accounting professionals are increasingly being encouraged to shift their focus from conventional accounting to accounting information as a result of new management strategies and ideas. Cybercrime and other attempts to exploit weaknesses in online systems have become more common in recent years. By introducing the concept of cloud computing and analyzing its logical structure, this research applies the technology and design model to the development of an Accounting Information Management System (AIMS). In accounting information technology administration, efficient resource allocation and decision-making are crucial for optimizing financial performance and strategic planning. Algorithms for dynamic planning are a useful tool in meeting these issues. To maximize efficiency in an accounting group’s allocation of resources, this study employs a dynamic planning method called value iteration. The research presented a new Bayesian optimized Restricted Boltzmann machine (BO-RBM) for acquittal IT management. The data set was first gathered and then pre-processed using z-score normalization. Then, an improved genetic algorithm was used to feature selection. After the system’s design and construction are complete, BO-RBM utilizes to both specify the cloud platform’s distributed storage mode and assess the cluster’s performance. The results show that the algorithm may boost financial performance, increase cost management, and accomplish strategic goals in the IT administration of accounting. The research in this study demonstrates that the cloud platform for handling massive amounts of data may accelerate processes and complete tasks quickly.