Optimized Deep Learning Framework for Intelligent Data Analytics in Information Systems

Abstract

The exponential expansion of data in contemporary information systems poses a significant barrier to the efficient, accurate, and scalable extraction of useful insights. High-dimensional and heterogeneous data are frequently difficult for conventional machine learning models to handle, which results in poor analytical performance and more computing overhead. The present investigation proposes an Optimized Deep Learning Framework for Intelligent Data Analytics in Information Systems in order to overcome these constraints. The model developed in this study achieved a success rate exceeding 98% across two test sets, indicating a clear advantage in processing accounting information. In the response time evaluation of the financial module, conducted over 60 trials, the system demonstrated an average response time of 0.5 seconds and maintained a 100% success rate, highlighting both its efficiency and reliability. The system can function normally if it can achieve 0.8 seconds and maintain a success rate above 98%. There are 70 test cases created specifically for the finance module in the system operational stability test, 70 of which are executed, with an execution rate of up to 100%. This indicates that the system can function effectively and won't malfunction while in use.