Deep Learning-Driven Cloud Intelligence Framework for SAP and Oracle-Based Business Management Systems with Adaptive Network Optimization
DOI:
https://doi.org/10.15680/IJCTECE.2025.0806009Keywords:
Deep Learning, Cloud Intelligence, SAP Integration, Oracle Database, Business Management Systems, Adaptive Network Optimization, SQL-Driven AnalyticsAbstract
This paper presents a Deep Learning-Driven Cloud Intelligence Framework designed to enhance the performance, reliability, and scalability of SAP and Oracle-based Business Management Systems (BMS) through adaptive network optimization. The proposed architecture integrates cloud computing, artificial intelligence, and deep learning algorithms to automate decision-making, resource allocation, and data-driven analytics across distributed enterprise environments. By leveraging SQL-driven data orchestration and hybrid cloud infrastructures, the framework ensures seamless interoperability between SAP modules and Oracle databases while maintaining data integrity and operational efficiency. Furthermore, the inclusion of adaptive network intelligence enables real-time monitoring and optimization of communication channels within dynamic business ecosystems. Experimental evaluations demonstrate that the proposed model significantly improves data processing speed, reduces latency, and enhances predictive accuracy for enterprise workflows. This research contributes to the development of intelligent, self-optimizing, and ethically aligned cloud ecosystems that empower organizations to achieve higher agility and resilience in complex business networks
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