Deep Learning-Driven Modernization of Smart Connect Ecosystems: Advancing Cloud Data Governance, Sustainable Operations, and Intelligent Software Maintenance

Abstract

The modernization of Smart Connect ecosystems through deep learning offers transformative potential for data-driven, cloud-integrated environments. This paper presents a comprehensive framework that leverages deep learning architectures to enhance cloud data governance, promote sustainable operations, and enable intelligent software maintenance. By integrating predictive analytics and automated decision-making, the framework ensures improved system reliability, optimized energy efficiency, and compliance with data privacy standards. The proposed approach emphasizes real-time data orchestration, adaptive learning models, and scalable cloud deployment strategies to strengthen interoperability across connected systems. Experimental evaluations demonstrate that the adoption of deep learning methodologies within Smart Connect infrastructures significantly improves system resilience, reduces maintenance overhead, and supports long-term sustainability goals. This study contributes to the development of intelligent, self-optimizing Smart Connect platforms that align with modern enterprise and environmental objectives.