Deep Learning–Driven Cloud-Native Framework for Industrial Effluent Quality Prediction and Targeted Healthcare ERP Advertising
DOI:
https://doi.org/10.15680/IJCTECE.2022.0506017Keywords:
Cloud-Native Architecture, Deep Learning, Industrial Effluent Quality Prediction, Big Data Analytics, Healthcare ERP Systems, Targeted Advertising, Artificial Intelligence, Predictive Modeling, Scalable AnalyticsAbstract
The increasing availability of large-scale industrial and enterprise data has created opportunities for intelligent, cloud-native analytics across diverse domains. This paper proposes a Deep Learning–Driven Cloud-Native Framework for Industrial Effluent Quality Prediction and Targeted Healthcare ERP Advertising that integrates advanced predictive modeling with scalable cloud infrastructure. For industrial applications, deep learning models are employed to accurately predict effluent quality under variable load and operational conditions, supporting proactive environmental monitoring and compliance. In parallel, the framework enables AI-driven targeted advertising within healthcare ERP systems by leveraging data analytics to improve personalization, engagement, and decision support while respecting data governance constraints. The cloud-native design ensures scalability, real-time processing, and seamless integration across heterogeneous data sources. Experimental evaluations demonstrate improved prediction accuracy, operational efficiency, and business intelligence, highlighting the framework’s effectiveness in unifying industrial analytics and healthcare enterprise applications within a single AI-driven ecosystem.
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