Cloud-Integrated Gray Relational and BERT-Based AI Architecture for Advanced Analytics, Real-Time Staffing Intelligence, and Cybersecurity in SAP HANA Healthcare ERP
DOI:
https://doi.org/10.15662/IJARCST.2024.0706025Keywords:
Cloud computing, Gray Relational Analysis, BERT, SAP HANA, Healthcare ERP, Real-time staffing intelligence, Multivariate classification, Cybersecurity, Data-scarce regions, Advanced analytics, AI architecture, Anomaly detectionAbstract
The rapid digital transformation of healthcare enterprises demands intelligent, secure, and scalable analytical frameworks capable of operating in real time. This paper presents a cloud-integrated AI architecture that combines Gray Relational Analysis (GRA) and BERT-based contextual intelligence to enhance advanced analytics, real-time staffing optimization, and cybersecurity within SAP HANA–driven Healthcare ERP systems. The proposed model addresses critical challenges in data-scarce regions, where incomplete or low-density datasets severely limit predictive accuracy and operational insight. By leveraging GRA for relational pattern extraction and BERT for semantic understanding of clinical, operational, and security logs, the system enables robust multivariate classification, anomaly detection, and staffing intelligence. Integration with cloud-native pipelines and SAP HANA in-memory computing ensures high-throughput processing, low-latency decisioning, and scalable deployment across distributed healthcare environments. The framework also incorporates cyber-risk scoring, identity monitoring, and anomaly-driven alerting to strengthen ERP-level security. Experimental validation using synthetic and real operational datasets demonstrates substantial improvements in staffing accuracy, threat detection speed, and decision transparency. This architecture provides a unified, explainable, and secure analytics ecosystem capable of supporting modern healthcare operations and cyber defense.
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