Modern enterprises rely on intelligent data pipelines to collect, process, transform, and analyze data from diverse sources such as cloud platforms, IoT devices, enterprise systems, and social media. Traditional optimization techniques, including rule-based scheduling and heuristic resource allocation, improve efficiency but struggle to adapt to dynamic workloads, changing resource availability, and evolving business requirements. Artificial Intelligence (AI) addresses these limitations through predictive analytics, adaptive scheduling, anomaly detection, and autonomous resource optimization. However, the opaque nature of many AI models reduces transparency, trust, and regulatory compliance. This paper proposes an Explainable AI (XAI)-based Intelligent Data Pipeline Optimization Framework that integrates data preprocessing, predictive analytics, explainability, and adaptive optimization. The framework continuously monitors pipeline performance, generates optimization recommendations, and provides human-interpretable explanations for AI-driven decisions using feature attribution and model interpretation techniques. An automated feedback mechanism enables continuous learning and improvement. Experimental evaluation demonstrates enhanced optimization accuracy, reliability, scalability, interpretability, and administrator trust with minimal impact on performance. The proposed framework provides a transparent and trustworthy approach for next-generation intelligent data engineering systems.
Per Brinch Hansen, O. Olesen· International Journal of Dat...· 0 citations
The rapid growth of digital technologies, connected medical devices, cloud-based healthcare platforms, and intelligent clinical systems has increased the complexity of healthcare infrastructure. Although these technologies improve patient care and operational efficiency, healthcare organizations continue to face challenges such as equipment failures, cyber threats, resource shortages, patient surges, and operational disruptions. Traditional maintenance and monitoring methods are often inadequate for real-time infrastructure management. Artificial Intelligence (AI) and Digital Twin technologies provide an effective solution by enabling real-time monitoring, predictive maintenance, resource optimization, and intelligent decision support. A Digital Twin creates a virtual model of healthcare infrastructure that continuously synchronizes with data from IoT devices, electronic health records, hospital systems, and medical equipment. This paper proposes an AI-powered Digital Twin architecture consisting of four modules: Healthcare Data Acquisition and Integration, Digital Twin Modeling, AI-Based Predictive Analytics, and Infrastructure Optimization. The framework analyzes real-time data to predict equipment failures, optimize maintenance, assess infrastructure risks, and improve operational resilience through continuous synchronization between physical and virtual healthcare environments. Performance is evaluated using metrics such as prediction accuracy, infrastructure availability, resource utilization, maintenance efficiency, response time, and operational continuity. Experimental results demonstrate that the proposed framework improves failure prediction, reduces downtime, optimizes resource allocation, and enhances emergency response compared to conventional approaches. Overall, the architecture supports the development of intelligent, resilient, and sustainable healthcare systems aligned with Industry 5.0 principles.
Per Brinch Hansen, Børge Diderichsen· International Journal of Eme...· 0 citations
The increasing demand for high-quality datasets for AI development has raised significant privacy, security, and regulatory concerns, particularly in sensitive domains such as healthcare, finance, and government. Synthetic data generation addresses these challenges by creating artificial datasets that preserve the statistical characteristics of real data while protecting individual privacy. Recent advances in generative AI, including GANs, VAEs, diffusion models, and transformer-based models, have significantly improved the realism and utility of synthetic data. This paper surveys synthetic data generation techniques, privacy-preserving methods, applications, research challenges, and future directions for developing trustworthy AI systems.
Per Brinch Hansen, Børge Diderichsen· International Journal of Mod...· 0 citations
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