Dynamic Intelligent Framework Using AI for Data Mining Federated Learning Financial Intelligence and Smart Healthcare Systems
DOI:
https://doi.org/10.15662/IJRAI.2023.0604009Keywords:
Artificial Intelligence, Data Mining, Federated Learning, Financial Intelligence, Smart Healthcare, Predictive Modeling, Privacy Preservation, Machine Learning, Big Data Analytics, Unified FrameworkAbstract
The rapid proliferation of data across industries has necessitated the development of unified frameworks that leverage Artificial Intelligence (AI) to extract actionable insights efficiently and securely. This study proposes a Unified Artificial Intelligence Framework (UAIF) integrating data mining, federated learning, financial intelligence, and smart healthcare applications. The framework employs advanced machine learning algorithms for data preprocessing, pattern recognition, and predictive modeling while preserving privacy through federated learning architectures. In financial intelligence, UAIF supports fraud detection, credit scoring, and market trend analysis by synthesizing multi-source financial data. In healthcare, the framework facilitates patient monitoring, disease prediction, and personalized treatment plans by analyzing distributed medical records while ensuring compliance with privacy regulations. The proposed framework emphasizes modularity, scalability, and interoperability across heterogeneous data sources. Experimental results demonstrate enhanced predictive accuracy, reduced data transmission overhead, and improved privacy preservation compared to conventional centralized models. This research highlights the potential of unified AI systems to bridge the gap between computational intelligence and real-world applications, contributing to smarter decision-making in finance and healthcare while promoting ethical and secure AI practices.
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