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A comprehensive sentiment analysis framework for financial market prediction using natural language processing and machine learning techniques and demonstrates how sentiment-driven models can assist traders, portfolio managers, and financial institutions in making informed investment decisions.
An analysis of edge computing architectures tailored for intelligent embedded robotic systems through an analysis of the architectural components, computational workflow, communication mechanisms, and performance characteristics shows that edge-based robotic systems enhance responsiveness, network efficiency, scalabili...
A Hybrid AI and Digital Twin Architecture for Enterprise Commerce and Financial Transformation that integrates intelligent data acquisition, digital twin modeling, hybrid AI analytics, financial intelligence, enterprise optimization, and continuous learning into a unified computational framework is proposed.
A privacy-aware Federated Data Engineering framework that integrates federated learning, distributed data engineering, and secure model aggregation for cross-enterprise analytics and incorporates privacy-enhancing technologies to ensure confidentiality, integrity, and transparency is presented.
The findings demonstrate that AI-driven disaster prediction systems significantly improve early warning capabilities, situational awareness, resource allocation, infrastructure protection, and emergency response, ultimately reducing disaster impacts and saving lives.
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