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An AI-driven Intelligent Multi-Sensor Data Fusion Framework that integrates heterogeneous sensors with deep learning, probabilistic fusion, and reinforcement learning for enhanced environmental perception, autonomous reasoning, and real-time control is proposed.
This paper discusses the design, implementation, and evaluation of a conversational interface that provides users with real-time, context-aware responses to queries, and demonstrates how such AI-powered systems enhance user experience, improve query accuracy, and streamline data discovery processes.
The proposed framework includes four stages: knowledge acquisition, data preprocessing, hybrid model integration, and predictive decision support, which improves prediction accuracy, reliability, transparency, and decision-making of next-generation intelligent systems.
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