Abstract

This paper presents a novel hybrid deep learning framework integrating convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for developing context-aware intelligent systems in smart cities. The proposed framework combines real-time sensor data processing with predictive analytics to optimize urban resource management. Our methodology incorporates multi-modal data fusion techniques, enabling seamless integration of traffic patterns, environmental monitoring, and citizen behavior analysis. The hybrid architecture demonstrates superior performance in handling temporal dependencies and spatial correlations inherent in urban data streams. Experimental validation across three metropolitan areas shows 23% improvement in traffic flow prediction accuracy and 18% reduction in energy consumption optimization tasks. The framework addresses scalability challenges through distributed computing architectures and edge-cloud integration strategies.