A Hybrid Deep Learning Framework for Intelligent Prediction and Recommendation Using Multimodal Data and Adaptive Learning

Abstract

The rapid advancement of artificial intelligence has enabled the development of intelligent systems capable of addressing complex challenges across multiple domains, including healthcare, finance, transportation, and digital media. However, most existing approaches focus on isolated tasks such as prediction, recommendation, or detection, limiting their effectiveness in real-world, data-rich environments. This paper proposes a hybrid deep learning framework that integrates recurrent neural networks for temporal prediction, graph neural networks and large language models for recommendation, and an enhanced YOLOv8 architecture for object detection within a unified system. The framework further incorporates a multimodal fusion mechanism to combine heterogeneous data sources and a reinforcement learning-based optimization strategy to enable adaptive and continuous learning. Experimental results across diverse datasets demonstrate that the proposed model significantly outperforms baseline methods in terms of prediction accuracy, recommendation quality, and detection performance. The findings highlight the effectiveness of integrating multiple artificial intelligence paradigms into a single scalable framework, providing a robust solution for intelligent decision-making in dynamic environments.