Deep Learning-Based Approaches for Real-Time Decision Support and Intelligent Data Processing

Abstract

  This study proposes deep learning-based approaches for real-time decision support and intelligent data processing, focusing on improving predictive accuracy, processing efficiency, and system scalability. The framework integrates advanced preprocessing techniques, deep learning architectures, and hybrid modeling strategies to enhance overall system performance. The effectiveness of the proposed approach is evaluated using multiple performance metrics, including accuracy, precision, recall, and F1-score. As illustrated, deep learning models significantly outperform traditional machine learning methods across all evaluation metrics, demonstrating superior capability in capturing complex data patterns and reducing prediction errors. This improvement highlights the effectiveness of deep neural networks in extracting meaningful features from large and heterogeneous datasets. A latency comparison among different computational approached, revealed that deep learning-based real-time systems achieve substantially lower latency compared to batch and stream-based machine learning models. The reduced latency ensures faster decision-making, making the proposed approach highly suitable for time-sensitive applications such as healthcare monitoring, autonomous systems, and smart infrastructure management. Furthermore, our data demonstrated the performance of various models, where a hybrid deep learning model combining convolutional and recurrent architecture achieves the highest accuracy. This indicates that integrating multiple deep learning techniques enhances model robustness and enables better handling of both spatial and temporal data. These findings contribute to the advancement of intelligent systems and highlight the potential of deep learning in enabling efficient, data-driven decision-making across various domains.