Exploring Deep Computational Intelligence Approaches for Enhanced Predictive Modeling in Big Data Environments

Abstract

The rapid growth of big data has created a pressing need for advanced predictive modeling techniques that can efficiently extract meaningful insights from massive, complex datasets. This study explores deep computational intelligence approaches to enhance predictive modeling in big data environments, focusing on the integration of deep learning, swarm intelligence, and hybrid optimization techniques. The proposed framework employs a Deep Neural Network (DNN) enhanced with Particle Swarm Optimization (PSO) and Adaptive Gradient Descent (AGD) for dynamic parameter tuning, leading to improved learning efficiency and accuracy. The framework is evaluated on real-world big data applications, including healthcare diagnostics, financial risk prediction, and energy consumption forecasting. Experimental results demonstrate a significant improvement in model performance, with an accuracy of 97.8% in healthcare diagnostics, a precision of 95.2% in financial risk prediction, and a mean absolute percentage error (MAPE) of 3.4% in energy forecasting. Additionally, the proposed approach achieves a 35% reduction in computational overhead compared to traditional DNNs and a 28% improvement in convergence speed due to the hybrid optimization. This work highlights the potential of integrating deep computational intelligence with big data analytics to achieve robust, scalable, and efficient predictive modeling. Future research will focus on extending the framework to accommodate real-time data streams and exploring its applicability across other big data domains.