Hybrid DeepXAI Fusion Network for Interpretable Big Data Analytics and Secure Cloud Decision-Making Systems

Abstract

Big data analytics supports modern intelligent systems, but model complexity and limited transparency can undermine interpretability, trust, and security in decision-making. This paper presents the Hybrid DeepXAI Fusion Network, a deep learning framework that integrates temporal, relational, and contextual features with explainable artificial intelligence (XAI) mechanisms. The architecture combines one-dimensional convolutional neural networks (1D-CNNs) for temporal feature extraction, Transformer encoders for contextual modeling, and graph neural networks (GNNs) for inter-feature dependency analysis. An attention-based fusion mechanism integrates these representations. Class Activation Mapping (CAM) and an explanation loss are incorporated to improve interpretability and explanation consistency. The framework is designed for deployment in a federated cloud architecture, using homomorphic encryption and blockchain-based auditing to support data privacy and accountability. Experimental evaluations across healthcare, finance, and energy datasets report 98.42% accuracy, 97.9% precision, 98.1% recall, and a 98.0% F1-score, outperforming the evaluated CNN-LSTM, Transformer-only, and XGBoost baselines. Under projected gradient descent (PGD) adversarial perturbations, the model reportedly retains accuracy above 91.5%. Scalability experiments report throughput of 80,000 messages per second with latency below 200 milliseconds. These results suggest that the proposed framework has potential for interpretable and secure cloud-based decision-support systems.

Six authors. Corresponding author: B. Nancharaiah