IRO Journals
Journal of Soft Computing Paradigm

HDNE-NR a Hybrid Deep Neural–Ensemble Framework for Robust Fraud Detection Under Controlled Noise Perturbations

Open Access
Published: 29 May, 2026
Pages: 158-183
  • Sumathi K. , Sumathi K.
    Associate Professor, Department of Information Technology, Kangeyam Institute of Technology (Autonomous), Kangayam, Tirupur, India
    Associate Professor, Department of Information Technology, Kangeyam Institute of Technology (Autonomous), Kangayam, Tirupur, India
  • Priyanga K. , Priyanga K.
    Assistant Professor, Department of Computer Science and Engineering, Kangeyam Institute of Technology (Autonomous), Kangayam, Tirupur, India
    Assistant Professor, Department of Computer Science and Engineering, Kangeyam Institute of Technology (Autonomous), Kangayam, Tirupur, India
  • Ramesh S. Ramesh S.
    Assistant Professor, Department of Computer Science and Engineering, Kangeyam Institute of Technology (Autonomous), Kangayam, Tirupur, India
    Assistant Professor, Department of Computer Science and Engineering, Kangeyam Institute of Technology (Autonomous), Kangayam, Tirupur, India
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How to Cite

K., Sumathi, Priyanga K., and Ramesh S. 2026. “HDNE-NR a Hybrid Deep Neural–Ensemble Framework for Robust Fraud Detection Under Controlled Noise Perturbations”. Journal of Soft Computing Paradigm 8 (2): 158-83. https://doi.org/10.36548/jscp.2026.2.005.
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Keywords

Noise Robustness
Ensemble Learning
Fraud Detection
Deep Neural Networks
Weighted Stacking
Imbalanced Classification

Abstract

This paper presents HDNE-NR (Hybrid Deep Neural Ensemble with Noise Robustness) in order to obtain constant fraud detection when subjected to controlled feature and label perturbations. The major purpose was to create a robustness-based predictive framework that can retain discrimination in imbalanced and noisy financial data with great intensity. HDNE-NR provides high-level performance when compared to the deep latent representation learning models; this is due to the combination of heterogeneous ensemble meta-classifiers and adaptive weighted stacking. In clean conditions, the framework had an F1-score of 0.933 and ROC-AUC of 0.992. At high noise of features (σ = 0.20) and corruption of labels (ϵ = 0.10), HDNE-NR maintained higher F1-scores (0.820 and 0.815, respectively) and lower decay curves. It is novel in the synergistic combination of any of the noise-aware learning of representations and adaptive weighting of the ensemble. Generally, HDNE-NR offers an uncertain and scalable framework in fraud analytics in the real world.

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