Institution profile

Murdoch University

Academic institutionaustralasia · au
Official website
Research library12linked papers
Opportunities0open roles
Selected work

Representative Papers

Prediction of bank transaction fraud using TabNet an adaptive deep learning architecture

Jul 20, 2026

This study addresses the critical need for high accuracy, interpretability, and scalability in online banking fraud detection by leveraging real-world transaction data from India. Through exploratory data analysis and SMOTE-based oversampling to mitigate class imbalance, the authors systematically evaluate five deep learning models: DNN, GRU, LSTM, 1D-CNN, and TabNet. Notably, they harness TabNet’s intrinsic sparse feature selection mechanism to simultaneously enhance model interpretability and generalization. Experimental results demonstrate that TabNet achieves a 97.39% accuracy and a 0.9739 ROC-AUC under three-fold cross-validation, significantly outperforming baseline models. The approach effectively reduces both false positives and false negatives, supports real-time deployment, and satisfies stringent financial regulatory requirements for model transparency.

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Multi-Modal Semantic Segmentation of Electrolyzer Components for Sustainable Hydrogen Technologies: A Dual-Branch Deep Learning Approach

Jul 17, 2026

This study addresses the significant challenges in semantic segmentation for automated disassembly of electrolyzer components, which arise from high visual similarity among materials, spectral overlap, irregular shapes, and severe class imbalance. To overcome these issues, the authors propose HREM-Net, a dual-branch deep network that effectively fuses hyperspectral and RGB imagery through a novel adaptive gated cross-modal fusion mechanism. The architecture integrates efficient channel attention, coordinate attention, Mobile Inverted Bottleneck blocks, and an atrous spatial pyramid pooling module, further enhanced by a composite loss function to strengthen multimodal feature synergy. Evaluated on the Electrolyzers-HSI dataset, the method achieves a mean class accuracy of 91.66% and an mIoU of 0.82, while demonstrating strong generalization on PCB-Vision with 96.91% accuracy and 0.93 mIoU.

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Metric Unreliability in Multimodal Machine Unlearning: A Systematic Analysis and Principled Unified Score

May 04, 2026

Existing evaluation metrics exhibit inconsistent behavior in multimodal machine unlearning tasks, making it difficult to reliably assess unlearning efficacy. This work systematically analyzes the conflicting rankings produced by five widely used metrics across three visual question answering (VQA) benchmarks and proposes a Unified Quality Score (UQS) that achieves more stable performance ranking by weighting each metric according to its distance correlation with an idealized reference model. Empirical evaluation on 36 variants of LLaVA-1.5-7B and BLIP-2 models reveals substantial discrepancies in metric-induced rankings. The proposed UQS demonstrates high stability under 100 random perturbations, achieving a Kendall’s τ of 0.647 ± 0.262. The authors publicly release the benchmark suite, model checkpoints, and an interactive leaderboard to support reproducible research in multimodal unlearning.

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Recent publications

Latest Papers

Prediction of bank transaction fraud using TabNet an adaptive deep learning architecture

Jul 20, 2026

This study addresses the critical need for high accuracy, interpretability, and scalability in online banking fraud detection by leveraging real-world transaction data from India. Through exploratory data analysis and SMOTE-based oversampling to mitigate class imbalance, the authors systematically evaluate five deep learning models: DNN, GRU, LSTM, 1D-CNN, and TabNet. Notably, they harness TabNet’s intrinsic sparse feature selection mechanism to simultaneously enhance model interpretability and generalization. Experimental results demonstrate that TabNet achieves a 97.39% accuracy and a 0.9739 ROC-AUC under three-fold cross-validation, significantly outperforming baseline models. The approach effectively reduces both false positives and false negatives, supports real-time deployment, and satisfies stringent financial regulatory requirements for model transparency.

0 citationsRead paper

Multi-Modal Semantic Segmentation of Electrolyzer Components for Sustainable Hydrogen Technologies: A Dual-Branch Deep Learning Approach

Jul 17, 2026

This study addresses the significant challenges in semantic segmentation for automated disassembly of electrolyzer components, which arise from high visual similarity among materials, spectral overlap, irregular shapes, and severe class imbalance. To overcome these issues, the authors propose HREM-Net, a dual-branch deep network that effectively fuses hyperspectral and RGB imagery through a novel adaptive gated cross-modal fusion mechanism. The architecture integrates efficient channel attention, coordinate attention, Mobile Inverted Bottleneck blocks, and an atrous spatial pyramid pooling module, further enhanced by a composite loss function to strengthen multimodal feature synergy. Evaluated on the Electrolyzers-HSI dataset, the method achieves a mean class accuracy of 91.66% and an mIoU of 0.82, while demonstrating strong generalization on PCB-Vision with 96.91% accuracy and 0.93 mIoU.

0 citationsRead paper

Metric Unreliability in Multimodal Machine Unlearning: A Systematic Analysis and Principled Unified Score

May 04, 2026

Existing evaluation metrics exhibit inconsistent behavior in multimodal machine unlearning tasks, making it difficult to reliably assess unlearning efficacy. This work systematically analyzes the conflicting rankings produced by five widely used metrics across three visual question answering (VQA) benchmarks and proposes a Unified Quality Score (UQS) that achieves more stable performance ranking by weighting each metric according to its distance correlation with an idealized reference model. Empirical evaluation on 36 variants of LLaVA-1.5-7B and BLIP-2 models reveals substantial discrepancies in metric-induced rankings. The proposed UQS demonstrates high stability under 100 random perturbations, achieving a Kendall’s τ of 0.647 ± 0.262. The authors publicly release the benchmark suite, model checkpoints, and an interactive leaderboard to support reproducible research in multimodal unlearning.

0 citationsRead paper