: The rapid expansion of the Internet of Things in critical industrial environments has significantly increased the attack surface, exposing systems to sophisticated cyber threats. Traditional pattern-based intrusion detection systems struggle to detect such advanced attacks, while deep learning approaches, despite achieving high detection accuracy, often suffer from high computational cost and latency, limiting their deployment in resource-constrained edge gateways. Quantum machine learning offers a promising alternative by enabling high-dimensional feature representation; however, current implementations are constrained by hardware noise, limited qubit availability and backend-dependent execution characteristics in the Noisy Intermediate-Scale Quantum era. To address these challenges, this paper proposes a Residual Hybrid Quantum-Classical Neural Network (RHQ-CNN) for efficient intrusion detection in Edge-Industrial Internet of Things (Edge-IIoT) environments under idealized quantum simulation conditions. The proposed framework employs a classical neural network encoder to compress high-dimensional network traffic into a compact latent representation suitable for quantum processing. A variational quantum circuit with serial data re-uploading is then utilised to model complex non-linear decision boundaries without increasing qubit requirements. In addition, a residual connection fuses classical and quantum representations to improve training stability and preserve latent feature information. The model is evaluated on the Edge-IIoTset dataset and achieves a test accuracy of 99.94%, with high weighted performance across the 15-class detection task, although the extremely low-sample Fingerprinting class remains comparatively more challenging. Additional controlled ablation, deployment-cost, and bootstrap confidence interval analyses demonstrate the test-set metric stability and computational trade-offs of the proposed architecture. These findings highlight the potential of hybrid quantum-classical models for next-generation cybersecurity in industrial IoT systems.
Alanoud Al Mazroa, A. Alamoudi, N. Karabayev et al.· Computers, Materials & C...· 0 citations
Accurate brain magnetic resonance imaging (MRI) segmentation remains challenging due to intensity inhomogeneity, acquisition-related bias fields, and ambiguous tissue boundaries. To address these challenges, a Multiplicative–Additive Bias Single-Function Dual-Level-Set (MAB-SFDLS) model is introduced within a Software-as-a-Service (SaaS)-based medical image analysis framework. The model incorporates both multiplicative and additive bias components into a unified variational energy formulation and employs a single level-set function with dual thresholds to achieve stable multi-region segmentation with smooth and continuous boundaries. The method was evaluated on the MRBrainS18 dataset, achieving Dice scores of 0.95 for white matter and 0.86 for gray matter, with a boundary deviation of 2.20 mm measured using HD95. Compared with the classical level-set formulation, notable improvements were observed in both overlap accuracy and boundary precision. The approach also demonstrated competitive performance against state-of-the-art deep learning models, including nnU-Net and U-Mamba, while maintaining lower computational requirements. Statistical analysis confirmed that the improvements were significant (p < 0.05). To enhance interpretability and practical applicability, the segmentation framework is integrated with a browser-based 3D visualization module that supports synchronized surface and volume rendering, as well as interactive region-of-interest exploration. This framework provides a practical, interpretable, computationally efficient, and scalable approach to robust brain MRI segmentation in a cloud-based medical imaging environment. The proposed model code and SaaS platform prototype are publicly available at
https://doi.org/10.5281/zenodo.20797546
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Ala’a R. Al-Shamasneh, Amal Alshardan, Suad Alramouni et al.· Scientific Reports· 0 citations
Digital Twin-Based Low-Energy Reinforcement Learning for Multi-Cell IoV (DT-LERL) distributed collaborative training architecture in cellular-based IoV scenarios is designed, which allows the twin to replace the end-side vehicular entities by introducing digital twins to carry out scenario interactions and model training.
A. Alamoudi, Abdullah S. Almansouri· Journal of Big Data· 0 citations