The growing use of machine learning in healthcare requires careful consideration of patient privacy, particularly when models are trained on small medical datasets where the risk of re-identification is heightened. This study analyzes how regression models operate under privacy-preserving constraints and assesses their susceptibility to privacy leakage. A stability-based membership-inference framework quantifies how much model outputs reveal about individual training samples. Noise-injection techniques are applied to reduce this risk, and their effects on privacy and predictive accuracy are evaluated. Experiments on a small real-world medical dataset show a trade-off between model utility and privacy, identifying conditions in which regression models remain reliable while limiting exposure to membership-inference attacks.
Scheduling scientific workflows in cloud computing environments, taking infrastructure provisioning into account, represents a complex optimisation problem, where minimising the total workflow completion time is a primary objective. This problem is computationally challenging, and metaheuristic methods such as genetic algorithms have been widely employed to obtain approximate solutions. In this study, an alternative approach based on a local search algorithm is proposed, employing four neighbourhood structures. These structures operate on the ordering of task assignments to virtual machines, enabling efficient exploration of the solution space through small, targeted modifications. A hybrid metaheuristic, known as a memetic algorithm, is used, combining the global search capabilities of genetic algorithms with the local refinement provided by the proposed local search. Experimental results demonstrate that the memetic algorithm produces competitive results compared with traditional genetic algorithms.
The analysis of retired electric vehicle battery packs is a critical enabler for sustainable circular economy strategies. However, real-world second-life battery data exhibit mixed chemistries, diverse topologies, and heterogeneous battery management system designs, which complicate reliable and scalable diagnostics. This paper introduces a physics-informed, ensemble-based semi-supervised machine learning framework to address these challenges. The proposed approach leverages multi-condition pulse testing to extract informative feature representations and integrates a simplified voltage-drop consistency constraint to enhance anomaly detection without reliance on manufacturer-specific models or proprietary parameters. Experimental results demonstrate strong generalization across battery chemistries and operating conditions while maintaining robust detection performance under a fixed false positive rate calibration.
This paper addresses a novel variant of the Vehicle Routing Problem that integrates two modern logistics problems: the use of electric vehicles and the optimisation of profit through zone-based pricing. The proposed problem, referred to as the Electric Vehicle Routing Problem with Zone-based Pricing, involves planning routes that maximise the profit, defined as the revenue obtained from serving a subset of customers that accept the service minus the travel energy costs. To tackle this complex problem, we propose two evolutionary algorithms as solution methods: a Genetic Algorithm, which searches directly in the space of potential routing solutions, and Genetic Programming, which evolves constructive heuristics that generate solutions. Experimental results demonstrate that Genetic Programming outperforms the Genetic Algorithm in all problem instances considered.
Cargo hijacking poses critical risks to global supply chains, yet most existing anomaly detection approaches are validated on single, homogeneous datasets and rely on static spatial boundaries that fail to capture realistic behavioral deviations. This study evaluates an unsupervised Long Short-Term Memory (LSTM) Autoencoder for real-time vehicle hijacking detection across heterogeneous logistics environments. The model was trained on a unified dataset of approximately 5.9 million GNSS observations integrating three distinct mobility profiles, and evaluated against road-constrained directional attacks generated via the Open Source Routing Machine (OSRM). A sensitivity-oriented decision threshold was adopted to prioritize threat detection over conservative filtering. Across 30 independent runs, the proposed framework achieved a mean Accuracy of 0.97 and a Recall of 0.99 on the unified dataset, with a clear separation between normal reconstruction error (MAE approximately 0.018) and attack error (MAE approximately 0.062). Inference latency averaged 0.69 s with a memory footprint of approximately 2.1 GB, without requiring GPU acceleration. These results confirm that training on diverse, heterogeneous data stabilizes the detection boundary and reduces false alarms without sacrificing sensitivity, providing a scalable and lightweight blueprint for securing logistics networks using only standard trajectory data.
