Abstract: Software Defect Prediction (SDP) is an issue of paramount importance for improvement of software quality but data isolation and privacy concerns across organizations affects efficacy of SDP. By making use of a Federated Learning (FL) framework in combination with Differential Privacy (DP) for collective learning without exchanging any raw data, we provide a unique solution to this problem. The study makes use of advanced deep learning... More

Abstract: Cardiovascular diseases (CVDs) are among the leading causes of mortality worldwide. Early detection and risk stratification are critical for preventive care. Traditional machine learning (ML) models can predict heart disease risk but often lack interpretability and fail to integrate with real-time clinical data. Recent advances in fine-tuned large language models (Custom GPT) offer natural language explanations but are limited by insufficient... More

Abstract: Wireless sensor networks (WSNs) play a vital role in modern applications such as environmental monitoring, industrial automation, and smart infrastructure, where reliable data transmission, robustness, and energy efficiency are essential. However, their distributed architecture and constrained computational resources make them highly vulnerable to a wide range of security threats, including Blackhole, Grayhole, Flooding, and Scheduling attacks.... More

Abstract: Much of the commercial and research writings on healthcare Real-Time Location Systems (RTLS) have concentrated on the considerable benefits of this technology for locating assets, patients and staff, with many applications developed, particularly for asset tracking. Less has been written about who is authorised to query and analyse healthcare RTLS data, including historical location data. We categorise use cases for the interactions which have... More

Abstract: Determining whether scan-observable network configurations capture meaningful characteristics of security exposure across large-scale Internet of Things (IoT) populations remains an open problem in measurement research. This study analyzes Internet-exposed IoT endpoints associated with the TR-069 protocol across a global population exceeding 37 million hosts, using a structured sample of 7,634 hosts derived from approximately 10,000 hosts... More