Industry 5.0 emphasizes human-centric, resilient, and sustainable manufacturing systems supported by intelligent digital technologies. Despite advances in digital twins, ensuring secure and transparent supply chain traceability remains a significant chall

Abstract

The increasing adoption of the Internet of Medical Things (IoMT) has enabled continuous health monitoring through wearable sensors and smart medical devices. However, cloud-centric healthcare systems suffer from high latency, bandwidth limitations, and privacy concerns. This paper proposes an edge intelligence framework that integrates IoMT devices with lightweight deep learning models for real-time disease prediction. The framework employs edge computing to preprocess physiological signals locally while utilizing optimized Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) for disease classification. Secure communication protocols and lightweight encryption mechanisms ensure patient data confidentiality during transmission. Experimental analysis demonstrates improvements in prediction accuracy, response time, and energy efficiency compared to conventional cloud-based approaches. The proposed framework provides a scalable and privacy-aware solution for next-generation intelligent healthcare systems capable of supporting continuous patient monitoring and early disease diagnosis.