federated learning

A REVIEW ON ARTIFICIAL INTELLIGENCE AND DIGITAL HEALTH IN VIRAL HEPATITIS CARE: CURRENT LANDSCAPE AND FUTURE HORIZONS

Viral hepatitis remains one of the most significant global public health challenges, affecting more than 300 million individuals worldwide and causing approximately 1.3 million deaths annually due to cirrhosis, liver failure, and hepatocellular carcinoma (HCC). Despite remarkable advances in vaccination strategies, hig...

Hybrid Quantum-Federated Learning Framework for Privacy-Preserving Fraud Detection: A Jordanian PDPL 2023 Compliance Study

Artificial Intelligence Frameworks in the Prevention and Early Diagnosis of Hepatitis B and C: A Comprehensive Clinical and Computational Review

Despite major medical breakthroughs, Hepatitis B (HBV) and Hepatitis C (HCV) continue to silent-kill millions globally, primarily because they go undetected until severe liver damage sets in. While direct-acting antivirals and suppressive therapies work wonders, our screening methods are lagging behind. We still rely o...

Privacy by Design in Digital Twin Ecosystem

Privacy-protecting digital twins are also crucial for fields like healthcare, finance, and smart cities, where sharing data in real time makes it harder to stay safe and follow the rules. This study discovered that digital twin ecosystems and innovative methodologies, including AI-driven adaptive privacy models, federa...

Code for: Detection-Aware Content-Level Differential Privacy for Lattice-Trapdoor Traceable Federated Prototype Learning

This repository contains the runnable implementation and experiment scripts accompanying the paper "Detection-Aware Content-Level Differential Privacy for Lattice-Trapdoor Traceable Federated Prototype Learning". It implements a lattice-trapdoor identity-tracing mechanism for federated prototype learning (FedLHT) combi...

IoT Architecture for End‐to‐End Visibility

This chapter provides a methodical basis of how predictive analytics works in theory and practice around the field related to consumer electronics. It also commences with an overview of the fundamental concepts, namely, data mining, machine learning, and statistical modeling, and the way they contribute to the success...

Additive Manufacturing and the Rise of Digital Inventory

The chapter discusses how additive manufacturing (AM) is helping speed the shift of the physical stockpile to a digital inventory on-demand, file-based production as close as possible to the point of need. This is transforming the coming times, reducing logistics costs, enhancing customization, and servicing of product...

AI-Driven Cyber Threat Prediction Framework for Smart Industrial Systems Using Federated Learning

Green and Responsible AI: Emerging Trends, Challenges, and Future Directions in Sustainable Computing

This review examines Green AI as an emerging paradigm for developing energy-efficient machine learning systems and explores its intersection with the principles of responsible AI, providing a roadmap for researchers and practitioners seeking to develop AI systems that balance computational performance with environmenta...

A Survey on Resource Management Solutions, Challenges, and Emerging Opportunities in Fog Computing

Fog computing positions computation and storage resources in close proximity to data sources, promisingmillisecond-scale responsiveness and data handling that the distant cloud cannot deliver. Over the pastdecade, researchers have proposed a constellation of mechanisms such as virtualisation, latency-awareservice place...

Deep Learning for Type 2 Diabetes Prediction: A Benchmarking Study Using the PIMA Indians Diabetes Dataset

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder with a growing global burden, making early and accurate risk prediction a public-health priority. This study develops and evaluates a deep neural network (DNN) for T2DM prediction using the PIMA Indian Diabetes dataset (768 records and 8 clinical features)...

Deep Learning for Type 2 Diabetes Prediction: A Benchmarking Study Using the PIMA Indians Diabetes Dataset

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder with a growing global burden, making early and accurate risk prediction a public-health priority. This study develops and evaluates a deep neural network (DNN) for T2DM prediction using the PIMA Indian Diabetes dataset (768 records and 8 clinical features)...