This paper explores AI-powered privacy-preserving techniques for secure data sharing in cloud computing environments. It proposes novel encryption methods and decentralized algorithms that ensure data integrity, confidentiality, and compliance with regulatory standards, making it ideal for industries requiring high security in data handling.

Martin Daniel Clark, Sarah Louise Adams, Michael James O’Connor, Rachel Kate Brown, Thomas Edward Hall

This paper introduces a machine learning-based approach for energy-efficient IoT networks, focusing on predictive maintenance and real-time monitoring. By leveraging machine learning algorithms, the system detects potential failures early, optimizing energy consumption and improving the performance of IoT systems in industrial settings.

Edward Michael Harrison, Olivia Grace Mitchell, Robert Charles Anderson, Jessica Anne Clark, Samuel George Taylor

This study presents a decentralized blockchain-based voting system aimed at securing online elections and polling. The proposed system uses blockchain's immutability and transparency to prevent vote tampering, ensuring accurate and confidential voting, ideal for governmental and organizational elections in a digital era.

Henry David Jones, Matthew James Harris, Elizabeth Sarah Parker, Linda Maria Williams, Andrew John Martin

This paper proposes a quantum computing-based approach to optimize network traffic in 5G communication systems. By utilizing quantum algorithms, the proposed solution reduces congestion, enhances bandwidth efficiency, and ensures secure data transmission, paving the way for future developments in 5G and beyond communication technologies.

James Daniel Carter, Charlotte Elizabeth Brown, William John Wilson, Helen Margaret Lee, Andrew James Moore

This paper discusses the implementation of edge computing for real-time traffic monitoring and smart city applications. By processing data locally, the system reduces latency and bandwidth usage, providing efficient traffic management, environmental monitoring, and emergency response systems, contributing to the development of smart urban environments.

Robert James Evans, Thomas John Harris, Michael David Roberts, Sophie Claire Richardson, John William Green

This research introduces a data-driven security framework for smart home networks using machine learning algorithms. The model detects vulnerabilities, predicts potential threats, and proactively defends against attacks, enhancing the security and resilience of IoT-enabled home automation systems.

Edward Matthew Lee, Caroline Ruth Parker, Richard John Thomas, Isabelle Marie Smith, George David Jackson

This paper investigates the application of artificial intelligence for predictive analytics in healthcare data management. By using machine learning techniques, it forecasts patient outcomes, optimizes resource allocation, and enhances decision-making, improving healthcare delivery and operational efficiency in medical institutions.

David Robert Harris, Susan Mary Clark, Frank Albert O’Neill, Patricia Jane Foster, Peter Thomas Young