This research explores the application of AI-driven real-time analytics in autonomous vehicles. The integration of machine learning and deep learning algorithms helps these systems interpret complex sensor data, enabling faster decision-making, improved navigation, and enhanced safety features in autonomous vehicle technologies.

Benjamin James Cook, Samuel Robert Evans, Daniel Thomas Walker, Emma Sophia Mitchell, Nathaniel Andrew Scott

This paper proposes a cloud-based big data framework for predictive healthcare monitoring. By analyzing large-scale health data from various sources, the system enables real-time monitoring of patient conditions and forecasts potential health risks, supporting proactive care and improving patient outcomes.

Oliver Michael Green, Henry Jacob Bennett, Amelia Sophia Turner, Lucas Mason Garcia, Ava Claire Williams

This research introduces a deep reinforcement learning model for autonomous drone navigation. The system learns to adapt its flight paths in dynamic environments, optimizing for efficiency and safety, allowing drones to navigate complex terrains while avoiding obstacles and hazards.

Matthew Joseph Harris, Lily Jane Turner, Jackson Connor Gray, Isabelle Marie Taylor, Samuel Ryan White

This paper investigates the role of cloud computing in advancing smart healthcare systems. By leveraging cloud technologies, healthcare providers can access real-time patient data, collaborate across platforms, and ensure more accurate diagnostics, improving efficiency and quality of care for patients.

Jack William Davis, Olivia Claire Harris, Matthew David Turner, Emily Grace Moore, Benjamin Samuel Young

This study proposes a predictive analytics approach to optimize resource allocation in multi-tenant cloud environments. By analyzing usage patterns, the model efficiently allocates resources, improving performance while minimizing costs. The method ensures that cloud systems operate with maximum efficiency and minimal downtime.

Oliver James Bell, Charlotte Olivia Davies, Gabriel Edward Mitchell, Amelia Grace Wilson, Noah Benjamin Harris

This paper discusses the integration of blockchain technology into cloud storage solutions to enhance data security in enterprises. By utilizing decentralized ledgers, the system ensures secure data sharing and prevents unauthorized access, fostering trust in cloud-based data management systems.

Lucas William Anderson, Ella Charlotte Taylor, Ethan Ryan Cooper, Sophie Amelia King, Daniel Nathan Scott

This research develops a framework for optimizing virtual machine scheduling in cloud data centers, aimed at improving resource utilization. By minimizing energy consumption and ensuring optimal workload distribution, the framework enhances the efficiency and sustainability of cloud infrastructure.

James Henry Clarke, Olivia Isabel Harris, Liam William Davis, Ella Amelia Johnson, Sophia Emily Evans

This paper introduces an AI-driven predictive maintenance system for IoT-based cloud platforms. By leveraging machine learning algorithms, the system predicts potential failures and optimizes maintenance schedules, reducing downtime and improving system reliability across IoT devices and cloud infrastructures.

Henry William Carter, Ella Charlotte Evans, Alexander Thomas Moore, George Nathaniel Brooks, Grace Isabella White