This study introduces a cloud-based automated monitoring and control system for smart grid energy management. The system uses real-time data processing and machine learning to optimize energy distribution, minimize operational costs, and ensure the stability and efficiency of modern energy grids.

Benjamin Ethan Scott, Ava Emily Parker, Liam Matthew Williams, Noah Jacob Taylor, Isabella Sophie Martinez

This paper explores AI-powered threat detection systems for enhancing cybersecurity in cloud networks. The proposed system uses deep learning models to analyze traffic patterns, identify anomalies, and prevent cyberattacks, ensuring secure and resilient cloud infrastructure for enterprises.

Lucas Alexander Evans, Emma Grace Lewis, William Henry Jackson, Olivia Sophia Moore, Jacob Carter Williams

This paper presents a real-time big data processing framework using cloud computing to improve healthcare services. By leveraging cloud resources, the framework enables fast data processing, efficient storage management, and real-time analysis for better patient monitoring and decision-making in healthcare environments.

Charlotte Isabelle Turner, Ethan Noah Harris, Benjamin Daniel Moore, Madison Scarlett Clark, Sophia Emma Anderson

This research discusses the use of AI-based predictive models in smart cities for data-driven decision-making. The models analyze urban data to predict trends in traffic, energy use, and resource management, enabling cities to optimize operations and improve sustainability and efficiency.

Jack Samuel Cooper, Ava Emily Lewis, Oliver James Harris, Ella Grace Martinez, William Lucas Robinson

This paper presents machine learning algorithms designed to optimize network traffic in cloud data centers. By analyzing network patterns and traffic flows, the algorithms predict and adapt to congestion, improving network performance, reducing latency, and enhancing overall data center efficiency.

Liam Ethan Carter, Noah James Lee, Olivia Madison Brown, Emma Lily Scott, Jacob Benjamin White

This paper explores a distributed cloud computing approach to handle scalable IoT data processing. By distributing workloads across cloud resources, the approach enhances performance, reduces latency, and ensures efficient data management, making it ideal for real-time IoT applications in smart environments.

Amelia Olivia Harris, Charlotte Grace Turner, Liam Daniel Walker, Isabella Madison Carter, James Henry Moore

This paper discusses the use of cloud-based infrastructure for secure data sharing in healthcare systems. By employing advanced encryption and access control mechanisms, the system ensures data privacy, improves collaboration between medical professionals, and enhances patient care management across various platforms.

Oliver Benjamin Scott, Emma Charlotte Green, William James Adams, Noah Matthew Reed, Olivia Sophia King