This study explores the application of advanced machine learning techniques to optimize network performance in cloud computing systems. The proposed framework adapts in real time to network conditions, ensuring high throughput and minimal latency, thus enhancing user experience and system efficiency.

Mark Joseph Anderson, Lisa Marie Williams, Thomas Richard Davis, Emily Grace Brown, Samuel Adam Clark

This paper proposes a blockchain-enabled distributed framework to ensure secure data sharing in cloud-based healthcare systems. The framework leverages the immutability and transparency of blockchain technology to protect sensitive patient data, enabling secure and efficient data exchanges between healthcare providers.

Robert Charles Harris, Maria Elena Lopez, Steven Michael Garcia, Linda Jane Young, David Matthew Clark

This paper investigates the use of deep reinforcement learning algorithms to improve resource allocation in cloud computing environments. The proposed model dynamically optimizes resource distribution based on demand and workload characteristics, ensuring high performance while reducing energy consumption in cloud data centers.

Jason William Harris, Robert Alan Johnson, Katherine Elizabeth Davis, Michael George Brown, Caroline Ruth Martin

This research develops an artificial intelligence-based cybersecurity framework designed to prevent Distributed Denial of Service (DDoS) attacks in cloud networks. The framework uses machine learning techniques to detect attack patterns and proactively mitigate threats, improving the resilience of cloud infrastructure.

John Thomas Miller, Sarah Elizabeth Lee, Nicholas James Scott, William Brian Roberts, Hannah Victoria Clark

This paper presents a cloud-based framework for secure and scalable management of IoT data using blockchain technology. The framework ensures data integrity, privacy, and transparency by combining the benefits of cloud computing and blockchain, making it suitable for large-scale IoT deployments.

Edward James Campbell, Victoria Marie Roberts, Daniel Andrew Walker, Jessica Maria Taylor, Alexander Samuel Evans

This study introduces an AI-powered framework for real-time monitoring and optimization of energy usage in cloud data centers. The framework dynamically adjusts power distribution based on usage patterns and environmental conditions, promoting energy efficiency while ensuring high system performance and reliability.

Benjamin Arthur Williams, Olivia Charlotte Green, Samuel Thomas Collins, Grace Eleanor Mitchell, Adam Christopher Morgan

This paper investigates the use of AI and machine learning techniques for predictive maintenance in Industrial IoT (IIoT) systems. By analyzing sensor data in real-time, the proposed model predicts equipment failures and suggests maintenance schedules, improving operational efficiency and reducing downtime in industrial environments.

Luke Christopher Turner, William Michael Green, Sarah Olivia Adams, Thomas Henry Harris, John Robert Powell