This research presents a deep reinforcement learning-based autonomous robot for warehouse management. The system optimizes item picking and sorting operations, improving efficiency in real-time environments. The proposed solution is scalable, adaptable, and significantly enhances operational productivity in logistics and warehousing applications.

John Michael Harris, Emily Grace Roberts, Nathaniel Thomas Clark, Samuel Isaac Edwards, Sophia Claire Bennett

This paper introduces an AI-powered surveillance system utilizing computer vision and edge computing for real-time threat detection. The system processes video feeds directly at the edge, reducing latency, and ensuring timely responses to security threats in large-scale public and private infrastructures.

David Alexander Mitchell, Olivia Claire Johnson, Benjamin Lucas Scott, Victoria Jane Moore, Emma Alice Robinson

This paper explores the integration of cloud computing with intelligent robotics for industrial automation. The system performs predictive maintenance by analyzing sensor data, ensuring timely interventions and enhancing the overall efficiency and longevity of manufacturing equipment in real-time production environments.

Matthew Samuel Green, Rachel Anna Peterson, Tyler Benjamin Lee, Jennifer Claire King, Oliver Robert Clark

This paper presents a multi-agent system for autonomous robots collaborating in smart factory environments. By utilizing reinforcement learning, the system allows robots to share information and optimize production workflows, improving efficiency and flexibility in manufacturing processes through decentralized decision-making.

Lucas Benjamin Turner, Ava Isabella Ford, Leo Daniel Harris, Ella Maria Carter, Daniel Thomas Stewart

This study discusses the application of edge computing in autonomous vehicle systems. The edge infrastructure supports real-time data processing, improving vehicle safety and traffic management. By enabling faster decision-making, the system contributes to reduced accidents and enhanced traffic flow in smart cities.

Christopher John Wilson, Daniel Joshua Clark, Lily Sophia Davis, Emma Charlotte Moore, Jack Samuel Turner

This research presents a system that integrates machine learning algorithms with cloud platforms to provide predictive analytics for healthcare applications. By analyzing patient data, the system predicts potential health risks, offering timely interventions and improving patient outcomes through personalized care models.

Emma Isabella Cooper, Jacob Daniel Harris, Olivia Grace Scott, Benjamin Henry Allen, Lily Victoria Evans

This paper discusses the application of artificial intelligence in real-time fraud detection for financial transactions. Leveraging big data analytics, the proposed system improves fraud detection accuracy by processing transaction data in real-time, ensuring higher security and reducing false-positive rates in financial institutions.

Alice Rebecca Morgan, James Edward Walker, Emily Sophia Phillips, Michael David Jackson, Olivia Grace Williams

This paper explores data privacy issues in edge computing and presents secure techniques for distributed data processing in Internet of Things (IoT) systems. By implementing encryption and decentralized processing, the system ensures data confidentiality and integrity, offering robust security solutions for IoT applications in sensitive environments.

Lucas Christopher Scott, Sarah Elizabeth Johnson, Ethan James Brown, Sophia Olivia Green, Daniel Alexander Mitchell