This study investigates the application of deep neural networks for predictive maintenance in industrial IoT systems. By analyzing sensor data from machines, the AI models can predict failures before they occur, enabling maintenance teams to address issues proactively and reduce downtime in critical systems.

Aditya Ravi Kulkarni, Wei Li Zhang, Jonathan Michael Peterson, Maria Elena Gomez, Elena Maria Rossi

This paper explores the use of AI-driven optimization techniques to improve energy efficiency in smart cities. By analyzing vast amounts of data from urban infrastructure, AI models can optimize energy usage, reduce waste, and lower carbon footprints, contributing to the development of sustainable urban environments.

Michael Julian Fields, Cheng Wei Li, Priya Nair, Wei Jun Tan, Taro Hiroshi Nakamura

This paper examines the use of AI and machine learning for fraud detection in financial systems. It highlights the challenges of developing robust fraud detection models, including dealing with unbalanced datasets, identifying new fraud patterns, and the importance of real-time detection for minimizing financial losses.

Aditya Ramesh Kulkarni, Wei Li Zhang, Jonathan Michael Peterson, Elena Maria Rossi, Maria Elena Gomez

This research explores the role of AI in autonomous vehicles, focusing on how machine learning algorithms are used to enhance safety, navigation, and decision-making in real-time. The study covers different AI approaches that allow autonomous vehicles to navigate complex environments safely and efficiently.

Christopher Robert Harris, Wei Jun Tan, Priya Nair, Isabella Claire Fisher, Michael Julian Fields

This paper discusses the role of AI in personalized healthcare, with a focus on how predictive modeling can improve patient outcomes. It covers various AI algorithms that analyze patient data, predict future health conditions, and enable tailored treatments to optimize recovery and quality of life.

Jonathan Michael Peterson, Wei Li Zhang, Maria Elena Gomez, Priya Nair, Taro Hiroshi Nakamura