TSYS School's Linqiang Ge Studies Integration of Machine Learning Algorithms into Cyber-Physical Systems

Cyber-physical systems (CPS), which integrate physical processes with computational resources, are fundamental to a variety of application domains, including smart grids, intelligent transportation, smart manufacturing, and smart healthcare, among others. These systems operate in complex, dynamic environments that generate continuous operational data requiring real-time processing. Traditional data analytics methods often struggle with CPS's complexity, heterogeneity, and security requirements. However, recent research advancements have focused on incorporating machine learning algorithms into these systems, leading to substantial improvements in automation, efficiency, and resilience. New research by TSYS School computer scientist Linqiang Ge, Auburn University's Jingyi Zheng and Towson University's Wei Yu provides a comprehensive exploration and review of machine learning techniques, including a detailed taxonomy of machine learning along with the representative algorithms within each type of machine learning model. Additionally, Ge and his colleagues discuss the deployment of these algorithms across various CPS, emphasizing the substantial benefits that machine learning can offer. To further demonstrate the efficacy of machine learning in CPS, a case study is demonstrated to validate the efficacy of a machine learning model within healthcare CPS.