Contextual AI-Based Systems for Predictive Computing in Autonomous Infrastructure Management
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V2I5P101Keywords:
Contextual Ai, Predictive Computing, Autonomous Infrastructure, Predictive Maintenance, Smart Grids, Intelligent Transportation Systems, Deep Learning, Real-Time AnalyticsAbstract
Introduction of Contextual Artificial Intelligence (AI) in autonomous infrastructure management systems is a paradigm shift in predictive computing, as the systems now have a way to undertake proactive monitoring of faults and optimization of critical infrastructures. The paper explores the design, development, and implementation of contextual AI systems to predictive maintenance and management in areas of smart grids, transportation networks, and smart buildings. Contextual AI is based on the premise of using environmental, operating, and historical data to predict anomalies in the system and allocate resources in the most efficient way. We discuss the use of modern machine learning algorithms, deep learning architectures and real time data analytics systems as a way of improving the decision making processes within infrastructure systems. Massive simulation outcomes prove that the predictive accuracy of contextual AI is much higher, the maintenance expenses are lower, and operational efficiency is improved overall. The research paper also covers a detailed approach to incorporating contextual AI in autonomous infrastructures, which also includes the system architecture, data acquisition, modeling, and performance analysis. The results offer understanding of the issues in the development of AI-based autonomous infrastructure management, the ethical aspects, and subsequent research opportunities
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