Revolutionizing Smart Infrastructure with Synergistic Applications of Artificial Intelligence and Machine Learning
Year: 2024
Type: article
Abstract: Smart infrastructure powered by Artificial Intelligence (AI) and Machine Learning (ML) transforms modern urban environments by optimizing energy distribution, water management, transportation, and security systems. This study presents an AI-ML-driven architecture for smart infrastructure deployment, integrating real-time and historical data from various sensors and IoT devices. The proposed system employs advanced predictive modeling techniques to enhance decision-making processes. To develop and analyze this architecture, a combination of tools, including Python, TensorFlow, Scikit-learn, and SQL-based databases, was used for data processing, model training, visualization, and system design. The dataset consists of real-time sensor readings from smart grids, water distribution networks, traffic monitoring systems, surveillance cameras, and historical records from cloud storage and edge computing environments. Key findings demonstrate that AI-driven models significantly improve resource management’s predictive accuracy, reduce data processing latency, and enhance overall system efficiency. Graphical representations, such as deployment diagrams and time-series forecasting graphs, illustrate the interactions between AI models, infrastructure components, and deployment services. The results underscore the potential of AI-ML frameworks to optimize smart infrastructure, making them more adaptive and resilient.
Author: Anjan Kumar Reddy Ayyadapu
Language: English
Topic: Internet of Things and AI
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4409971826