Next-Generation AI-Based Real-Time Computational Framework for Smart Environments

Abstract

The rapid growth of smart environments and intelligent digital systems has increased the demand for efficient real-time computational frameworks powered by Artificial Intelligence (AI). Modern applications such as smart cities, healthcare monitoring, industrial automation, transportation, and IoT networks require intelligent systems capable of processing large volumes of real-time data with high accuracy, low latency, and adaptive decision-making capabilities. Traditional computational systems often face challenges related to scalability, processing delays, security, and dynamic resource management in real-time environments.

This paper proposes a Next-Generation AI-Based Real-Time Computational Framework for Smart Environments that integrates machine learning algorithms, real-time analytics, cloud-edge computing, and intelligent automation to improve system performance and decision-making efficiency. The proposed framework is designed to support scalable data processing, predictive analysis, resource optimization, and secure communication among interconnected smart devices. The architecture enables adaptive learning and autonomous responses for dynamic environments while maintaining reliability and computational efficiency. The proposed model can be applied in multiple domains including smart healthcare, smart transportation, industrial IoT, and intelligent surveillance systems. Experimental analysis demonstrates improved response time, system accuracy, and optimized resource utilization compared to conventional real-time systems.