Authors:
V. Ellappan, S. Tamilarasi, S. Sathish Kumar, M. Nishanth, S. Vinoth Kumar, R. Bagavathi Lakshmi, Jaime Alfonso Flores Navas

Addresses:
Department of Electronics and Communication and Engineering, Mahendra Institute of Technology, Mallasamudram, Tamil Nadu, India. Department of Computer Applications, Vels Institute of Science, Technology and Advanced Studies, Chennai, Tamil Nadu, India. Department of Environmental Sciences, National Autonomous University of Mexico, Coyoacán, Mexico City, Mexico.

Abstract:

Healthcare, industrial automation, and sustainable energy management are all transforming due to the integration of Artificial Intelligence (AI) and cutting-edge electronics. The paper introduces a novel AI-integrated electronics framework that synergistically integrates embedded machine learning, edge computing, Internet of Things (IoT) sensor networks, and real-time signal processing, enabling context-aware, adaptive intelligent systems. The proposed architecture is based on a five-layer design that includes sensing, signal conditioning, edge processing, AI decision making, and secure communication layers. CNNs, LSTM networks, and federated learning are applied to low-latency edge inference on ARM Cortex-M7 and FPGA hardware platforms. System accuracy is proven by experimental evaluation (95.7%), power consumption is reduced by 68%, and end-to-end latency is less than 12 ms in healthcare monitoring, predictive maintenance and smart grid energy optimisation. The results show that major improvements were achieved in prediction accuracy, computational efficiency, response time, reliability and system scalability for various application fields. The research highlights are clearly summarised, highlighting the advancement of a unified framework between AI and electronics, thorough experiments to validate the system, increased intelligence in the system, and application in healthcare monitoring, industrial process automation, and sustainable energy management, offering readers a quick yet informative overview of the entire research work.

Keywords: Artificial Intelligence; Edge Computing; Embedded Systems; Federated Learning; Healthcare IoT; Industrial Automation; Sustainable Electronics; Predictive Maintenance; Smart Grid.

Received on: 27/08/2025, Revised on: 14/10/2025, Accepted on: 18/12/2025, Published on: 31/03/2026

DOI: 10.69888/FTSCIS.2026.000712

FMDB Transactions on Sustainable Critical Infrastructures, 2026 Vol. 1 No. 1, Pages: 45-57

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