AI-BASED INFORMATION SYSTEMS FOR REAL-TIME SIGNAL PROCESSING AND SMART INDUSTRIAL AUTOMATION APPLICATIONS

Authors

  • Indrajit Goswami School of Commerce and Management Studies, Dayananda Sagar University, Innovation Campus, Hosur Road, Bengaluru – 560114, Karnataka, India
  • Guru Basava Aradhya S. Department of Master of Business Administration, Atria Institute of Technology, Anandnagar, Hebbal, Bengaluru – 560024, Karnataka, India
  • Maitri School of Business and Management, Noida International University, Gautam Buddha Nagar, Greater Noida, Uttar Pradesh, India.
  • Pallavi N. R. Department of Computer Science and Engineering, BGS Institute of Technology, Adichunchanagiri University, B.G. Nagara, Nagamangala Taluk, Mandya – 571448, Karnataka, India.
  • Bincy Pothen Department of Hospital Administration, School of Management & Commerce Studies, Shri Guru Ram Rai University, Dehradun, Uttarakhand, India.
  • Geetha M. Department of Computer Science and Engineering, BGS Institute of Technology, Adichunchanagiri University, B.G. Nagara, Nagamangala Taluk, Mandya District – 571448, Karnataka, India.

DOI:

https://doi.org/10.70917/ijcisim-2026-2038

Keywords:

Artificial Intelligence, Real-Time Signal Processing, Smart Industrial Automation, Industry 4.0, Machine Learning, Industrial Information Systems

Abstract

Artificial Intelligence (AI) has revolutionized industrial information systems by making it possible to process signals in real-time and automate processes within various manufacturing settings. In today's industrial landscape, AI-powered information systems are being adopted by modern sectors to improve the quality and reliability of their operations, predictive maintenance, resource optimization, quality assurance, and decision-making. With the ever-increasing volume of data produced by industrial sensors, devices connected to the Internet of Things (IoT), embedded systems and cyber-physical infrastructure, real-time signal processing is key to making sense of that data. Conventional automation systems often face limitations in handling dynamic industrial environments due to their dependence on predefined rules and static control architectures. These challenges are addressed by AI-based information systems using machine learning algorithms, deep neural networks, edge computing, and adaptive control systems that can learn and make decisions on their own. The study explores the architecture, components, methodologies, applications, benefits, and challenges of AI-powered real-time signal processing systems in smart industrial automation. The study emphasizes the capabilities of AI technologies to enable intelligent monitoring, fault detection, process optimization, and predictive analytics within the context of Industry 4.0. In addition, new concepts like digital twins, federated learning, explainable AI and autonomous industrial ecosystems are explored. The results show that the use of AI-driven information systems can clearly enhance productivity, reliability, and operational flexibility, as well as minimize downtime and maintenance expenses, and contribute positively to sustainability. The study shows that AI supported signal processing technologies are a key enabler for realizing next generation smart industries and autonomous manufacturing systems.

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Published

2026-06-20

How to Cite

Indrajit Goswami, Guru Basava Aradhya S., Maitri, Pallavi N. R., Bincy Pothen, & Geetha M. (2026). AI-BASED INFORMATION SYSTEMS FOR REAL-TIME SIGNAL PROCESSING AND SMART INDUSTRIAL AUTOMATION APPLICATIONS. International Journal of Computer Information Systems and Industrial Management Applications, 18(2s), 508–521. https://doi.org/10.70917/ijcisim-2026-2038

Issue

Vol. 18 No. 2s (2026)

Section

Original Articles