Decision Support System based on Industry 5.0 in Artificial Intelligence

Authors

  • S. Srinivasan Professor, Department of Biomedical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Saveetha Nagar, Thandalam, Chennai-602105
  • D. Deva Hema Assistant Professor (Sr.G), Department of CSE, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamilnadu
  • Balaji Singaram Software Developer, Compunnel Inc., Plainsboro, New Jersey, USA, 08536
  • D. Praveena Associate Professor, Department of Information Technology, R.M.D. Engineering College, Kavaraipettai, Tamil Nadu
  • K. B. Kishore Mohan Professor and Head, Department of Biomedical Engineering, SSCET, Sankari, Salem
  • M. Preetha Professor & Head, Department of Computer Science and Engineering, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, Tamilnadu

Keywords:

Process Mining (PM), Decision Support System, industry 5.0, Artificial Intelligence

Abstract

The term "Industry 5.0" was created to address personalized production and the empowerment of humans in manufacturing processes, as Industry 4.0 was unable to meet the increasing need for customization. There are differing opinions about what Industry 5.0 is and what comprises the reconciliation of humans and robots from the term's inception. A new "proof of concept" for enhanced Process Mining is to be adopted to automate decision-making, optimize machine settings, and conduct maintenance interventions to provide a novel method for modeling production management in industry. Both supervised and unsupervised artificial intelligence techniques are incorporated into the PM model's complex electrical sensing and actuation subsystems. These systems facilitate intelligent decision-making by suggesting theoretical process workflows that are powered by a Decision Support System (DSS) engine. By solving these identified obstacles, future researchers may enhance the decision assistance systems. Every decision support system is subjected to a methodical analysis. An extensive assessment is carried out considering many factors such as compatibility, expandability, ease of use, and so on. Future issues are identified and outlined based on the evaluation's findings, which also reveal development trends and offer suggestions for future research directions.

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Published

07.02.2024

How to Cite

Srinivasan, S. ., Hema, D. D. ., Singaram, B. ., Praveena, D. ., Mohan, K. B. K. ., & Preetha, M. . (2024). Decision Support System based on Industry 5.0 in Artificial Intelligence. International Journal of Intelligent Systems and Applications in Engineering, 12(15s), 172–178. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4731

Issue

Vol. 12 No. 15s (2024)

Section

Research Article

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