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All issues Volume 680 (2025) E3S Web Conf., 680 (2025) 00104 References
Open Access
Issue
E3S Web Conf.
Volume 680, 2025
The 4th International Conference on Energy and Green Computing (ICEGC’2025)
Article Number 00104
Number of page(s) 14
DOI https://doi.org/10.1051/e3sconf/202568000104
Published online 19 December 2025
  1. Marmoucha, Zouhair, SOULHI, Aziz, et EL KHAILI, Mohamed. Comparative Study of Machine Learning Models Applied to PdM. In: 2025 5th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET). IEEE, 2025 [Google Scholar]
  2. El Hailouch, Hayat, SAATI, Hanae, NHAILA, Hasna, et EL KHAILI, Mohamed. Study of the Roles of IoT in the Transformation of Supplier Relationships in Industry 4.0. In: 2025 5th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET). IEEE, 2025 [Google Scholar]
  3. Wang, W., Yang, H., Zhang, Y., & Xu, J. (2018). IoT-enabled real-time energy efficiency optimisation method for energy-intensive manufacturing enterprises. International Journal of Computer Integrated Manufacturing, 31(4–5), 362–379. [Google Scholar]
  4. S. S. Kamble, A. Gunasekaran, and R. Sharma, “Analysis of the role of artificial intelligence in Industry 4.0: Applications in predictive analytics, planning optimization, and operational efficiency,” Journal of Manufacturing Systems, vol. 67, pp. 336–347, 2023 [Google Scholar]
  5. A. Billey and C. Wuest, “Energy digital twins in smart manufacturing systems: A case study,” Computers & Industrial Engineering, vol. 176, p. 102729, 2024, [Google Scholar]
  6. S. Aldrete, “Optimize production lines to reduce lifecycle energy use,” Siemens Digital Industries Software Blog, Mar. 18, 2015. [Online]. Available: https://blogs.sw.siemens.com/tecnomatix/optimize-production-lines-to-reduce-lifecycle-energy-use/ [Google Scholar]
  7. Kadhim M.H. & Mardukhi F,”A Novel IoT Application Recommendation System Using Metaheuristic Multi-Criteria Analysis”, Computer Systems Science & Engineering, 2020 [Google Scholar]
  8. Urrea P. et al., “Optimizing IIoT Performance: Intelligent Selection of SDN Controllers through AHP Analysis”, International Journal of Intelligent Systems,2024 [Google Scholar]
  9. Y. Hu, Q. Jia, Y. Yao, Y. Lee, M. Lee, C. Wang, X. Zhou, R. Xie, and F. R. Yu, “Industrial Internet of Things Intelligence Empowering, Smart Manufacturing: A Literature Review,” 2024 [Google Scholar]
  10. O. M. Okuyelu and O. Adaji, “AI-Driven Real-time Quality Monitoring and Process Optimization for Enhanced Manufacturing Performance,” Journal of Advances in Mathematics and Computer Science, vol. 39, no. 4, pp. 81–89, Mar. 2024 [Google Scholar]
  11. M. Achouch, M. Dimitrova, R. Dhouib, H. Ibrahim, M. Adda, S. Sattarpanah Karganroudi, K. Ziane, and A. Aminzadeh, “Predictive Maintenance and Fault Monitoring Enabled by Machine Learning: Experimental Analysis of a TA-48 Multistage Centrifugal Plant Compressor,” *Applied Sciences*, vol. 13, no. 3, art. 1790, 2023 [Google Scholar]
  12. Soori, M., Arezoo, B., & Dastres, R. (2023). Internet of Things for Smart Factories in Industry 4.0, a Review. Internet of Things and Cyber-Physical Systems, 3, 192–204 [CrossRef] [Google Scholar]
  13. B. Alex and J. Mwebaze, “A Framework for IoT-Enabled Smart Manufacturing for Energy and Resource Optimization,” *arXiv preprint arXiv:2502.03040*, Feb. 2025. [Google Scholar]
  14. B. Dwyer and J. Bassa, “Combining IoT, Industry 4.0, and energy management suggests exciting future,” *InTech Magazine*, March–April 2018 [Google Scholar]
  15. M. A. Shabur, “A comprehensive review on the impact of Industry 4.0 on the development of a sustainable environment,” *Discover Sustainability*, vol. 5, art. 97, May 2024 [Google Scholar]
  16. L. Zhang, Y. Wang, and H. Zhao, “Artificial intelligence adoption and its impact on industrial energy consumption,” Systems, vol. 13, no. 9, p. 757, 2023 [Google Scholar]
  17. M. Chen, J. Li, and S. Kumar, “Deep learning approaches for predictive energy management in manufacturing systems,” Energy and AI, vol. 12, p. 100123, 2023 [Google Scholar]
  18. Bazi, S., Nhaila, H., & El Khaili, M. (2024, May). Artificial intelligence for diagnosing power transformer faults. In 2024 4th International Conference on Innovative [Google Scholar]
  19. M. Rahman, R. Singh, and A. Patel, “Integration of AI-based predictive maintenance for energy-efficient mechanical systems,” International Journal of Advanced Manufacturing Technology, vol. 135, no. 7–8, pp. 3459–3476, 2024 [Google Scholar]
  20. L.Réveszová, D. Paľová, Základy modelovania podnikových procesov, Košice, Technická univerzita v Košiciach ,2009 [Google Scholar]
  21. L. Pachniková, Ľ. Šidlovská, Simulation as support tool for design of manufacturing systems, Transfer inovácií 18, 2010 [Google Scholar]
  22. S. Kłos and J. Patalas-Maliszewska, “The impact of ERP on maintenance management,” Management and Production Engineering Review, vol. 4, no. 3, pp. 15–25, 2013 [Google Scholar]
  23. M. Ghahramani, Y. Qiao, M. Zhou, A. O’Hagan, and J. Sweeney, “AI-based modeling and data-driven evaluation for smart manufacturing processes,” *arXiv preprint arXiv:2008.12987*, Aug. 2020. [Google Scholar]
  24. A. Azadeh, S. N. Shirkouhi, and K. Rezaie, “A robust decision-making methodology for evaluation and selection of simulation software package,” The International Journal of Advanced Manufacturing Technology, vol. 47, no. 1–4, pp. 381–393, 2010 [Google Scholar]
  25. B. Subramanian, P. Stark, and A. Wiesner, “Application of AHP in selection of software tools for digital factory in automotive,” *Procedia CIRP*, vol. 93, pp. 1361–1366, 2020 [Google Scholar]
  26. I. El Hassani, C. El Mazgualdi, and T. Masrour, “Artificial Intelligence and Machine Learning to Predict and Improve Efficiency in Manufacturing Industry,” *arXiv preprint arXiv:1901.02256*, Feb. 2019 [Google Scholar]

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