Issue
Mechanics & Industry
Volume 27, 2026
Artificial Intelligence in Mechanical Manufacturing: From Machine Learning to Generative Pre-trained Transformer
Article Number 2
Number of page(s) 12
DOI https://doi.org/10.1051/meca/2025029
Published online 09 January 2026
  1. Y. Chen, Research on collaborative innovation of key common technologies in the new energy vehicle industry based on digital twin technology, Energy Rep. 8, 15399–15407 (2022) [Google Scholar]
  2. E. Aliwa, O. Rana, C. Perera, P. Burnap, Cyberattacks and countermeasures for in-vehicle networks, ACM Comput. Surv. (CSUR). 54, 1–37 (2021) [Google Scholar]
  3. A. Abu-Khadrah, M. Jarrah, H. Alrababah, Z. N. Alqattan, H. Akbar, Pervasive computing of adaptable recommendation system for head-up display in smart transportation, Comput. Electr. Eng. 102, 108204 (2022) [Google Scholar]
  4. E. Vieira, J. Almeida, J. Ferreira, T. Ferreira, S.A. Vieira, L. Vieira, A Roadside and cloud-based vehicular communications framework for the provision of C-ITS services, Information. 14, 153 (2023) [Google Scholar]
  5. M. Sadaf, Z. Iqbal, A.R. Javed, I. Saba, M. Krichen, S. Majeed, A. Raza, Connected and automated vehicles: Infrastructure, applications, security, critical challenges, and future aspects, Technol. 11, 117 (2023) [Google Scholar]
  6. A. Singh, B.B. Gupta, Distributed denial-of-service (DDoS) attacks and defense mechanisms in various web-enabled computing platforms: issues, challenges, and future research directions, Int. J. Semant. Web Inf. (IJSWIS). 18, 1–43 (2022) [Google Scholar]
  7. A. Kim, M. Park, D.H. Lee, AI-IDS: application of deep learning to real-time Web intrusion detection, IEEE Access. 8, 70245–70261 (2020) [Google Scholar]
  8. M. Markevych, M. Dawson, A review of enhancing intrusion detection systems for cybersecurity using artificial intelligence (ai), in: International Conference Knowledge-based Organization, 2023, Vol. 29, No. 3, pp. 30–37 [Google Scholar]
  9. M. Yu, Construction of regional intelligent transportation system in smart city road network via 5G network, IEEE T Intell. Transp. 24, 2208–2216 (2022) [Google Scholar]
  10. Y. Pan, L. Zhang, Roles of artificial intelligence in construction engineering and management: a critical review and future trends, Automat Constr. 122, 103517 (2021) [Google Scholar]
  11. S. Wu, Spatiotemporal dynamic forecasting and analysis of regional traffic flow in urban road networks using deep learning convolutional neural network, IEEE T Intell. Transp. 23, 1607–1615 (2021) [Google Scholar]
  12. W. Wei, K.C. Chen, A. Rayes, R. Scherer, Guest editorial introduction to the special issue on graph-based machine learning for intelligent transportation systems, IEEE T Intell. Transp. 24, 8393–8398 (2023) [Google Scholar]
  13. H. Gao, Z. Li, Y. Wang, Privacy-Preserved collaborative estimation for networked vehicles with application to road anomaly detection. (2020). https://doi.org/10.48550/arXiv.2008.02928 [Google Scholar]
  14. M. Ragab, H.A. Abdushkour, L. Maghrabi, D. Alsalman, A.G. Fayoumi, A.A.M. AL-Ghamd, Improved artificial rabbits optimization with ensemble learning-based traffic flow monitoring on intelligent transportation system, Sustainability. 15 12601 (2023) [Google Scholar]
  15. M. Hassan, A. Kanwal, M. Jarrah, M. Pradhan, A. Hussain, B. Mago, Smart city intelligent traffic control for connected road junction congestion awareness with deep extreme learning machine, in: 2022 International Conference on Business Analytics for Technology and Security (ICBATS), Dubai, United Arab Emirates, 2022, pp. 1–4 [Google Scholar]
  16. B. Wang, Y. Han, S. Wang, D. Tian, M. Cai, M. Liu, L. Wang, A review of intelligent connected vehicle cooperative driving development, Mathematics. 10, 3635 (2022) [Google Scholar]
  17. C. Christy, A. Nirmala, A.M.O. Teena, A.I. Amali, Machine learning based multi-stage intrusion detection system and feature selection ensemble security in cloud assisted vehicular ad hoc networks, Sci. Rep. 15, 27058 (2025) [Google Scholar]
  18. F. Ashfaq, R.M. Ghoniem, N.Z. Jhanjhi, N.A. Khan, A.D. Algarni, Using dual attention BiLSTM to predict vehicle lane changing maneuvers on highway dataset, Systems. 11, 196 (2023) [Google Scholar]
  19. F.M. Khan et al., Vehicular network security through optimized deep learning model with feature selection techniques, ICCK Trans. Sens. Commun. Control 1.2, 136–153 (2024) [Google Scholar]
  20. F.M. Khan, T. Rahman, A. Zeb, Z.A. Haider, I.U. Khan, H. Bilal, M.A. Khan, I. Ullah, Vehicular network security through optimized deep learning model with feature selection techniques, ICCK Trans. Sens. Commun. Control 1, 136–153 (2024) [Google Scholar]
  21. S. Olugbade, S. Ojo, A.L. Imoize, J. Isabona, M.O. Alaba, A review of artificial intelligence and machine learning for incident detectors in road transport systems, Math. Comput. App. 27, 77 (2022) [Google Scholar]
  22. X. Wang, Q. Wang, An abnormal traffic detection method using GCN-BiLSTM-Attention in the internet of vehicles environment, EURASIP J. Wirel. Comm. 70 (2023) [Google Scholar]
  23. A. Manderna, S. Kumar, U. Dohare, M. Aljaidi, O. Kaiwartya, J. Lloret, Vehicular network intrusion detection using a cascaded deep learning approach with multi-variant metaheuristic, Sensors. 23, 8772 (2023) [Google Scholar]
  24. S. Ebrahimi-Nejad, M. Kheybari, S.V.N. Borujerd, Multi-objective optimization of a sports car suspension system using simplified quarter-car models, Mech. Industr. 21, 4 (2020) [Google Scholar]
  25. M.S. Daoud, M. Shehab, H.M. Al-Mimi, L. Abualigah, R.A. Zitar, M.K.Y. Shambour, Gradient-based optimizer (GBO): a review, theory, variants, and applications, Arch. Comput. Methods Eng. 30, 2431–2449 (2023) [Google Scholar]
  26. I. Ahmadianfar, O.B. Haddad, X. Chu, Gradient-based optimizer: a new metaheuristic optimization algorithm, Inf. Sci. 540, 131–159 (2020) [Google Scholar]
  27. X. Chen, S. Han, T. Luo, D. Guo, Investigation of sliding mode control for nonlinear suspension systems with state estimation, Mechanics & Industry. 21, 611 (2020) [Google Scholar]
  28. A.A. Abdellatif, C.F. Chiasserini, F. Malandrino, A. Mohamed, A. Erbad, Active learning with noisy labelers for improving classification accuracy of connected vehicles, IEEE Trans. Veh. Technol. 70, 3059–3070 (2021) [Google Scholar]
  29. D.J. Hand, P. Christen, N. Kirielle, F*: an interpretable transformation of the F-measure, Machine Learning. 110, 451–456 (2021) [Google Scholar]
  30. https://www.kaggle.com/datasets/galaxyh/kdd-cup-1999−data [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.