Page 74 - IJAMD-2-2
P. 74
International Journal of AI for
Materials and Design
A unified industrial AI foundation framework
Knowl Inf Syst. 2022;64(12):3197-3234. comprehensive survey. Neurocomputing. 2021;459(1):249-289.
doi: 10.1007/s10115-022-01756-8 doi: 10.1016/j.neucom.2021.04.112
54. Lundberg SM, Lee SI. A Unified Approach to Interpreting 63. Wang L, Zhang X, Su H, Zhu J. A comprehensive survey of
Model Predictions. In: Advances in Neural Information continual learning: Theory, method and application. IEEE
Processing Systems 30 (NIPS 2017). Vol. 30. Curran Trans Pattern Anal Mach Intell. 2024;46(8):5362-5383.
Associates, Inc.; 2017. doi: 10.1109/tpami.2024.3367329
55. Ribeiro MT, Singh S, Guestrin C. “Why Should I Trust You?”: 64. Yang L, Shami A. On hyperparameter optimization
Explaining the Predictions of Any Classifier. In: Proceedings of machine learning algorithms: Theory and practice.
of the 22 ACM SIGKDD International Conference on Neurocomputing. 2020;415:295-316.
nd
Knowledge Discovery and Data Mining - KDD ’16. 2016.
p. 1135-1144. doi: 10.1016/j.neucom.2020.07.061
doi: 10.1145/2939672.2939778 65. Elsken T, Metzen JH, Hutter F. Neural architecture search: A
survey. J Mach Learn Res. 2019;20(55):1-21.
56. Li Z, Guo X, Qiang S. A survey of deep causal models and
their industrial applications. Artif Intell Rev. 2024;57(11):298. doi: 10.48550/arXiv.1808.05377
66. Deng BL, Li G, Han S, Shi L, Xie Y. Model compression and
doi: 10.1007/s10462-024-10886-0
hardware acceleration for neural networks: A comprehensive
57. Singhal P, Walambe R, Ramanna S, Kotecha K. Domain survey. Proc IEEE. 2020;108(4):485-532.
adaptation: Challenges, methods, datasets, and applications. doi: 10.1109/jproc.2020.2976475
IEEE Access. 2023;11:6973-7020.
67. Gou J, Gou J, Yu B, Maybank SJ, Tao D. Knowledge distillation:
doi: 10.1109/access.2023.3237025
A survey. Int J Comput Vis. 2021;129(6):1789-1819.
58. Li W, Huang R, Li J, et al. A perspective survey on deep doi: 10.1007/S11263-021-01453-Z
transfer learning for fault diagnosis in industrial scenarios:
Theories, applications and challenges. Mech Syst Signal 68. Kim S, Kim I, You D. Multi-condition multi-objective
Process. 2022;167:108487. optimization using deep reinforcement learning. J Comput
Phys. 2022;462:111263.
doi: 10.1016/j.ymssp.2021.108487
doi: 10.1016/j.jcp.2022.111263
59. Banabilah S, Aloqaily M, Alsayed E, Malik N, Jararweh Y.
Federated learning review: Fundamentals, enabling 69. Su H, Lee J. An advanced diagnostic model for gearbox
technologies, and future applications. Inf Process Manag. degradation prediction under various operating conditions
2022;59(6):103061. and degradation levels. Annu Conf PHM Soc. 2024;16(1).
doi: 10.1016/j.ipm.2022.103061 doi: 10.36001/phmconf.2024.v16i1.3869
60. Song Y, Wang T, Cai P, Mondal SK, Sahoo JP. A 70. Vaerenberg R, Marx D, Hosseinli SA, et al. Preprocessing and
comprehensive survey of few-shot learning: Evolution, modeling approach for gearbox pitting severity prediction
applications, challenges, and opportunities. ACM Comput under unseen operating conditions and fault severities. Int J
Surv. 2023;55:1-40. Progn Health Manag. 2024;15(1):1-12.
doi: 10.36001/ijphm.2024.v15i1.3808
doi: 10.1145/3582688
71. Gauriat CM, Pencolé Y, Ribot P, Brouillet G. Multi-
61. Wang W, Zheng VW, Yu H, Miao C. A survey of zero-shot
learning: Settings, methods, and applications. ACM Trans class Neural Additive Models: An Interpretable Supervised
Learning Method for Gearbox Degradation Detection. In:
Intell Syst Technol. 2019;10(2):1-37.
2024 IEEE International Conference on Prognostics and
doi: 10.1145/3293318 Health Management (ICPHM). 2024. p. 41-48.
62. Hoi SCH, Sahoo D, Lu J, Zhao P. Online learning: A doi: 10.1109/icphm61352.2024.10627522
Volume 2 Issue 2 (2025) 68 doi: 10.36922/IJAMD025080006

