ARCHIVES
Original Article
Adaptive Fault Tolerance in Machine Learning Systems: A Self-Healing Framework
Faiza Fathima1
Adwaith Raj2
Jojo George3
Nandana Babu4
Ashmila K.P5
Dr. S. Vadhana Kumari6
1 2 3 4 5 6 Computer Science and Engineering and Business Systems, Vimal Jyothi Engineering College, Kannur, Kerala, India.
Published Online: January-April 2026
Pages: 214-220
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260501030
References
1. L. Myllyaho, M. Raatikainen, T. M¨annist¨o, J. K. Nurminen, and T. Mikkonen,“On misbehaviour and fault tolerance in machine learning
systems,” Journal of Systems and Software, vol. 183, p. 111096, 2022. [Online].
Available:https://www.sciencedirect.com/science/article/pii/S016412122100193X
2. L. Myllyaho, J. K. Nurminen, and T. Mikkonen, “Node co-activations as a means of error detection—towards fault-tolerant neural networks,”
Array, vol. 15, p. 100201, 2022. [Online].Available:https://www.sciencedirect.com/science/article/pii/S2590005622000509
3. Liu, D., Zhang, S., Wang, S. et al. Realization and research of self-healing technology of power communication equipment based on power
safety and controllability. Energy Inform 8, 1 (2025). https://doi.org/10.1186/s42162-024-00460-x
4. E. Oluwagbade, “Self-healing codebases: Using nlp and ml for automatic coderepair,” 03 2023.
https://www.researchgate.net/publication/390929429
5. Barrera, J.M., Reina, A., Mate, A. et al. Fault detection and diagnosis for industrial processes based on clustering and autoencoders: a case of
gas turbines. Int. J. Mach. Learn. & Cyber. 13, 3113–3129 (2022). https://doi.org/10.1007/s13042-022-01583-x
6. Seba, A.M., Gemeda, K.A. & Ramulu, P.J. Prediction and classification of IoT sensor faults using hybrid deep learning model. Discov Appl
Sci 6, 9 (2024). https://doi.org/10.1007/s42452-024-05633-7
7. Nikam, D., Chukwuemeke, A., Nigam, A. et al. On the application of YOLO-based object detection models to classify and detect defects in
laser-directed energy deposition process. Prog Addit Manuf 10, 7609–7624 (2025). https://doi.org/10.1007/s40964-025-01056-x
8. Elhoseny, M., Rao, D.D., Veerasamy, B.D. et al. Deep Learning Algorithm for Optimized Sensor Data Fusion in Fault Diagnosis and
Tolerance. Int J Comput Intell Syst 17, 299 (2024). https://doi.org/10.1007/s44196-024-00692-5
9. Ortiz-Garces, I., Villegas-Ch, W. & Luján-Mora, S. Implementation of edge AI for early fault detection in IoT networks: evaluation of
performance and scalability in complex applications. Discov Internet Things 5, 108 (2025). https://doi.org/10.1007/s43926-025-00196-4
10. Arie Gurfinkel, Marijn Heule Tools and Algorithms for the Construction and Analysis of Systemshttps://doi.org/10.1007/978-3-031-90643-
5
11. D. Saxena and A. K. Singh, “A self-healing and fault-tolerant cloud-based digital twin processing management model,” IEEE Transactions
on Industrial Informatics, early access, 2025.https://doi.org/10.1109/TII.2025.3540498
systems,” Journal of Systems and Software, vol. 183, p. 111096, 2022. [Online].
Available:https://www.sciencedirect.com/science/article/pii/S016412122100193X
2. L. Myllyaho, J. K. Nurminen, and T. Mikkonen, “Node co-activations as a means of error detection—towards fault-tolerant neural networks,”
Array, vol. 15, p. 100201, 2022. [Online].Available:https://www.sciencedirect.com/science/article/pii/S2590005622000509
3. Liu, D., Zhang, S., Wang, S. et al. Realization and research of self-healing technology of power communication equipment based on power
safety and controllability. Energy Inform 8, 1 (2025). https://doi.org/10.1186/s42162-024-00460-x
4. E. Oluwagbade, “Self-healing codebases: Using nlp and ml for automatic coderepair,” 03 2023.
https://www.researchgate.net/publication/390929429
5. Barrera, J.M., Reina, A., Mate, A. et al. Fault detection and diagnosis for industrial processes based on clustering and autoencoders: a case of
gas turbines. Int. J. Mach. Learn. & Cyber. 13, 3113–3129 (2022). https://doi.org/10.1007/s13042-022-01583-x
6. Seba, A.M., Gemeda, K.A. & Ramulu, P.J. Prediction and classification of IoT sensor faults using hybrid deep learning model. Discov Appl
Sci 6, 9 (2024). https://doi.org/10.1007/s42452-024-05633-7
7. Nikam, D., Chukwuemeke, A., Nigam, A. et al. On the application of YOLO-based object detection models to classify and detect defects in
laser-directed energy deposition process. Prog Addit Manuf 10, 7609–7624 (2025). https://doi.org/10.1007/s40964-025-01056-x
8. Elhoseny, M., Rao, D.D., Veerasamy, B.D. et al. Deep Learning Algorithm for Optimized Sensor Data Fusion in Fault Diagnosis and
Tolerance. Int J Comput Intell Syst 17, 299 (2024). https://doi.org/10.1007/s44196-024-00692-5
9. Ortiz-Garces, I., Villegas-Ch, W. & Luján-Mora, S. Implementation of edge AI for early fault detection in IoT networks: evaluation of
performance and scalability in complex applications. Discov Internet Things 5, 108 (2025). https://doi.org/10.1007/s43926-025-00196-4
10. Arie Gurfinkel, Marijn Heule Tools and Algorithms for the Construction and Analysis of Systemshttps://doi.org/10.1007/978-3-031-90643-
5
11. D. Saxena and A. K. Singh, “A self-healing and fault-tolerant cloud-based digital twin processing management model,” IEEE Transactions
on Industrial Informatics, early access, 2025.https://doi.org/10.1109/TII.2025.3540498
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