Hierarchical Anomaly Detection and SHAP-Based Root-Cause Attribution for Robotic Process Automation Workflows
References
- Cao, Y., Cao, Y., Xiang, H., Zhang, Y., Zhu, Y., & Ting, K. (2024). Anomaly Detection Based on Isolation Mechanisms: A Survey. Mach. Intell. Res. 22, 849–865 doi: 10.1007/s11633-025-1554-4.
- Chirayil Nandakumar, S., Mitchell, D., Suphi Erden, M., Flynn, D., & Lim, T. (2024). Anomaly Detection Methods in Autonomous Robotic Missions, Sensors (Basel, Switzerland). Multidisciplinary Digital Publishing Institute (MDPI), 24(4), p. 1330. doi: 10.3390/s24041330.
- Deng, B., Wang, D., Li, H., Wang, Z., Zhang, L. & Shu, Y. (2024). Deep Learning Enabled Scheduling System Data Anomaly Analysis in Robotic Process Automation, International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024. Institute of Electrical and Electronics Engineers Inc. doi: 10.1109/ICDSCNC62492.2024.10939815.
- Essakki, P. & Bagavathi, R.(2025). Lightweight TinyML-based Anomaly Detection for Intelligent Robotic Process Automation Workflows, Proceedings of the 4th International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2025. Institute of Electrical and Electronics Engineers Inc., pp. 93–97. doi: 10.1109/ICIMIA67127.2025.11200680.
- Kumar, V., Srivastava, V., Mahjabin, S., Pal, A., Kluttermann, S. & Muller, E. (2024). Autoencoder Optimization for Anomaly Detection: A Comparative Study with Shallow Algorithms, Proceedings of the International Joint Conference on Neural Networks. Institute of Electrical and Electronics Engineers Inc. doi: 10.1109/IJCNN60899.2024.10650057.
- Landauer, M., Onder, S., Skopik, F. & Wurzenberger, M. (2023a). Deep learning for anomaly detection in log data: A survey, Machine Learning with Applications. Elsevier, 12(1), p. 100470. doi: 10.1016/j.mlwa.2023.100470.
- Muaz, M., Sajid, S., Schulze, T., Liu, C., Klasen, N., & Drescher, B. (2025). Explainable AI for correct root cause analysis of product quality in injection moulding, Journal of Manufacturing Processes. Elsevier, 145, pp. 371–380. doi: 10.1016/j.jmapro.2025.03.114.
- Prucha, P. (2023). Towards Discovering Erratic Behavior in Robotic Process Automation with Statistical Process Control, Information Systems and e-Business Management. Springer Science and Business Media Deutschland GmbH, 22(4), pp. 741–758. http://arxiv.org/abs/2305.00205
- Renero, J., Maestre, R., Ochoa, I. & Madrid, B. (2025). REX: Causal discovery based on machine learning and explainability techniques. doi: 10.1016/j.patcog.2025.112491.
- Santos, P., Rocha, M., & Krohling, A. (2025). Combining SHAP and Causal Analysis for Interpretable Fault Detection in Industrial Processes. https://arxiv.org/pdf/2510.23817v1
- Shi, Y., Zhang, N., Song, X., Li, H. & Zhu, Q. (2024). Novel approach for industrial process anomaly detection based on process mining, Journal of Process Control. Elsevier, 136, p. 103165. doi: 10.1016/j.jprocont.2024.103165.
- Shukla, V., Shukla, A., Surya, S., & Shukla, S. (2025). A systematic survey: role of deep learning-based image anomaly detection in industrial inspection contexts, Frontiers in Robotics and AI. Frontiers Media SA, 12, p. 1554196. doi: 10.3389/frobt.2025.1554196.
- Tayeh, T. & Shami, A. (2021). Anomaly Detection in Smart Manufacturing with an Application Focus on Robotic Finishing Systems: A Review. https://arXiv:2107.05053
DOI: https://doi.org/10.2478/picbe-2026-0043 | Journal eISSN: 2558-9652
Language: English
Page range: 538 - 552
Published on: Jul 16, 2026
Published by: Bucharest University of Economic Studies
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
Related subjects:
© 2026 Yanka ALEKSANDROVA, Mihail RADEV, Mila GEORGIEVA, Desislava KOLEVA, published by Bucharest University of Economic Studies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.