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Hierarchical Anomaly Detection and SHAP-Based Root-Cause Attribution for Robotic Process Automation Workflows Cover

Hierarchical Anomaly Detection and SHAP-Based Root-Cause Attribution for Robotic Process Automation Workflows

Proceedings of the International Conference on Business Excellence
Volume 20 (2026): Issue 1 (July 2026)
By: ,  ,   and    
Open Access
|Jul 2026
Authors

Authors

Yanka ALEKSANDROVA

yalexandrova@ue-varna.bg

University of Economics – Varna, Varna, Bulgaria

Mihail RADEV

radev@ue-varna.bg

University of Economics – Varna, Varna, Bulgaria

Mila GEORGIEVA

mila@mu-varna.bg

Medical University – Varna, Varna, Bulgaria

Desislava KOLEVA

desi_koleva@ue-varna.bg

University of Economics – Varna, Varna, Bulgaria
DOI: https://doi.org/10.2478/picbe-2026-0043 | Journal eISSN: 2558-9652
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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:
Robotic Process Automation,
hierarchical anomaly detection,
root cause analysis,
explainable artificial intelligence,
Isolation Forest,
Long Short-Term Memory Autoencoder,
operational diagnostics
Related subjects:
Business and economics,
Political economics,
Political economics, other,
Business management,
Business management, other,
Industrial chemistry,
Energy harvesting and conversion

© 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.

Volume 20 (2026): Issue 1 (July 2026)
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