Anomaly explanation: A review
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Dhatterwal, J. S., & Kaswan, K. S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95315-3.00001-2 · Cited by 1 · see citing works
Stege, N., & Breitner, M. H. (2024) · Data & Knowledge Engineering · doi:10.1016/j.datak.2024.102299 · Cited by 1 · see citing works
Industry-Specific Applications of AI and ML
Singhal, S., Sharma, A. K., Singh, A. K., Pandey, A., & Sharma, A. (2024) · Advances in systems analysis, software engineering, and high performance computing book series · doi:10.4018/979-8-3693-3502-4.ch008 · Cited by 1 · see citing works
A Protocol for Creating and Modifying the Outlier Detection System's General FrameworkPreprint
Divya, D., Bhasi, M., & Santoshkumar, M. B. (2022) · Research Square · doi:10.21203/rs.3.rs-1653583/v2
Yepmo, V. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/844
Self-citationA Protocol for Creating and Modifying the Outlier Detection System's General FrameworkPreprint
Divya, D., Bhasi, M., & Santoshkumar, M. B. (2022) · Research Square · doi:10.21203/rs.3.rs-1653583/v1
Fuzzy-Vocabulary-Based Detection and Explanation of Anomalies
Nath, R., Smits, G., & Pivert, O. (2023) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz52849.2023.10309771
Self-citationExplainable Contextual Anomaly Detection: A Focus on QCAD
Gumbs, G., Togbe, M. U., Chabchoub, Y., & Perrot, P. (2025) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-96522-7_9
Leveraging Graph Neural Networks to Detect Anomalies in Mobile Money TransactionsPreprint
Ssali, B. S., Namugera, F., & Bbosa, F. F. (2025) · Research Square · doi:10.21203/rs.3.rs-7784995/v1
The Role of Artificial Intelligence in Smart Healthcare System
Singhal, S., Sharma, A. K., Sharma, A., Chadha, S., & Chauhan, R. (2025) · Studies in computational intelligence · doi:10.1007/978-3-031-99946-8_1
Singh, S. J., & Agrawal, P. (2025) · International Conference on Computing Communication Control and automation · doi:10.1109/iccubea65967.2025.11284166
Hamza, M. H., Radhi, A. D., Fallah, D., Al-Muttalib, M. Y. M., Almaiah, M. A., Mahdi, S. A., Obeed, O. J., Sekhar, R., & Itankar, N. (2025) · International Conference on Cyber Resilience (ICCR) · doi:10.1109/iccr67387.2025.11292160
Characterization and detection of semantic textual outliers
Pantin, J. (2023) · theses.fr (ABES) · doi:10.70675/3abc7c1bz47b8z44b9z9a09z23c263b7a2af
AutoRecover: Predictive IT Incident Resolution Through AI-Driven Automation in Cloud Infrastructure
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- Authors
- Yepmo, V., Smits, G., & Pivert, O.
- Affiliations
- Institut de Recherche en Informatique et Systèmes Aléatoires (FR)
- Corresponding author
- Yepmo, Véronne
- Venue
- Data & Knowledge Engineering
- Publisher
- Elsevier BV
- Published
- 2022-01
- Volume
- 137
- Pages
- 101946
- ISSN
- 0169-023X (ISSN-L)
- OpenAlex
- W3214757078
- Cited by
- 52
- References
- 80
- Citations by year
- 2025: 13 · 2024: 20 · 2023: 10 · 2022: 9
- FWCI
- 3.45
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (am, 708 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Data & Knowledge Engineering (acceptedVersion)
- Funders
- DGA
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Keywords
- anomaly explanation, anomaly detection, explainable AI, model interpretability
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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