The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
A survey of anomaly detection techniques in financial domain
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Cites
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
Neural Networks for Pattern Recognition
Bishop, C. M. (1995) · doi:10.1093/oso/9780198538493.001.0001 · Cited by 12,287
LOF: identifying density-based local outliers
Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335388 · Cited by 5,207
Cited by
The k-means Algorithm: A Comprehensive Survey and Performance Evaluation
Ahmed, M., Seraj, R., & Islam, S. M. S. (2020) · Electronics · doi:10.3390/electronics9081295 · Cited by 1,717
Self-citationMachine Learning for Anomaly Detection: A Systematic Review
Nassif, A. B., Talib, M. A., Nasir, Q., & Dakalbab, F. M. (2021) · IEEE Access · doi:10.1109/access.2021.3083060 · Cited by 748
Himeur, Y., Ghanem, K., Alsalemi, A., Bensaali, F., & Amira, A. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.116601 · Cited by 660
Eling, M., Nuessle, D., & Staubli, J. (2022) · The Geneva Papers on Risk and Insurance Issues and Practice · doi:10.1057/s41288-020-00201-7 · Cited by 156
“Among these are pattern and anomaly detection (e.g. for fraud mitigation, see e.g. Ahmed et al. 2016), speech recognition and natural language generation (e.g. for the development of chatbots, see e.g. Dale 2016), recommendation engines (e.g. for automated product suggestions, see e.g. Marchand and…”
A Survey on Explainable Anomaly Detection
Li, Z., Zhu, Y., & Van Leeuwen, M. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3609333 · Cited by 139
Metadata
- Authors
- Ahmed, M., Mahmood, A. N., & Islam, M. R.
- Venue
- Future Generation Computer Systems
- Publisher
- Elsevier BV
- Published
- 2016-02
- Volume
- 55
- Pages
- 278-288
- ISSN
- 0167-739X (ISSN-L)
- OpenAlex
- W2084512860
- Cited by
- 528
- References
- 3
- FWCI
- 18.77
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: A survey of anomaly detection techniques in financial domain. https://app.cochat.ai/papers/10.1016/j.future.2015.01.001
- The paper
- Ahmed, M., Mahmood, A. N., & Islam, M. R. (2016). A survey of anomaly detection techniques in financial domain. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2015.01.001
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
