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Explainable ensemble learning using SHAP for ERP anomaly detection.

Overview

Authors: Adiah Qazi1, Ammad Ali Khan Jadoon2
ORCID iDs: Adiah Qazi
  1. Department of Information Security, Military College of Signals, National University of Sciences and Technology (NUST), Islamabad, Pakistan
  2. Department of Electrical Engineering, Military College of Signals, National University of Sciences and Technology (NUST), Islamabad, Pakistan
Journal: Scientific reports, volume 16, issue 1, article 27125
Dates: received 10 April 2026; accepted 10 June 2026; published online 14 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-57913-4 · PMID 42289525 · PMCID PMC13527133 · OpenAlex W7164742241
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), methods / tools (subfield)
Methods: Connectivity, Statistics, Machine learning, Evoked potentials
Keywords: ERP systems, Anomaly detection, Explainable AI, Ensemble learning, SHAP analysis, Fraud detection, Engineering, Mathematics and computing
Topic: Explainable Artificial Intelligence (XAI) (Artificial Intelligence, Computer Science), according to OpenAlex
Citations: cited by 1 paper (Europe PMC); 41 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Code

No file of the authors' code could be read here: it is described below, and read at its source.

Zenodo 20609426

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 2 files
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41598-026-57913-4.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 0 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • it points to the authors' code: Zenodo 20609426
  • it says that the data are available on request

Read it in the paper: doi.org/10.1038/s41598-026-57913-4.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 8 keywords, 10 references.

Cite

This paper

Qazi, A., & Jadoon, A. A. K. (2026). Explainable ensemble learning using SHAP for ERP anomaly detection. Scientific reports, 16(1), 27125. https://doi.org/10.1038/s41598-026-57913-4

BibTeX

@article{qazi2026explainable,
author = {Qazi, Adiah and Jadoon, Ammad Ali Khan},
title = {{Explainable ensemble learning using SHAP for ERP anomaly detection}},
journal = {Scientific reports},
year = {2026},
month = jun,
volume = {16},
number = {1},
pages = {27125},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-57913-4},
url = {https://doi.org/10.1038/s41598-026-57913-4},
pmid = {42289525},
pmcid = {PMC13527133}
}

RIS

TY - JOUR
AU - Qazi, Adiah
AU - Jadoon, Ammad Ali Khan
TI - Explainable ensemble learning using SHAP for ERP anomaly detection
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/06/14
VL - 16
IS - 1
SP - 27125
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-57913-4
UR - https://doi.org/10.1038/s41598-026-57913-4
LA - en
ER -

CSL-JSON

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