Benchmarking Modern Deep Learning Models for Electroluminescence-Based Solar Cell Defect Detection.
- DOI
- 10.3390/s26134256
- Published
- 2026 Jul 4
- Container
- Sensors (Basel, Switzerland)
- Publisher
- Not recorded
- Open access
- yes
Credibility signals
limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Cite this work
BibTeX
@article{allodium:10.3390/s26134256,
title = {Benchmarking Modern Deep Learning Models for Electroluminescence-Based Solar Cell Defect Detection.},
author = {Şahin G and Rüstemli AC and Al-Ani AYB and Rüstemli S and Akin E},
year = {2026},
journal = {Sensors (Basel, Switzerland)},
doi = {10.3390/s26134256},
url = {https://doi.org/10.3390/s26134256}
}RIS
TY - JOUR TI - Benchmarking Modern Deep Learning Models for Electroluminescence-Based Solar Cell Defect Detection. AU - Şahin G AU - Rüstemli AC AU - Al-Ani AYB AU - Rüstemli S AU - Akin E PY - 2026 JO - Sensors (Basel, Switzerland) DO - 10.3390/s26134256 UR - https://doi.org/10.3390/s26134256 ER -
APA
G, Ş., AC, R., AYB, A., S, R., & E, A. (2026). Benchmarking Modern Deep Learning Models for Electroluminescence-Based Solar Cell Defect Detection.. Sensors (Basel, Switzerland). https://doi.org/10.3390/s26134256
Source records
- pubmed · retrieved 2026-11-10T20:31:13.380Z