Interpretable Self-Supervised Learning for Fault Identification in Printed Circuit Board Assembly Testing Article Swipe
Md. Rakibul Islam
,
Shahina Begum
,
Mobyen Uddin Ahmed
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/app151810080
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/app151810080
Fault identification in Printed Circuit Board Assembly (PCBA) testing is essential for assuring product quality; nevertheless, conventional methods still have difficulties due to the lack of labeled faulty data and the “black box” nature of advanced models. This study introduces a label-free, interpretable self-supervised framework that uses two pretext tasks: (i) an autoencoder (reconstruction error and two latent features) and (ii) isolation forest (faulty score) to form a four-dimensional representation of each test sequence. A two-component Gaussian Mixture Model is used, and the samples are clustered into normal and fault groups. The decision is explained with cluster mean differences, SHAP (LinearSHAP or LinearExplainer on a logistic-regression surrogate), and a shallow decision tree that generated if–then rules. On real PCBA data, internal indices showed compact and well-separated clusters (Silhouette 0.85, Calinski–Harabasz 50,344.19, Davies–Bouldin 0.39), external metrics were high (ARI 0.72; NMI 0.59; Fowlkes–Mallows 0.98), and the clustered result used as a fault predictor reached 0.98 accuracy, 0.98 precision, and 0.99 recall. Explanations show that the IForest score and reconstruction error drive most decisions, causing simple thresholds that can guide inspection. An ablation without the self-supervised tasks results in degraded clustering quality. The proposed approach offers accurate, label-free fault prediction with transparent reasoning and is suitable for deployment in industrial test lines.
Related Topics
Concepts
No concepts available.
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app151810080
- https://www.mdpi.com/2076-3417/15/18/10080/pdf?version=1758015044
- OA Status
- gold
- References
- 43
- OpenAlex ID
- https://openalex.org/W4414208921
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4414208921Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/app151810080Digital Object Identifier
- Title
-
Interpretable Self-Supervised Learning for Fault Identification in Printed Circuit Board Assembly TestingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-09-15Full publication date if available
- Authors
-
Md. Rakibul Islam, Shahina Begum, Mobyen Uddin AhmedList of authors in order
- Landing page
-
https://doi.org/10.3390/app151810080Publisher landing page
- PDF URL
-
https://www.mdpi.com/2076-3417/15/18/10080/pdf?version=1758015044Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2076-3417/15/18/10080/pdf?version=1758015044Direct OA link when available
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
43Number of works referenced by this work
Full payload
| id | https://openalex.org/W4414208921 |
|---|---|
| doi | https://doi.org/10.3390/app151810080 |
| ids.doi | https://doi.org/10.3390/app151810080 |
| ids.openalex | https://openalex.org/W4414208921 |
| fwci | 0.0 |
| type | article |
| title | Interpretable Self-Supervised Learning for Fault Identification in Printed Circuit Board Assembly Testing |
| biblio.issue | 18 |
| biblio.volume | 15 |
| biblio.last_page | 10080 |
| biblio.first_page | 10080 |
| topics[0].id | https://openalex.org/T12111 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9714000225067139 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2209 |
| topics[0].subfield.display_name | Industrial and Manufacturing Engineering |
| topics[0].display_name | Industrial Vision Systems and Defect Detection |
| topics[1].id | https://openalex.org/T10876 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9036999940872192 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2207 |
| topics[1].subfield.display_name | Control and Systems Engineering |
| topics[1].display_name | Fault Detection and Control Systems |
| is_xpac | False |
| apc_list.value | 2300 |
| apc_list.currency | CHF |
| apc_list.value_usd | 2490 |
| apc_paid.value | 2300 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 2490 |
| language | en |
| locations[0].id | doi:10.3390/app151810080 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210205812 |
| locations[0].source.issn | 2076-3417 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2076-3417 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Applied Sciences |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.mdpi.com/2076-3417/15/18/10080/pdf?version=1758015044 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Applied Sciences |
| locations[0].landing_page_url | https://doi.org/10.3390/app151810080 |
| locations[1].id | pmh:oai:doaj.org/article:772fdcac57ea4d27bb0e22815cfb4c5b |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306401280 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Applied Sciences, Vol 15, Iss 18, p 10080 (2025) |
| locations[1].landing_page_url | https://doaj.org/article/772fdcac57ea4d27bb0e22815cfb4c5b |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5100710126 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-0904-9268 |
| authorships[0].author.display_name | Md. Rakibul Islam |
| authorships[0].countries | SE |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I82509713 |
| authorships[0].affiliations[0].raw_affiliation_string | Artificial Intelligence and Intelligent Systems Research Group, School of Innovation, Design and Engineering,Mälardalen University, Högskoleplan 1, 722 20 Västerås, Sweden |
| authorships[0].institutions[0].id | https://openalex.org/I82509713 |
| authorships[0].institutions[0].ror | https://ror.org/033vfbz75 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I82509713 |
| authorships[0].institutions[0].country_code | SE |
| authorships[0].institutions[0].display_name | Mälardalen University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Md Rakibul Islam |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Artificial Intelligence and Intelligent Systems Research Group, School of Innovation, Design and Engineering,Mälardalen University, Högskoleplan 1, 722 20 Västerås, Sweden |
| authorships[1].author.id | https://openalex.org/A5036681394 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-1212-7637 |
| authorships[1].author.display_name | Shahina Begum |
| authorships[1].countries | SE |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I82509713 |
| authorships[1].affiliations[0].raw_affiliation_string | Artificial Intelligence and Intelligent Systems Research Group, School of Innovation, Design and Engineering,Mälardalen University, Högskoleplan 1, 722 20 Västerås, Sweden |
| authorships[1].institutions[0].id | https://openalex.org/I82509713 |
| authorships[1].institutions[0].ror | https://ror.org/033vfbz75 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I82509713 |
| authorships[1].institutions[0].country_code | SE |
| authorships[1].institutions[0].display_name | Mälardalen University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Shahina Begum |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Artificial Intelligence and Intelligent Systems Research Group, School of Innovation, Design and Engineering,Mälardalen University, Högskoleplan 1, 722 20 Västerås, Sweden |
| authorships[2].author.id | https://openalex.org/A5007258873 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-1953-6086 |
| authorships[2].author.display_name | Mobyen Uddin Ahmed |
| authorships[2].countries | SE |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I82509713 |
| authorships[2].affiliations[0].raw_affiliation_string | Artificial Intelligence and Intelligent Systems Research Group, School of Innovation, Design and Engineering,Mälardalen University, Högskoleplan 1, 722 20 Västerås, Sweden |
| authorships[2].institutions[0].id | https://openalex.org/I82509713 |
| authorships[2].institutions[0].ror | https://ror.org/033vfbz75 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I82509713 |
| authorships[2].institutions[0].country_code | SE |
| authorships[2].institutions[0].display_name | Mälardalen University |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Mobyen Uddin Ahmed |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Artificial Intelligence and Intelligent Systems Research Group, School of Innovation, Design and Engineering,Mälardalen University, Högskoleplan 1, 722 20 Västerås, Sweden |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.mdpi.com/2076-3417/15/18/10080/pdf?version=1758015044 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Interpretable Self-Supervised Learning for Fault Identification in Printed Circuit Board Assembly Testing |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T12111 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9714000225067139 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2209 |
| primary_topic.subfield.display_name | Industrial and Manufacturing Engineering |
| primary_topic.display_name | Industrial Vision Systems and Defect Detection |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | doi:10.3390/app151810080 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210205812 |
| best_oa_location.source.issn | 2076-3417 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2076-3417 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Applied Sciences |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.mdpi.com/2076-3417/15/18/10080/pdf?version=1758015044 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Applied Sciences |
| best_oa_location.landing_page_url | https://doi.org/10.3390/app151810080 |
| primary_location.id | doi:10.3390/app151810080 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210205812 |
| primary_location.source.issn | 2076-3417 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2076-3417 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Applied Sciences |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/2076-3417/15/18/10080/pdf?version=1758015044 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Applied Sciences |
| primary_location.landing_page_url | https://doi.org/10.3390/app151810080 |
| publication_date | 2025-09-15 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W3186516637, https://openalex.org/W4327970389, https://openalex.org/W3012920143, https://openalex.org/W4402351974, https://openalex.org/W4414165902, https://openalex.org/W4409228446, https://openalex.org/W4409328712, https://openalex.org/W4320712931, https://openalex.org/W4387189885, https://openalex.org/W4323430047, https://openalex.org/W4406044960, https://openalex.org/W4401729026, https://openalex.org/W4200368269, https://openalex.org/W4313891026, https://openalex.org/W2296719434, https://openalex.org/W4390886362, https://openalex.org/W4412587687, https://openalex.org/W4284970331, https://openalex.org/W4318778685, https://openalex.org/W4410171794, https://openalex.org/W4401487891, https://openalex.org/W4391616365, https://openalex.org/W4319319155, https://openalex.org/W3107871149, https://openalex.org/W4407113914, https://openalex.org/W4405363817, https://openalex.org/W4401083046, https://openalex.org/W4229021999, https://openalex.org/W2573302502, https://openalex.org/W4388015821, https://openalex.org/W3185256938, https://openalex.org/W3107330311, https://openalex.org/W3130814454, https://openalex.org/W3160361541, https://openalex.org/W4313590336, https://openalex.org/W2586262374, https://openalex.org/W4411987095, https://openalex.org/W4403785409, https://openalex.org/W3034781633, https://openalex.org/W4410945406, https://openalex.org/W3202586029, https://openalex.org/W4388097102, https://openalex.org/W3089294463 |
| referenced_works_count | 43 |
| abstract_inverted_index.A | 74 |
| abstract_inverted_index.a | 40, 67, 104, 108, 149 |
| abstract_inverted_index.An | 179 |
| abstract_inverted_index.On | 116 |
| abstract_inverted_index.an | 51 |
| abstract_inverted_index.as | 148 |
| abstract_inverted_index.in | 2, 186, 206 |
| abstract_inverted_index.is | 9, 79, 93, 202 |
| abstract_inverted_index.of | 25, 34, 70 |
| abstract_inverted_index.on | 103 |
| abstract_inverted_index.or | 101 |
| abstract_inverted_index.to | 22, 65 |
| abstract_inverted_index.(i) | 50 |
| abstract_inverted_index.NMI | 139 |
| abstract_inverted_index.The | 91, 190 |
| abstract_inverted_index.and | 29, 55, 59, 81, 88, 107, 124, 143, 157, 166, 201 |
| abstract_inverted_index.are | 84 |
| abstract_inverted_index.can | 176 |
| abstract_inverted_index.due | 21 |
| abstract_inverted_index.for | 11, 204 |
| abstract_inverted_index.the | 23, 30, 82, 144, 163, 182 |
| abstract_inverted_index.two | 47, 56 |
| abstract_inverted_index.(ARI | 137 |
| abstract_inverted_index.(ii) | 60 |
| abstract_inverted_index.0.98 | 153, 155 |
| abstract_inverted_index.0.99 | 158 |
| abstract_inverted_index.PCBA | 118 |
| abstract_inverted_index.SHAP | 99 |
| abstract_inverted_index.This | 37 |
| abstract_inverted_index.data | 28 |
| abstract_inverted_index.each | 71 |
| abstract_inverted_index.form | 66 |
| abstract_inverted_index.have | 19 |
| abstract_inverted_index.high | 136 |
| abstract_inverted_index.into | 86 |
| abstract_inverted_index.lack | 24 |
| abstract_inverted_index.mean | 97 |
| abstract_inverted_index.most | 170 |
| abstract_inverted_index.real | 117 |
| abstract_inverted_index.show | 161 |
| abstract_inverted_index.test | 72, 208 |
| abstract_inverted_index.that | 45, 112, 162, 175 |
| abstract_inverted_index.tree | 111 |
| abstract_inverted_index.used | 147 |
| abstract_inverted_index.uses | 46 |
| abstract_inverted_index.were | 135 |
| abstract_inverted_index.with | 95, 198 |
| abstract_inverted_index.0.59; | 140 |
| abstract_inverted_index.0.72; | 138 |
| abstract_inverted_index.0.85, | 128 |
| abstract_inverted_index.Board | 5 |
| abstract_inverted_index.Fault | 0 |
| abstract_inverted_index.Model | 78 |
| abstract_inverted_index.data, | 119 |
| abstract_inverted_index.drive | 169 |
| abstract_inverted_index.error | 54, 168 |
| abstract_inverted_index.fault | 89, 150, 196 |
| abstract_inverted_index.guide | 177 |
| abstract_inverted_index.score | 165 |
| abstract_inverted_index.still | 18 |
| abstract_inverted_index.study | 38 |
| abstract_inverted_index.tasks | 184 |
| abstract_inverted_index.used, | 80 |
| abstract_inverted_index.(PCBA) | 7 |
| abstract_inverted_index.0.39), | 132 |
| abstract_inverted_index.0.98), | 142 |
| abstract_inverted_index.box” | 32 |
| abstract_inverted_index.faulty | 27 |
| abstract_inverted_index.forest | 62 |
| abstract_inverted_index.latent | 57 |
| abstract_inverted_index.lines. | 209 |
| abstract_inverted_index.nature | 33 |
| abstract_inverted_index.normal | 87 |
| abstract_inverted_index.offers | 193 |
| abstract_inverted_index.result | 146 |
| abstract_inverted_index.rules. | 115 |
| abstract_inverted_index.score) | 64 |
| abstract_inverted_index.showed | 122 |
| abstract_inverted_index.simple | 173 |
| abstract_inverted_index.tasks: | 49 |
| abstract_inverted_index.(faulty | 63 |
| abstract_inverted_index.Circuit | 4 |
| abstract_inverted_index.IForest | 164 |
| abstract_inverted_index.Mixture | 77 |
| abstract_inverted_index.Printed | 3 |
| abstract_inverted_index.causing | 172 |
| abstract_inverted_index.cluster | 96 |
| abstract_inverted_index.compact | 123 |
| abstract_inverted_index.groups. | 90 |
| abstract_inverted_index.indices | 121 |
| abstract_inverted_index.labeled | 26 |
| abstract_inverted_index.methods | 17 |
| abstract_inverted_index.metrics | 134 |
| abstract_inverted_index.models. | 36 |
| abstract_inverted_index.pretext | 48 |
| abstract_inverted_index.product | 13 |
| abstract_inverted_index.reached | 152 |
| abstract_inverted_index.recall. | 159 |
| abstract_inverted_index.results | 185 |
| abstract_inverted_index.samples | 83 |
| abstract_inverted_index.shallow | 109 |
| abstract_inverted_index.testing | 8 |
| abstract_inverted_index.without | 181 |
| abstract_inverted_index.Assembly | 6 |
| abstract_inverted_index.Gaussian | 76 |
| abstract_inverted_index.ablation | 180 |
| abstract_inverted_index.advanced | 35 |
| abstract_inverted_index.approach | 192 |
| abstract_inverted_index.assuring | 12 |
| abstract_inverted_index.clusters | 126 |
| abstract_inverted_index.decision | 92, 110 |
| abstract_inverted_index.degraded | 187 |
| abstract_inverted_index.external | 133 |
| abstract_inverted_index.internal | 120 |
| abstract_inverted_index.proposed | 191 |
| abstract_inverted_index.quality. | 189 |
| abstract_inverted_index.quality; | 14 |
| abstract_inverted_index.suitable | 203 |
| abstract_inverted_index.“black | 31 |
| abstract_inverted_index.accuracy, | 154 |
| abstract_inverted_index.accurate, | 194 |
| abstract_inverted_index.clustered | 85, 145 |
| abstract_inverted_index.essential | 10 |
| abstract_inverted_index.explained | 94 |
| abstract_inverted_index.features) | 58 |
| abstract_inverted_index.framework | 44 |
| abstract_inverted_index.generated | 113 |
| abstract_inverted_index.if–then | 114 |
| abstract_inverted_index.isolation | 61 |
| abstract_inverted_index.predictor | 151 |
| abstract_inverted_index.reasoning | 200 |
| abstract_inverted_index.sequence. | 73 |
| abstract_inverted_index.50,344.19, | 130 |
| abstract_inverted_index.clustering | 188 |
| abstract_inverted_index.decisions, | 171 |
| abstract_inverted_index.deployment | 205 |
| abstract_inverted_index.industrial | 207 |
| abstract_inverted_index.introduces | 39 |
| abstract_inverted_index.label-free | 195 |
| abstract_inverted_index.precision, | 156 |
| abstract_inverted_index.prediction | 197 |
| abstract_inverted_index.thresholds | 174 |
| abstract_inverted_index.(LinearSHAP | 100 |
| abstract_inverted_index.(Silhouette | 127 |
| abstract_inverted_index.autoencoder | 52 |
| abstract_inverted_index.inspection. | 178 |
| abstract_inverted_index.label-free, | 41 |
| abstract_inverted_index.surrogate), | 106 |
| abstract_inverted_index.transparent | 199 |
| abstract_inverted_index.Explanations | 160 |
| abstract_inverted_index.conventional | 16 |
| abstract_inverted_index.differences, | 98 |
| abstract_inverted_index.difficulties | 20 |
| abstract_inverted_index.interpretable | 42 |
| abstract_inverted_index.nevertheless, | 15 |
| abstract_inverted_index.two-component | 75 |
| abstract_inverted_index.identification | 1 |
| abstract_inverted_index.reconstruction | 167 |
| abstract_inverted_index.representation | 69 |
| abstract_inverted_index.well-separated | 125 |
| abstract_inverted_index.(reconstruction | 53 |
| abstract_inverted_index.LinearExplainer | 102 |
| abstract_inverted_index.self-supervised | 43, 183 |
| abstract_inverted_index.Davies–Bouldin | 131 |
| abstract_inverted_index.four-dimensional | 68 |
| abstract_inverted_index.Fowlkes–Mallows | 141 |
| abstract_inverted_index.Calinski–Harabasz | 129 |
| abstract_inverted_index.logistic-regression | 105 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.55229222 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |