Enhancing aviation control security through ADS-B injection detection using ensemble meta-learning models with Explainable AI Article Swipe
Vajratiya Vajrobol
,
Geetika Jain Saxena
,
Sanjeev Singh
,
Amit Pundir
,
Brij B. Gupta
,
Akshat Gaurav
,
Kwok Tai Chui
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.aej.2024.10.042
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.aej.2024.10.042
The increasing use of Automatic Dependent Surveillance-Broadcast (ADS-B) technology in flight control systems has created many serious concerns.These weaknesses threaten the security and safety of our aviation industry. Therefore, to enhance aviation control security and better deal with these problems, this research focuses on developing a strong ADS-B injection detection system. It combines XGBoost and Random Forest with Logistic Regression in an Ensemble Learning Meta-Learning Model to identify ADS-B injection risks and categorise them. Ensemble methods, which combine several models can increase the detection accuracy and robustness of the model used to identify the threat. In addition, Explainable AI (XAI) methods are employed to enhance the process of explaining how the model reaches its decisions and building trust in aviation security systems. The system’s training, testing, and evaluation are conducted with ADS-B data. This result indicates that the Stacked Random Forest and XGBoost with Logistic Regression Meta-Learner with 99.60% accuracy, along with good recall rates (99.49%) and precision (99.41%). Also, aviation control authorities are reassured by the model’s transparent and applicable decision logic through the application of XAI techniques. This research contributes to enhanced aviation security by proposing a new, highly accurate ADS-B injection detection system with explainable outcomes. A strategy like this can help flight control systems maintain integrity amidst an ever-digitising aviation reality.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.aej.2024.10.042
- OA Status
- gold
- Cited By
- 8
- References
- 53
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403970529
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4403970529Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.aej.2024.10.042Digital Object Identifier
- Title
-
Enhancing aviation control security through ADS-B injection detection using ensemble meta-learning models with Explainable AIWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-11-01Full publication date if available
- Authors
-
Vajratiya Vajrobol, Geetika Jain Saxena, Sanjeev Singh, Amit Pundir, Brij B. Gupta, Akshat Gaurav, Kwok Tai ChuiList of authors in order
- Landing page
-
https://doi.org/10.1016/j.aej.2024.10.042Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.aej.2024.10.042Direct OA link when available
- Concepts
-
Aviation, Airport security, Control (management), Ensemble learning, Security controls, Computer science, Aeronautics, Engineering, Computer security, Artificial intelligence, Aerospace engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 7, 2024: 1Per-year citation counts (last 5 years)
- References (count)
-
53Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4403970529 |
|---|---|
| doi | https://doi.org/10.1016/j.aej.2024.10.042 |
| ids.doi | https://doi.org/10.1016/j.aej.2024.10.042 |
| ids.openalex | https://openalex.org/W4403970529 |
| fwci | 5.11022839 |
| type | article |
| title | Enhancing aviation control security through ADS-B injection detection using ensemble meta-learning models with Explainable AI |
| biblio.issue | |
| biblio.volume | 112 |
| biblio.last_page | 73 |
| biblio.first_page | 63 |
| topics[0].id | https://openalex.org/T12026 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.996999979019165 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Explainable Artificial Intelligence (XAI) |
| topics[1].id | https://openalex.org/T11689 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9968000054359436 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Adversarial Robustness in Machine Learning |
| topics[2].id | https://openalex.org/T11512 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9711999893188477 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1702 |
| topics[2].subfield.display_name | Artificial Intelligence |
| topics[2].display_name | Anomaly Detection Techniques and Applications |
| is_xpac | False |
| apc_list.value | 860 |
| apc_list.currency | USD |
| apc_list.value_usd | 860 |
| apc_paid.value | 860 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 860 |
| concepts[0].id | https://openalex.org/C74448152 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7336642742156982 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q765633 |
| concepts[0].display_name | Aviation |
| concepts[1].id | https://openalex.org/C180646296 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6472011804580688 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1378517 |
| concepts[1].display_name | Airport security |
| concepts[2].id | https://openalex.org/C2775924081 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5571656227111816 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q55608371 |
| concepts[2].display_name | Control (management) |
| concepts[3].id | https://openalex.org/C45942800 |
| concepts[3].level | 2 |
| concepts[3].score | 0.507632851600647 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q245652 |
| concepts[3].display_name | Ensemble learning |
| concepts[4].id | https://openalex.org/C178148461 |
| concepts[4].level | 3 |
| concepts[4].score | 0.5040875673294067 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q1632136 |
| concepts[4].display_name | Security controls |
| concepts[5].id | https://openalex.org/C41008148 |
| concepts[5].level | 0 |
| concepts[5].score | 0.49718859791755676 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[5].display_name | Computer science |
| concepts[6].id | https://openalex.org/C178802073 |
| concepts[6].level | 1 |
| concepts[6].score | 0.4644733667373657 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q8421 |
| concepts[6].display_name | Aeronautics |
| concepts[7].id | https://openalex.org/C127413603 |
| concepts[7].level | 0 |
| concepts[7].score | 0.3264825940132141 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[7].display_name | Engineering |
| concepts[8].id | https://openalex.org/C38652104 |
| concepts[8].level | 1 |
| concepts[8].score | 0.31848496198654175 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q3510521 |
| concepts[8].display_name | Computer security |
| concepts[9].id | https://openalex.org/C154945302 |
| concepts[9].level | 1 |
| concepts[9].score | 0.28066083788871765 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[9].display_name | Artificial intelligence |
| concepts[10].id | https://openalex.org/C146978453 |
| concepts[10].level | 1 |
| concepts[10].score | 0.19848838448524475 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q3798668 |
| concepts[10].display_name | Aerospace engineering |
| keywords[0].id | https://openalex.org/keywords/aviation |
| keywords[0].score | 0.7336642742156982 |
| keywords[0].display_name | Aviation |
| keywords[1].id | https://openalex.org/keywords/airport-security |
| keywords[1].score | 0.6472011804580688 |
| keywords[1].display_name | Airport security |
| keywords[2].id | https://openalex.org/keywords/control |
| keywords[2].score | 0.5571656227111816 |
| keywords[2].display_name | Control (management) |
| keywords[3].id | https://openalex.org/keywords/ensemble-learning |
| keywords[3].score | 0.507632851600647 |
| keywords[3].display_name | Ensemble learning |
| keywords[4].id | https://openalex.org/keywords/security-controls |
| keywords[4].score | 0.5040875673294067 |
| keywords[4].display_name | Security controls |
| keywords[5].id | https://openalex.org/keywords/computer-science |
| keywords[5].score | 0.49718859791755676 |
| keywords[5].display_name | Computer science |
| keywords[6].id | https://openalex.org/keywords/aeronautics |
| keywords[6].score | 0.4644733667373657 |
| keywords[6].display_name | Aeronautics |
| keywords[7].id | https://openalex.org/keywords/engineering |
| keywords[7].score | 0.3264825940132141 |
| keywords[7].display_name | Engineering |
| keywords[8].id | https://openalex.org/keywords/computer-security |
| keywords[8].score | 0.31848496198654175 |
| keywords[8].display_name | Computer security |
| keywords[9].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[9].score | 0.28066083788871765 |
| keywords[9].display_name | Artificial intelligence |
| keywords[10].id | https://openalex.org/keywords/aerospace-engineering |
| keywords[10].score | 0.19848838448524475 |
| keywords[10].display_name | Aerospace engineering |
| language | en |
| locations[0].id | doi:10.1016/j.aej.2024.10.042 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S2764413287 |
| locations[0].source.issn | 1110-0168, 2090-2670 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1110-0168 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Alexandria Engineering Journal |
| locations[0].source.host_organization | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_name | Elsevier BV |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_lineage_names | Elsevier BV |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| 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 | Alexandria Engineering Journal |
| locations[0].landing_page_url | https://doi.org/10.1016/j.aej.2024.10.042 |
| locations[1].id | pmh:oai:doaj.org/article:c72d6ee8361744beae143faa92f76e97 |
| 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 | Alexandria Engineering Journal, Vol 112, Iss , Pp 63-73 (2025) |
| locations[1].landing_page_url | https://doaj.org/article/c72d6ee8361744beae143faa92f76e97 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5028040949 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-2717-9253 |
| authorships[0].author.display_name | Vajratiya Vajrobol |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Vajratiya Vajrobol |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5090982622 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-5828-8049 |
| authorships[1].author.display_name | Geetika Jain Saxena |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Geetika Jain Saxena |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5005999906 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-5276-2160 |
| authorships[2].author.display_name | Sanjeev Singh |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Sanjeev Singh |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5034870275 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-7764-5278 |
| authorships[3].author.display_name | Amit Pundir |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Amit Pundir |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5071261948 |
| authorships[4].author.orcid | https://orcid.org/0000-0003-4929-4698 |
| authorships[4].author.display_name | Brij B. Gupta |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Brij B. Gupta |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5042846465 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-5796-9424 |
| authorships[5].author.display_name | Akshat Gaurav |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Akshat Gaurav |
| authorships[5].is_corresponding | False |
| authorships[6].author.id | https://openalex.org/A5086102702 |
| authorships[6].author.orcid | https://orcid.org/0000-0001-7992-9901 |
| authorships[6].author.display_name | Kwok Tai Chui |
| authorships[6].author_position | last |
| authorships[6].raw_author_name | Kwok Tai Chui |
| authorships[6].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1016/j.aej.2024.10.042 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Enhancing aviation control security through ADS-B injection detection using ensemble meta-learning models with Explainable AI |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T12026 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.996999979019165 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Explainable Artificial Intelligence (XAI) |
| related_works | https://openalex.org/W2254414496, https://openalex.org/W2890665354, https://openalex.org/W591202335, https://openalex.org/W319941286, https://openalex.org/W587136344, https://openalex.org/W851444952, https://openalex.org/W3184101823, https://openalex.org/W2228139345, https://openalex.org/W78353031, https://openalex.org/W2375131120 |
| cited_by_count | 8 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 7 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1016/j.aej.2024.10.042 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2764413287 |
| best_oa_location.source.issn | 1110-0168, 2090-2670 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1110-0168 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Alexandria Engineering Journal |
| best_oa_location.source.host_organization | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_name | Elsevier BV |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_lineage_names | Elsevier BV |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| 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 | Alexandria Engineering Journal |
| best_oa_location.landing_page_url | https://doi.org/10.1016/j.aej.2024.10.042 |
| primary_location.id | doi:10.1016/j.aej.2024.10.042 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S2764413287 |
| primary_location.source.issn | 1110-0168, 2090-2670 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1110-0168 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Alexandria Engineering Journal |
| primary_location.source.host_organization | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_name | Elsevier BV |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_lineage_names | Elsevier BV |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| 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 | Alexandria Engineering Journal |
| primary_location.landing_page_url | https://doi.org/10.1016/j.aej.2024.10.042 |
| publication_date | 2024-11-01 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W6788245621, https://openalex.org/W6846342268, https://openalex.org/W4213101073, https://openalex.org/W3128339542, https://openalex.org/W4210263542, https://openalex.org/W3183441209, https://openalex.org/W4213298225, https://openalex.org/W6850425164, https://openalex.org/W4312475441, https://openalex.org/W2089255914, https://openalex.org/W6922200139, https://openalex.org/W6856118644, https://openalex.org/W6757457238, https://openalex.org/W6686936072, https://openalex.org/W2767200452, https://openalex.org/W3042356186, https://openalex.org/W4224219490, https://openalex.org/W4220768885, https://openalex.org/W4295854586, https://openalex.org/W4387341418, https://openalex.org/W2891503716, https://openalex.org/W4313648826, https://openalex.org/W4319080686, https://openalex.org/W4283215813, https://openalex.org/W6684911825, https://openalex.org/W4386249434, https://openalex.org/W6632006591, https://openalex.org/W2328120369, https://openalex.org/W6748328607, https://openalex.org/W3025125428, https://openalex.org/W6861352137, https://openalex.org/W3165453550, https://openalex.org/W4387264633, https://openalex.org/W2944851425, https://openalex.org/W4200559994, https://openalex.org/W6804506347, https://openalex.org/W6857750972, https://openalex.org/W4388274365, https://openalex.org/W2282821441, https://openalex.org/W4296473449, https://openalex.org/W3138613066, https://openalex.org/W3191161603, https://openalex.org/W6860173891, https://openalex.org/W3006905685, https://openalex.org/W2978631110, https://openalex.org/W4254057358, https://openalex.org/W4235799440, https://openalex.org/W4387425005, https://openalex.org/W3217618723, https://openalex.org/W4387043541, https://openalex.org/W4391721723, https://openalex.org/W2789758093, https://openalex.org/W2803699176 |
| referenced_works_count | 53 |
| abstract_inverted_index.A | 199 |
| abstract_inverted_index.a | 45, 188 |
| abstract_inverted_index.AI | 98 |
| abstract_inverted_index.In | 95 |
| abstract_inverted_index.It | 51 |
| abstract_inverted_index.an | 61, 211 |
| abstract_inverted_index.by | 165, 186 |
| abstract_inverted_index.in | 9, 60, 118 |
| abstract_inverted_index.of | 3, 24, 87, 107, 176 |
| abstract_inverted_index.on | 43 |
| abstract_inverted_index.to | 29, 66, 91, 103, 182 |
| abstract_inverted_index.The | 0, 122 |
| abstract_inverted_index.XAI | 177 |
| abstract_inverted_index.and | 22, 34, 54, 71, 85, 115, 126, 141, 156, 169 |
| abstract_inverted_index.are | 101, 128, 163 |
| abstract_inverted_index.can | 80, 203 |
| abstract_inverted_index.has | 13 |
| abstract_inverted_index.how | 109 |
| abstract_inverted_index.its | 113 |
| abstract_inverted_index.our | 25 |
| abstract_inverted_index.the | 20, 82, 88, 93, 105, 110, 137, 166, 174 |
| abstract_inverted_index.use | 2 |
| abstract_inverted_index.This | 133, 179 |
| abstract_inverted_index.deal | 36 |
| abstract_inverted_index.good | 152 |
| abstract_inverted_index.help | 204 |
| abstract_inverted_index.like | 201 |
| abstract_inverted_index.many | 15 |
| abstract_inverted_index.new, | 189 |
| abstract_inverted_index.that | 136 |
| abstract_inverted_index.this | 40, 202 |
| abstract_inverted_index.used | 90 |
| abstract_inverted_index.with | 37, 57, 130, 143, 147, 151, 196 |
| abstract_inverted_index.(XAI) | 99 |
| abstract_inverted_index.ADS-B | 47, 68, 131, 192 |
| abstract_inverted_index.Also, | 159 |
| abstract_inverted_index.Model | 65 |
| abstract_inverted_index.along | 150 |
| abstract_inverted_index.data. | 132 |
| abstract_inverted_index.logic | 172 |
| abstract_inverted_index.model | 89, 111 |
| abstract_inverted_index.rates | 154 |
| abstract_inverted_index.risks | 70 |
| abstract_inverted_index.them. | 73 |
| abstract_inverted_index.these | 38 |
| abstract_inverted_index.trust | 117 |
| abstract_inverted_index.which | 76 |
| abstract_inverted_index.99.60% | 148 |
| abstract_inverted_index.Forest | 56, 140 |
| abstract_inverted_index.Random | 55, 139 |
| abstract_inverted_index.amidst | 210 |
| abstract_inverted_index.better | 35 |
| abstract_inverted_index.flight | 10, 205 |
| abstract_inverted_index.highly | 190 |
| abstract_inverted_index.models | 79 |
| abstract_inverted_index.recall | 153 |
| abstract_inverted_index.result | 134 |
| abstract_inverted_index.safety | 23 |
| abstract_inverted_index.strong | 46 |
| abstract_inverted_index.system | 195 |
| abstract_inverted_index.(ADS-B) | 7 |
| abstract_inverted_index.Stacked | 138 |
| abstract_inverted_index.XGBoost | 53, 142 |
| abstract_inverted_index.combine | 77 |
| abstract_inverted_index.control | 11, 32, 161, 206 |
| abstract_inverted_index.created | 14 |
| abstract_inverted_index.enhance | 30, 104 |
| abstract_inverted_index.focuses | 42 |
| abstract_inverted_index.methods | 100 |
| abstract_inverted_index.process | 106 |
| abstract_inverted_index.reaches | 112 |
| abstract_inverted_index.serious | 16 |
| abstract_inverted_index.several | 78 |
| abstract_inverted_index.system. | 50 |
| abstract_inverted_index.systems | 12, 207 |
| abstract_inverted_index.threat. | 94 |
| abstract_inverted_index.through | 173 |
| abstract_inverted_index.(99.49%) | 155 |
| abstract_inverted_index.Ensemble | 62, 74 |
| abstract_inverted_index.Learning | 63 |
| abstract_inverted_index.Logistic | 58, 144 |
| abstract_inverted_index.accuracy | 84 |
| abstract_inverted_index.accurate | 191 |
| abstract_inverted_index.aviation | 26, 31, 119, 160, 184, 213 |
| abstract_inverted_index.building | 116 |
| abstract_inverted_index.combines | 52 |
| abstract_inverted_index.decision | 171 |
| abstract_inverted_index.employed | 102 |
| abstract_inverted_index.enhanced | 183 |
| abstract_inverted_index.identify | 67, 92 |
| abstract_inverted_index.increase | 81 |
| abstract_inverted_index.maintain | 208 |
| abstract_inverted_index.methods, | 75 |
| abstract_inverted_index.reality. | 214 |
| abstract_inverted_index.research | 41, 180 |
| abstract_inverted_index.security | 21, 33, 120, 185 |
| abstract_inverted_index.strategy | 200 |
| abstract_inverted_index.systems. | 121 |
| abstract_inverted_index.testing, | 125 |
| abstract_inverted_index.threaten | 19 |
| abstract_inverted_index.(99.41%). | 158 |
| abstract_inverted_index.Automatic | 4 |
| abstract_inverted_index.Dependent | 5 |
| abstract_inverted_index.accuracy, | 149 |
| abstract_inverted_index.addition, | 96 |
| abstract_inverted_index.conducted | 129 |
| abstract_inverted_index.decisions | 114 |
| abstract_inverted_index.detection | 49, 83, 194 |
| abstract_inverted_index.indicates | 135 |
| abstract_inverted_index.industry. | 27 |
| abstract_inverted_index.injection | 48, 69, 193 |
| abstract_inverted_index.integrity | 209 |
| abstract_inverted_index.model’s | 167 |
| abstract_inverted_index.outcomes. | 198 |
| abstract_inverted_index.precision | 157 |
| abstract_inverted_index.problems, | 39 |
| abstract_inverted_index.proposing | 187 |
| abstract_inverted_index.reassured | 164 |
| abstract_inverted_index.training, | 124 |
| abstract_inverted_index.Regression | 59, 145 |
| abstract_inverted_index.Therefore, | 28 |
| abstract_inverted_index.applicable | 170 |
| abstract_inverted_index.categorise | 72 |
| abstract_inverted_index.developing | 44 |
| abstract_inverted_index.evaluation | 127 |
| abstract_inverted_index.explaining | 108 |
| abstract_inverted_index.increasing | 1 |
| abstract_inverted_index.robustness | 86 |
| abstract_inverted_index.system’s | 123 |
| abstract_inverted_index.technology | 8 |
| abstract_inverted_index.weaknesses | 18 |
| abstract_inverted_index.Explainable | 97 |
| abstract_inverted_index.application | 175 |
| abstract_inverted_index.authorities | 162 |
| abstract_inverted_index.contributes | 181 |
| abstract_inverted_index.explainable | 197 |
| abstract_inverted_index.techniques. | 178 |
| abstract_inverted_index.transparent | 168 |
| abstract_inverted_index.Meta-Learner | 146 |
| abstract_inverted_index.Meta-Learning | 64 |
| abstract_inverted_index.concerns.These | 17 |
| abstract_inverted_index.ever-digitising | 212 |
| abstract_inverted_index.Surveillance-Broadcast | 6 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.94244486 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |