Integrating Two-Tier Optimization Algorithm With Convolutional Bi-LSTM Model for Robust Anomaly Detection in Autonomous Vehicles Article Swipe
Moneerah Alotaibi
,
Manal Abdullah Alohali
,
Khalid Mahmood
,
Asma A. Alhashmi
,
Jehad Saad Alqurni
,
Sultan Alotaibi
,
Ahmad A. Alzahrani
,
Imène Issaouı
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3523539
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3523539
Industrial development has changed vehicles of traditional into autonomous vehicles (AVs). AVs play a significant role since they are measured as a vital module of smart cities. The AV is an advanced automobile efficient in preserving secure driving by evading collisions formed by drivers. In contrast with traditional vehicles, which are fully coordinated and functioned by humans, AVs gather information regarding the exterior environment utilizing sensors to guarantee secure navigation. AVs decrease environmental effects since they regularly employ electricity to function rather than fossil fuel, thus diminishing greenhouse gasses. However, AVs might be exposed to cyber-attacks, causing dangers to human life. Machine learning (ML) and deep learning (DL) based anomaly recognition has progressed as a new study track in autonomous driving. ML and DL-based anomaly detection scholars have focused on improving accuracy as a typical classification task without aiming at mischievous information. This article develops an improved whale optimization algorithm-based feature selection using explainable artificial intelligence for robust anomaly detection (IWOAFS-XAIAD) technique in autonomous driving. The major aim of the IWOAFS-XAIAD technique is an endwise XAI structure to construe and imagine the anomaly recognition classifications prepared by AI models securing autonomous driving systems. Initially, the IWOAFS-XAIAD technique utilizes the Z-score data normalization method to convert input data into a compatible layout. Besides, the IWOAFS-XAIAD technique employs an improved whale optimization algorithm (IWOA)-based feature subset selection to pick an optimum set of features. An attention mechanism with the CNN-BiLSTM (CNN-BiLSTM-A) model is employed for anomaly detection and classification. Moreover, the catch-fish optimization algorithm (CFOA) selects the hyperparameters connected to the CNN-BiLSTM-A model. Finally, utilizing the SHAP XAI method, the IWOAFS-XAIAD technique performs local and global descriptions for the black-box AI model. To demonstrate the optimum classification outcome of the IWOAFS-XAIAD technique, a wide range of experiments is performed on a VeReMi dataset. The experimental validation of the IWOAFS-XAIAD technique portrayed a superior accuracy value of 98.52% over the existing methods.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2024.3523539
- OA Status
- gold
- Cited By
- 4
- References
- 53
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405907386
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4405907386Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2024.3523539Digital Object Identifier
- Title
-
Integrating Two-Tier Optimization Algorithm With Convolutional Bi-LSTM Model for Robust Anomaly Detection in Autonomous VehiclesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-12-30Full publication date if available
- Authors
-
Moneerah Alotaibi, Manal Abdullah Alohali, Khalid Mahmood, Asma A. Alhashmi, Jehad Saad Alqurni, Sultan Alotaibi, Ahmad A. Alzahrani, Imène IssaouıList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2024.3523539Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1109/access.2024.3523539Direct OA link when available
- Concepts
-
Computer science, Anomaly detection, Anomaly (physics), Algorithm, Artificial intelligence, Convolutional code, Pattern recognition (psychology), Condensed matter physics, Physics, Decoding methodsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4Per-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/W4405907386 |
|---|---|
| doi | https://doi.org/10.1109/access.2024.3523539 |
| ids.doi | https://doi.org/10.1109/access.2024.3523539 |
| ids.openalex | https://openalex.org/W4405907386 |
| fwci | 2.5551142 |
| type | article |
| title | Integrating Two-Tier Optimization Algorithm With Convolutional Bi-LSTM Model for Robust Anomaly Detection in Autonomous Vehicles |
| biblio.issue | |
| biblio.volume | 13 |
| biblio.last_page | 6833 |
| biblio.first_page | 6820 |
| topics[0].id | https://openalex.org/T11512 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9598000049591064 |
| 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 | Anomaly Detection Techniques and Applications |
| is_xpac | False |
| apc_list.value | 1850 |
| apc_list.currency | USD |
| apc_list.value_usd | 1850 |
| apc_paid.value | 1850 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 1850 |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.7188929915428162 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C739882 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6192830801010132 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q3560506 |
| concepts[1].display_name | Anomaly detection |
| concepts[2].id | https://openalex.org/C12997251 |
| concepts[2].level | 2 |
| concepts[2].score | 0.48849761486053467 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q567560 |
| concepts[2].display_name | Anomaly (physics) |
| concepts[3].id | https://openalex.org/C11413529 |
| concepts[3].level | 1 |
| concepts[3].score | 0.4634077250957489 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[3].display_name | Algorithm |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.443717896938324 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C157899210 |
| concepts[5].level | 3 |
| concepts[5].score | 0.43685922026634216 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1395022 |
| concepts[5].display_name | Convolutional code |
| concepts[6].id | https://openalex.org/C153180895 |
| concepts[6].level | 2 |
| concepts[6].score | 0.3785499632358551 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[6].display_name | Pattern recognition (psychology) |
| concepts[7].id | https://openalex.org/C26873012 |
| concepts[7].level | 1 |
| concepts[7].score | 0.0 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q214781 |
| concepts[7].display_name | Condensed matter physics |
| concepts[8].id | https://openalex.org/C121332964 |
| concepts[8].level | 0 |
| concepts[8].score | 0.0 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[8].display_name | Physics |
| concepts[9].id | https://openalex.org/C57273362 |
| concepts[9].level | 2 |
| concepts[9].score | 0.0 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q576722 |
| concepts[9].display_name | Decoding methods |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.7188929915428162 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/anomaly-detection |
| keywords[1].score | 0.6192830801010132 |
| keywords[1].display_name | Anomaly detection |
| keywords[2].id | https://openalex.org/keywords/anomaly |
| keywords[2].score | 0.48849761486053467 |
| keywords[2].display_name | Anomaly (physics) |
| keywords[3].id | https://openalex.org/keywords/algorithm |
| keywords[3].score | 0.4634077250957489 |
| keywords[3].display_name | Algorithm |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.443717896938324 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/convolutional-code |
| keywords[5].score | 0.43685922026634216 |
| keywords[5].display_name | Convolutional code |
| keywords[6].id | https://openalex.org/keywords/pattern-recognition |
| keywords[6].score | 0.3785499632358551 |
| keywords[6].display_name | Pattern recognition (psychology) |
| language | en |
| locations[0].id | doi:10.1109/access.2024.3523539 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S2485537415 |
| locations[0].source.issn | 2169-3536 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2169-3536 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | IEEE Access |
| locations[0].source.host_organization | https://openalex.org/P4310319808 |
| locations[0].source.host_organization_name | Institute of Electrical and Electronics Engineers |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319808 |
| locations[0].source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| 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 | IEEE Access |
| locations[0].landing_page_url | https://doi.org/10.1109/access.2024.3523539 |
| locations[1].id | pmh:oai:doaj.org/article:f833b789207d4855b847483a90d28db1 |
| 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 | IEEE Access, Vol 13, Pp 6820-6833 (2025) |
| locations[1].landing_page_url | https://doaj.org/article/f833b789207d4855b847483a90d28db1 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5071479102 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-0074-8153 |
| authorships[0].author.display_name | Moneerah Alotaibi |
| authorships[0].countries | SA |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I206935292 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Computer Science, College of Science and Humanities Dawadmi, Shaqra University, Saudi Arabia |
| authorships[0].institutions[0].id | https://openalex.org/I206935292 |
| authorships[0].institutions[0].ror | https://ror.org/05hawb687 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I206935292 |
| authorships[0].institutions[0].country_code | SA |
| authorships[0].institutions[0].display_name | Shaqra University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Moneerah Alotaibi |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Computer Science, College of Science and Humanities Dawadmi, Shaqra University, Saudi Arabia |
| authorships[1].author.id | https://openalex.org/A5018019909 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-1975-5345 |
| authorships[1].author.display_name | Manal Abdullah Alohali |
| authorships[1].countries | SA |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I106778892 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi Arabia |
| authorships[1].institutions[0].id | https://openalex.org/I106778892 |
| authorships[1].institutions[0].ror | https://ror.org/05b0cyh02 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I106778892 |
| authorships[1].institutions[0].country_code | SA |
| authorships[1].institutions[0].display_name | Princess Nourah bint Abdulrahman University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Manal Abdullah Alohali |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi Arabia |
| authorships[2].author.id | https://openalex.org/A5101557363 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-8236-7441 |
| authorships[2].author.display_name | Khalid Mahmood |
| authorships[2].countries | SA |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I82952536 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Information Systems, Applied College at Mahayil, King Khalid University, Saudi Arabia |
| authorships[2].institutions[0].id | https://openalex.org/I82952536 |
| authorships[2].institutions[0].ror | https://ror.org/052kwzs30 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I82952536 |
| authorships[2].institutions[0].country_code | SA |
| authorships[2].institutions[0].display_name | King Khalid University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Khalid Mahmood |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Information Systems, Applied College at Mahayil, King Khalid University, Saudi Arabia |
| authorships[3].author.id | https://openalex.org/A5012876317 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-7871-7069 |
| authorships[3].author.display_name | Asma A. Alhashmi |
| authorships[3].countries | SA |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I118590987 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Computer Science, College of Science, Northern Border University, Arar, Saudi Arabia |
| authorships[3].institutions[0].id | https://openalex.org/I118590987 |
| authorships[3].institutions[0].ror | https://ror.org/03j9tzj20 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I118590987 |
| authorships[3].institutions[0].country_code | SA |
| authorships[3].institutions[0].display_name | Northern Border University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Asma A. Alhashmi |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Computer Science, College of Science, Northern Border University, Arar, Saudi Arabia |
| authorships[4].author.id | https://openalex.org/A5070381631 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-4834-9039 |
| authorships[4].author.display_name | Jehad Saad Alqurni |
| authorships[4].countries | SA |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I76571253 |
| authorships[4].affiliations[0].raw_affiliation_string | Department of Educational Technologies, College of Education, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, Saudi Arabia |
| authorships[4].institutions[0].id | https://openalex.org/I76571253 |
| authorships[4].institutions[0].ror | https://ror.org/038cy8j79 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I76571253 |
| authorships[4].institutions[0].country_code | SA |
| authorships[4].institutions[0].display_name | Imam Abdulrahman Bin Faisal University |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Jehad Saad Alqurni |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Department of Educational Technologies, College of Education, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, Saudi Arabia |
| authorships[5].author.id | https://openalex.org/A5088482440 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-9165-3036 |
| authorships[5].author.display_name | Sultan Alotaibi |
| authorships[5].countries | SA |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I120238654 |
| authorships[5].affiliations[0].raw_affiliation_string | Department of Computer Science, College of Computing and Informatics, Saudi Electronic University, Saudi Arabia |
| authorships[5].institutions[0].id | https://openalex.org/I120238654 |
| authorships[5].institutions[0].ror | https://ror.org/05ndh7v49 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I120238654 |
| authorships[5].institutions[0].country_code | SA |
| authorships[5].institutions[0].display_name | Saudi Electronic University |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Sultan Refa Alotaibi |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Department of Computer Science, College of Computing and Informatics, Saudi Electronic University, Saudi Arabia |
| authorships[6].author.id | https://openalex.org/A5100701629 |
| authorships[6].author.orcid | https://orcid.org/0000-0003-1573-0367 |
| authorships[6].author.display_name | Ahmad A. Alzahrani |
| authorships[6].countries | SA |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I199693650 |
| authorships[6].affiliations[0].raw_affiliation_string | Department of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, Saudi Arabia |
| authorships[6].institutions[0].id | https://openalex.org/I199693650 |
| authorships[6].institutions[0].ror | https://ror.org/01xjqrm90 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I199693650 |
| authorships[6].institutions[0].country_code | SA |
| authorships[6].institutions[0].display_name | Umm al-Qura University |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Ahmad A. Alzahrani |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Department of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, Saudi Arabia |
| authorships[7].author.id | https://openalex.org/A5033132546 |
| authorships[7].author.orcid | https://orcid.org/0000-0003-2466-8550 |
| authorships[7].author.display_name | Imène Issaouı |
| authorships[7].countries | SA |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I156216236 |
| authorships[7].affiliations[0].raw_affiliation_string | Unit of Scientific Research, Applied College, Qassim University, Buraydah, Saudi Arabia |
| authorships[7].institutions[0].id | https://openalex.org/I156216236 |
| authorships[7].institutions[0].ror | https://ror.org/01wsfe280 |
| authorships[7].institutions[0].type | education |
| authorships[7].institutions[0].lineage | https://openalex.org/I156216236 |
| authorships[7].institutions[0].country_code | SA |
| authorships[7].institutions[0].display_name | Qassim University |
| authorships[7].author_position | last |
| authorships[7].raw_author_name | Imène Issaoui |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | Unit of Scientific Research, Applied College, Qassim University, Buraydah, Saudi Arabia |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1109/access.2024.3523539 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Integrating Two-Tier Optimization Algorithm With Convolutional Bi-LSTM Model for Robust Anomaly Detection in Autonomous Vehicles |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11512 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9598000049591064 |
| 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 | Anomaly Detection Techniques and Applications |
| related_works | https://openalex.org/W2806741695, https://openalex.org/W4290647774, https://openalex.org/W3189286258, https://openalex.org/W3207797160, https://openalex.org/W3210364259, https://openalex.org/W4300558037, https://openalex.org/W2667207928, https://openalex.org/W2912112202, https://openalex.org/W4377864969, https://openalex.org/W3120251014 |
| cited_by_count | 4 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 4 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1109/access.2024.3523539 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2485537415 |
| best_oa_location.source.issn | 2169-3536 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2169-3536 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | IEEE Access |
| best_oa_location.source.host_organization | https://openalex.org/P4310319808 |
| best_oa_location.source.host_organization_name | Institute of Electrical and Electronics Engineers |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319808 |
| best_oa_location.source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| 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 | IEEE Access |
| best_oa_location.landing_page_url | https://doi.org/10.1109/access.2024.3523539 |
| primary_location.id | doi:10.1109/access.2024.3523539 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S2485537415 |
| primary_location.source.issn | 2169-3536 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2169-3536 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | IEEE Access |
| primary_location.source.host_organization | https://openalex.org/P4310319808 |
| primary_location.source.host_organization_name | Institute of Electrical and Electronics Engineers |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319808 |
| primary_location.source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| 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 | IEEE Access |
| primary_location.landing_page_url | https://doi.org/10.1109/access.2024.3523539 |
| publication_date | 2024-12-30 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W4292787455, https://openalex.org/W4296870355, https://openalex.org/W3100096660, https://openalex.org/W4296870283, https://openalex.org/W2341935269, https://openalex.org/W2567664875, https://openalex.org/W3169654173, https://openalex.org/W4387060234, https://openalex.org/W3089138201, https://openalex.org/W4205306613, https://openalex.org/W4393405277, https://openalex.org/W4391640464, https://openalex.org/W4382203413, https://openalex.org/W4367146810, https://openalex.org/W4401325122, https://openalex.org/W4310874327, https://openalex.org/W4388814239, https://openalex.org/W3132444044, https://openalex.org/W4385429522, https://openalex.org/W3212518961, https://openalex.org/W4366748065, https://openalex.org/W4401163642, https://openalex.org/W4386168745, https://openalex.org/W4402029940, https://openalex.org/W4381327531, https://openalex.org/W4387366038, https://openalex.org/W4386032003, https://openalex.org/W4391881308, https://openalex.org/W4376464656, https://openalex.org/W4402375090, https://openalex.org/W4319663699, https://openalex.org/W4401246625, https://openalex.org/W4392940874, https://openalex.org/W4389859971, https://openalex.org/W4387489729, https://openalex.org/W4396745094, https://openalex.org/W4361025967, https://openalex.org/W4404480071, https://openalex.org/W4302291044, https://openalex.org/W4396609033, https://openalex.org/W4310353354, https://openalex.org/W4401540155, https://openalex.org/W4323256912, https://openalex.org/W4400316269, https://openalex.org/W4377700897, https://openalex.org/W4399602438, https://openalex.org/W4377115572, https://openalex.org/W2110940238, https://openalex.org/W4401811430, https://openalex.org/W4401974455, https://openalex.org/W4401889102, https://openalex.org/W4283167377, https://openalex.org/W4402035872 |
| referenced_works_count | 53 |
| abstract_inverted_index.a | 13, 21, 114, 133, 208, 290, 298, 309 |
| abstract_inverted_index.AI | 187, 278 |
| abstract_inverted_index.AV | 28 |
| abstract_inverted_index.An | 232 |
| abstract_inverted_index.In | 44 |
| abstract_inverted_index.ML | 121 |
| abstract_inverted_index.To | 280 |
| abstract_inverted_index.an | 30, 145, 173, 216, 227 |
| abstract_inverted_index.as | 20, 113, 132 |
| abstract_inverted_index.at | 139 |
| abstract_inverted_index.be | 92 |
| abstract_inverted_index.by | 38, 42, 55, 186 |
| abstract_inverted_index.in | 34, 118, 162 |
| abstract_inverted_index.is | 29, 172, 240, 295 |
| abstract_inverted_index.of | 5, 24, 168, 230, 286, 293, 304, 313 |
| abstract_inverted_index.on | 129, 297 |
| abstract_inverted_index.to | 66, 79, 94, 98, 177, 203, 225, 257 |
| abstract_inverted_index.AVs | 11, 57, 70, 90 |
| abstract_inverted_index.The | 27, 165, 301 |
| abstract_inverted_index.XAI | 175, 265 |
| abstract_inverted_index.aim | 167 |
| abstract_inverted_index.and | 53, 104, 122, 179, 245, 272 |
| abstract_inverted_index.are | 18, 50 |
| abstract_inverted_index.for | 156, 242, 275 |
| abstract_inverted_index.has | 2, 111 |
| abstract_inverted_index.new | 115 |
| abstract_inverted_index.set | 229 |
| abstract_inverted_index.the | 61, 169, 181, 194, 198, 212, 236, 248, 254, 258, 263, 267, 276, 282, 287, 305, 316 |
| abstract_inverted_index.(DL) | 107 |
| abstract_inverted_index.(ML) | 103 |
| abstract_inverted_index.SHAP | 264 |
| abstract_inverted_index.This | 142 |
| abstract_inverted_index.data | 200, 206 |
| abstract_inverted_index.deep | 105 |
| abstract_inverted_index.have | 127 |
| abstract_inverted_index.into | 7, 207 |
| abstract_inverted_index.over | 315 |
| abstract_inverted_index.pick | 226 |
| abstract_inverted_index.play | 12 |
| abstract_inverted_index.role | 15 |
| abstract_inverted_index.task | 136 |
| abstract_inverted_index.than | 82 |
| abstract_inverted_index.they | 17, 75 |
| abstract_inverted_index.thus | 85 |
| abstract_inverted_index.wide | 291 |
| abstract_inverted_index.with | 46, 235 |
| abstract_inverted_index.based | 108 |
| abstract_inverted_index.fuel, | 84 |
| abstract_inverted_index.fully | 51 |
| abstract_inverted_index.human | 99 |
| abstract_inverted_index.input | 205 |
| abstract_inverted_index.life. | 100 |
| abstract_inverted_index.local | 271 |
| abstract_inverted_index.major | 166 |
| abstract_inverted_index.might | 91 |
| abstract_inverted_index.model | 239 |
| abstract_inverted_index.range | 292 |
| abstract_inverted_index.since | 16, 74 |
| abstract_inverted_index.smart | 25 |
| abstract_inverted_index.study | 116 |
| abstract_inverted_index.track | 117 |
| abstract_inverted_index.using | 152 |
| abstract_inverted_index.value | 312 |
| abstract_inverted_index.vital | 22 |
| abstract_inverted_index.whale | 147, 218 |
| abstract_inverted_index.which | 49 |
| abstract_inverted_index.(AVs). | 10 |
| abstract_inverted_index.(CFOA) | 252 |
| abstract_inverted_index.98.52% | 314 |
| abstract_inverted_index.VeReMi | 299 |
| abstract_inverted_index.aiming | 138 |
| abstract_inverted_index.employ | 77 |
| abstract_inverted_index.formed | 41 |
| abstract_inverted_index.fossil | 83 |
| abstract_inverted_index.gather | 58 |
| abstract_inverted_index.global | 273 |
| abstract_inverted_index.method | 202 |
| abstract_inverted_index.model. | 260, 279 |
| abstract_inverted_index.models | 188 |
| abstract_inverted_index.module | 23 |
| abstract_inverted_index.rather | 81 |
| abstract_inverted_index.robust | 157 |
| abstract_inverted_index.secure | 36, 68 |
| abstract_inverted_index.subset | 223 |
| abstract_inverted_index.Machine | 101 |
| abstract_inverted_index.Z-score | 199 |
| abstract_inverted_index.anomaly | 109, 124, 158, 182, 243 |
| abstract_inverted_index.article | 143 |
| abstract_inverted_index.causing | 96 |
| abstract_inverted_index.changed | 3 |
| abstract_inverted_index.cities. | 26 |
| abstract_inverted_index.convert | 204 |
| abstract_inverted_index.dangers | 97 |
| abstract_inverted_index.driving | 37, 191 |
| abstract_inverted_index.effects | 73 |
| abstract_inverted_index.employs | 215 |
| abstract_inverted_index.endwise | 174 |
| abstract_inverted_index.evading | 39 |
| abstract_inverted_index.exposed | 93 |
| abstract_inverted_index.feature | 150, 222 |
| abstract_inverted_index.focused | 128 |
| abstract_inverted_index.gasses. | 88 |
| abstract_inverted_index.humans, | 56 |
| abstract_inverted_index.imagine | 180 |
| abstract_inverted_index.layout. | 210 |
| abstract_inverted_index.method, | 266 |
| abstract_inverted_index.optimum | 228, 283 |
| abstract_inverted_index.outcome | 285 |
| abstract_inverted_index.selects | 253 |
| abstract_inverted_index.sensors | 65 |
| abstract_inverted_index.typical | 134 |
| abstract_inverted_index.without | 137 |
| abstract_inverted_index.Besides, | 211 |
| abstract_inverted_index.DL-based | 123 |
| abstract_inverted_index.Finally, | 261 |
| abstract_inverted_index.However, | 89 |
| abstract_inverted_index.accuracy | 131, 311 |
| abstract_inverted_index.advanced | 31 |
| abstract_inverted_index.construe | 178 |
| abstract_inverted_index.contrast | 45 |
| abstract_inverted_index.dataset. | 300 |
| abstract_inverted_index.decrease | 71 |
| abstract_inverted_index.develops | 144 |
| abstract_inverted_index.drivers. | 43 |
| abstract_inverted_index.driving. | 120, 164 |
| abstract_inverted_index.employed | 241 |
| abstract_inverted_index.existing | 317 |
| abstract_inverted_index.exterior | 62 |
| abstract_inverted_index.function | 80 |
| abstract_inverted_index.improved | 146, 217 |
| abstract_inverted_index.learning | 102, 106 |
| abstract_inverted_index.measured | 19 |
| abstract_inverted_index.methods. | 318 |
| abstract_inverted_index.performs | 270 |
| abstract_inverted_index.prepared | 185 |
| abstract_inverted_index.scholars | 126 |
| abstract_inverted_index.securing | 189 |
| abstract_inverted_index.superior | 310 |
| abstract_inverted_index.systems. | 192 |
| abstract_inverted_index.utilizes | 197 |
| abstract_inverted_index.vehicles | 4, 9 |
| abstract_inverted_index.Moreover, | 247 |
| abstract_inverted_index.algorithm | 220, 251 |
| abstract_inverted_index.attention | 233 |
| abstract_inverted_index.black-box | 277 |
| abstract_inverted_index.connected | 256 |
| abstract_inverted_index.detection | 125, 159, 244 |
| abstract_inverted_index.efficient | 33 |
| abstract_inverted_index.features. | 231 |
| abstract_inverted_index.guarantee | 67 |
| abstract_inverted_index.improving | 130 |
| abstract_inverted_index.mechanism | 234 |
| abstract_inverted_index.performed | 296 |
| abstract_inverted_index.portrayed | 308 |
| abstract_inverted_index.regarding | 60 |
| abstract_inverted_index.regularly | 76 |
| abstract_inverted_index.selection | 151, 224 |
| abstract_inverted_index.structure | 176 |
| abstract_inverted_index.technique | 161, 171, 196, 214, 269, 307 |
| abstract_inverted_index.utilizing | 64, 262 |
| abstract_inverted_index.vehicles, | 48 |
| abstract_inverted_index.CNN-BiLSTM | 237 |
| abstract_inverted_index.Industrial | 0 |
| abstract_inverted_index.Initially, | 193 |
| abstract_inverted_index.artificial | 154 |
| abstract_inverted_index.automobile | 32 |
| abstract_inverted_index.autonomous | 8, 119, 163, 190 |
| abstract_inverted_index.catch-fish | 249 |
| abstract_inverted_index.collisions | 40 |
| abstract_inverted_index.compatible | 209 |
| abstract_inverted_index.functioned | 54 |
| abstract_inverted_index.greenhouse | 87 |
| abstract_inverted_index.preserving | 35 |
| abstract_inverted_index.progressed | 112 |
| abstract_inverted_index.technique, | 289 |
| abstract_inverted_index.validation | 303 |
| abstract_inverted_index.coordinated | 52 |
| abstract_inverted_index.demonstrate | 281 |
| abstract_inverted_index.development | 1 |
| abstract_inverted_index.diminishing | 86 |
| abstract_inverted_index.electricity | 78 |
| abstract_inverted_index.environment | 63 |
| abstract_inverted_index.experiments | 294 |
| abstract_inverted_index.explainable | 153 |
| abstract_inverted_index.information | 59 |
| abstract_inverted_index.mischievous | 140 |
| abstract_inverted_index.navigation. | 69 |
| abstract_inverted_index.recognition | 110, 183 |
| abstract_inverted_index.significant | 14 |
| abstract_inverted_index.traditional | 6, 47 |
| abstract_inverted_index.(IWOA)-based | 221 |
| abstract_inverted_index.CNN-BiLSTM-A | 259 |
| abstract_inverted_index.IWOAFS-XAIAD | 170, 195, 213, 268, 288, 306 |
| abstract_inverted_index.descriptions | 274 |
| abstract_inverted_index.experimental | 302 |
| abstract_inverted_index.information. | 141 |
| abstract_inverted_index.intelligence | 155 |
| abstract_inverted_index.optimization | 148, 219, 250 |
| abstract_inverted_index.environmental | 72 |
| abstract_inverted_index.normalization | 201 |
| abstract_inverted_index.(CNN-BiLSTM-A) | 238 |
| abstract_inverted_index.(IWOAFS-XAIAD) | 160 |
| abstract_inverted_index.classification | 135, 284 |
| abstract_inverted_index.cyber-attacks, | 95 |
| abstract_inverted_index.algorithm-based | 149 |
| abstract_inverted_index.classification. | 246 |
| abstract_inverted_index.classifications | 184 |
| abstract_inverted_index.hyperparameters | 255 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.5899999737739563 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.88828524 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |