A Two-Stages Unsupervised/Supervised Statistical Learning Approach for Drone Behaviour Prediction Article Swipe
Gurpreet Singh
,
Adolfo Perrusquía
,
Weisi Guo
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/codit58514.2023.10284495
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/codit58514.2023.10284495
Drones are prone to abuse due to their low cost and their pool of potential illegal applications that can compromise safety of national infrastructures and facilities. Hence, drone detection and predict its behaviour is crucial to ensure smooth operation of services. In this paper, an unsupervised/supervised statistical learning algorithm for drone behaviour prediction is proposed. The algorithm is based on drone detection data collected from any radar or RF- sensor. The architecture of the approach is comprised of two stages: i) the first stage attempts to study the drone detection data using either unsupervised or supervised learning methods to model low dimensional expert’s features, and ii) in the second stage a real time drone behaviour predictor model is proposed based on the Kolmogorov-Smirnov and Wasserstein distances. Simulation studies using synthetic data obtained from the AirSim simulator are given to provide the evidence-base for future improvements in the field of drone behaviour prediction.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/codit58514.2023.10284495
- OA Status
- green
- Cited By
- 8
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387914455
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4387914455Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/codit58514.2023.10284495Digital Object Identifier
- Title
-
A Two-Stages Unsupervised/Supervised Statistical Learning Approach for Drone Behaviour PredictionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-07-03Full publication date if available
- Authors
-
Gurpreet Singh, Adolfo Perrusquía, Weisi GuoList of authors in order
- Landing page
-
https://doi.org/10.1109/codit58514.2023.10284495Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://dspace.lib.cranfield.ac.uk/handle/1826/20448Direct OA link when available
- Concepts
-
Drone, Computer science, Unsupervised learning, Artificial intelligence, Machine learning, Field (mathematics), Radar, Supervised learning, Data modeling, Data mining, Artificial neural network, Mathematics, Database, Biology, Pure mathematics, Telecommunications, GeneticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 6, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
37Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4387914455 |
|---|---|
| doi | https://doi.org/10.1109/codit58514.2023.10284495 |
| ids.doi | https://doi.org/10.1109/codit58514.2023.10284495 |
| ids.openalex | https://openalex.org/W4387914455 |
| fwci | 2.0435431 |
| type | article |
| title | A Two-Stages Unsupervised/Supervised Statistical Learning Approach for Drone Behaviour Prediction |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | 6 |
| biblio.first_page | 1 |
| topics[0].id | https://openalex.org/T10711 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9987000226974487 |
| 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 | Target Tracking and Data Fusion in Sensor Networks |
| topics[1].id | https://openalex.org/T11133 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9976000189781189 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2202 |
| topics[1].subfield.display_name | Aerospace Engineering |
| topics[1].display_name | UAV Applications and Optimization |
| topics[2].id | https://openalex.org/T11489 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9948999881744385 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2202 |
| topics[2].subfield.display_name | Aerospace Engineering |
| topics[2].display_name | Air Traffic Management and Optimization |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C59519942 |
| concepts[0].level | 2 |
| concepts[0].score | 0.9673222303390503 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q650665 |
| concepts[0].display_name | Drone |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.7239975333213806 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C8038995 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6537231802940369 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1152135 |
| concepts[2].display_name | Unsupervised learning |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.6188782453536987 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C119857082 |
| concepts[4].level | 1 |
| concepts[4].score | 0.6154070496559143 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[4].display_name | Machine learning |
| concepts[5].id | https://openalex.org/C9652623 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5450471043586731 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q190109 |
| concepts[5].display_name | Field (mathematics) |
| concepts[6].id | https://openalex.org/C554190296 |
| concepts[6].level | 2 |
| concepts[6].score | 0.49782252311706543 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q47528 |
| concepts[6].display_name | Radar |
| concepts[7].id | https://openalex.org/C136389625 |
| concepts[7].level | 3 |
| concepts[7].score | 0.49605593085289 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q334384 |
| concepts[7].display_name | Supervised learning |
| concepts[8].id | https://openalex.org/C67186912 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4137928783893585 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q367664 |
| concepts[8].display_name | Data modeling |
| concepts[9].id | https://openalex.org/C124101348 |
| concepts[9].level | 1 |
| concepts[9].score | 0.377594918012619 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[9].display_name | Data mining |
| concepts[10].id | https://openalex.org/C50644808 |
| concepts[10].level | 2 |
| concepts[10].score | 0.1533636748790741 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[10].display_name | Artificial neural network |
| concepts[11].id | https://openalex.org/C33923547 |
| concepts[11].level | 0 |
| concepts[11].score | 0.09777134656906128 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[11].display_name | Mathematics |
| concepts[12].id | https://openalex.org/C77088390 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q8513 |
| concepts[12].display_name | Database |
| concepts[13].id | https://openalex.org/C86803240 |
| concepts[13].level | 0 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[13].display_name | Biology |
| concepts[14].id | https://openalex.org/C202444582 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q837863 |
| concepts[14].display_name | Pure mathematics |
| concepts[15].id | https://openalex.org/C76155785 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q418 |
| concepts[15].display_name | Telecommunications |
| concepts[16].id | https://openalex.org/C54355233 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q7162 |
| concepts[16].display_name | Genetics |
| keywords[0].id | https://openalex.org/keywords/drone |
| keywords[0].score | 0.9673222303390503 |
| keywords[0].display_name | Drone |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.7239975333213806 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/unsupervised-learning |
| keywords[2].score | 0.6537231802940369 |
| keywords[2].display_name | Unsupervised learning |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.6188782453536987 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/machine-learning |
| keywords[4].score | 0.6154070496559143 |
| keywords[4].display_name | Machine learning |
| keywords[5].id | https://openalex.org/keywords/field |
| keywords[5].score | 0.5450471043586731 |
| keywords[5].display_name | Field (mathematics) |
| keywords[6].id | https://openalex.org/keywords/radar |
| keywords[6].score | 0.49782252311706543 |
| keywords[6].display_name | Radar |
| keywords[7].id | https://openalex.org/keywords/supervised-learning |
| keywords[7].score | 0.49605593085289 |
| keywords[7].display_name | Supervised learning |
| keywords[8].id | https://openalex.org/keywords/data-modeling |
| keywords[8].score | 0.4137928783893585 |
| keywords[8].display_name | Data modeling |
| keywords[9].id | https://openalex.org/keywords/data-mining |
| keywords[9].score | 0.377594918012619 |
| keywords[9].display_name | Data mining |
| keywords[10].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[10].score | 0.1533636748790741 |
| keywords[10].display_name | Artificial neural network |
| keywords[11].id | https://openalex.org/keywords/mathematics |
| keywords[11].score | 0.09777134656906128 |
| keywords[11].display_name | Mathematics |
| language | en |
| locations[0].id | doi:10.1109/codit58514.2023.10284495 |
| locations[0].is_oa | False |
| locations[0].source | |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | proceedings-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | 2023 9th International Conference on Control, Decision and Information Technologies (CoDIT) |
| locations[0].landing_page_url | https://doi.org/10.1109/codit58514.2023.10284495 |
| locations[1].id | pmh:oai:dspace.lib.cranfield.ac.uk:1826/20448 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306401778 |
| 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 | CERES (Cranfield University) |
| locations[1].source.host_organization | https://openalex.org/I82284825 |
| locations[1].source.host_organization_name | Cranfield University |
| locations[1].source.host_organization_lineage | https://openalex.org/I82284825 |
| locations[1].license | cc-by-nc |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | Conference paper |
| locations[1].license_id | https://openalex.org/licenses/cc-by-nc |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://dspace.lib.cranfield.ac.uk/handle/1826/20448 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5018302833 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-3696-965X |
| authorships[0].author.display_name | Gurpreet Singh |
| authorships[0].countries | GB |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I82284825 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Aerospace, Transport and Manufacturing, Cranfield University,Bedford,UK,MK43 OAL |
| authorships[0].institutions[0].id | https://openalex.org/I82284825 |
| authorships[0].institutions[0].ror | https://ror.org/05cncd958 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I82284825 |
| authorships[0].institutions[0].country_code | GB |
| authorships[0].institutions[0].display_name | Cranfield University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Gurpreet Singh |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | School of Aerospace, Transport and Manufacturing, Cranfield University,Bedford,UK,MK43 OAL |
| authorships[1].author.id | https://openalex.org/A5065156490 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-2290-1160 |
| authorships[1].author.display_name | Adolfo Perrusquía |
| authorships[1].countries | GB |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I82284825 |
| authorships[1].affiliations[0].raw_affiliation_string | School of Aerospace, Transport and Manufacturing, Cranfield University,Bedford,UK,MK43 OAL |
| authorships[1].institutions[0].id | https://openalex.org/I82284825 |
| authorships[1].institutions[0].ror | https://ror.org/05cncd958 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I82284825 |
| authorships[1].institutions[0].country_code | GB |
| authorships[1].institutions[0].display_name | Cranfield University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Adolfo Perrusquía |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | School of Aerospace, Transport and Manufacturing, Cranfield University,Bedford,UK,MK43 OAL |
| authorships[2].author.id | https://openalex.org/A5062362866 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-3524-3953 |
| authorships[2].author.display_name | Weisi Guo |
| authorships[2].countries | GB |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I82284825 |
| authorships[2].affiliations[0].raw_affiliation_string | School of Aerospace, Transport and Manufacturing, Cranfield University,Bedford,UK,MK43 OAL |
| authorships[2].institutions[0].id | https://openalex.org/I82284825 |
| authorships[2].institutions[0].ror | https://ror.org/05cncd958 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I82284825 |
| authorships[2].institutions[0].country_code | GB |
| authorships[2].institutions[0].display_name | Cranfield University |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Weisi Guo |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | School of Aerospace, Transport and Manufacturing, Cranfield University,Bedford,UK,MK43 OAL |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://dspace.lib.cranfield.ac.uk/handle/1826/20448 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | A Two-Stages Unsupervised/Supervised Statistical Learning Approach for Drone Behaviour Prediction |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10711 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9987000226974487 |
| 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 | Target Tracking and Data Fusion in Sensor Networks |
| related_works | https://openalex.org/W3148060700, https://openalex.org/W3080681248, https://openalex.org/W4376646226, https://openalex.org/W3047177827, https://openalex.org/W4287685660, https://openalex.org/W2057778272, https://openalex.org/W4319302697, https://openalex.org/W2986085304, https://openalex.org/W2163389298, https://openalex.org/W205223620 |
| cited_by_count | 8 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 6 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:dspace.lib.cranfield.ac.uk:1826/20448 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306401778 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | CERES (Cranfield University) |
| best_oa_location.source.host_organization | https://openalex.org/I82284825 |
| best_oa_location.source.host_organization_name | Cranfield University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I82284825 |
| best_oa_location.license | cc-by-nc |
| best_oa_location.pdf_url | |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | Conference paper |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by-nc |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://dspace.lib.cranfield.ac.uk/handle/1826/20448 |
| primary_location.id | doi:10.1109/codit58514.2023.10284495 |
| primary_location.is_oa | False |
| primary_location.source | |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | proceedings-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | 2023 9th International Conference on Control, Decision and Information Technologies (CoDIT) |
| primary_location.landing_page_url | https://doi.org/10.1109/codit58514.2023.10284495 |
| publication_date | 2023-07-03 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W4308092584, https://openalex.org/W2974922583, https://openalex.org/W3201976123, https://openalex.org/W3206820790, https://openalex.org/W2944086006, https://openalex.org/W3109894257, https://openalex.org/W3007973969, https://openalex.org/W4220656210, https://openalex.org/W6605961244, https://openalex.org/W4360605581, https://openalex.org/W2972280472, https://openalex.org/W4223631735, https://openalex.org/W1966852974, https://openalex.org/W3115167583, https://openalex.org/W3041139862, https://openalex.org/W3202758424, https://openalex.org/W6811005120, https://openalex.org/W6923009506, https://openalex.org/W3212717258, https://openalex.org/W2913367279, https://openalex.org/W3125616174, https://openalex.org/W2947505091, https://openalex.org/W4282002157, https://openalex.org/W3018983829, https://openalex.org/W4296705663, https://openalex.org/W3112976396, https://openalex.org/W3155413787, https://openalex.org/W4313598816, https://openalex.org/W4321484304, https://openalex.org/W3005782158, https://openalex.org/W3007626257, https://openalex.org/W4283724018, https://openalex.org/W2147862810, https://openalex.org/W2986358680, https://openalex.org/W3119990284, https://openalex.org/W145423968, https://openalex.org/W4226343310 |
| referenced_works_count | 37 |
| abstract_inverted_index.a | 110 |
| abstract_inverted_index.In | 41 |
| abstract_inverted_index.an | 44 |
| abstract_inverted_index.i) | 80 |
| abstract_inverted_index.in | 106, 145 |
| abstract_inverted_index.is | 33, 53, 57, 75, 117 |
| abstract_inverted_index.of | 13, 21, 39, 72, 77, 148 |
| abstract_inverted_index.on | 59, 120 |
| abstract_inverted_index.or | 67, 94 |
| abstract_inverted_index.to | 3, 6, 35, 85, 98, 138 |
| abstract_inverted_index.RF- | 68 |
| abstract_inverted_index.The | 55, 70 |
| abstract_inverted_index.and | 10, 24, 29, 104, 123 |
| abstract_inverted_index.any | 65 |
| abstract_inverted_index.are | 1, 136 |
| abstract_inverted_index.can | 18 |
| abstract_inverted_index.due | 5 |
| abstract_inverted_index.for | 49, 142 |
| abstract_inverted_index.ii) | 105 |
| abstract_inverted_index.its | 31 |
| abstract_inverted_index.low | 8, 100 |
| abstract_inverted_index.the | 73, 81, 87, 107, 121, 133, 140, 146 |
| abstract_inverted_index.two | 78 |
| abstract_inverted_index.cost | 9 |
| abstract_inverted_index.data | 62, 90, 130 |
| abstract_inverted_index.from | 64, 132 |
| abstract_inverted_index.pool | 12 |
| abstract_inverted_index.real | 111 |
| abstract_inverted_index.that | 17 |
| abstract_inverted_index.this | 42 |
| abstract_inverted_index.time | 112 |
| abstract_inverted_index.abuse | 4 |
| abstract_inverted_index.based | 58, 119 |
| abstract_inverted_index.drone | 27, 50, 60, 88, 113, 149 |
| abstract_inverted_index.field | 147 |
| abstract_inverted_index.first | 82 |
| abstract_inverted_index.given | 137 |
| abstract_inverted_index.model | 99, 116 |
| abstract_inverted_index.prone | 2 |
| abstract_inverted_index.radar | 66 |
| abstract_inverted_index.stage | 83, 109 |
| abstract_inverted_index.study | 86 |
| abstract_inverted_index.their | 7, 11 |
| abstract_inverted_index.using | 91, 128 |
| abstract_inverted_index.AirSim | 134 |
| abstract_inverted_index.Drones | 0 |
| abstract_inverted_index.Hence, | 26 |
| abstract_inverted_index.either | 92 |
| abstract_inverted_index.ensure | 36 |
| abstract_inverted_index.future | 143 |
| abstract_inverted_index.paper, | 43 |
| abstract_inverted_index.safety | 20 |
| abstract_inverted_index.second | 108 |
| abstract_inverted_index.smooth | 37 |
| abstract_inverted_index.crucial | 34 |
| abstract_inverted_index.illegal | 15 |
| abstract_inverted_index.methods | 97 |
| abstract_inverted_index.predict | 30 |
| abstract_inverted_index.provide | 139 |
| abstract_inverted_index.sensor. | 69 |
| abstract_inverted_index.stages: | 79 |
| abstract_inverted_index.studies | 127 |
| abstract_inverted_index.approach | 74 |
| abstract_inverted_index.attempts | 84 |
| abstract_inverted_index.learning | 47, 96 |
| abstract_inverted_index.national | 22 |
| abstract_inverted_index.obtained | 131 |
| abstract_inverted_index.proposed | 118 |
| abstract_inverted_index.algorithm | 48, 56 |
| abstract_inverted_index.behaviour | 32, 51, 114, 150 |
| abstract_inverted_index.collected | 63 |
| abstract_inverted_index.comprised | 76 |
| abstract_inverted_index.detection | 28, 61, 89 |
| abstract_inverted_index.features, | 103 |
| abstract_inverted_index.operation | 38 |
| abstract_inverted_index.potential | 14 |
| abstract_inverted_index.predictor | 115 |
| abstract_inverted_index.proposed. | 54 |
| abstract_inverted_index.services. | 40 |
| abstract_inverted_index.simulator | 135 |
| abstract_inverted_index.synthetic | 129 |
| abstract_inverted_index.Simulation | 126 |
| abstract_inverted_index.compromise | 19 |
| abstract_inverted_index.distances. | 125 |
| abstract_inverted_index.expert’s | 102 |
| abstract_inverted_index.prediction | 52 |
| abstract_inverted_index.supervised | 95 |
| abstract_inverted_index.Wasserstein | 124 |
| abstract_inverted_index.dimensional | 101 |
| abstract_inverted_index.facilities. | 25 |
| abstract_inverted_index.prediction. | 151 |
| abstract_inverted_index.statistical | 46 |
| abstract_inverted_index.applications | 16 |
| abstract_inverted_index.architecture | 71 |
| abstract_inverted_index.improvements | 144 |
| abstract_inverted_index.unsupervised | 93 |
| abstract_inverted_index.evidence-base | 141 |
| abstract_inverted_index.infrastructures | 23 |
| abstract_inverted_index.Kolmogorov-Smirnov | 122 |
| abstract_inverted_index.unsupervised/supervised | 45 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.6600000262260437 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.87227521 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |