Integrating machine learning and network analytics to model project cost, time and quality performance Article Swipe
Shahadat Uddin
,
Stephen Ong
,
Haohui Lu
,
Petr Matouš
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1080/09537287.2023.2196256
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1080/09537287.2023.2196256
This study aims to connect project management, network science and machine learning in an accessible overview applied to a real original dataset. Based on an initial literature review of applicable project performance measures and attributes, relevant project data were collected through an online survey. The information was split into three categories, including the basic project measures (five attributes), project stakeholder network measures (seven attributes), and project complexity measures (seven attributes). In total, 70 responses were collected, and five machine learning approaches (i.e. support vector machine, logistic regression, k-nearest neighbour, random forest and extreme gradient boosting) were applied to model the relationships between project attributes, networks and the Iron Triangle of project cost, time and quality. The results confirm the expected trends affecting project performance and provide an example for the discussion of the applicability of integrated machine learning and network analytics approaches to modelling project performance. The article demonstrates in an accessible way a real case of integration of machine learning, network science and project management and suggests avenues for further research and applications in practice.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1080/09537287.2023.2196256
- OA Status
- hybrid
- Cited By
- 10
- References
- 97
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4363648075
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4363648075Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1080/09537287.2023.2196256Digital Object Identifier
- Title
-
Integrating machine learning and network analytics to model project cost, time and quality performanceWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-04-10Full publication date if available
- Authors
-
Shahadat Uddin, Stephen Ong, Haohui Lu, Petr MatoušList of authors in order
- Landing page
-
https://doi.org/10.1080/09537287.2023.2196256Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1080/09537287.2023.2196256Direct OA link when available
- Concepts
-
Computer science, Gradient boosting, Project management, Random forest, Machine learning, Artificial intelligence, Analytics, Support vector machine, Data science, Quality (philosophy), Engineering, Systems engineering, Epistemology, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
10Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 3, 2023: 2Per-year citation counts (last 5 years)
- References (count)
-
97Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4363648075 |
|---|---|
| doi | https://doi.org/10.1080/09537287.2023.2196256 |
| ids.doi | https://doi.org/10.1080/09537287.2023.2196256 |
| ids.openalex | https://openalex.org/W4363648075 |
| fwci | 3.23489043 |
| type | article |
| title | Integrating machine learning and network analytics to model project cost, time and quality performance |
| biblio.issue | 12 |
| biblio.volume | 35 |
| biblio.last_page | 1489 |
| biblio.first_page | 1475 |
| topics[0].id | https://openalex.org/T10395 |
| topics[0].field.id | https://openalex.org/fields/18 |
| topics[0].field.display_name | Decision Sciences |
| topics[0].score | 0.9983999729156494 |
| topics[0].domain.id | https://openalex.org/domains/2 |
| topics[0].domain.display_name | Social Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1803 |
| topics[0].subfield.display_name | Management Science and Operations Research |
| topics[0].display_name | Construction Project Management and Performance |
| topics[1].id | https://openalex.org/T11006 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9776999950408936 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2215 |
| topics[1].subfield.display_name | Building and Construction |
| topics[1].display_name | BIM and Construction Integration |
| topics[2].id | https://openalex.org/T10809 |
| topics[2].field.id | https://openalex.org/fields/36 |
| topics[2].field.display_name | Health Professions |
| topics[2].score | 0.9765999913215637 |
| topics[2].domain.id | https://openalex.org/domains/4 |
| topics[2].domain.display_name | Health Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/3614 |
| topics[2].subfield.display_name | Radiological and Ultrasound Technology |
| topics[2].display_name | Occupational Health and Safety Research |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.6728745698928833 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C70153297 |
| concepts[1].level | 3 |
| concepts[1].score | 0.5599504709243774 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q5591907 |
| concepts[1].display_name | Gradient boosting |
| concepts[2].id | https://openalex.org/C15952604 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5241770148277283 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q179012 |
| concepts[2].display_name | Project management |
| concepts[3].id | https://openalex.org/C169258074 |
| concepts[3].level | 2 |
| concepts[3].score | 0.520964503288269 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q245748 |
| concepts[3].display_name | Random forest |
| concepts[4].id | https://openalex.org/C119857082 |
| concepts[4].level | 1 |
| concepts[4].score | 0.4670283794403076 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[4].display_name | Machine learning |
| concepts[5].id | https://openalex.org/C154945302 |
| concepts[5].level | 1 |
| concepts[5].score | 0.4446811079978943 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[5].display_name | Artificial intelligence |
| concepts[6].id | https://openalex.org/C79158427 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4375627338886261 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q485396 |
| concepts[6].display_name | Analytics |
| concepts[7].id | https://openalex.org/C12267149 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4269412159919739 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q282453 |
| concepts[7].display_name | Support vector machine |
| concepts[8].id | https://openalex.org/C2522767166 |
| concepts[8].level | 1 |
| concepts[8].score | 0.4240989089012146 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q2374463 |
| concepts[8].display_name | Data science |
| concepts[9].id | https://openalex.org/C2779530757 |
| concepts[9].level | 2 |
| concepts[9].score | 0.4228234589099884 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q1207505 |
| concepts[9].display_name | Quality (philosophy) |
| concepts[10].id | https://openalex.org/C127413603 |
| concepts[10].level | 0 |
| concepts[10].score | 0.2110656499862671 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[10].display_name | Engineering |
| concepts[11].id | https://openalex.org/C201995342 |
| concepts[11].level | 1 |
| concepts[11].score | 0.20473915338516235 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q682496 |
| concepts[11].display_name | Systems engineering |
| concepts[12].id | https://openalex.org/C111472728 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q9471 |
| concepts[12].display_name | Epistemology |
| concepts[13].id | https://openalex.org/C138885662 |
| concepts[13].level | 0 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[13].display_name | Philosophy |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.6728745698928833 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/gradient-boosting |
| keywords[1].score | 0.5599504709243774 |
| keywords[1].display_name | Gradient boosting |
| keywords[2].id | https://openalex.org/keywords/project-management |
| keywords[2].score | 0.5241770148277283 |
| keywords[2].display_name | Project management |
| keywords[3].id | https://openalex.org/keywords/random-forest |
| keywords[3].score | 0.520964503288269 |
| keywords[3].display_name | Random forest |
| keywords[4].id | https://openalex.org/keywords/machine-learning |
| keywords[4].score | 0.4670283794403076 |
| keywords[4].display_name | Machine learning |
| keywords[5].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[5].score | 0.4446811079978943 |
| keywords[5].display_name | Artificial intelligence |
| keywords[6].id | https://openalex.org/keywords/analytics |
| keywords[6].score | 0.4375627338886261 |
| keywords[6].display_name | Analytics |
| keywords[7].id | https://openalex.org/keywords/support-vector-machine |
| keywords[7].score | 0.4269412159919739 |
| keywords[7].display_name | Support vector machine |
| keywords[8].id | https://openalex.org/keywords/data-science |
| keywords[8].score | 0.4240989089012146 |
| keywords[8].display_name | Data science |
| keywords[9].id | https://openalex.org/keywords/quality |
| keywords[9].score | 0.4228234589099884 |
| keywords[9].display_name | Quality (philosophy) |
| keywords[10].id | https://openalex.org/keywords/engineering |
| keywords[10].score | 0.2110656499862671 |
| keywords[10].display_name | Engineering |
| keywords[11].id | https://openalex.org/keywords/systems-engineering |
| keywords[11].score | 0.20473915338516235 |
| keywords[11].display_name | Systems engineering |
| language | en |
| locations[0].id | doi:10.1080/09537287.2023.2196256 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S165057322 |
| locations[0].source.issn | 0953-7287, 1366-5871 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 0953-7287 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Production Planning & Control |
| locations[0].source.host_organization | https://openalex.org/P4310320547 |
| locations[0].source.host_organization_name | Taylor & Francis |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320547 |
| locations[0].source.host_organization_lineage_names | Taylor & Francis |
| 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 | Production Planning & Control |
| locations[0].landing_page_url | https://doi.org/10.1080/09537287.2023.2196256 |
| locations[1].id | pmh:oai:figshare.com:article/22581223 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400572 |
| 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 | OPAL (Open@LaTrobe) (La Trobe University) |
| locations[1].source.host_organization | https://openalex.org/I196829312 |
| locations[1].source.host_organization_name | La Trobe University |
| locations[1].source.host_organization_lineage | https://openalex.org/I196829312 |
| locations[1].license | cc-by |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | Text |
| locations[1].license_id | https://openalex.org/licenses/cc-by |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://figshare.com/articles/journal_contribution/Integrating_machine_learning_and_network_analytics_to_model_project_cost_time_and_quality_performance/22581223 |
| locations[2].id | doi:10.6084/m9.figshare.22581223.v1 |
| locations[2].is_oa | True |
| locations[2].source | |
| locations[2].license | cc-by |
| locations[2].pdf_url | |
| locations[2].version | |
| locations[2].raw_type | article-journal |
| locations[2].license_id | https://openalex.org/licenses/cc-by |
| locations[2].is_accepted | False |
| locations[2].is_published | |
| locations[2].raw_source_name | |
| locations[2].landing_page_url | https://doi.org/10.6084/m9.figshare.22581223.v1 |
| indexed_in | crossref, datacite |
| authorships[0].author.id | https://openalex.org/A5073472514 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-0091-6919 |
| authorships[0].author.display_name | Shahadat Uddin |
| authorships[0].countries | AU |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I129604602 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Project Management, The University of Sydney, Sydney, Australia |
| authorships[0].institutions[0].id | https://openalex.org/I129604602 |
| authorships[0].institutions[0].ror | https://ror.org/0384j8v12 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I129604602 |
| authorships[0].institutions[0].country_code | AU |
| authorships[0].institutions[0].display_name | The University of Sydney |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Shahadat Uddin |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | School of Project Management, The University of Sydney, Sydney, Australia |
| authorships[1].author.id | https://openalex.org/A5101073475 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Stephen Ong |
| authorships[1].countries | AU |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I129604602 |
| authorships[1].affiliations[0].raw_affiliation_string | School of Project Management, The University of Sydney, Sydney, Australia |
| authorships[1].institutions[0].id | https://openalex.org/I129604602 |
| authorships[1].institutions[0].ror | https://ror.org/0384j8v12 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I129604602 |
| authorships[1].institutions[0].country_code | AU |
| authorships[1].institutions[0].display_name | The University of Sydney |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Stephen Ong |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | School of Project Management, The University of Sydney, Sydney, Australia |
| authorships[2].author.id | https://openalex.org/A5054496165 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-5338-156X |
| authorships[2].author.display_name | Haohui Lu |
| authorships[2].countries | AU |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I129604602 |
| authorships[2].affiliations[0].raw_affiliation_string | School of Project Management, The University of Sydney, Sydney, Australia |
| authorships[2].institutions[0].id | https://openalex.org/I129604602 |
| authorships[2].institutions[0].ror | https://ror.org/0384j8v12 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I129604602 |
| authorships[2].institutions[0].country_code | AU |
| authorships[2].institutions[0].display_name | The University of Sydney |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Haohui Lu |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | School of Project Management, The University of Sydney, Sydney, Australia |
| authorships[3].author.id | https://openalex.org/A5046798586 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-6140-6843 |
| authorships[3].author.display_name | Petr Matouš |
| authorships[3].countries | AU |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I129604602 |
| authorships[3].affiliations[0].raw_affiliation_string | School of Project Management, The University of Sydney, Sydney, Australia |
| authorships[3].institutions[0].id | https://openalex.org/I129604602 |
| authorships[3].institutions[0].ror | https://ror.org/0384j8v12 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I129604602 |
| authorships[3].institutions[0].country_code | AU |
| authorships[3].institutions[0].display_name | The University of Sydney |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Petr Matous |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | School of Project Management, The University of Sydney, Sydney, Australia |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1080/09537287.2023.2196256 |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Integrating machine learning and network analytics to model project cost, time and quality performance |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10395 |
| primary_topic.field.id | https://openalex.org/fields/18 |
| primary_topic.field.display_name | Decision Sciences |
| primary_topic.score | 0.9983999729156494 |
| primary_topic.domain.id | https://openalex.org/domains/2 |
| primary_topic.domain.display_name | Social Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1803 |
| primary_topic.subfield.display_name | Management Science and Operations Research |
| primary_topic.display_name | Construction Project Management and Performance |
| related_works | https://openalex.org/W4200112873, https://openalex.org/W2955796858, https://openalex.org/W4224941037, https://openalex.org/W4386690025, https://openalex.org/W2004826645, https://openalex.org/W3208169454, https://openalex.org/W3135818052, https://openalex.org/W4322710485, https://openalex.org/W4315777889, https://openalex.org/W3008856725 |
| cited_by_count | 10 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 5 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 3 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 2 |
| locations_count | 3 |
| best_oa_location.id | doi:10.1080/09537287.2023.2196256 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S165057322 |
| best_oa_location.source.issn | 0953-7287, 1366-5871 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 0953-7287 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Production Planning & Control |
| best_oa_location.source.host_organization | https://openalex.org/P4310320547 |
| best_oa_location.source.host_organization_name | Taylor & Francis |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320547 |
| best_oa_location.source.host_organization_lineage_names | Taylor & Francis |
| 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 | Production Planning & Control |
| best_oa_location.landing_page_url | https://doi.org/10.1080/09537287.2023.2196256 |
| primary_location.id | doi:10.1080/09537287.2023.2196256 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S165057322 |
| primary_location.source.issn | 0953-7287, 1366-5871 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 0953-7287 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Production Planning & Control |
| primary_location.source.host_organization | https://openalex.org/P4310320547 |
| primary_location.source.host_organization_name | Taylor & Francis |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320547 |
| primary_location.source.host_organization_lineage_names | Taylor & Francis |
| 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 | Production Planning & Control |
| primary_location.landing_page_url | https://doi.org/10.1080/09537287.2023.2196256 |
| publication_date | 2023-04-10 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2128728535, https://openalex.org/W1580108325, https://openalex.org/W3214947676, https://openalex.org/W2102636708, https://openalex.org/W2092587332, https://openalex.org/W2122842398, https://openalex.org/W2066149784, https://openalex.org/W6631691190, https://openalex.org/W2025844353, https://openalex.org/W2804720989, https://openalex.org/W2991131289, https://openalex.org/W2954378598, https://openalex.org/W2615128651, https://openalex.org/W2911964244, https://openalex.org/W1973885566, https://openalex.org/W2124956521, https://openalex.org/W3177986309, https://openalex.org/W2295598076, https://openalex.org/W2024184269, https://openalex.org/W2506208000, https://openalex.org/W4206804737, https://openalex.org/W2956153508, https://openalex.org/W2402251040, https://openalex.org/W1979295071, https://openalex.org/W1997752435, https://openalex.org/W3203495899, https://openalex.org/W1967699267, https://openalex.org/W2011066533, https://openalex.org/W2158698691, https://openalex.org/W2761764885, https://openalex.org/W3116940373, https://openalex.org/W2056944867, https://openalex.org/W2085281262, https://openalex.org/W2017849362, https://openalex.org/W2982358567, https://openalex.org/W2109469951, https://openalex.org/W2136132422, https://openalex.org/W6611257893, https://openalex.org/W4280551256, https://openalex.org/W3142712062, https://openalex.org/W2498119267, https://openalex.org/W2059569762, https://openalex.org/W1901616594, https://openalex.org/W2106217029, https://openalex.org/W2995066234, https://openalex.org/W2150344954, https://openalex.org/W2054870039, https://openalex.org/W2114439486, https://openalex.org/W2122508994, https://openalex.org/W3080993712, https://openalex.org/W4200111052, https://openalex.org/W2536538677, https://openalex.org/W2017558960, https://openalex.org/W2043411841, https://openalex.org/W2775324908, https://openalex.org/W2004169327, https://openalex.org/W2064063942, https://openalex.org/W3137544425, https://openalex.org/W1964940342, https://openalex.org/W3084250253, https://openalex.org/W2797300136, https://openalex.org/W2133277024, https://openalex.org/W2770963638, https://openalex.org/W2017668305, https://openalex.org/W2012566553, https://openalex.org/W2990784565, https://openalex.org/W2761177211, https://openalex.org/W3027176846, https://openalex.org/W4236362309, https://openalex.org/W3015250136, https://openalex.org/W6995832217, https://openalex.org/W2093860406, https://openalex.org/W3214925128, https://openalex.org/W3048586388, https://openalex.org/W3020660240, https://openalex.org/W2024478733, https://openalex.org/W2791207658, https://openalex.org/W6780666841, https://openalex.org/W2729741099, https://openalex.org/W2995098893, https://openalex.org/W4295067252, https://openalex.org/W2112090702, https://openalex.org/W2162088004, https://openalex.org/W3134456924, https://openalex.org/W2058509250, https://openalex.org/W2042469728, https://openalex.org/W3156853717, https://openalex.org/W3091895038, https://openalex.org/W3049667020, https://openalex.org/W3159649695, https://openalex.org/W1533198000, https://openalex.org/W328766226, https://openalex.org/W2769614055, https://openalex.org/W162156362, https://openalex.org/W2315902422, https://openalex.org/W3041520694, https://openalex.org/W1570448133 |
| referenced_works_count | 97 |
| abstract_inverted_index.a | 18, 153 |
| abstract_inverted_index.70 | 72 |
| abstract_inverted_index.In | 70 |
| abstract_inverted_index.an | 13, 24, 41, 126, 150 |
| abstract_inverted_index.in | 12, 149, 174 |
| abstract_inverted_index.of | 28, 109, 131, 134, 156, 158 |
| abstract_inverted_index.on | 23 |
| abstract_inverted_index.to | 3, 17, 97, 142 |
| abstract_inverted_index.The | 44, 115, 146 |
| abstract_inverted_index.and | 9, 33, 64, 76, 91, 105, 113, 124, 138, 163, 166, 172 |
| abstract_inverted_index.for | 128, 169 |
| abstract_inverted_index.the | 52, 99, 106, 118, 129, 132 |
| abstract_inverted_index.was | 46 |
| abstract_inverted_index.way | 152 |
| abstract_inverted_index.Iron | 107 |
| abstract_inverted_index.This | 0 |
| abstract_inverted_index.aims | 2 |
| abstract_inverted_index.case | 155 |
| abstract_inverted_index.data | 37 |
| abstract_inverted_index.five | 77 |
| abstract_inverted_index.into | 48 |
| abstract_inverted_index.real | 19, 154 |
| abstract_inverted_index.time | 112 |
| abstract_inverted_index.were | 38, 74, 95 |
| abstract_inverted_index.(five | 56 |
| abstract_inverted_index.(i.e. | 81 |
| abstract_inverted_index.Based | 22 |
| abstract_inverted_index.basic | 53 |
| abstract_inverted_index.cost, | 111 |
| abstract_inverted_index.model | 98 |
| abstract_inverted_index.split | 47 |
| abstract_inverted_index.study | 1 |
| abstract_inverted_index.three | 49 |
| abstract_inverted_index.(seven | 62, 68 |
| abstract_inverted_index.forest | 90 |
| abstract_inverted_index.online | 42 |
| abstract_inverted_index.random | 89 |
| abstract_inverted_index.review | 27 |
| abstract_inverted_index.total, | 71 |
| abstract_inverted_index.trends | 120 |
| abstract_inverted_index.vector | 83 |
| abstract_inverted_index.applied | 16, 96 |
| abstract_inverted_index.article | 147 |
| abstract_inverted_index.avenues | 168 |
| abstract_inverted_index.between | 101 |
| abstract_inverted_index.confirm | 117 |
| abstract_inverted_index.connect | 4 |
| abstract_inverted_index.example | 127 |
| abstract_inverted_index.extreme | 92 |
| abstract_inverted_index.further | 170 |
| abstract_inverted_index.initial | 25 |
| abstract_inverted_index.machine | 10, 78, 136, 159 |
| abstract_inverted_index.network | 7, 60, 139, 161 |
| abstract_inverted_index.project | 5, 30, 36, 54, 58, 65, 102, 110, 122, 144, 164 |
| abstract_inverted_index.provide | 125 |
| abstract_inverted_index.results | 116 |
| abstract_inverted_index.science | 8, 162 |
| abstract_inverted_index.support | 82 |
| abstract_inverted_index.survey. | 43 |
| abstract_inverted_index.through | 40 |
| abstract_inverted_index.Triangle | 108 |
| abstract_inverted_index.dataset. | 21 |
| abstract_inverted_index.expected | 119 |
| abstract_inverted_index.gradient | 93 |
| abstract_inverted_index.learning | 11, 79, 137 |
| abstract_inverted_index.logistic | 85 |
| abstract_inverted_index.machine, | 84 |
| abstract_inverted_index.measures | 32, 55, 61, 67 |
| abstract_inverted_index.networks | 104 |
| abstract_inverted_index.original | 20 |
| abstract_inverted_index.overview | 15 |
| abstract_inverted_index.quality. | 114 |
| abstract_inverted_index.relevant | 35 |
| abstract_inverted_index.research | 171 |
| abstract_inverted_index.suggests | 167 |
| abstract_inverted_index.affecting | 121 |
| abstract_inverted_index.analytics | 140 |
| abstract_inverted_index.boosting) | 94 |
| abstract_inverted_index.collected | 39 |
| abstract_inverted_index.including | 51 |
| abstract_inverted_index.learning, | 160 |
| abstract_inverted_index.modelling | 143 |
| abstract_inverted_index.practice. | 175 |
| abstract_inverted_index.responses | 73 |
| abstract_inverted_index.accessible | 14, 151 |
| abstract_inverted_index.applicable | 29 |
| abstract_inverted_index.approaches | 80, 141 |
| abstract_inverted_index.collected, | 75 |
| abstract_inverted_index.complexity | 66 |
| abstract_inverted_index.discussion | 130 |
| abstract_inverted_index.integrated | 135 |
| abstract_inverted_index.literature | 26 |
| abstract_inverted_index.management | 165 |
| abstract_inverted_index.neighbour, | 88 |
| abstract_inverted_index.attributes, | 34, 103 |
| abstract_inverted_index.categories, | 50 |
| abstract_inverted_index.information | 45 |
| abstract_inverted_index.integration | 157 |
| abstract_inverted_index.management, | 6 |
| abstract_inverted_index.performance | 31, 123 |
| abstract_inverted_index.regression, | 86 |
| abstract_inverted_index.stakeholder | 59 |
| abstract_inverted_index.applications | 173 |
| abstract_inverted_index.attributes), | 57, 63 |
| abstract_inverted_index.attributes). | 69 |
| abstract_inverted_index.demonstrates | 148 |
| abstract_inverted_index.performance. | 145 |
| abstract_inverted_index.applicability | 133 |
| abstract_inverted_index.relationships | 100 |
| abstract_inverted_index.<i>k</i>-nearest | 87 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 94 |
| corresponding_author_ids | https://openalex.org/A5073472514 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I129604602 |
| citation_normalized_percentile.value | 0.9081658 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |