Enhancing gait recognition by multimodal fusion of mobilenetv1 and xception features via PCA for OaA-SVM classification Article Swipe
Akash Pundir
,
Manmohan Sharma
,
Ankita Pundir
,
Dipen Saini
,
Khmaies Ouahada
,
Salil Bharany
,
Ateeq Ur Rehman
,
Habib Hamam
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-024-68053-y
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-024-68053-y
Gait recognition has become an increasingly promising area of research in the search for noninvasive and effective methods of person identification. Its potential applications in security systems and medical diagnosis make it an exciting field with wide-ranging implications. However, precisely recognizing and assessing gait patterns is difficult, particularly in changing situations or from multiple perspectives. In this study, we utilized the widely used CASIA-B dataset to observe the performance of our proposed gait recognition model, with the aim of addressing some of the existing limitations in this field. Fifty individuals are randomly selected from the dataset, and the resulting data are split evenly for training and testing purposes. We begin by excerpting features from gait photos using two well-known deep learning networks, MobileNetV1 and Xception. We then combined these features and reduced their dimensionality via principal component analysis (PCA) to improve the model's performance. We subsequently assessed the model using two distinct classifiers: a random forest and a one against all support vector machine (OaA-SVM). The findings indicate that the OaA-SVM classifier manifests superior performance compared to the others, with a mean accuracy of 98.77% over eleven different viewing angles. This study is conducive to the development of effective gait recognition algorithms that can be applied to heighten people's security and promote their well-being.
Related Topics
Concepts
Artificial intelligence
Computer science
Support vector machine
Random forest
Biometrics
Gait
Pattern recognition (psychology)
Principal component analysis
Classifier (UML)
Machine learning
Identification (biology)
Curse of dimensionality
Field (mathematics)
Physical medicine and rehabilitation
Medicine
Mathematics
Biology
Pure mathematics
Botany
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-024-68053-y
- OA Status
- gold
- Cited By
- 10
- References
- 29
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401009765
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4401009765Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1038/s41598-024-68053-yDigital Object Identifier
- Title
-
Enhancing gait recognition by multimodal fusion of mobilenetv1 and xception features via PCA for OaA-SVM classificationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-26Full publication date if available
- Authors
-
Akash Pundir, Manmohan Sharma, Ankita Pundir, Dipen Saini, Khmaies Ouahada, Salil Bharany, Ateeq Ur Rehman, Habib HamamList of authors in order
- Landing page
-
https://doi.org/10.1038/s41598-024-68053-yPublisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1038/s41598-024-68053-yDirect OA link when available
- Concepts
-
Artificial intelligence, Computer science, Support vector machine, Random forest, Biometrics, Gait, Pattern recognition (psychology), Principal component analysis, Classifier (UML), Machine learning, Identification (biology), Curse of dimensionality, Field (mathematics), Physical medicine and rehabilitation, Medicine, Mathematics, Biology, Pure mathematics, BotanyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
10Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6, 2024: 4Per-year citation counts (last 5 years)
- References (count)
-
29Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4401009765 |
|---|---|
| doi | https://doi.org/10.1038/s41598-024-68053-y |
| ids.doi | https://doi.org/10.1038/s41598-024-68053-y |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/39060307 |
| ids.openalex | https://openalex.org/W4401009765 |
| fwci | 3.67525963 |
| mesh[0].qualifier_ui | |
| mesh[0].descriptor_ui | D006801 |
| mesh[0].is_major_topic | False |
| mesh[0].qualifier_name | |
| mesh[0].descriptor_name | Humans |
| mesh[1].qualifier_ui | |
| mesh[1].descriptor_ui | D060388 |
| mesh[1].is_major_topic | True |
| mesh[1].qualifier_name | |
| mesh[1].descriptor_name | Support Vector Machine |
| mesh[2].qualifier_ui | Q000502 |
| mesh[2].descriptor_ui | D005684 |
| mesh[2].is_major_topic | True |
| mesh[2].qualifier_name | physiology |
| mesh[2].descriptor_name | Gait |
| mesh[3].qualifier_ui | |
| mesh[3].descriptor_ui | D025341 |
| mesh[3].is_major_topic | True |
| mesh[3].qualifier_name | |
| mesh[3].descriptor_name | Principal Component Analysis |
| mesh[4].qualifier_ui | |
| mesh[4].descriptor_ui | D000465 |
| mesh[4].is_major_topic | False |
| mesh[4].qualifier_name | |
| mesh[4].descriptor_name | Algorithms |
| mesh[5].qualifier_ui | |
| mesh[5].descriptor_ui | D000077321 |
| mesh[5].is_major_topic | False |
| mesh[5].qualifier_name | |
| mesh[5].descriptor_name | Deep Learning |
| mesh[6].qualifier_ui | |
| mesh[6].descriptor_ui | D005260 |
| mesh[6].is_major_topic | False |
| mesh[6].qualifier_name | |
| mesh[6].descriptor_name | Female |
| mesh[7].qualifier_ui | |
| mesh[7].descriptor_ui | D008297 |
| mesh[7].is_major_topic | False |
| mesh[7].qualifier_name | |
| mesh[7].descriptor_name | Male |
| mesh[8].qualifier_ui | Q000379 |
| mesh[8].descriptor_ui | D010363 |
| mesh[8].is_major_topic | False |
| mesh[8].qualifier_name | methods |
| mesh[8].descriptor_name | Pattern Recognition, Automated |
| mesh[9].qualifier_ui | |
| mesh[9].descriptor_ui | D000328 |
| mesh[9].is_major_topic | False |
| mesh[9].qualifier_name | |
| mesh[9].descriptor_name | Adult |
| mesh[10].qualifier_ui | |
| mesh[10].descriptor_ui | D006801 |
| mesh[10].is_major_topic | False |
| mesh[10].qualifier_name | |
| mesh[10].descriptor_name | Humans |
| mesh[11].qualifier_ui | |
| mesh[11].descriptor_ui | D060388 |
| mesh[11].is_major_topic | True |
| mesh[11].qualifier_name | |
| mesh[11].descriptor_name | Support Vector Machine |
| mesh[12].qualifier_ui | Q000502 |
| mesh[12].descriptor_ui | D005684 |
| mesh[12].is_major_topic | True |
| mesh[12].qualifier_name | physiology |
| mesh[12].descriptor_name | Gait |
| mesh[13].qualifier_ui | |
| mesh[13].descriptor_ui | D025341 |
| mesh[13].is_major_topic | True |
| mesh[13].qualifier_name | |
| mesh[13].descriptor_name | Principal Component Analysis |
| mesh[14].qualifier_ui | |
| mesh[14].descriptor_ui | D000465 |
| mesh[14].is_major_topic | False |
| mesh[14].qualifier_name | |
| mesh[14].descriptor_name | Algorithms |
| mesh[15].qualifier_ui | |
| mesh[15].descriptor_ui | D000077321 |
| mesh[15].is_major_topic | False |
| mesh[15].qualifier_name | |
| mesh[15].descriptor_name | Deep Learning |
| mesh[16].qualifier_ui | |
| mesh[16].descriptor_ui | D005260 |
| mesh[16].is_major_topic | False |
| mesh[16].qualifier_name | |
| mesh[16].descriptor_name | Female |
| mesh[17].qualifier_ui | |
| mesh[17].descriptor_ui | D008297 |
| mesh[17].is_major_topic | False |
| mesh[17].qualifier_name | |
| mesh[17].descriptor_name | Male |
| mesh[18].qualifier_ui | Q000379 |
| mesh[18].descriptor_ui | D010363 |
| mesh[18].is_major_topic | False |
| mesh[18].qualifier_name | methods |
| mesh[18].descriptor_name | Pattern Recognition, Automated |
| mesh[19].qualifier_ui | |
| mesh[19].descriptor_ui | D000328 |
| mesh[19].is_major_topic | False |
| mesh[19].qualifier_name | |
| mesh[19].descriptor_name | Adult |
| mesh[20].qualifier_ui | |
| mesh[20].descriptor_ui | D006801 |
| mesh[20].is_major_topic | False |
| mesh[20].qualifier_name | |
| mesh[20].descriptor_name | Humans |
| mesh[21].qualifier_ui | |
| mesh[21].descriptor_ui | D060388 |
| mesh[21].is_major_topic | True |
| mesh[21].qualifier_name | |
| mesh[21].descriptor_name | Support Vector Machine |
| mesh[22].qualifier_ui | Q000502 |
| mesh[22].descriptor_ui | D005684 |
| mesh[22].is_major_topic | True |
| mesh[22].qualifier_name | physiology |
| mesh[22].descriptor_name | Gait |
| mesh[23].qualifier_ui | |
| mesh[23].descriptor_ui | D025341 |
| mesh[23].is_major_topic | True |
| mesh[23].qualifier_name | |
| mesh[23].descriptor_name | Principal Component Analysis |
| mesh[24].qualifier_ui | |
| mesh[24].descriptor_ui | D000465 |
| mesh[24].is_major_topic | False |
| mesh[24].qualifier_name | |
| mesh[24].descriptor_name | Algorithms |
| mesh[25].qualifier_ui | |
| mesh[25].descriptor_ui | D000077321 |
| mesh[25].is_major_topic | False |
| mesh[25].qualifier_name | |
| mesh[25].descriptor_name | Deep Learning |
| mesh[26].qualifier_ui | |
| mesh[26].descriptor_ui | D005260 |
| mesh[26].is_major_topic | False |
| mesh[26].qualifier_name | |
| mesh[26].descriptor_name | Female |
| mesh[27].qualifier_ui | |
| mesh[27].descriptor_ui | D008297 |
| mesh[27].is_major_topic | False |
| mesh[27].qualifier_name | |
| mesh[27].descriptor_name | Male |
| mesh[28].qualifier_ui | Q000379 |
| mesh[28].descriptor_ui | D010363 |
| mesh[28].is_major_topic | False |
| mesh[28].qualifier_name | methods |
| mesh[28].descriptor_name | Pattern Recognition, Automated |
| mesh[29].qualifier_ui | |
| mesh[29].descriptor_ui | D000328 |
| mesh[29].is_major_topic | False |
| mesh[29].qualifier_name | |
| mesh[29].descriptor_name | Adult |
| mesh[30].qualifier_ui | |
| mesh[30].descriptor_ui | D006801 |
| mesh[30].is_major_topic | False |
| mesh[30].qualifier_name | |
| mesh[30].descriptor_name | Humans |
| mesh[31].qualifier_ui | |
| mesh[31].descriptor_ui | D060388 |
| mesh[31].is_major_topic | True |
| mesh[31].qualifier_name | |
| mesh[31].descriptor_name | Support Vector Machine |
| mesh[32].qualifier_ui | Q000502 |
| mesh[32].descriptor_ui | D005684 |
| mesh[32].is_major_topic | True |
| mesh[32].qualifier_name | physiology |
| mesh[32].descriptor_name | Gait |
| mesh[33].qualifier_ui | |
| mesh[33].descriptor_ui | D025341 |
| mesh[33].is_major_topic | True |
| mesh[33].qualifier_name | |
| mesh[33].descriptor_name | Principal Component Analysis |
| mesh[34].qualifier_ui | |
| mesh[34].descriptor_ui | D000465 |
| mesh[34].is_major_topic | False |
| mesh[34].qualifier_name | |
| mesh[34].descriptor_name | Algorithms |
| mesh[35].qualifier_ui | |
| mesh[35].descriptor_ui | D000077321 |
| mesh[35].is_major_topic | False |
| mesh[35].qualifier_name | |
| mesh[35].descriptor_name | Deep Learning |
| mesh[36].qualifier_ui | |
| mesh[36].descriptor_ui | D005260 |
| mesh[36].is_major_topic | False |
| mesh[36].qualifier_name | |
| mesh[36].descriptor_name | Female |
| mesh[37].qualifier_ui | |
| mesh[37].descriptor_ui | D008297 |
| mesh[37].is_major_topic | False |
| mesh[37].qualifier_name | |
| mesh[37].descriptor_name | Male |
| mesh[38].qualifier_ui | Q000379 |
| mesh[38].descriptor_ui | D010363 |
| mesh[38].is_major_topic | False |
| mesh[38].qualifier_name | methods |
| mesh[38].descriptor_name | Pattern Recognition, Automated |
| mesh[39].qualifier_ui | |
| mesh[39].descriptor_ui | D000328 |
| mesh[39].is_major_topic | False |
| mesh[39].qualifier_name | |
| mesh[39].descriptor_name | Adult |
| type | article |
| title | Enhancing gait recognition by multimodal fusion of mobilenetv1 and xception features via PCA for OaA-SVM classification |
| biblio.issue | 1 |
| biblio.volume | 14 |
| biblio.last_page | 17155 |
| biblio.first_page | 17155 |
| topics[0].id | https://openalex.org/T12740 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 1.0 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2204 |
| topics[0].subfield.display_name | Biomedical Engineering |
| topics[0].display_name | Gait Recognition and Analysis |
| topics[1].id | https://openalex.org/T11512 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9822999835014343 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Anomaly Detection Techniques and Applications |
| topics[2].id | https://openalex.org/T10331 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9790999889373779 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1707 |
| topics[2].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[2].display_name | Video Surveillance and Tracking Methods |
| is_xpac | False |
| apc_list.value | 1890 |
| apc_list.currency | EUR |
| apc_list.value_usd | 2190 |
| apc_paid.value | 1890 |
| apc_paid.currency | EUR |
| apc_paid.value_usd | 2190 |
| concepts[0].id | https://openalex.org/C154945302 |
| concepts[0].level | 1 |
| concepts[0].score | 0.776455283164978 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[0].display_name | Artificial intelligence |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.73415207862854 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C12267149 |
| concepts[2].level | 2 |
| concepts[2].score | 0.7331627607345581 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q282453 |
| concepts[2].display_name | Support vector machine |
| concepts[3].id | https://openalex.org/C169258074 |
| concepts[3].level | 2 |
| concepts[3].score | 0.6838274598121643 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q245748 |
| concepts[3].display_name | Random forest |
| concepts[4].id | https://openalex.org/C184297639 |
| concepts[4].level | 2 |
| concepts[4].score | 0.622838020324707 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q177765 |
| concepts[4].display_name | Biometrics |
| concepts[5].id | https://openalex.org/C151800584 |
| concepts[5].level | 2 |
| concepts[5].score | 0.6171258091926575 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q2370000 |
| concepts[5].display_name | Gait |
| concepts[6].id | https://openalex.org/C153180895 |
| concepts[6].level | 2 |
| concepts[6].score | 0.611327588558197 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[6].display_name | Pattern recognition (psychology) |
| concepts[7].id | https://openalex.org/C27438332 |
| concepts[7].level | 2 |
| concepts[7].score | 0.5902990698814392 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q2873 |
| concepts[7].display_name | Principal component analysis |
| concepts[8].id | https://openalex.org/C95623464 |
| concepts[8].level | 2 |
| concepts[8].score | 0.570095956325531 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q1096149 |
| concepts[8].display_name | Classifier (UML) |
| concepts[9].id | https://openalex.org/C119857082 |
| concepts[9].level | 1 |
| concepts[9].score | 0.5228484272956848 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[9].display_name | Machine learning |
| concepts[10].id | https://openalex.org/C116834253 |
| concepts[10].level | 2 |
| concepts[10].score | 0.46397051215171814 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q2039217 |
| concepts[10].display_name | Identification (biology) |
| concepts[11].id | https://openalex.org/C111030470 |
| concepts[11].level | 2 |
| concepts[11].score | 0.4611826539039612 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q1430460 |
| concepts[11].display_name | Curse of dimensionality |
| concepts[12].id | https://openalex.org/C9652623 |
| concepts[12].level | 2 |
| concepts[12].score | 0.44283604621887207 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q190109 |
| concepts[12].display_name | Field (mathematics) |
| concepts[13].id | https://openalex.org/C99508421 |
| concepts[13].level | 1 |
| concepts[13].score | 0.14111235737800598 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q2678675 |
| concepts[13].display_name | Physical medicine and rehabilitation |
| concepts[14].id | https://openalex.org/C71924100 |
| concepts[14].level | 0 |
| concepts[14].score | 0.09420934319496155 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[14].display_name | Medicine |
| concepts[15].id | https://openalex.org/C33923547 |
| concepts[15].level | 0 |
| concepts[15].score | 0.08794420957565308 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[15].display_name | Mathematics |
| concepts[16].id | https://openalex.org/C86803240 |
| concepts[16].level | 0 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[16].display_name | Biology |
| concepts[17].id | https://openalex.org/C202444582 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q837863 |
| concepts[17].display_name | Pure mathematics |
| concepts[18].id | https://openalex.org/C59822182 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q441 |
| concepts[18].display_name | Botany |
| keywords[0].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[0].score | 0.776455283164978 |
| keywords[0].display_name | Artificial intelligence |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.73415207862854 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/support-vector-machine |
| keywords[2].score | 0.7331627607345581 |
| keywords[2].display_name | Support vector machine |
| keywords[3].id | https://openalex.org/keywords/random-forest |
| keywords[3].score | 0.6838274598121643 |
| keywords[3].display_name | Random forest |
| keywords[4].id | https://openalex.org/keywords/biometrics |
| keywords[4].score | 0.622838020324707 |
| keywords[4].display_name | Biometrics |
| keywords[5].id | https://openalex.org/keywords/gait |
| keywords[5].score | 0.6171258091926575 |
| keywords[5].display_name | Gait |
| keywords[6].id | https://openalex.org/keywords/pattern-recognition |
| keywords[6].score | 0.611327588558197 |
| keywords[6].display_name | Pattern recognition (psychology) |
| keywords[7].id | https://openalex.org/keywords/principal-component-analysis |
| keywords[7].score | 0.5902990698814392 |
| keywords[7].display_name | Principal component analysis |
| keywords[8].id | https://openalex.org/keywords/classifier |
| keywords[8].score | 0.570095956325531 |
| keywords[8].display_name | Classifier (UML) |
| keywords[9].id | https://openalex.org/keywords/machine-learning |
| keywords[9].score | 0.5228484272956848 |
| keywords[9].display_name | Machine learning |
| keywords[10].id | https://openalex.org/keywords/identification |
| keywords[10].score | 0.46397051215171814 |
| keywords[10].display_name | Identification (biology) |
| keywords[11].id | https://openalex.org/keywords/curse-of-dimensionality |
| keywords[11].score | 0.4611826539039612 |
| keywords[11].display_name | Curse of dimensionality |
| keywords[12].id | https://openalex.org/keywords/field |
| keywords[12].score | 0.44283604621887207 |
| keywords[12].display_name | Field (mathematics) |
| keywords[13].id | https://openalex.org/keywords/physical-medicine-and-rehabilitation |
| keywords[13].score | 0.14111235737800598 |
| keywords[13].display_name | Physical medicine and rehabilitation |
| keywords[14].id | https://openalex.org/keywords/medicine |
| keywords[14].score | 0.09420934319496155 |
| keywords[14].display_name | Medicine |
| keywords[15].id | https://openalex.org/keywords/mathematics |
| keywords[15].score | 0.08794420957565308 |
| keywords[15].display_name | Mathematics |
| language | en |
| locations[0].id | doi:10.1038/s41598-024-68053-y |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S196734849 |
| locations[0].source.issn | 2045-2322 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2045-2322 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Scientific Reports |
| locations[0].source.host_organization | https://openalex.org/P4310319908 |
| locations[0].source.host_organization_name | Nature Portfolio |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319908, https://openalex.org/P4310319965 |
| locations[0].source.host_organization_lineage_names | Nature Portfolio, Springer Nature |
| locations[0].license | cc-by-nc-nd |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by-nc-nd |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Scientific Reports |
| locations[0].landing_page_url | https://doi.org/10.1038/s41598-024-68053-y |
| locations[1].id | pmid:39060307 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| 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 | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Scientific reports |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/39060307 |
| locations[2].id | pmh:oai:doaj.org/article:4b949f95363145699ca856a9b6471faf |
| locations[2].is_oa | False |
| locations[2].source.id | https://openalex.org/S4306401280 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[2].source.host_organization | |
| locations[2].source.host_organization_name | |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | article |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Scientific Reports, Vol 14, Iss 1, Pp 1-17 (2024) |
| locations[2].landing_page_url | https://doaj.org/article/4b949f95363145699ca856a9b6471faf |
| locations[3].id | pmh:oai:pubmedcentral.nih.gov:11282096 |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S2764455111 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | False |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | PubMed Central |
| locations[3].source.host_organization | https://openalex.org/I1299303238 |
| locations[3].source.host_organization_name | National Institutes of Health |
| locations[3].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[3].license | other-oa |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | Text |
| locations[3].license_id | https://openalex.org/licenses/other-oa |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | Sci Rep |
| locations[3].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/11282096 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5039715220 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-4365-1319 |
| authorships[0].author.display_name | Akash Pundir |
| authorships[0].countries | IN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I110360157 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Computer Science and Engineering, Lovely Professional University, Phagwara, India |
| authorships[0].institutions[0].id | https://openalex.org/I110360157 |
| authorships[0].institutions[0].ror | https://ror.org/00et6q107 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I110360157 |
| authorships[0].institutions[0].country_code | IN |
| authorships[0].institutions[0].display_name | Lovely Professional University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Akash Pundir |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | School of Computer Science and Engineering, Lovely Professional University, Phagwara, India |
| authorships[1].author.id | https://openalex.org/A5045470776 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-9445-5898 |
| authorships[1].author.display_name | Manmohan Sharma |
| authorships[1].countries | IN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I110360157 |
| authorships[1].affiliations[0].raw_affiliation_string | School of Computer Science and Engineering, Lovely Professional University, Phagwara, India |
| authorships[1].institutions[0].id | https://openalex.org/I110360157 |
| authorships[1].institutions[0].ror | https://ror.org/00et6q107 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I110360157 |
| authorships[1].institutions[0].country_code | IN |
| authorships[1].institutions[0].display_name | Lovely Professional University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Manmohan Sharma |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | School of Computer Science and Engineering, Lovely Professional University, Phagwara, India |
| authorships[2].author.id | https://openalex.org/A5092188005 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Ankita Pundir |
| authorships[2].countries | IN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I110360157 |
| authorships[2].affiliations[0].raw_affiliation_string | School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, India |
| authorships[2].institutions[0].id | https://openalex.org/I110360157 |
| authorships[2].institutions[0].ror | https://ror.org/00et6q107 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I110360157 |
| authorships[2].institutions[0].country_code | IN |
| authorships[2].institutions[0].display_name | Lovely Professional University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Ankita Pundir |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, India |
| authorships[3].author.id | https://openalex.org/A5041213328 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Dipen Saini |
| authorships[3].countries | IN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I110360157 |
| authorships[3].affiliations[0].raw_affiliation_string | School of Computer Science and Engineering, Lovely Professional University, Phagwara, India |
| authorships[3].institutions[0].id | https://openalex.org/I110360157 |
| authorships[3].institutions[0].ror | https://ror.org/00et6q107 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I110360157 |
| authorships[3].institutions[0].country_code | IN |
| authorships[3].institutions[0].display_name | Lovely Professional University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Dipen Saini |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | School of Computer Science and Engineering, Lovely Professional University, Phagwara, India |
| authorships[4].author.id | https://openalex.org/A5089836379 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Khmaies Ouahada |
| authorships[4].countries | ZA |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I24027795 |
| authorships[4].affiliations[0].raw_affiliation_string | Department of Electrical and Electronic Engineering Science, School of Electrical Engineering, University of Johannesburg, Johannesburg, 2006, South Africa |
| authorships[4].institutions[0].id | https://openalex.org/I24027795 |
| authorships[4].institutions[0].ror | https://ror.org/04z6c2n17 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I24027795 |
| authorships[4].institutions[0].country_code | ZA |
| authorships[4].institutions[0].display_name | University of Johannesburg |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Khmaies Ouahada |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Department of Electrical and Electronic Engineering Science, School of Electrical Engineering, University of Johannesburg, Johannesburg, 2006, South Africa |
| authorships[5].author.id | https://openalex.org/A5055981334 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-2282-0419 |
| authorships[5].author.display_name | Salil Bharany |
| authorships[5].countries | IN |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I74319210 |
| authorships[5].affiliations[0].raw_affiliation_string | Institute of Engineering and Technology, Chitkara University, Chitkara University, Punjab, India |
| authorships[5].institutions[0].id | https://openalex.org/I74319210 |
| authorships[5].institutions[0].ror | https://ror.org/057d6z539 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I74319210 |
| authorships[5].institutions[0].country_code | IN |
| authorships[5].institutions[0].display_name | Chitkara University |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Salil bharany |
| authorships[5].is_corresponding | True |
| authorships[5].raw_affiliation_strings | Institute of Engineering and Technology, Chitkara University, Chitkara University, Punjab, India |
| authorships[6].author.id | https://openalex.org/A5037676122 |
| authorships[6].author.orcid | https://orcid.org/0000-0001-5203-0621 |
| authorships[6].author.display_name | Ateeq Ur Rehman |
| authorships[6].countries | KR |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I12832649 |
| authorships[6].affiliations[0].raw_affiliation_string | School of Computing, Gachon University, Seongnam, 13120, Republic of Korea |
| authorships[6].institutions[0].id | https://openalex.org/I12832649 |
| authorships[6].institutions[0].ror | https://ror.org/03ryywt80 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I12832649 |
| authorships[6].institutions[0].country_code | KR |
| authorships[6].institutions[0].display_name | Gachon University |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Ateeq Ur Rehman |
| authorships[6].is_corresponding | True |
| authorships[6].raw_affiliation_strings | School of Computing, Gachon University, Seongnam, 13120, Republic of Korea |
| authorships[7].author.id | https://openalex.org/A5091127899 |
| authorships[7].author.orcid | https://orcid.org/0000-0002-5320-1012 |
| authorships[7].author.display_name | Habib Hamam |
| authorships[7].countries | ZA |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I24027795 |
| authorships[7].affiliations[0].raw_affiliation_string | Department of Electrical and Electronic Engineering Science, School of Electrical Engineering, University of Johannesburg, Johannesburg, 2006, South Africa |
| authorships[7].institutions[0].id | https://openalex.org/I24027795 |
| authorships[7].institutions[0].ror | https://ror.org/04z6c2n17 |
| authorships[7].institutions[0].type | education |
| authorships[7].institutions[0].lineage | https://openalex.org/I24027795 |
| authorships[7].institutions[0].country_code | ZA |
| authorships[7].institutions[0].display_name | University of Johannesburg |
| authorships[7].author_position | last |
| authorships[7].raw_author_name | Habib Hamam |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | Department of Electrical and Electronic Engineering Science, School of Electrical Engineering, University of Johannesburg, Johannesburg, 2006, South Africa |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1038/s41598-024-68053-y |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Enhancing gait recognition by multimodal fusion of mobilenetv1 and xception features via PCA for OaA-SVM classification |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T12740 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 1.0 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2204 |
| primary_topic.subfield.display_name | Biomedical Engineering |
| primary_topic.display_name | Gait Recognition and Analysis |
| related_works | https://openalex.org/W2076845124, https://openalex.org/W2183964146, https://openalex.org/W2379932303, https://openalex.org/W3147744369, https://openalex.org/W1975632186, https://openalex.org/W4241440711, https://openalex.org/W3185413894, https://openalex.org/W1905194803, https://openalex.org/W2137411393, https://openalex.org/W1923394858 |
| cited_by_count | 10 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 6 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 4 |
| locations_count | 4 |
| best_oa_location.id | doi:10.1038/s41598-024-68053-y |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S196734849 |
| best_oa_location.source.issn | 2045-2322 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2045-2322 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Scientific Reports |
| best_oa_location.source.host_organization | https://openalex.org/P4310319908 |
| best_oa_location.source.host_organization_name | Nature Portfolio |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319908, https://openalex.org/P4310319965 |
| best_oa_location.source.host_organization_lineage_names | Nature Portfolio, Springer Nature |
| best_oa_location.license | cc-by-nc-nd |
| 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-nc-nd |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Scientific Reports |
| best_oa_location.landing_page_url | https://doi.org/10.1038/s41598-024-68053-y |
| primary_location.id | doi:10.1038/s41598-024-68053-y |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S196734849 |
| primary_location.source.issn | 2045-2322 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2045-2322 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Scientific Reports |
| primary_location.source.host_organization | https://openalex.org/P4310319908 |
| primary_location.source.host_organization_name | Nature Portfolio |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319908, https://openalex.org/P4310319965 |
| primary_location.source.host_organization_lineage_names | Nature Portfolio, Springer Nature |
| primary_location.license | cc-by-nc-nd |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Scientific Reports |
| primary_location.landing_page_url | https://doi.org/10.1038/s41598-024-68053-y |
| publication_date | 2024-07-26 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W2983901841, https://openalex.org/W2980102731, https://openalex.org/W4213110061, https://openalex.org/W2209524052, https://openalex.org/W4321021816, https://openalex.org/W2962459300, https://openalex.org/W4288049693, https://openalex.org/W2254652002, https://openalex.org/W4205268692, https://openalex.org/W2801396221, https://openalex.org/W2573716527, https://openalex.org/W2104335344, https://openalex.org/W4323044467, https://openalex.org/W4319335657, https://openalex.org/W3009888228, https://openalex.org/W4207075225, https://openalex.org/W1984031350, https://openalex.org/W3197576140, https://openalex.org/W3203290016, https://openalex.org/W3212607673, https://openalex.org/W4366374028, https://openalex.org/W4285495163, https://openalex.org/W4297775537, https://openalex.org/W2531409750, https://openalex.org/W2294798173, https://openalex.org/W4308620628, https://openalex.org/W4285041802, https://openalex.org/W4385776789, https://openalex.org/W4225697201 |
| referenced_works_count | 29 |
| abstract_inverted_index.a | 153, 157, 180 |
| abstract_inverted_index.In | 55 |
| abstract_inverted_index.We | 108, 125, 144 |
| abstract_inverted_index.an | 4, 32 |
| abstract_inverted_index.be | 204 |
| abstract_inverted_index.by | 110 |
| abstract_inverted_index.in | 10, 24, 48, 85 |
| abstract_inverted_index.is | 45, 192 |
| abstract_inverted_index.it | 31 |
| abstract_inverted_index.of | 8, 18, 69, 78, 81, 183, 197 |
| abstract_inverted_index.or | 51 |
| abstract_inverted_index.to | 65, 139, 176, 194, 206 |
| abstract_inverted_index.we | 58 |
| abstract_inverted_index.Its | 21 |
| abstract_inverted_index.The | 165 |
| abstract_inverted_index.aim | 77 |
| abstract_inverted_index.all | 160 |
| abstract_inverted_index.and | 15, 27, 41, 96, 105, 123, 130, 156, 210 |
| abstract_inverted_index.are | 90, 100 |
| abstract_inverted_index.can | 203 |
| abstract_inverted_index.for | 13, 103 |
| abstract_inverted_index.has | 2 |
| abstract_inverted_index.one | 158 |
| abstract_inverted_index.our | 70 |
| abstract_inverted_index.the | 11, 60, 67, 76, 82, 94, 97, 141, 147, 169, 177, 195 |
| abstract_inverted_index.two | 117, 150 |
| abstract_inverted_index.via | 134 |
| abstract_inverted_index.Gait | 0 |
| abstract_inverted_index.This | 190 |
| abstract_inverted_index.area | 7 |
| abstract_inverted_index.data | 99 |
| abstract_inverted_index.deep | 119 |
| abstract_inverted_index.from | 52, 93, 113 |
| abstract_inverted_index.gait | 43, 72, 114, 199 |
| abstract_inverted_index.make | 30 |
| abstract_inverted_index.mean | 181 |
| abstract_inverted_index.over | 185 |
| abstract_inverted_index.some | 80 |
| abstract_inverted_index.that | 168, 202 |
| abstract_inverted_index.then | 126 |
| abstract_inverted_index.this | 56, 86 |
| abstract_inverted_index.used | 62 |
| abstract_inverted_index.with | 35, 75, 179 |
| abstract_inverted_index.(PCA) | 138 |
| abstract_inverted_index.Fifty | 88 |
| abstract_inverted_index.begin | 109 |
| abstract_inverted_index.field | 34 |
| abstract_inverted_index.model | 148 |
| abstract_inverted_index.split | 101 |
| abstract_inverted_index.study | 191 |
| abstract_inverted_index.their | 132, 212 |
| abstract_inverted_index.these | 128 |
| abstract_inverted_index.using | 116, 149 |
| abstract_inverted_index.98.77% | 184 |
| abstract_inverted_index.become | 3 |
| abstract_inverted_index.eleven | 186 |
| abstract_inverted_index.evenly | 102 |
| abstract_inverted_index.field. | 87 |
| abstract_inverted_index.forest | 155 |
| abstract_inverted_index.model, | 74 |
| abstract_inverted_index.person | 19 |
| abstract_inverted_index.photos | 115 |
| abstract_inverted_index.random | 154 |
| abstract_inverted_index.search | 12 |
| abstract_inverted_index.study, | 57 |
| abstract_inverted_index.vector | 162 |
| abstract_inverted_index.widely | 61 |
| abstract_inverted_index.CASIA-B | 63 |
| abstract_inverted_index.OaA-SVM | 170 |
| abstract_inverted_index.against | 159 |
| abstract_inverted_index.angles. | 189 |
| abstract_inverted_index.applied | 205 |
| abstract_inverted_index.dataset | 64 |
| abstract_inverted_index.improve | 140 |
| abstract_inverted_index.machine | 163 |
| abstract_inverted_index.medical | 28 |
| abstract_inverted_index.methods | 17 |
| abstract_inverted_index.model's | 142 |
| abstract_inverted_index.observe | 66 |
| abstract_inverted_index.others, | 178 |
| abstract_inverted_index.promote | 211 |
| abstract_inverted_index.reduced | 131 |
| abstract_inverted_index.support | 161 |
| abstract_inverted_index.systems | 26 |
| abstract_inverted_index.testing | 106 |
| abstract_inverted_index.viewing | 188 |
| abstract_inverted_index.However, | 38 |
| abstract_inverted_index.accuracy | 182 |
| abstract_inverted_index.analysis | 137 |
| abstract_inverted_index.assessed | 146 |
| abstract_inverted_index.changing | 49 |
| abstract_inverted_index.combined | 127 |
| abstract_inverted_index.compared | 175 |
| abstract_inverted_index.dataset, | 95 |
| abstract_inverted_index.distinct | 151 |
| abstract_inverted_index.exciting | 33 |
| abstract_inverted_index.existing | 83 |
| abstract_inverted_index.features | 112, 129 |
| abstract_inverted_index.findings | 166 |
| abstract_inverted_index.heighten | 207 |
| abstract_inverted_index.indicate | 167 |
| abstract_inverted_index.learning | 120 |
| abstract_inverted_index.multiple | 53 |
| abstract_inverted_index.patterns | 44 |
| abstract_inverted_index.people's | 208 |
| abstract_inverted_index.proposed | 71 |
| abstract_inverted_index.randomly | 91 |
| abstract_inverted_index.research | 9 |
| abstract_inverted_index.security | 25, 209 |
| abstract_inverted_index.selected | 92 |
| abstract_inverted_index.superior | 173 |
| abstract_inverted_index.training | 104 |
| abstract_inverted_index.utilized | 59 |
| abstract_inverted_index.Xception. | 124 |
| abstract_inverted_index.assessing | 42 |
| abstract_inverted_index.component | 136 |
| abstract_inverted_index.conducive | 193 |
| abstract_inverted_index.diagnosis | 29 |
| abstract_inverted_index.different | 187 |
| abstract_inverted_index.effective | 16, 198 |
| abstract_inverted_index.manifests | 172 |
| abstract_inverted_index.networks, | 121 |
| abstract_inverted_index.potential | 22 |
| abstract_inverted_index.precisely | 39 |
| abstract_inverted_index.principal | 135 |
| abstract_inverted_index.promising | 6 |
| abstract_inverted_index.purposes. | 107 |
| abstract_inverted_index.resulting | 98 |
| abstract_inverted_index.(OaA-SVM). | 164 |
| abstract_inverted_index.addressing | 79 |
| abstract_inverted_index.algorithms | 201 |
| abstract_inverted_index.classifier | 171 |
| abstract_inverted_index.difficult, | 46 |
| abstract_inverted_index.excerpting | 111 |
| abstract_inverted_index.situations | 50 |
| abstract_inverted_index.well-known | 118 |
| abstract_inverted_index.MobileNetV1 | 122 |
| abstract_inverted_index.development | 196 |
| abstract_inverted_index.individuals | 89 |
| abstract_inverted_index.limitations | 84 |
| abstract_inverted_index.noninvasive | 14 |
| abstract_inverted_index.performance | 68, 174 |
| abstract_inverted_index.recognition | 1, 73, 200 |
| abstract_inverted_index.recognizing | 40 |
| abstract_inverted_index.well-being. | 213 |
| abstract_inverted_index.applications | 23 |
| abstract_inverted_index.classifiers: | 152 |
| abstract_inverted_index.increasingly | 5 |
| abstract_inverted_index.particularly | 47 |
| abstract_inverted_index.performance. | 143 |
| abstract_inverted_index.subsequently | 145 |
| abstract_inverted_index.wide-ranging | 36 |
| abstract_inverted_index.implications. | 37 |
| abstract_inverted_index.perspectives. | 54 |
| abstract_inverted_index.dimensionality | 133 |
| abstract_inverted_index.identification. | 20 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5055981334, https://openalex.org/A5037676122 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 8 |
| corresponding_institution_ids | https://openalex.org/I12832649, https://openalex.org/I74319210 |
| citation_normalized_percentile.value | 0.89137786 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |