Anemia Detection from Eyes, Palm and Fingernails with Machine Learning Models Article Swipe
A.S Nousir
,
Amr A. A. Attia
,
M. Salama
,
Amany Mostafa
,
Mohamed Osama
,
Hussein A. Khalil
,
Basma Mohamed
,
Dina Darwish
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.5121/ijcses.2024.15601
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.5121/ijcses.2024.15601
This literature review provides a comprehensive examination of non-invasive methods for detecting anemia using advanced machine learning (ML) models, with a focus on analyzing images of hands, palms, and fingernails. Anemia, a prevalent global health issue, particularly affects vulnerable groups such as children and pregnant women. Traditional diagnostic methods, while accurate, are often invasive and less accessible in resource-limited settings, creating the need for alternative approaches. By synthesizing current research, this review explores various ML techniques, including Convolutional Neural Networks (CNNs) and ensemble learning methods, assessing their accuracy and reliability in diagnosing anemia based on image analysis. A unique aspect of this research is the use of smartphone technology for capturing images, making the diagnostic process more accessible, user-friendly, and cost-effective. The findings underscore the promise of non-invasive ML-based approaches for detecting anemia, particularly in underserved populations, but also reveal significant gaps in current research. These include the need for larger, more diverse datasets and improved algorithms that can enhance diagnostic precision and adapt to real-world conditions. While existing models, ranging from conventional machine learning to more advanced neural networks, have shown considerable improvement, further development is necessary for effective real-time testing and application. By leveraging advancements in image processing and ML, this review highlights the potential for these technologies to offer timely medical interventions, improving health outcomes for millions affected by anemia worldwide.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5121/ijcses.2024.15601
- OA Status
- diamond
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406438913
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4406438913Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5121/ijcses.2024.15601Digital Object Identifier
- Title
-
Anemia Detection from Eyes, Palm and Fingernails with Machine Learning ModelsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-12-28Full publication date if available
- Authors
-
A.S Nousir, Amr A. A. Attia, M. Salama, Amany Mostafa, Mohamed Osama, Hussein A. Khalil, Basma Mohamed, Dina DarwishList of authors in order
- Landing page
-
https://doi.org/10.5121/ijcses.2024.15601Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.5121/ijcses.2024.15601Direct OA link when available
- Concepts
-
Palm, Anemia, Computer science, Artificial intelligence, Machine learning, Medicine, Internal medicine, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4406438913 |
|---|---|
| doi | https://doi.org/10.5121/ijcses.2024.15601 |
| ids.doi | https://doi.org/10.5281/zenodo.14587573 |
| ids.openalex | https://openalex.org/W4406438913 |
| fwci | 0.0 |
| type | article |
| title | Anemia Detection from Eyes, Palm and Fingernails with Machine Learning Models |
| biblio.issue | 6 |
| biblio.volume | 15 |
| biblio.last_page | 09 |
| biblio.first_page | 01 |
| topics[0].id | https://openalex.org/T13761 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 0.7325000166893005 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2739 |
| topics[0].subfield.display_name | Public Health, Environmental and Occupational Health |
| topics[0].display_name | HIV/AIDS oral health manifestations |
| topics[1].id | https://openalex.org/T10751 |
| topics[1].field.id | https://openalex.org/fields/13 |
| topics[1].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[1].score | 0.642799973487854 |
| topics[1].domain.id | https://openalex.org/domains/1 |
| topics[1].domain.display_name | Life Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1311 |
| topics[1].subfield.display_name | Genetics |
| topics[1].display_name | Forensic and Genetic Research |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C94598645 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8014758825302124 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q2347874 |
| concepts[0].display_name | Palm |
| concepts[1].id | https://openalex.org/C2778248108 |
| concepts[1].level | 2 |
| concepts[1].score | 0.500291109085083 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q5445 |
| concepts[1].display_name | Anemia |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.4707629978656769 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.4374038577079773 |
| 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 | 0.4063495397567749 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[4].display_name | Machine learning |
| concepts[5].id | https://openalex.org/C71924100 |
| concepts[5].level | 0 |
| concepts[5].score | 0.35053667426109314 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[5].display_name | Medicine |
| concepts[6].id | https://openalex.org/C126322002 |
| concepts[6].level | 1 |
| concepts[6].score | 0.11956658959388733 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q11180 |
| concepts[6].display_name | Internal medicine |
| concepts[7].id | https://openalex.org/C121332964 |
| concepts[7].level | 0 |
| concepts[7].score | 0.06114000082015991 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[7].display_name | Physics |
| concepts[8].id | https://openalex.org/C62520636 |
| concepts[8].level | 1 |
| concepts[8].score | 0.0 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q944 |
| concepts[8].display_name | Quantum mechanics |
| keywords[0].id | https://openalex.org/keywords/palm |
| keywords[0].score | 0.8014758825302124 |
| keywords[0].display_name | Palm |
| keywords[1].id | https://openalex.org/keywords/anemia |
| keywords[1].score | 0.500291109085083 |
| keywords[1].display_name | Anemia |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.4707629978656769 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.4374038577079773 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/machine-learning |
| keywords[4].score | 0.4063495397567749 |
| keywords[4].display_name | Machine learning |
| keywords[5].id | https://openalex.org/keywords/medicine |
| keywords[5].score | 0.35053667426109314 |
| keywords[5].display_name | Medicine |
| keywords[6].id | https://openalex.org/keywords/internal-medicine |
| keywords[6].score | 0.11956658959388733 |
| keywords[6].display_name | Internal medicine |
| keywords[7].id | https://openalex.org/keywords/physics |
| keywords[7].score | 0.06114000082015991 |
| keywords[7].display_name | Physics |
| language | en |
| locations[0].id | doi:10.5121/ijcses.2024.15601 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210221709 |
| locations[0].source.issn | 2319-5398, 2319-8990 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2319-5398 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | International Journal of Chaos Control Modelling and Simulation |
| locations[0].source.host_organization | |
| locations[0].source.host_organization_name | |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | International Journal of Chaos, Control, Modelling and Simulation |
| locations[0].landing_page_url | https://doi.org/10.5121/ijcses.2024.15601 |
| locations[1].id | doi:10.5281/zenodo.14587573 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400562 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | Zenodo (CERN European Organization for Nuclear Research) |
| locations[1].source.host_organization | https://openalex.org/I67311998 |
| locations[1].source.host_organization_name | European Organization for Nuclear Research |
| locations[1].source.host_organization_lineage | https://openalex.org/I67311998 |
| locations[1].license | cc-by |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | https://openalex.org/licenses/cc-by |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.5281/zenodo.14587573 |
| indexed_in | crossref, datacite |
| authorships[0].author.id | https://openalex.org/A5115900291 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | A.S Nousir |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | A.S Nousir |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5059739662 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-6604-6795 |
| authorships[1].author.display_name | Amr A. A. Attia |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Aya.M Attia |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5101444516 |
| authorships[2].author.orcid | https://orcid.org/0009-0009-6210-2575 |
| authorships[2].author.display_name | M. Salama |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | M Salama |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5086070290 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-4013-0742 |
| authorships[3].author.display_name | Amany Mostafa |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | A Mostafa |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5008898830 |
| authorships[4].author.orcid | https://orcid.org/0000-0003-2257-4868 |
| authorships[4].author.display_name | Mohamed Osama |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | M Osama |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5034276298 |
| authorships[5].author.orcid | |
| authorships[5].author.display_name | Hussein A. Khalil |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | H. A Khalil |
| authorships[5].is_corresponding | False |
| authorships[6].author.id | https://openalex.org/A5088655209 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-3214-2914 |
| authorships[6].author.display_name | Basma Mohamed |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | B Mohamed |
| authorships[6].is_corresponding | False |
| authorships[7].author.id | https://openalex.org/A5067916435 |
| authorships[7].author.orcid | https://orcid.org/0000-0002-6708-649X |
| authorships[7].author.display_name | Dina Darwish |
| authorships[7].author_position | last |
| authorships[7].raw_author_name | Dina Darwish |
| authorships[7].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.5121/ijcses.2024.15601 |
| open_access.oa_status | diamond |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-01-16T00:00:00 |
| display_name | Anemia Detection from Eyes, Palm and Fingernails with Machine Learning Models |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T13761 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 0.7325000166893005 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2739 |
| primary_topic.subfield.display_name | Public Health, Environmental and Occupational Health |
| primary_topic.display_name | HIV/AIDS oral health manifestations |
| related_works | https://openalex.org/W2961085424, https://openalex.org/W4306674287, https://openalex.org/W4387369504, https://openalex.org/W3046775127, https://openalex.org/W4394896187, https://openalex.org/W3170094116, https://openalex.org/W4386462264, https://openalex.org/W3107602296, https://openalex.org/W4364306694, https://openalex.org/W4312192474 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | doi:10.5121/ijcses.2024.15601 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210221709 |
| best_oa_location.source.issn | 2319-5398, 2319-8990 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2319-5398 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | International Journal of Chaos Control Modelling and Simulation |
| best_oa_location.source.host_organization | |
| best_oa_location.source.host_organization_name | |
| best_oa_location.license | |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | International Journal of Chaos, Control, Modelling and Simulation |
| best_oa_location.landing_page_url | https://doi.org/10.5121/ijcses.2024.15601 |
| primary_location.id | doi:10.5121/ijcses.2024.15601 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210221709 |
| primary_location.source.issn | 2319-5398, 2319-8990 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2319-5398 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | International Journal of Chaos Control Modelling and Simulation |
| primary_location.source.host_organization | |
| primary_location.source.host_organization_name | |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | International Journal of Chaos, Control, Modelling and Simulation |
| primary_location.landing_page_url | https://doi.org/10.5121/ijcses.2024.15601 |
| publication_date | 2024-12-28 |
| publication_year | 2024 |
| referenced_works_count | 0 |
| abstract_inverted_index.A | 97 |
| abstract_inverted_index.a | 4, 20, 31 |
| abstract_inverted_index.By | 66, 194 |
| abstract_inverted_index.ML | 74 |
| abstract_inverted_index.as | 41 |
| abstract_inverted_index.by | 221 |
| abstract_inverted_index.in | 57, 90, 134, 142, 197 |
| abstract_inverted_index.is | 103, 186 |
| abstract_inverted_index.of | 7, 25, 100, 106, 126 |
| abstract_inverted_index.on | 22, 94 |
| abstract_inverted_index.to | 164, 175, 210 |
| abstract_inverted_index.ML, | 201 |
| abstract_inverted_index.The | 121 |
| abstract_inverted_index.and | 28, 43, 54, 81, 88, 119, 154, 162, 192, 200 |
| abstract_inverted_index.are | 51 |
| abstract_inverted_index.but | 137 |
| abstract_inverted_index.can | 158 |
| abstract_inverted_index.for | 10, 63, 109, 130, 149, 188, 207, 218 |
| abstract_inverted_index.the | 61, 104, 113, 124, 147, 205 |
| abstract_inverted_index.use | 105 |
| abstract_inverted_index.(ML) | 17 |
| abstract_inverted_index.This | 0 |
| abstract_inverted_index.also | 138 |
| abstract_inverted_index.from | 171 |
| abstract_inverted_index.gaps | 141 |
| abstract_inverted_index.have | 180 |
| abstract_inverted_index.less | 55 |
| abstract_inverted_index.more | 116, 151, 176 |
| abstract_inverted_index.need | 62, 148 |
| abstract_inverted_index.such | 40 |
| abstract_inverted_index.that | 157 |
| abstract_inverted_index.this | 70, 101, 202 |
| abstract_inverted_index.with | 19 |
| abstract_inverted_index.These | 145 |
| abstract_inverted_index.While | 167 |
| abstract_inverted_index.adapt | 163 |
| abstract_inverted_index.based | 93 |
| abstract_inverted_index.focus | 21 |
| abstract_inverted_index.image | 95, 198 |
| abstract_inverted_index.offer | 211 |
| abstract_inverted_index.often | 52 |
| abstract_inverted_index.shown | 181 |
| abstract_inverted_index.their | 86 |
| abstract_inverted_index.these | 208 |
| abstract_inverted_index.using | 13 |
| abstract_inverted_index.while | 49 |
| abstract_inverted_index.(CNNs) | 80 |
| abstract_inverted_index.Neural | 78 |
| abstract_inverted_index.anemia | 12, 92, 222 |
| abstract_inverted_index.aspect | 99 |
| abstract_inverted_index.global | 33 |
| abstract_inverted_index.groups | 39 |
| abstract_inverted_index.hands, | 26 |
| abstract_inverted_index.health | 34, 216 |
| abstract_inverted_index.images | 24 |
| abstract_inverted_index.issue, | 35 |
| abstract_inverted_index.making | 112 |
| abstract_inverted_index.neural | 178 |
| abstract_inverted_index.palms, | 27 |
| abstract_inverted_index.reveal | 139 |
| abstract_inverted_index.review | 2, 71, 203 |
| abstract_inverted_index.timely | 212 |
| abstract_inverted_index.unique | 98 |
| abstract_inverted_index.women. | 45 |
| abstract_inverted_index.Anemia, | 30 |
| abstract_inverted_index.affects | 37 |
| abstract_inverted_index.anemia, | 132 |
| abstract_inverted_index.current | 68, 143 |
| abstract_inverted_index.diverse | 152 |
| abstract_inverted_index.enhance | 159 |
| abstract_inverted_index.further | 184 |
| abstract_inverted_index.images, | 111 |
| abstract_inverted_index.include | 146 |
| abstract_inverted_index.larger, | 150 |
| abstract_inverted_index.machine | 15, 173 |
| abstract_inverted_index.medical | 213 |
| abstract_inverted_index.methods | 9 |
| abstract_inverted_index.models, | 18, 169 |
| abstract_inverted_index.process | 115 |
| abstract_inverted_index.promise | 125 |
| abstract_inverted_index.ranging | 170 |
| abstract_inverted_index.testing | 191 |
| abstract_inverted_index.various | 73 |
| abstract_inverted_index.ML-based | 128 |
| abstract_inverted_index.Networks | 79 |
| abstract_inverted_index.accuracy | 87 |
| abstract_inverted_index.advanced | 14, 177 |
| abstract_inverted_index.affected | 220 |
| abstract_inverted_index.children | 42 |
| abstract_inverted_index.creating | 60 |
| abstract_inverted_index.datasets | 153 |
| abstract_inverted_index.ensemble | 82 |
| abstract_inverted_index.existing | 168 |
| abstract_inverted_index.explores | 72 |
| abstract_inverted_index.findings | 122 |
| abstract_inverted_index.improved | 155 |
| abstract_inverted_index.invasive | 53 |
| abstract_inverted_index.learning | 16, 83, 174 |
| abstract_inverted_index.methods, | 48, 84 |
| abstract_inverted_index.millions | 219 |
| abstract_inverted_index.outcomes | 217 |
| abstract_inverted_index.pregnant | 44 |
| abstract_inverted_index.provides | 3 |
| abstract_inverted_index.research | 102 |
| abstract_inverted_index.accurate, | 50 |
| abstract_inverted_index.analysis. | 96 |
| abstract_inverted_index.analyzing | 23 |
| abstract_inverted_index.assessing | 85 |
| abstract_inverted_index.capturing | 110 |
| abstract_inverted_index.detecting | 11, 131 |
| abstract_inverted_index.effective | 189 |
| abstract_inverted_index.improving | 215 |
| abstract_inverted_index.including | 76 |
| abstract_inverted_index.necessary | 187 |
| abstract_inverted_index.networks, | 179 |
| abstract_inverted_index.potential | 206 |
| abstract_inverted_index.precision | 161 |
| abstract_inverted_index.prevalent | 32 |
| abstract_inverted_index.real-time | 190 |
| abstract_inverted_index.research, | 69 |
| abstract_inverted_index.research. | 144 |
| abstract_inverted_index.settings, | 59 |
| abstract_inverted_index.accessible | 56 |
| abstract_inverted_index.algorithms | 156 |
| abstract_inverted_index.approaches | 129 |
| abstract_inverted_index.diagnosing | 91 |
| abstract_inverted_index.diagnostic | 47, 114, 160 |
| abstract_inverted_index.highlights | 204 |
| abstract_inverted_index.leveraging | 195 |
| abstract_inverted_index.literature | 1 |
| abstract_inverted_index.processing | 199 |
| abstract_inverted_index.real-world | 165 |
| abstract_inverted_index.smartphone | 107 |
| abstract_inverted_index.technology | 108 |
| abstract_inverted_index.underscore | 123 |
| abstract_inverted_index.vulnerable | 38 |
| abstract_inverted_index.worldwide. | 223 |
| abstract_inverted_index.Traditional | 46 |
| abstract_inverted_index.accessible, | 117 |
| abstract_inverted_index.alternative | 64 |
| abstract_inverted_index.approaches. | 65 |
| abstract_inverted_index.conditions. | 166 |
| abstract_inverted_index.development | 185 |
| abstract_inverted_index.examination | 6 |
| abstract_inverted_index.reliability | 89 |
| abstract_inverted_index.significant | 140 |
| abstract_inverted_index.techniques, | 75 |
| abstract_inverted_index.underserved | 135 |
| abstract_inverted_index.advancements | 196 |
| abstract_inverted_index.application. | 193 |
| abstract_inverted_index.considerable | 182 |
| abstract_inverted_index.conventional | 172 |
| abstract_inverted_index.fingernails. | 29 |
| abstract_inverted_index.improvement, | 183 |
| abstract_inverted_index.non-invasive | 8, 127 |
| abstract_inverted_index.particularly | 36, 133 |
| abstract_inverted_index.populations, | 136 |
| abstract_inverted_index.synthesizing | 67 |
| abstract_inverted_index.technologies | 209 |
| abstract_inverted_index.Convolutional | 77 |
| abstract_inverted_index.comprehensive | 5 |
| abstract_inverted_index.interventions, | 214 |
| abstract_inverted_index.user-friendly, | 118 |
| abstract_inverted_index.cost-effective. | 120 |
| abstract_inverted_index.resource-limited | 58 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile.value | 0.42973673 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |