Energy Demand Forecasting for Electric Vehicles Using Blockchain-Based Federated Learning Article Swipe
Firdous Kausar
,
Rami Al-Hamouz
,
Sajid Hussain
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3377661
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3377661
The widespread adoption of electric cars (EVs) can be attributed to their many advantages over conventional gas-powered automobiles. However, there may be difficulties in incorporating EVs into the grid due to increased energy demand and peak load. We propose a blockchain-based federated learning scheme using different linear regression algorithms for energy demand prediction for EVs. The information gathered from EVs is stored on the blockchain network. Only those with the proper credentials can decrypt the data from its encrypted storage. Data from EVs is utilized to train a machine learning model with the use of a federated learning algorithm. Each EV is used to train a model, and then the models’ parameters are distributed throughout the blockchain. Our approach is innovative in analyzing of BCFL communications overhead and latency issues, while delving deeper into its dynamics to measure and reduce communication delays to maximize system efficiency. The implementation results verify the effectiveness of our system in anticipating EVs’ energy requirements. For the training of the BCFL model, a huge real-world dataset was used from over 60,000 transactions at EV charging stations in Boulder city, Colorado. The results show that the framework is reliable, since all the models have R2 values above 0.91, which indicates a high degree of accuracy in predicting energy use.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2024.3377661
- https://ieeexplore.ieee.org/ielx7/6287639/6514899/10472974.pdf
- OA Status
- gold
- Cited By
- 13
- References
- 17
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392902189
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4392902189Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2024.3377661Digital Object Identifier
- Title
-
Energy Demand Forecasting for Electric Vehicles Using Blockchain-Based Federated LearningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-01Full publication date if available
- Authors
-
Firdous Kausar, Rami Al-Hamouz, Sajid HussainList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2024.3377661Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/6514899/10472974.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://ieeexplore.ieee.org/ielx7/6287639/6514899/10472974.pdfDirect OA link when available
- Concepts
-
Blockchain, Computer science, Computer securityTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
13Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 9, 2024: 4Per-year citation counts (last 5 years)
- References (count)
-
17Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4392902189 |
|---|---|
| doi | https://doi.org/10.1109/access.2024.3377661 |
| ids.doi | https://doi.org/10.1109/access.2024.3377661 |
| ids.openalex | https://openalex.org/W4392902189 |
| fwci | 19.85945343 |
| type | article |
| title | Energy Demand Forecasting for Electric Vehicles Using Blockchain-Based Federated Learning |
| biblio.issue | |
| biblio.volume | 12 |
| biblio.last_page | 41298 |
| biblio.first_page | 41287 |
| topics[0].id | https://openalex.org/T10270 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9977999925613403 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1710 |
| topics[0].subfield.display_name | Information Systems |
| topics[0].display_name | Blockchain Technology Applications and Security |
| topics[1].id | https://openalex.org/T12617 |
| topics[1].field.id | https://openalex.org/fields/21 |
| topics[1].field.display_name | Energy |
| topics[1].score | 0.989799976348877 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2105 |
| topics[1].subfield.display_name | Renewable Energy, Sustainability and the Environment |
| topics[1].display_name | Energy, Environment, and Transportation Policies |
| topics[2].id | https://openalex.org/T10768 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9858999848365784 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2208 |
| topics[2].subfield.display_name | Electrical and Electronic Engineering |
| topics[2].display_name | Electric Vehicles and Infrastructure |
| is_xpac | False |
| apc_list.value | 1850 |
| apc_list.currency | USD |
| apc_list.value_usd | 1850 |
| apc_paid.value | 1850 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 1850 |
| concepts[0].id | https://openalex.org/C2779687700 |
| concepts[0].level | 2 |
| concepts[0].score | 0.9446810483932495 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q20514253 |
| concepts[0].display_name | Blockchain |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.6874724626541138 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C38652104 |
| concepts[2].level | 1 |
| concepts[2].score | 0.23231187462806702 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q3510521 |
| concepts[2].display_name | Computer security |
| keywords[0].id | https://openalex.org/keywords/blockchain |
| keywords[0].score | 0.9446810483932495 |
| keywords[0].display_name | Blockchain |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.6874724626541138 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/computer-security |
| keywords[2].score | 0.23231187462806702 |
| keywords[2].display_name | Computer security |
| language | en |
| locations[0].id | doi:10.1109/access.2024.3377661 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S2485537415 |
| locations[0].source.issn | 2169-3536 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2169-3536 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | IEEE Access |
| locations[0].source.host_organization | https://openalex.org/P4310319808 |
| locations[0].source.host_organization_name | Institute of Electrical and Electronics Engineers |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319808 |
| locations[0].source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| locations[0].license | |
| locations[0].pdf_url | https://ieeexplore.ieee.org/ielx7/6287639/6514899/10472974.pdf |
| 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 | IEEE Access |
| locations[0].landing_page_url | https://doi.org/10.1109/access.2024.3377661 |
| locations[1].id | pmh:oai:doaj.org/article:e79379d7432c4452b98624277ec85020 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306401280 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | IEEE Access, Vol 12, Pp 41287-41298 (2024) |
| locations[1].landing_page_url | https://doaj.org/article/e79379d7432c4452b98624277ec85020 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5082200006 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-8922-549X |
| authorships[0].author.display_name | Firdous Kausar |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I9254433 |
| authorships[0].affiliations[0].raw_affiliation_string | Mathematics and Computer Science Department, Fisk University, Nashville, TN, USA |
| authorships[0].institutions[0].id | https://openalex.org/I9254433 |
| authorships[0].institutions[0].ror | https://ror.org/01bbs6821 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I9254433 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | Fisk University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Firdous Kausar |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Mathematics and Computer Science Department, Fisk University, Nashville, TN, USA |
| authorships[1].author.id | https://openalex.org/A5094178810 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Rami Al-Hamouz |
| authorships[1].countries | OM |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I47818738 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Electrical and Computer Engineering, College of Engineering, Sultan Qaboos University, Muscat, Oman |
| authorships[1].institutions[0].id | https://openalex.org/I47818738 |
| authorships[1].institutions[0].ror | https://ror.org/04wq8zb47 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I47818738 |
| authorships[1].institutions[0].country_code | OM |
| authorships[1].institutions[0].display_name | Sultan Qaboos University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Rami Al-Hamouz |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Electrical and Computer Engineering, College of Engineering, Sultan Qaboos University, Muscat, Oman |
| authorships[2].author.id | https://openalex.org/A5102009927 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-2972-7049 |
| authorships[2].author.display_name | Sajid Hussain |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I9254433 |
| authorships[2].affiliations[0].raw_affiliation_string | Mathematics and Computer Science Department, Fisk University, Nashville, TN, USA |
| authorships[2].institutions[0].id | https://openalex.org/I9254433 |
| authorships[2].institutions[0].ror | https://ror.org/01bbs6821 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I9254433 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | Fisk University |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Sajid Hussain |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Mathematics and Computer Science Department, Fisk University, Nashville, TN, USA |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://ieeexplore.ieee.org/ielx7/6287639/6514899/10472974.pdf |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Energy Demand Forecasting for Electric Vehicles Using Blockchain-Based Federated Learning |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10270 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9977999925613403 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1710 |
| primary_topic.subfield.display_name | Information Systems |
| primary_topic.display_name | Blockchain Technology Applications and Security |
| related_works | https://openalex.org/W2748952813, https://openalex.org/W4210406818, https://openalex.org/W4306779889, https://openalex.org/W4386732777, https://openalex.org/W3211706803, https://openalex.org/W4382775358, https://openalex.org/W4246942721, https://openalex.org/W3209862047, https://openalex.org/W4304136894, https://openalex.org/W2989851257 |
| cited_by_count | 13 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 9 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 4 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1109/access.2024.3377661 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2485537415 |
| best_oa_location.source.issn | 2169-3536 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2169-3536 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | IEEE Access |
| best_oa_location.source.host_organization | https://openalex.org/P4310319808 |
| best_oa_location.source.host_organization_name | Institute of Electrical and Electronics Engineers |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319808 |
| best_oa_location.source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://ieeexplore.ieee.org/ielx7/6287639/6514899/10472974.pdf |
| 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 | IEEE Access |
| best_oa_location.landing_page_url | https://doi.org/10.1109/access.2024.3377661 |
| primary_location.id | doi:10.1109/access.2024.3377661 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S2485537415 |
| primary_location.source.issn | 2169-3536 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2169-3536 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | IEEE Access |
| primary_location.source.host_organization | https://openalex.org/P4310319808 |
| primary_location.source.host_organization_name | Institute of Electrical and Electronics Engineers |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319808 |
| primary_location.source.host_organization_lineage_names | Institute of Electrical and Electronics Engineers |
| primary_location.license | |
| primary_location.pdf_url | https://ieeexplore.ieee.org/ielx7/6287639/6514899/10472974.pdf |
| 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 | IEEE Access |
| primary_location.landing_page_url | https://doi.org/10.1109/access.2024.3377661 |
| publication_date | 2024-01-01 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W4292409483, https://openalex.org/W6728757088, https://openalex.org/W2919115771, https://openalex.org/W4320029344, https://openalex.org/W4308544908, https://openalex.org/W3149578452, https://openalex.org/W3007607795, https://openalex.org/W4310990750, https://openalex.org/W4281763929, https://openalex.org/W2748692995, https://openalex.org/W3135032840, https://openalex.org/W6684249991, https://openalex.org/W2974429275, https://openalex.org/W3211053788, https://openalex.org/W2754529701, https://openalex.org/W6769285895, https://openalex.org/W4318619660 |
| referenced_works_count | 17 |
| abstract_inverted_index.a | 39, 87, 95, 105, 167, 204 |
| abstract_inverted_index.EV | 100, 178 |
| abstract_inverted_index.R2 | 198 |
| abstract_inverted_index.We | 37 |
| abstract_inverted_index.at | 177 |
| abstract_inverted_index.be | 8, 21 |
| abstract_inverted_index.in | 23, 121, 155, 181, 209 |
| abstract_inverted_index.is | 60, 83, 101, 119, 191 |
| abstract_inverted_index.of | 3, 94, 123, 152, 163, 207 |
| abstract_inverted_index.on | 62 |
| abstract_inverted_index.to | 10, 30, 85, 103, 136, 142 |
| abstract_inverted_index.EVs | 25, 59, 82 |
| abstract_inverted_index.For | 160 |
| abstract_inverted_index.Our | 117 |
| abstract_inverted_index.The | 0, 55, 146, 185 |
| abstract_inverted_index.all | 194 |
| abstract_inverted_index.and | 34, 107, 127, 138 |
| abstract_inverted_index.are | 112 |
| abstract_inverted_index.can | 7, 72 |
| abstract_inverted_index.due | 29 |
| abstract_inverted_index.for | 49, 53 |
| abstract_inverted_index.its | 77, 134 |
| abstract_inverted_index.may | 20 |
| abstract_inverted_index.our | 153 |
| abstract_inverted_index.the | 27, 63, 69, 74, 92, 109, 115, 150, 161, 164, 189, 195 |
| abstract_inverted_index.use | 93 |
| abstract_inverted_index.was | 171 |
| abstract_inverted_index.BCFL | 124, 165 |
| abstract_inverted_index.Data | 80 |
| abstract_inverted_index.EVs. | 54 |
| abstract_inverted_index.Each | 99 |
| abstract_inverted_index.Only | 66 |
| abstract_inverted_index.cars | 5 |
| abstract_inverted_index.data | 75 |
| abstract_inverted_index.from | 58, 76, 81, 173 |
| abstract_inverted_index.grid | 28 |
| abstract_inverted_index.have | 197 |
| abstract_inverted_index.high | 205 |
| abstract_inverted_index.huge | 168 |
| abstract_inverted_index.into | 26, 133 |
| abstract_inverted_index.many | 12 |
| abstract_inverted_index.over | 14, 174 |
| abstract_inverted_index.peak | 35 |
| abstract_inverted_index.show | 187 |
| abstract_inverted_index.that | 188 |
| abstract_inverted_index.then | 108 |
| abstract_inverted_index.use. | 212 |
| abstract_inverted_index.used | 102, 172 |
| abstract_inverted_index.with | 68, 91 |
| abstract_inverted_index.(EVs) | 6 |
| abstract_inverted_index.0.91, | 201 |
| abstract_inverted_index.above | 200 |
| abstract_inverted_index.city, | 183 |
| abstract_inverted_index.load. | 36 |
| abstract_inverted_index.model | 90 |
| abstract_inverted_index.since | 193 |
| abstract_inverted_index.their | 11 |
| abstract_inverted_index.there | 19 |
| abstract_inverted_index.those | 67 |
| abstract_inverted_index.train | 86, 104 |
| abstract_inverted_index.using | 44 |
| abstract_inverted_index.which | 202 |
| abstract_inverted_index.while | 130 |
| abstract_inverted_index.60,000 | 175 |
| abstract_inverted_index.deeper | 132 |
| abstract_inverted_index.degree | 206 |
| abstract_inverted_index.delays | 141 |
| abstract_inverted_index.demand | 33, 51 |
| abstract_inverted_index.energy | 32, 50, 158, 211 |
| abstract_inverted_index.linear | 46 |
| abstract_inverted_index.model, | 106, 166 |
| abstract_inverted_index.models | 196 |
| abstract_inverted_index.proper | 70 |
| abstract_inverted_index.reduce | 139 |
| abstract_inverted_index.scheme | 43 |
| abstract_inverted_index.stored | 61 |
| abstract_inverted_index.system | 144, 154 |
| abstract_inverted_index.values | 199 |
| abstract_inverted_index.verify | 149 |
| abstract_inverted_index.Boulder | 182 |
| abstract_inverted_index.dataset | 170 |
| abstract_inverted_index.decrypt | 73 |
| abstract_inverted_index.delving | 131 |
| abstract_inverted_index.issues, | 129 |
| abstract_inverted_index.latency | 128 |
| abstract_inverted_index.machine | 88 |
| abstract_inverted_index.measure | 137 |
| abstract_inverted_index.propose | 38 |
| abstract_inverted_index.results | 148, 186 |
| abstract_inverted_index.However, | 18 |
| abstract_inverted_index.accuracy | 208 |
| abstract_inverted_index.adoption | 2 |
| abstract_inverted_index.approach | 118 |
| abstract_inverted_index.charging | 179 |
| abstract_inverted_index.dynamics | 135 |
| abstract_inverted_index.electric | 4 |
| abstract_inverted_index.gathered | 57 |
| abstract_inverted_index.learning | 42, 89, 97 |
| abstract_inverted_index.maximize | 143 |
| abstract_inverted_index.network. | 65 |
| abstract_inverted_index.overhead | 126 |
| abstract_inverted_index.stations | 180 |
| abstract_inverted_index.storage. | 79 |
| abstract_inverted_index.training | 162 |
| abstract_inverted_index.utilized | 84 |
| abstract_inverted_index.Colorado. | 184 |
| abstract_inverted_index.analyzing | 122 |
| abstract_inverted_index.different | 45 |
| abstract_inverted_index.encrypted | 78 |
| abstract_inverted_index.federated | 41, 96 |
| abstract_inverted_index.framework | 190 |
| abstract_inverted_index.increased | 31 |
| abstract_inverted_index.indicates | 203 |
| abstract_inverted_index.reliable, | 192 |
| abstract_inverted_index.advantages | 13 |
| abstract_inverted_index.algorithm. | 98 |
| abstract_inverted_index.algorithms | 48 |
| abstract_inverted_index.attributed | 9 |
| abstract_inverted_index.blockchain | 64 |
| abstract_inverted_index.innovative | 120 |
| abstract_inverted_index.parameters | 111 |
| abstract_inverted_index.predicting | 210 |
| abstract_inverted_index.prediction | 52 |
| abstract_inverted_index.real-world | 169 |
| abstract_inverted_index.regression | 47 |
| abstract_inverted_index.throughout | 114 |
| abstract_inverted_index.widespread | 1 |
| abstract_inverted_index.EVs’ | 157 |
| abstract_inverted_index.blockchain. | 116 |
| abstract_inverted_index.credentials | 71 |
| abstract_inverted_index.distributed | 113 |
| abstract_inverted_index.efficiency. | 145 |
| abstract_inverted_index.gas-powered | 16 |
| abstract_inverted_index.information | 56 |
| abstract_inverted_index.anticipating | 156 |
| abstract_inverted_index.automobiles. | 17 |
| abstract_inverted_index.conventional | 15 |
| abstract_inverted_index.difficulties | 22 |
| abstract_inverted_index.transactions | 176 |
| abstract_inverted_index.communication | 140 |
| abstract_inverted_index.effectiveness | 151 |
| abstract_inverted_index.incorporating | 24 |
| abstract_inverted_index.requirements. | 159 |
| abstract_inverted_index.communications | 125 |
| abstract_inverted_index.implementation | 147 |
| abstract_inverted_index.models’ | 110 |
| abstract_inverted_index.blockchain-based | 40 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.8399999737739563 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.98652527 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |