HYBRID DEEP LEARNING ALGORITHM FOR FRACTAL HUMAN ACTIVITY RECOGNITION USING SMART IOT-EDGE-CLOUD CONTINUUM Article Swipe
Randa Allafi
,
Fadwa Alrowais
,
Mohammed Aljebreen
,
Noha Negm
,
Ahmed S. Salama
,
Radwa Marzouk
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1142/s0218348x25400262
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1142/s0218348x25400262
Human activity recognition (HAR) employs a broad range of sensors that generate massive volumes of data. Traditional server-based and cloud computing methods require all sensor data to be sent to servers or clouds for processing, which leads to high latency and bandwidth costs. The long-term data transfer between servers and sensors maximizes the cost of latency and bandwidth. Real-time processing is, nevertheless, highly required for human action identification. By bringing processing and quick data storage to the sensors instead of depending on a central database, edge computing is rapidly emerging as a solution to this issue. Artificial intelligence is responsible for most HAR, which demands a lot of processing power and calculation. Artificial intelligence (AI) needs more computation which is not allowed by edge computing. So Edge intelligence, which allows AI to operate at the network edge for actual-time applications, has been made possible by the advent of binarized neural networks. To provide less latency and less memory for human activity identification at the edge network, we construct a hybrid deep learning-based binarized neural network (HDL-Binary Dilated DenseNet) in this research. Fractal HAR optimization algorithms could be applied to these algorithms. For example, fractal-HAR optimization techniques might be used to provide less latency and less memory human activity identification at the edge network. Using three sensors-based human activity detection datasets such as Radar HAR dataset, UCI HAR dataset and UniMib-SHAR dataset, we implemented the Hybrid Binary Dilated Dense Net. It is then assessed using four criteria. Comparatively, the Hybrid Binary Dilated DenseNet performs better with 99.6% radar HAR dataset which is highest than other models like CNN-BiLSTM and GoogLeNet.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1142/s0218348x25400262
- OA Status
- hybrid
- References
- 29
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405823008
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4405823008Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1142/s0218348x25400262Digital Object Identifier
- Title
-
HYBRID DEEP LEARNING ALGORITHM FOR FRACTAL HUMAN ACTIVITY RECOGNITION USING SMART IOT-EDGE-CLOUD CONTINUUMWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-12-26Full publication date if available
- Authors
-
Randa Allafi, Fadwa Alrowais, Mohammed Aljebreen, Noha Negm, Ahmed S. Salama, Radwa MarzoukList of authors in order
- Landing page
-
https://doi.org/10.1142/s0218348x25400262Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1142/s0218348x25400262Direct OA link when available
- Concepts
-
Fractal, Cloud computing, Internet of Things, Enhanced Data Rates for GSM Evolution, Computer science, Artificial intelligence, Deep learning, Statistical physics, Mathematics, Physics, Mathematical analysis, Computer security, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
29Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4405823008 |
|---|---|
| doi | https://doi.org/10.1142/s0218348x25400262 |
| ids.doi | https://doi.org/10.1142/s0218348x25400262 |
| ids.openalex | https://openalex.org/W4405823008 |
| fwci | 0.0 |
| type | article |
| title | HYBRID DEEP LEARNING ALGORITHM FOR FRACTAL HUMAN ACTIVITY RECOGNITION USING SMART IOT-EDGE-CLOUD CONTINUUM |
| biblio.issue | 02 |
| biblio.volume | 33 |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T13731 |
| topics[0].field.id | https://openalex.org/fields/33 |
| topics[0].field.display_name | Social Sciences |
| topics[0].score | 0.6093000173568726 |
| topics[0].domain.id | https://openalex.org/domains/2 |
| topics[0].domain.display_name | Social Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/3322 |
| topics[0].subfield.display_name | Urban Studies |
| topics[0].display_name | Advanced Computing and Algorithms |
| topics[1].id | https://openalex.org/T13647 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.5916000008583069 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1710 |
| topics[1].subfield.display_name | Information Systems |
| topics[1].display_name | AI and Big Data Applications |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C40636538 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7920888066291809 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q81392 |
| concepts[0].display_name | Fractal |
| concepts[1].id | https://openalex.org/C79974875 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6765384674072266 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q483639 |
| concepts[1].display_name | Cloud computing |
| concepts[2].id | https://openalex.org/C81860439 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6337937116622925 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q251212 |
| concepts[2].display_name | Internet of Things |
| concepts[3].id | https://openalex.org/C162307627 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5262324213981628 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q204833 |
| concepts[3].display_name | Enhanced Data Rates for GSM Evolution |
| concepts[4].id | https://openalex.org/C41008148 |
| concepts[4].level | 0 |
| concepts[4].score | 0.48215004801750183 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[4].display_name | Computer science |
| concepts[5].id | https://openalex.org/C154945302 |
| concepts[5].level | 1 |
| concepts[5].score | 0.4691820442676544 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[5].display_name | Artificial intelligence |
| concepts[6].id | https://openalex.org/C108583219 |
| concepts[6].level | 2 |
| concepts[6].score | 0.44677969813346863 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[6].display_name | Deep learning |
| concepts[7].id | https://openalex.org/C121864883 |
| concepts[7].level | 1 |
| concepts[7].score | 0.36036771535873413 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q677916 |
| concepts[7].display_name | Statistical physics |
| concepts[8].id | https://openalex.org/C33923547 |
| concepts[8].level | 0 |
| concepts[8].score | 0.27062779664993286 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[8].display_name | Mathematics |
| concepts[9].id | https://openalex.org/C121332964 |
| concepts[9].level | 0 |
| concepts[9].score | 0.15255853533744812 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[9].display_name | Physics |
| concepts[10].id | https://openalex.org/C134306372 |
| concepts[10].level | 1 |
| concepts[10].score | 0.12558135390281677 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q7754 |
| concepts[10].display_name | Mathematical analysis |
| concepts[11].id | https://openalex.org/C38652104 |
| concepts[11].level | 1 |
| concepts[11].score | 0.10183402895927429 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q3510521 |
| concepts[11].display_name | Computer security |
| concepts[12].id | https://openalex.org/C111919701 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q9135 |
| concepts[12].display_name | Operating system |
| keywords[0].id | https://openalex.org/keywords/fractal |
| keywords[0].score | 0.7920888066291809 |
| keywords[0].display_name | Fractal |
| keywords[1].id | https://openalex.org/keywords/cloud-computing |
| keywords[1].score | 0.6765384674072266 |
| keywords[1].display_name | Cloud computing |
| keywords[2].id | https://openalex.org/keywords/internet-of-things |
| keywords[2].score | 0.6337937116622925 |
| keywords[2].display_name | Internet of Things |
| keywords[3].id | https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution |
| keywords[3].score | 0.5262324213981628 |
| keywords[3].display_name | Enhanced Data Rates for GSM Evolution |
| keywords[4].id | https://openalex.org/keywords/computer-science |
| keywords[4].score | 0.48215004801750183 |
| keywords[4].display_name | Computer science |
| keywords[5].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[5].score | 0.4691820442676544 |
| keywords[5].display_name | Artificial intelligence |
| keywords[6].id | https://openalex.org/keywords/deep-learning |
| keywords[6].score | 0.44677969813346863 |
| keywords[6].display_name | Deep learning |
| keywords[7].id | https://openalex.org/keywords/statistical-physics |
| keywords[7].score | 0.36036771535873413 |
| keywords[7].display_name | Statistical physics |
| keywords[8].id | https://openalex.org/keywords/mathematics |
| keywords[8].score | 0.27062779664993286 |
| keywords[8].display_name | Mathematics |
| keywords[9].id | https://openalex.org/keywords/physics |
| keywords[9].score | 0.15255853533744812 |
| keywords[9].display_name | Physics |
| keywords[10].id | https://openalex.org/keywords/mathematical-analysis |
| keywords[10].score | 0.12558135390281677 |
| keywords[10].display_name | Mathematical analysis |
| keywords[11].id | https://openalex.org/keywords/computer-security |
| keywords[11].score | 0.10183402895927429 |
| keywords[11].display_name | Computer security |
| language | en |
| locations[0].id | doi:10.1142/s0218348x25400262 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S204703277 |
| locations[0].source.issn | 0218-348X, 1793-6543 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 0218-348X |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Fractals |
| locations[0].source.host_organization | https://openalex.org/P4310319815 |
| locations[0].source.host_organization_name | World Scientific |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319815 |
| locations[0].source.host_organization_lineage_names | World Scientific |
| 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 | Fractals |
| locations[0].landing_page_url | https://doi.org/10.1142/s0218348x25400262 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5021273283 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-9858-5160 |
| authorships[0].author.display_name | Randa Allafi |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Randa Allafi |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5063985986 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-8447-198X |
| authorships[1].author.display_name | Fadwa Alrowais |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Fadwa Alrowais |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5025820678 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-6295-733X |
| authorships[2].author.display_name | Mohammed Aljebreen |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Mohammed Aljebreen |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5066330330 |
| authorships[3].author.orcid | https://orcid.org/0009-0005-5911-1033 |
| authorships[3].author.display_name | Noha Negm |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Noha Negm |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5101966780 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-5362-0113 |
| authorships[4].author.display_name | Ahmed S. Salama |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Ahmed S. Salama |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5062547644 |
| authorships[5].author.orcid | |
| authorships[5].author.display_name | Radwa Marzouk |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Radwa Marzouk |
| authorships[5].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/10.1142/s0218348x25400262 |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | HYBRID DEEP LEARNING ALGORITHM FOR FRACTAL HUMAN ACTIVITY RECOGNITION USING SMART IOT-EDGE-CLOUD CONTINUUM |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T13731 |
| primary_topic.field.id | https://openalex.org/fields/33 |
| primary_topic.field.display_name | Social Sciences |
| primary_topic.score | 0.6093000173568726 |
| primary_topic.domain.id | https://openalex.org/domains/2 |
| primary_topic.domain.display_name | Social Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/3322 |
| primary_topic.subfield.display_name | Urban Studies |
| primary_topic.display_name | Advanced Computing and Algorithms |
| related_works | https://openalex.org/W4244478748, https://openalex.org/W4245926026, https://openalex.org/W3150465815, https://openalex.org/W4223488648, https://openalex.org/W2134969820, https://openalex.org/W2251605416, https://openalex.org/W1997222214, https://openalex.org/W2560439919, https://openalex.org/W4389340727, https://openalex.org/W2802581102 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1142/s0218348x25400262 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S204703277 |
| best_oa_location.source.issn | 0218-348X, 1793-6543 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 0218-348X |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Fractals |
| best_oa_location.source.host_organization | https://openalex.org/P4310319815 |
| best_oa_location.source.host_organization_name | World Scientific |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319815 |
| best_oa_location.source.host_organization_lineage_names | World Scientific |
| 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 | Fractals |
| best_oa_location.landing_page_url | https://doi.org/10.1142/s0218348x25400262 |
| primary_location.id | doi:10.1142/s0218348x25400262 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S204703277 |
| primary_location.source.issn | 0218-348X, 1793-6543 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 0218-348X |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Fractals |
| primary_location.source.host_organization | https://openalex.org/P4310319815 |
| primary_location.source.host_organization_name | World Scientific |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319815 |
| primary_location.source.host_organization_lineage_names | World Scientific |
| 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 | Fractals |
| primary_location.landing_page_url | https://doi.org/10.1142/s0218348x25400262 |
| publication_date | 2024-12-26 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W4390738727, https://openalex.org/W4309154445, https://openalex.org/W4388192025, https://openalex.org/W4390660035, https://openalex.org/W4385647779, https://openalex.org/W4387010854, https://openalex.org/W4392001995, https://openalex.org/W4392903375, https://openalex.org/W4224211730, https://openalex.org/W4386634591, https://openalex.org/W4389331912, https://openalex.org/W4323364752, https://openalex.org/W4214514125, https://openalex.org/W4206045754, https://openalex.org/W3188870776, https://openalex.org/W4205964587, https://openalex.org/W4200317051, https://openalex.org/W3150937970, https://openalex.org/W4225714398, https://openalex.org/W4376877198, https://openalex.org/W4286906772, https://openalex.org/W4221017297, https://openalex.org/W4392309194, https://openalex.org/W4360998004, https://openalex.org/W4312383953, https://openalex.org/W4319596424, https://openalex.org/W4390843541, https://openalex.org/W4366284225, https://openalex.org/W4383957140 |
| referenced_works_count | 29 |
| abstract_inverted_index.a | 5, 82, 91, 105, 168 |
| abstract_inverted_index.AI | 130 |
| abstract_inverted_index.By | 68 |
| abstract_inverted_index.It | 239 |
| abstract_inverted_index.So | 125 |
| abstract_inverted_index.To | 151 |
| abstract_inverted_index.as | 90, 221 |
| abstract_inverted_index.at | 133, 162, 209 |
| abstract_inverted_index.be | 27, 186, 197 |
| abstract_inverted_index.by | 122, 144 |
| abstract_inverted_index.in | 178 |
| abstract_inverted_index.is | 87, 98, 119, 240, 260 |
| abstract_inverted_index.of | 8, 14, 54, 79, 107, 147 |
| abstract_inverted_index.on | 81 |
| abstract_inverted_index.or | 31 |
| abstract_inverted_index.to | 26, 29, 37, 75, 93, 131, 188, 199 |
| abstract_inverted_index.we | 166, 231 |
| abstract_inverted_index.For | 191 |
| abstract_inverted_index.HAR | 182, 223, 226, 257 |
| abstract_inverted_index.The | 43 |
| abstract_inverted_index.UCI | 225 |
| abstract_inverted_index.all | 23 |
| abstract_inverted_index.and | 18, 40, 49, 56, 71, 110, 155, 203, 228, 267 |
| abstract_inverted_index.for | 33, 64, 100, 137, 158 |
| abstract_inverted_index.has | 140 |
| abstract_inverted_index.is, | 60 |
| abstract_inverted_index.lot | 106 |
| abstract_inverted_index.not | 120 |
| abstract_inverted_index.the | 52, 76, 134, 145, 163, 210, 233, 247 |
| abstract_inverted_index.(AI) | 114 |
| abstract_inverted_index.Edge | 126 |
| abstract_inverted_index.HAR, | 102 |
| abstract_inverted_index.Net. | 238 |
| abstract_inverted_index.been | 141 |
| abstract_inverted_index.cost | 53 |
| abstract_inverted_index.data | 25, 45, 73 |
| abstract_inverted_index.deep | 170 |
| abstract_inverted_index.edge | 85, 123, 136, 164, 211 |
| abstract_inverted_index.four | 244 |
| abstract_inverted_index.high | 38 |
| abstract_inverted_index.less | 153, 156, 201, 204 |
| abstract_inverted_index.like | 265 |
| abstract_inverted_index.made | 142 |
| abstract_inverted_index.more | 116 |
| abstract_inverted_index.most | 101 |
| abstract_inverted_index.sent | 28 |
| abstract_inverted_index.such | 220 |
| abstract_inverted_index.than | 262 |
| abstract_inverted_index.that | 10 |
| abstract_inverted_index.then | 241 |
| abstract_inverted_index.this | 94, 179 |
| abstract_inverted_index.used | 198 |
| abstract_inverted_index.with | 254 |
| abstract_inverted_index.(HAR) | 3 |
| abstract_inverted_index.99.6% | 255 |
| abstract_inverted_index.Dense | 237 |
| abstract_inverted_index.Human | 0 |
| abstract_inverted_index.Radar | 222 |
| abstract_inverted_index.Using | 213 |
| abstract_inverted_index.broad | 6 |
| abstract_inverted_index.cloud | 19 |
| abstract_inverted_index.could | 185 |
| abstract_inverted_index.data. | 15 |
| abstract_inverted_index.human | 65, 159, 206, 216 |
| abstract_inverted_index.leads | 36 |
| abstract_inverted_index.might | 196 |
| abstract_inverted_index.needs | 115 |
| abstract_inverted_index.other | 263 |
| abstract_inverted_index.power | 109 |
| abstract_inverted_index.quick | 72 |
| abstract_inverted_index.radar | 256 |
| abstract_inverted_index.range | 7 |
| abstract_inverted_index.these | 189 |
| abstract_inverted_index.three | 214 |
| abstract_inverted_index.using | 243 |
| abstract_inverted_index.which | 35, 103, 118, 128, 259 |
| abstract_inverted_index.Binary | 235, 249 |
| abstract_inverted_index.Hybrid | 234, 248 |
| abstract_inverted_index.action | 66 |
| abstract_inverted_index.advent | 146 |
| abstract_inverted_index.allows | 129 |
| abstract_inverted_index.better | 253 |
| abstract_inverted_index.clouds | 32 |
| abstract_inverted_index.costs. | 42 |
| abstract_inverted_index.highly | 62 |
| abstract_inverted_index.hybrid | 169 |
| abstract_inverted_index.issue. | 95 |
| abstract_inverted_index.memory | 157, 205 |
| abstract_inverted_index.models | 264 |
| abstract_inverted_index.neural | 149, 173 |
| abstract_inverted_index.sensor | 24 |
| abstract_inverted_index.Dilated | 176, 236, 250 |
| abstract_inverted_index.Fractal | 181 |
| abstract_inverted_index.allowed | 121 |
| abstract_inverted_index.applied | 187 |
| abstract_inverted_index.between | 47 |
| abstract_inverted_index.central | 83 |
| abstract_inverted_index.dataset | 227, 258 |
| abstract_inverted_index.demands | 104 |
| abstract_inverted_index.employs | 4 |
| abstract_inverted_index.highest | 261 |
| abstract_inverted_index.instead | 78 |
| abstract_inverted_index.latency | 39, 55, 154, 202 |
| abstract_inverted_index.massive | 12 |
| abstract_inverted_index.methods | 21 |
| abstract_inverted_index.network | 135, 174 |
| abstract_inverted_index.operate | 132 |
| abstract_inverted_index.provide | 152, 200 |
| abstract_inverted_index.rapidly | 88 |
| abstract_inverted_index.require | 22 |
| abstract_inverted_index.sensors | 9, 50, 77 |
| abstract_inverted_index.servers | 30, 48 |
| abstract_inverted_index.storage | 74 |
| abstract_inverted_index.volumes | 13 |
| abstract_inverted_index.DenseNet | 251 |
| abstract_inverted_index.activity | 1, 160, 207, 217 |
| abstract_inverted_index.assessed | 242 |
| abstract_inverted_index.bringing | 69 |
| abstract_inverted_index.dataset, | 224, 230 |
| abstract_inverted_index.datasets | 219 |
| abstract_inverted_index.emerging | 89 |
| abstract_inverted_index.example, | 192 |
| abstract_inverted_index.generate | 11 |
| abstract_inverted_index.network, | 165 |
| abstract_inverted_index.network. | 212 |
| abstract_inverted_index.performs | 252 |
| abstract_inverted_index.possible | 143 |
| abstract_inverted_index.required | 63 |
| abstract_inverted_index.solution | 92 |
| abstract_inverted_index.transfer | 46 |
| abstract_inverted_index.DenseNet) | 177 |
| abstract_inverted_index.Real-time | 58 |
| abstract_inverted_index.bandwidth | 41 |
| abstract_inverted_index.binarized | 148, 172 |
| abstract_inverted_index.computing | 20, 86 |
| abstract_inverted_index.construct | 167 |
| abstract_inverted_index.criteria. | 245 |
| abstract_inverted_index.database, | 84 |
| abstract_inverted_index.depending | 80 |
| abstract_inverted_index.detection | 218 |
| abstract_inverted_index.long-term | 44 |
| abstract_inverted_index.maximizes | 51 |
| abstract_inverted_index.networks. | 150 |
| abstract_inverted_index.research. | 180 |
| abstract_inverted_index.Artificial | 96, 112 |
| abstract_inverted_index.CNN-BiLSTM | 266 |
| abstract_inverted_index.GoogLeNet. | 268 |
| abstract_inverted_index.algorithms | 184 |
| abstract_inverted_index.bandwidth. | 57 |
| abstract_inverted_index.computing. | 124 |
| abstract_inverted_index.processing | 59, 70, 108 |
| abstract_inverted_index.techniques | 195 |
| abstract_inverted_index.(HDL-Binary | 175 |
| abstract_inverted_index.Traditional | 16 |
| abstract_inverted_index.UniMib-SHAR | 229 |
| abstract_inverted_index.actual-time | 138 |
| abstract_inverted_index.algorithms. | 190 |
| abstract_inverted_index.computation | 117 |
| abstract_inverted_index.fractal-HAR | 193 |
| abstract_inverted_index.implemented | 232 |
| abstract_inverted_index.processing, | 34 |
| abstract_inverted_index.recognition | 2 |
| abstract_inverted_index.responsible | 99 |
| abstract_inverted_index.calculation. | 111 |
| abstract_inverted_index.intelligence | 97, 113 |
| abstract_inverted_index.optimization | 183, 194 |
| abstract_inverted_index.server-based | 17 |
| abstract_inverted_index.applications, | 139 |
| abstract_inverted_index.intelligence, | 127 |
| abstract_inverted_index.nevertheless, | 61 |
| abstract_inverted_index.sensors-based | 215 |
| abstract_inverted_index.Comparatively, | 246 |
| abstract_inverted_index.identification | 161, 208 |
| abstract_inverted_index.learning-based | 171 |
| abstract_inverted_index.identification. | 67 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.46700643 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |