AN EFFICIENT FRACTAL CARDIO DISEASES ANALYSIS USING OPTIMIZED DEEP LEARNING MODEL IN CLOUD OF THING CONTINUUM ARCHITECTURE Article Swipe
Manal Abdullah Alohali
,
Munya A. Arasi
,
Saad Alahmari
,
Asma Alshuhail
,
Wafa Almukadi
,
Bandar M. Alghamdi
,
Fouad Shoie Alallah
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1142/s0218348x25400237
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1142/s0218348x25400237
Exercise has long been known to improve cardiovascular health, energy metabolism, and well-being. However, myocardial cell responses to exercise are complex and multifaceted due to their molecular pathways. To understand cardiac physiology and path physiology, one must understand these pathways, including energy autophagy. In recent years, deep learning techniques, IoT devices, and cloud computing infrastructure have enabled real-time, large-scale biological data analysis. The objective of this work is to extract and analyze autophagy properties in exercise-induced cardiac cells in a cloud-IoT context using deep learning, more especially an autoencoder. The Shanghai University of Sport Ethics Committee for Science Research gave its approval for the data collection, which involved 150 male Sprague–Dawley (SD) rats that were eight weeks old and in good health. The [Formula: see text]-score normalization method was used to standardize the data. Fractal optimization methods could be applied to these algorithms. For example, fractal-inspired optimization techniques might be used to analyze deep learning with Autoencoder, the autography energy of exercise myocardial cells within a cloud-IoT. To capture the intricate myocardial energy autophagy during exercise, we introduced the DMO-GCNN-Autoencoder, a Dwarf Mongoose Optimized Graph Convolutional Neural Network. The results showed that the proposed network’s performance matches that of the existing methods.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1142/s0218348x25400237
- OA Status
- hybrid
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401920708
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4401920708Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1142/s0218348x25400237Digital Object Identifier
- Title
-
AN EFFICIENT FRACTAL CARDIO DISEASES ANALYSIS USING OPTIMIZED DEEP LEARNING MODEL IN CLOUD OF THING CONTINUUM ARCHITECTUREWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-08-27Full publication date if available
- Authors
-
Manal Abdullah Alohali, Munya A. Arasi, Saad Alahmari, Asma Alshuhail, Wafa Almukadi, Bandar M. Alghamdi, Fouad Shoie AlallahList of authors in order
- Landing page
-
https://doi.org/10.1142/s0218348x25400237Publisher 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/s0218348x25400237Direct OA link when available
- Concepts
-
Deep learning, Autoencoder, Cloud computing, Artificial intelligence, Computer science, Context (archaeology), Convolutional neural network, Machine learning, Biology, Operating system, PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
31Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4401920708 |
|---|---|
| doi | https://doi.org/10.1142/s0218348x25400237 |
| ids.doi | https://doi.org/10.1142/s0218348x25400237 |
| ids.openalex | https://openalex.org/W4401920708 |
| fwci | 0.0 |
| type | article |
| title | AN EFFICIENT FRACTAL CARDIO DISEASES ANALYSIS USING OPTIMIZED DEEP LEARNING MODEL IN CLOUD OF THING CONTINUUM ARCHITECTURE |
| awards[0].id | https://openalex.org/G6868481806 |
| awards[0].funder_id | https://openalex.org/F4320324433 |
| awards[0].display_name | |
| awards[0].funder_award_id | RGP2/13/45 |
| awards[0].funder_display_name | King Khalid University |
| biblio.issue | 09n10 |
| biblio.volume | 32 |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10745 |
| topics[0].field.id | https://openalex.org/fields/27 |
| topics[0].field.display_name | Medicine |
| topics[0].score | 0.9661999940872192 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2705 |
| topics[0].subfield.display_name | Cardiology and Cardiovascular Medicine |
| topics[0].display_name | Heart Rate Variability and Autonomic Control |
| topics[1].id | https://openalex.org/T13522 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.9315999746322632 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2705 |
| topics[1].subfield.display_name | Cardiology and Cardiovascular Medicine |
| topics[1].display_name | Cardiovascular Health and Risk Factors |
| topics[2].id | https://openalex.org/T10924 |
| topics[2].field.id | https://openalex.org/fields/27 |
| topics[2].field.display_name | Medicine |
| topics[2].score | 0.9128000140190125 |
| topics[2].domain.id | https://openalex.org/domains/4 |
| topics[2].domain.display_name | Health Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2705 |
| topics[2].subfield.display_name | Cardiology and Cardiovascular Medicine |
| topics[2].display_name | Cardiovascular Health and Disease Prevention |
| funders[0].id | https://openalex.org/F4320324433 |
| funders[0].ror | https://ror.org/052kwzs30 |
| funders[0].display_name | King Khalid University |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C108583219 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8278084993362427 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[0].display_name | Deep learning |
| concepts[1].id | https://openalex.org/C101738243 |
| concepts[1].level | 3 |
| concepts[1].score | 0.8036839365959167 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q786435 |
| concepts[1].display_name | Autoencoder |
| concepts[2].id | https://openalex.org/C79974875 |
| concepts[2].level | 2 |
| concepts[2].score | 0.663844108581543 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q483639 |
| concepts[2].display_name | Cloud computing |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.6268224716186523 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C41008148 |
| concepts[4].level | 0 |
| concepts[4].score | 0.6048660278320312 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[4].display_name | Computer science |
| concepts[5].id | https://openalex.org/C2779343474 |
| concepts[5].level | 2 |
| concepts[5].score | 0.47950178384780884 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q3109175 |
| concepts[5].display_name | Context (archaeology) |
| concepts[6].id | https://openalex.org/C81363708 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4516460597515106 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[6].display_name | Convolutional neural network |
| concepts[7].id | https://openalex.org/C119857082 |
| concepts[7].level | 1 |
| concepts[7].score | 0.3837928771972656 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[7].display_name | Machine learning |
| concepts[8].id | https://openalex.org/C86803240 |
| concepts[8].level | 0 |
| concepts[8].score | 0.1518368422985077 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[8].display_name | Biology |
| concepts[9].id | https://openalex.org/C111919701 |
| concepts[9].level | 1 |
| concepts[9].score | 0.0 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q9135 |
| concepts[9].display_name | Operating system |
| concepts[10].id | https://openalex.org/C151730666 |
| concepts[10].level | 1 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q7205 |
| concepts[10].display_name | Paleontology |
| keywords[0].id | https://openalex.org/keywords/deep-learning |
| keywords[0].score | 0.8278084993362427 |
| keywords[0].display_name | Deep learning |
| keywords[1].id | https://openalex.org/keywords/autoencoder |
| keywords[1].score | 0.8036839365959167 |
| keywords[1].display_name | Autoencoder |
| keywords[2].id | https://openalex.org/keywords/cloud-computing |
| keywords[2].score | 0.663844108581543 |
| keywords[2].display_name | Cloud computing |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.6268224716186523 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/computer-science |
| keywords[4].score | 0.6048660278320312 |
| keywords[4].display_name | Computer science |
| keywords[5].id | https://openalex.org/keywords/context |
| keywords[5].score | 0.47950178384780884 |
| keywords[5].display_name | Context (archaeology) |
| keywords[6].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[6].score | 0.4516460597515106 |
| keywords[6].display_name | Convolutional neural network |
| keywords[7].id | https://openalex.org/keywords/machine-learning |
| keywords[7].score | 0.3837928771972656 |
| keywords[7].display_name | Machine learning |
| keywords[8].id | https://openalex.org/keywords/biology |
| keywords[8].score | 0.1518368422985077 |
| keywords[8].display_name | Biology |
| language | en |
| locations[0].id | doi:10.1142/s0218348x25400237 |
| 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/s0218348x25400237 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5018019909 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-1975-5345 |
| authorships[0].author.display_name | Manal Abdullah Alohali |
| authorships[0].countries | SA |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I106778892 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh 11671, Saudi Arabia |
| authorships[0].institutions[0].id | https://openalex.org/I106778892 |
| authorships[0].institutions[0].ror | https://ror.org/05b0cyh02 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I106778892 |
| authorships[0].institutions[0].country_code | SA |
| authorships[0].institutions[0].display_name | Princess Nourah bint Abdulrahman University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | MANAL ABDULLAH ALOHALI |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh 11671, Saudi Arabia |
| authorships[1].author.id | https://openalex.org/A5115721781 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-4049-5090 |
| authorships[1].author.display_name | Munya A. Arasi |
| authorships[1].countries | SA |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I82952536 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Computer Science, Applied College at Rijal Almaa, King Khalid University, Saudi Arabia |
| authorships[1].institutions[0].id | https://openalex.org/I82952536 |
| authorships[1].institutions[0].ror | https://ror.org/052kwzs30 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I82952536 |
| authorships[1].institutions[0].country_code | SA |
| authorships[1].institutions[0].display_name | King Khalid University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | MUNYA A. ARASI |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Computer Science, Applied College at Rijal Almaa, King Khalid University, Saudi Arabia |
| authorships[2].author.id | https://openalex.org/A5022718239 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-3757-7300 |
| authorships[2].author.display_name | Saad Alahmari |
| authorships[2].countries | SA |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I118590987 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Computer Science, Applied College Northern Border University, Arar 91431, Saudi Arabia |
| authorships[2].institutions[0].id | https://openalex.org/I118590987 |
| authorships[2].institutions[0].ror | https://ror.org/03j9tzj20 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I118590987 |
| authorships[2].institutions[0].country_code | SA |
| authorships[2].institutions[0].display_name | Northern Border University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | SAAD ALAHMARI |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Computer Science, Applied College Northern Border University, Arar 91431, Saudi Arabia |
| authorships[3].author.id | https://openalex.org/A5056520942 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-4110-9657 |
| authorships[3].author.display_name | Asma Alshuhail |
| authorships[3].countries | SA |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I4626487 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Information Systems, College of Computer Sciences & Information Technology, King Faisal University, Saudi Arabia |
| authorships[3].institutions[0].id | https://openalex.org/I4626487 |
| authorships[3].institutions[0].ror | https://ror.org/00dn43547 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I4626487 |
| authorships[3].institutions[0].country_code | SA |
| authorships[3].institutions[0].display_name | King Faisal University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | ASMA ALSHUHAIL |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Information Systems, College of Computer Sciences & Information Technology, King Faisal University, Saudi Arabia |
| authorships[4].author.id | https://openalex.org/A5065270510 |
| authorships[4].author.orcid | https://orcid.org/0000-0003-3980-0710 |
| authorships[4].author.display_name | Wafa Almukadi |
| authorships[4].countries | SA |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I4210099699 |
| authorships[4].affiliations[0].raw_affiliation_string | Department of Software Engineering, College of Engineering and Computer Science, University of Jeddah, Saudi Arabia |
| authorships[4].institutions[0].id | https://openalex.org/I4210099699 |
| authorships[4].institutions[0].ror | https://ror.org/015ya8798 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I4210099699 |
| authorships[4].institutions[0].country_code | SA |
| authorships[4].institutions[0].display_name | University of Jeddah |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | WAFA SULAIMAN ALMUKADI |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Department of Software Engineering, College of Engineering and Computer Science, University of Jeddah, Saudi Arabia |
| authorships[5].author.id | https://openalex.org/A5114910421 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-0873-7985 |
| authorships[5].author.display_name | Bandar M. Alghamdi |
| authorships[5].countries | SA |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I185163786 |
| authorships[5].affiliations[0].raw_affiliation_string | Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia |
| authorships[5].institutions[0].id | https://openalex.org/I185163786 |
| authorships[5].institutions[0].ror | https://ror.org/02ma4wv74 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I185163786 |
| authorships[5].institutions[0].country_code | SA |
| authorships[5].institutions[0].display_name | King Abdulaziz University |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | BANDAR M. ALGHAMDI |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia |
| authorships[6].author.id | https://openalex.org/A5024791801 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-1569-8558 |
| authorships[6].author.display_name | Fouad Shoie Alallah |
| authorships[6].countries | SA |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I185163786 |
| authorships[6].affiliations[0].raw_affiliation_string | Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia |
| authorships[6].institutions[0].id | https://openalex.org/I185163786 |
| authorships[6].institutions[0].ror | https://ror.org/02ma4wv74 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I185163786 |
| authorships[6].institutions[0].country_code | SA |
| authorships[6].institutions[0].display_name | King Abdulaziz University |
| authorships[6].author_position | last |
| authorships[6].raw_author_name | FOUAD SHOIE ALALLAH |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia |
| 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/s0218348x25400237 |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | AN EFFICIENT FRACTAL CARDIO DISEASES ANALYSIS USING OPTIMIZED DEEP LEARNING MODEL IN CLOUD OF THING CONTINUUM ARCHITECTURE |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10745 |
| primary_topic.field.id | https://openalex.org/fields/27 |
| primary_topic.field.display_name | Medicine |
| primary_topic.score | 0.9661999940872192 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2705 |
| primary_topic.subfield.display_name | Cardiology and Cardiovascular Medicine |
| primary_topic.display_name | Heart Rate Variability and Autonomic Control |
| related_works | https://openalex.org/W3013693939, https://openalex.org/W2669956259, https://openalex.org/W4249005693, https://openalex.org/W4392946183, https://openalex.org/W4405887298, https://openalex.org/W4226493464, https://openalex.org/W3133861977, https://openalex.org/W2951211570, https://openalex.org/W3103566983, https://openalex.org/W4380075502 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1142/s0218348x25400237 |
| 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/s0218348x25400237 |
| primary_location.id | doi:10.1142/s0218348x25400237 |
| 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/s0218348x25400237 |
| publication_date | 2024-08-27 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W3011824135, https://openalex.org/W3110042863, https://openalex.org/W3203572143, https://openalex.org/W4313477864, https://openalex.org/W3126896156, https://openalex.org/W3083676618, https://openalex.org/W4280609860, https://openalex.org/W3171203325, https://openalex.org/W3000813720, https://openalex.org/W3211685926, https://openalex.org/W3158244287, https://openalex.org/W3165762063, https://openalex.org/W3010307996, https://openalex.org/W3106327490, https://openalex.org/W4386762822, https://openalex.org/W4386187583, https://openalex.org/W4362456036, https://openalex.org/W4220980551, https://openalex.org/W4220778579, https://openalex.org/W3012690105, https://openalex.org/W4323027726, https://openalex.org/W3140416476, https://openalex.org/W3197451482, https://openalex.org/W3037697000, https://openalex.org/W2753813400, https://openalex.org/W2998223455, https://openalex.org/W3044699007, https://openalex.org/W4399425273, https://openalex.org/W4378385237, https://openalex.org/W4304614134, https://openalex.org/W4319297891 |
| referenced_works_count | 31 |
| abstract_inverted_index.a | 79, 165, 180 |
| abstract_inverted_index.In | 43 |
| abstract_inverted_index.To | 28, 167 |
| abstract_inverted_index.an | 87 |
| abstract_inverted_index.be | 138, 149 |
| abstract_inverted_index.in | 74, 78, 119 |
| abstract_inverted_index.is | 67 |
| abstract_inverted_index.of | 64, 92, 160, 198 |
| abstract_inverted_index.to | 5, 17, 24, 68, 130, 140, 151 |
| abstract_inverted_index.we | 176 |
| abstract_inverted_index.150 | 108 |
| abstract_inverted_index.For | 143 |
| abstract_inverted_index.IoT | 49 |
| abstract_inverted_index.The | 62, 89, 122, 188 |
| abstract_inverted_index.and | 11, 21, 32, 51, 70, 118 |
| abstract_inverted_index.are | 19 |
| abstract_inverted_index.due | 23 |
| abstract_inverted_index.for | 96, 102 |
| abstract_inverted_index.has | 1 |
| abstract_inverted_index.its | 100 |
| abstract_inverted_index.old | 117 |
| abstract_inverted_index.one | 35 |
| abstract_inverted_index.see | 124 |
| abstract_inverted_index.the | 103, 132, 157, 169, 178, 192, 199 |
| abstract_inverted_index.was | 128 |
| abstract_inverted_index.(SD) | 111 |
| abstract_inverted_index.been | 3 |
| abstract_inverted_index.cell | 15 |
| abstract_inverted_index.data | 60, 104 |
| abstract_inverted_index.deep | 46, 83, 153 |
| abstract_inverted_index.gave | 99 |
| abstract_inverted_index.good | 120 |
| abstract_inverted_index.have | 55 |
| abstract_inverted_index.long | 2 |
| abstract_inverted_index.male | 109 |
| abstract_inverted_index.more | 85 |
| abstract_inverted_index.must | 36 |
| abstract_inverted_index.path | 33 |
| abstract_inverted_index.rats | 112 |
| abstract_inverted_index.that | 113, 191, 197 |
| abstract_inverted_index.this | 65 |
| abstract_inverted_index.used | 129, 150 |
| abstract_inverted_index.were | 114 |
| abstract_inverted_index.with | 155 |
| abstract_inverted_index.work | 66 |
| abstract_inverted_index.Dwarf | 181 |
| abstract_inverted_index.Graph | 184 |
| abstract_inverted_index.Sport | 93 |
| abstract_inverted_index.cells | 77, 163 |
| abstract_inverted_index.cloud | 52 |
| abstract_inverted_index.could | 137 |
| abstract_inverted_index.data. | 133 |
| abstract_inverted_index.eight | 115 |
| abstract_inverted_index.known | 4 |
| abstract_inverted_index.might | 148 |
| abstract_inverted_index.their | 25 |
| abstract_inverted_index.these | 38, 141 |
| abstract_inverted_index.using | 82 |
| abstract_inverted_index.weeks | 116 |
| abstract_inverted_index.which | 106 |
| abstract_inverted_index.Ethics | 94 |
| abstract_inverted_index.Neural | 186 |
| abstract_inverted_index.during | 174 |
| abstract_inverted_index.energy | 9, 41, 159, 172 |
| abstract_inverted_index.method | 127 |
| abstract_inverted_index.recent | 44 |
| abstract_inverted_index.showed | 190 |
| abstract_inverted_index.within | 164 |
| abstract_inverted_index.years, | 45 |
| abstract_inverted_index.Fractal | 134 |
| abstract_inverted_index.Science | 97 |
| abstract_inverted_index.analyze | 71, 152 |
| abstract_inverted_index.applied | 139 |
| abstract_inverted_index.capture | 168 |
| abstract_inverted_index.cardiac | 30, 76 |
| abstract_inverted_index.complex | 20 |
| abstract_inverted_index.context | 81 |
| abstract_inverted_index.enabled | 56 |
| abstract_inverted_index.extract | 69 |
| abstract_inverted_index.health, | 8 |
| abstract_inverted_index.health. | 121 |
| abstract_inverted_index.improve | 6 |
| abstract_inverted_index.matches | 196 |
| abstract_inverted_index.methods | 136 |
| abstract_inverted_index.results | 189 |
| abstract_inverted_index.Exercise | 0 |
| abstract_inverted_index.However, | 13 |
| abstract_inverted_index.Mongoose | 182 |
| abstract_inverted_index.Network. | 187 |
| abstract_inverted_index.Research | 98 |
| abstract_inverted_index.Shanghai | 90 |
| abstract_inverted_index.approval | 101 |
| abstract_inverted_index.devices, | 50 |
| abstract_inverted_index.example, | 144 |
| abstract_inverted_index.exercise | 18, 161 |
| abstract_inverted_index.existing | 200 |
| abstract_inverted_index.involved | 107 |
| abstract_inverted_index.learning | 47, 154 |
| abstract_inverted_index.methods. | 201 |
| abstract_inverted_index.proposed | 193 |
| abstract_inverted_index.Committee | 95 |
| abstract_inverted_index.Optimized | 183 |
| abstract_inverted_index.[Formula: | 123 |
| abstract_inverted_index.analysis. | 61 |
| abstract_inverted_index.autophagy | 72, 173 |
| abstract_inverted_index.cloud-IoT | 80 |
| abstract_inverted_index.computing | 53 |
| abstract_inverted_index.exercise, | 175 |
| abstract_inverted_index.including | 40 |
| abstract_inverted_index.intricate | 170 |
| abstract_inverted_index.learning, | 84 |
| abstract_inverted_index.molecular | 26 |
| abstract_inverted_index.objective | 63 |
| abstract_inverted_index.pathways, | 39 |
| abstract_inverted_index.pathways. | 27 |
| abstract_inverted_index.responses | 16 |
| abstract_inverted_index.University | 91 |
| abstract_inverted_index.autography | 158 |
| abstract_inverted_index.autophagy. | 42 |
| abstract_inverted_index.biological | 59 |
| abstract_inverted_index.cloud-IoT. | 166 |
| abstract_inverted_index.especially | 86 |
| abstract_inverted_index.introduced | 177 |
| abstract_inverted_index.myocardial | 14, 162, 171 |
| abstract_inverted_index.physiology | 31 |
| abstract_inverted_index.properties | 73 |
| abstract_inverted_index.real-time, | 57 |
| abstract_inverted_index.techniques | 147 |
| abstract_inverted_index.understand | 29, 37 |
| abstract_inverted_index.algorithms. | 142 |
| abstract_inverted_index.collection, | 105 |
| abstract_inverted_index.large-scale | 58 |
| abstract_inverted_index.metabolism, | 10 |
| abstract_inverted_index.network’s | 194 |
| abstract_inverted_index.performance | 195 |
| abstract_inverted_index.physiology, | 34 |
| abstract_inverted_index.standardize | 131 |
| abstract_inverted_index.techniques, | 48 |
| abstract_inverted_index.text]-score | 125 |
| abstract_inverted_index.well-being. | 12 |
| abstract_inverted_index.Autoencoder, | 156 |
| abstract_inverted_index.autoencoder. | 88 |
| abstract_inverted_index.multifaceted | 22 |
| abstract_inverted_index.optimization | 135, 146 |
| abstract_inverted_index.Convolutional | 185 |
| abstract_inverted_index.normalization | 126 |
| abstract_inverted_index.cardiovascular | 7 |
| abstract_inverted_index.infrastructure | 54 |
| abstract_inverted_index.Sprague–Dawley | 110 |
| abstract_inverted_index.exercise-induced | 75 |
| abstract_inverted_index.fractal-inspired | 145 |
| abstract_inverted_index.DMO-GCNN-Autoencoder, | 179 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.27878157 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |