Constraints on Hyper-parameters in Deep Learning Convolutional Neural Networks Article Swipe
Ubaid M. Al‐Saggaf
,
Abdelaziz Botalb
,
Muhammad Faisal
,
Muhammad Moinuddin
,
Abdulrahman U. Alsaggaf
,
Sulhi A. Alfakeh
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.14569/ijacsa.2022.0131150
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.14569/ijacsa.2022.0131150
Convolutional Neural Network (CNN), a type of Deep Learning, has a very large number of hyper-meters in contrast to the Artificial Neural Network (ANN) which makes the task of CNN training more demanding. The reason why the task of tuning parameters optimization is difficult in the CNN is the existence of a huge optimization space comprising a large number of hyper-parameters such as the number of layers, number of neurons, number of kernels, stride, padding, rows or columns truncation, parameters of the backpropagation algorithm, etc. Moreover, most of the existing techniques in the literature for the selection of these parameters are based on random practice which is developed for some specific datasets. In this work, we empirically investigated and proved that CNN performance is linked not only to choosing the right hyper-parameters but also to its implementation. More specifically, it is found that the performance is also depending on how it deals when the CNN operations require setting of hyper-parameters that do not symmetrically fit the input volume. We demonstrated two different implementations, crop or pad the input volume to make it fit. Our analysis shows that padding performs better than cropping in terms of prediction accuracy (85.58% in contrast to 82.62%) while takes lesser training time (8 minutes lesser).
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.14569/ijacsa.2022.0131150
- http://thesai.org/Downloads/Volume13No11/Paper_50-Constraints_on_Hyper_parameters_in_Deep_Learning.pdf
- OA Status
- diamond
- Cited By
- 1
- References
- 24
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4312581726
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4312581726Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.14569/ijacsa.2022.0131150Digital Object Identifier
- Title
-
Constraints on Hyper-parameters in Deep Learning Convolutional Neural NetworksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-01Full publication date if available
- Authors
-
Ubaid M. Al‐Saggaf, Abdelaziz Botalb, Muhammad Faisal, Muhammad Moinuddin, Abdulrahman U. Alsaggaf, Sulhi A. AlfakehList of authors in order
- Landing page
-
https://doi.org/10.14569/ijacsa.2022.0131150Publisher landing page
- PDF URL
-
https://thesai.org/Downloads/Volume13No11/Paper_50-Constraints_on_Hyper_parameters_in_Deep_Learning.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://thesai.org/Downloads/Volume13No11/Paper_50-Constraints_on_Hyper_parameters_in_Deep_Learning.pdfDirect OA link when available
- Concepts
-
Computer science, Padding, Convolutional neural network, Artificial intelligence, Deep learning, Backpropagation, Contrast (vision), Task (project management), Artificial neural network, Machine learning, Pattern recognition (psychology), Algorithm, Economics, Management, Computer securityTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1Per-year citation counts (last 5 years)
- References (count)
-
24Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4312581726 |
|---|---|
| doi | https://doi.org/10.14569/ijacsa.2022.0131150 |
| ids.doi | https://doi.org/10.14569/ijacsa.2022.0131150 |
| ids.openalex | https://openalex.org/W4312581726 |
| fwci | 0.1237918 |
| type | article |
| title | Constraints on Hyper-parameters in Deep Learning Convolutional Neural Networks |
| biblio.issue | 11 |
| biblio.volume | 13 |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10036 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9904999732971191 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1707 |
| topics[0].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[0].display_name | Advanced Neural Network Applications |
| topics[1].id | https://openalex.org/T10320 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9725000262260437 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Neural Networks and Applications |
| topics[2].id | https://openalex.org/T10812 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9483000040054321 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1707 |
| topics[2].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[2].display_name | Human Pose and Action Recognition |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.9192725419998169 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C165435473 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7560591697692871 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1509884 |
| concepts[1].display_name | Padding |
| concepts[2].id | https://openalex.org/C81363708 |
| concepts[2].level | 2 |
| concepts[2].score | 0.7174880504608154 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[2].display_name | Convolutional neural network |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.6340717077255249 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C108583219 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5514906644821167 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[4].display_name | Deep learning |
| concepts[5].id | https://openalex.org/C155032097 |
| concepts[5].level | 3 |
| concepts[5].score | 0.5395092368125916 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q798503 |
| concepts[5].display_name | Backpropagation |
| concepts[6].id | https://openalex.org/C2776502983 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5232952237129211 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q690182 |
| concepts[6].display_name | Contrast (vision) |
| concepts[7].id | https://openalex.org/C2780451532 |
| concepts[7].level | 2 |
| concepts[7].score | 0.5217084288597107 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q759676 |
| concepts[7].display_name | Task (project management) |
| concepts[8].id | https://openalex.org/C50644808 |
| concepts[8].level | 2 |
| concepts[8].score | 0.47542598843574524 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[8].display_name | Artificial neural network |
| concepts[9].id | https://openalex.org/C119857082 |
| concepts[9].level | 1 |
| concepts[9].score | 0.42622482776641846 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[9].display_name | Machine learning |
| concepts[10].id | https://openalex.org/C153180895 |
| concepts[10].level | 2 |
| concepts[10].score | 0.38564419746398926 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[10].display_name | Pattern recognition (psychology) |
| concepts[11].id | https://openalex.org/C11413529 |
| concepts[11].level | 1 |
| concepts[11].score | 0.32185035943984985 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[11].display_name | Algorithm |
| concepts[12].id | https://openalex.org/C162324750 |
| concepts[12].level | 0 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q8134 |
| concepts[12].display_name | Economics |
| concepts[13].id | https://openalex.org/C187736073 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q2920921 |
| concepts[13].display_name | Management |
| concepts[14].id | https://openalex.org/C38652104 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q3510521 |
| concepts[14].display_name | Computer security |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.9192725419998169 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/padding |
| keywords[1].score | 0.7560591697692871 |
| keywords[1].display_name | Padding |
| keywords[2].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[2].score | 0.7174880504608154 |
| keywords[2].display_name | Convolutional neural network |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.6340717077255249 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/deep-learning |
| keywords[4].score | 0.5514906644821167 |
| keywords[4].display_name | Deep learning |
| keywords[5].id | https://openalex.org/keywords/backpropagation |
| keywords[5].score | 0.5395092368125916 |
| keywords[5].display_name | Backpropagation |
| keywords[6].id | https://openalex.org/keywords/contrast |
| keywords[6].score | 0.5232952237129211 |
| keywords[6].display_name | Contrast (vision) |
| keywords[7].id | https://openalex.org/keywords/task |
| keywords[7].score | 0.5217084288597107 |
| keywords[7].display_name | Task (project management) |
| keywords[8].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[8].score | 0.47542598843574524 |
| keywords[8].display_name | Artificial neural network |
| keywords[9].id | https://openalex.org/keywords/machine-learning |
| keywords[9].score | 0.42622482776641846 |
| keywords[9].display_name | Machine learning |
| keywords[10].id | https://openalex.org/keywords/pattern-recognition |
| keywords[10].score | 0.38564419746398926 |
| keywords[10].display_name | Pattern recognition (psychology) |
| keywords[11].id | https://openalex.org/keywords/algorithm |
| keywords[11].score | 0.32185035943984985 |
| keywords[11].display_name | Algorithm |
| language | en |
| locations[0].id | doi:10.14569/ijacsa.2022.0131150 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S23629721 |
| locations[0].source.issn | 2156-5570, 2158-107X |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2156-5570 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | International Journal of Advanced Computer Science and Applications |
| locations[0].source.host_organization | https://openalex.org/P4310311819 |
| locations[0].source.host_organization_name | Science and Information Organization |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310311819 |
| locations[0].source.host_organization_lineage_names | Science and Information Organization |
| locations[0].license | cc-by |
| locations[0].pdf_url | http://thesai.org/Downloads/Volume13No11/Paper_50-Constraints_on_Hyper_parameters_in_Deep_Learning.pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | International Journal of Advanced Computer Science and Applications |
| locations[0].landing_page_url | https://doi.org/10.14569/ijacsa.2022.0131150 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5014958087 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-2925-5184 |
| authorships[0].author.display_name | Ubaid M. Al‐Saggaf |
| authorships[0].countries | SA |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I185163786 |
| authorships[0].affiliations[0].raw_affiliation_string | Electricaland Computer Engineering Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia |
| authorships[0].institutions[0].id | https://openalex.org/I185163786 |
| authorships[0].institutions[0].ror | https://ror.org/02ma4wv74 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I185163786 |
| authorships[0].institutions[0].country_code | SA |
| authorships[0].institutions[0].display_name | King Abdulaziz University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Ubaid M. Al-Saggaf |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Electricaland Computer Engineering Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia |
| authorships[1].author.id | https://openalex.org/A5040651687 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Abdelaziz Botalb |
| authorships[1].countries | SA |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I185163786 |
| authorships[1].affiliations[0].raw_affiliation_string | Electricaland Computer Engineering Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia |
| authorships[1].institutions[0].id | https://openalex.org/I185163786 |
| authorships[1].institutions[0].ror | https://ror.org/02ma4wv74 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I185163786 |
| authorships[1].institutions[0].country_code | SA |
| authorships[1].institutions[0].display_name | King Abdulaziz University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Abdelaziz Botalb |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Electricaland Computer Engineering Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia |
| authorships[2].author.id | https://openalex.org/A5100748011 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-0818-3177 |
| authorships[2].author.display_name | Muhammad Faisal |
| authorships[2].countries | SA |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I185163786 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Internal Medicine, Child and Adolescent Psychiatrist Faculty of Medicine, King Abdulaziz University, Jeddah 21589, Saudi Arabia |
| authorships[2].institutions[0].id | https://openalex.org/I185163786 |
| authorships[2].institutions[0].ror | https://ror.org/02ma4wv74 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I185163786 |
| authorships[2].institutions[0].country_code | SA |
| authorships[2].institutions[0].display_name | King Abdulaziz University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Muhammad Faisal |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Internal Medicine, Child and Adolescent Psychiatrist Faculty of Medicine, King Abdulaziz University, Jeddah 21589, Saudi Arabia |
| authorships[3].author.id | https://openalex.org/A5057263563 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-4735-0692 |
| authorships[3].author.display_name | Muhammad Moinuddin |
| authorships[3].countries | SA |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I185163786 |
| authorships[3].affiliations[0].raw_affiliation_string | Electricaland Computer Engineering Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia |
| authorships[3].institutions[0].id | https://openalex.org/I185163786 |
| authorships[3].institutions[0].ror | https://ror.org/02ma4wv74 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I185163786 |
| authorships[3].institutions[0].country_code | SA |
| authorships[3].institutions[0].display_name | King Abdulaziz University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Muhammad Moinuddin |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Electricaland Computer Engineering Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia |
| authorships[4].author.id | https://openalex.org/A5080153475 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-5910-8217 |
| authorships[4].author.display_name | Abdulrahman U. Alsaggaf |
| authorships[4].countries | SA |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I185163786 |
| authorships[4].affiliations[0].raw_affiliation_string | Center of Excellence in Intelligent Engineering Systems (CEIES), King Abdulaziz University, Jeddah 21589, Saudi Arabia |
| authorships[4].affiliations[1].institution_ids | https://openalex.org/I134085113 |
| authorships[4].affiliations[1].raw_affiliation_string | Computer & Information Technology Dept., Dammam Community College King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia |
| authorships[4].institutions[0].id | https://openalex.org/I185163786 |
| authorships[4].institutions[0].ror | https://ror.org/02ma4wv74 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I185163786 |
| authorships[4].institutions[0].country_code | SA |
| authorships[4].institutions[0].display_name | King Abdulaziz University |
| authorships[4].institutions[1].id | https://openalex.org/I134085113 |
| authorships[4].institutions[1].ror | https://ror.org/03yez3163 |
| authorships[4].institutions[1].type | education |
| authorships[4].institutions[1].lineage | https://openalex.org/I134085113 |
| authorships[4].institutions[1].country_code | SA |
| authorships[4].institutions[1].display_name | King Fahd University of Petroleum and Minerals |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Abdulrahman U. Alsaggaf |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Center of Excellence in Intelligent Engineering Systems (CEIES), King Abdulaziz University, Jeddah 21589, Saudi Arabia, Computer & Information Technology Dept., Dammam Community College King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia |
| authorships[5].author.id | https://openalex.org/A5047807302 |
| authorships[5].author.orcid | https://orcid.org/0000-0003-1371-876X |
| authorships[5].author.display_name | Sulhi A. Alfakeh |
| authorships[5].countries | SA |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I185163786 |
| authorships[5].affiliations[0].raw_affiliation_string | Electricaland Computer Engineering Department, 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 | last |
| authorships[5].raw_author_name | Sulhi Ali Alfakeh |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Electricaland Computer Engineering Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | http://thesai.org/Downloads/Volume13No11/Paper_50-Constraints_on_Hyper_parameters_in_Deep_Learning.pdf |
| open_access.oa_status | diamond |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Constraints on Hyper-parameters in Deep Learning Convolutional Neural Networks |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10036 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9904999732971191 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1707 |
| primary_topic.subfield.display_name | Computer Vision and Pattern Recognition |
| primary_topic.display_name | Advanced Neural Network Applications |
| related_works | https://openalex.org/W2142641794, https://openalex.org/W2503150695, https://openalex.org/W2946726629, https://openalex.org/W4384947563, https://openalex.org/W4231428344, https://openalex.org/W4312417841, https://openalex.org/W3193565141, https://openalex.org/W3133861977, https://openalex.org/W3167935049, https://openalex.org/W3029198973 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2024 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 1 |
| best_oa_location.id | doi:10.14569/ijacsa.2022.0131150 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S23629721 |
| best_oa_location.source.issn | 2156-5570, 2158-107X |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2156-5570 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | International Journal of Advanced Computer Science and Applications |
| best_oa_location.source.host_organization | https://openalex.org/P4310311819 |
| best_oa_location.source.host_organization_name | Science and Information Organization |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310311819 |
| best_oa_location.source.host_organization_lineage_names | Science and Information Organization |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | http://thesai.org/Downloads/Volume13No11/Paper_50-Constraints_on_Hyper_parameters_in_Deep_Learning.pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | International Journal of Advanced Computer Science and Applications |
| best_oa_location.landing_page_url | https://doi.org/10.14569/ijacsa.2022.0131150 |
| primary_location.id | doi:10.14569/ijacsa.2022.0131150 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S23629721 |
| primary_location.source.issn | 2156-5570, 2158-107X |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2156-5570 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | International Journal of Advanced Computer Science and Applications |
| primary_location.source.host_organization | https://openalex.org/P4310311819 |
| primary_location.source.host_organization_name | Science and Information Organization |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310311819 |
| primary_location.source.host_organization_lineage_names | Science and Information Organization |
| primary_location.license | cc-by |
| primary_location.pdf_url | http://thesai.org/Downloads/Volume13No11/Paper_50-Constraints_on_Hyper_parameters_in_Deep_Learning.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | International Journal of Advanced Computer Science and Applications |
| primary_location.landing_page_url | https://doi.org/10.14569/ijacsa.2022.0131150 |
| publication_date | 2022-01-01 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W2097998348, https://openalex.org/W2617308160, https://openalex.org/W2080525614, https://openalex.org/W2131241448, https://openalex.org/W2266822037, https://openalex.org/W2755520760, https://openalex.org/W2767790836, https://openalex.org/W2556522401, https://openalex.org/W6738712246, https://openalex.org/W2772089072, https://openalex.org/W2586021185, https://openalex.org/W2344075909, https://openalex.org/W6630126088, https://openalex.org/W3011131643, https://openalex.org/W4206415710, https://openalex.org/W2304648132, https://openalex.org/W4297915673, https://openalex.org/W2892156163, https://openalex.org/W4295185264, https://openalex.org/W2884166952, https://openalex.org/W2106411961, https://openalex.org/W1994197834, https://openalex.org/W2964306294, https://openalex.org/W2963565814 |
| referenced_works_count | 24 |
| abstract_inverted_index.a | 4, 10, 51, 56 |
| abstract_inverted_index.(8 | 207 |
| abstract_inverted_index.In | 112 |
| abstract_inverted_index.We | 168 |
| abstract_inverted_index.as | 62 |
| abstract_inverted_index.do | 161 |
| abstract_inverted_index.in | 16, 44, 91, 192, 198 |
| abstract_inverted_index.is | 42, 47, 106, 123, 140, 145 |
| abstract_inverted_index.it | 139, 150, 181 |
| abstract_inverted_index.of | 6, 14, 28, 38, 50, 59, 65, 68, 71, 80, 87, 97, 158, 194 |
| abstract_inverted_index.on | 102, 148 |
| abstract_inverted_index.or | 76, 174 |
| abstract_inverted_index.to | 18, 127, 134, 179, 200 |
| abstract_inverted_index.we | 115 |
| abstract_inverted_index.CNN | 29, 46, 121, 154 |
| abstract_inverted_index.Our | 183 |
| abstract_inverted_index.The | 33 |
| abstract_inverted_index.and | 118 |
| abstract_inverted_index.are | 100 |
| abstract_inverted_index.but | 132 |
| abstract_inverted_index.fit | 164 |
| abstract_inverted_index.for | 94, 108 |
| abstract_inverted_index.has | 9 |
| abstract_inverted_index.how | 149 |
| abstract_inverted_index.its | 135 |
| abstract_inverted_index.not | 125, 162 |
| abstract_inverted_index.pad | 175 |
| abstract_inverted_index.the | 19, 26, 36, 45, 48, 63, 81, 88, 92, 95, 129, 143, 153, 165, 176 |
| abstract_inverted_index.two | 170 |
| abstract_inverted_index.why | 35 |
| abstract_inverted_index.Deep | 7 |
| abstract_inverted_index.More | 137 |
| abstract_inverted_index.also | 133, 146 |
| abstract_inverted_index.crop | 173 |
| abstract_inverted_index.etc. | 84 |
| abstract_inverted_index.fit. | 182 |
| abstract_inverted_index.huge | 52 |
| abstract_inverted_index.make | 180 |
| abstract_inverted_index.more | 31 |
| abstract_inverted_index.most | 86 |
| abstract_inverted_index.only | 126 |
| abstract_inverted_index.rows | 75 |
| abstract_inverted_index.some | 109 |
| abstract_inverted_index.such | 61 |
| abstract_inverted_index.task | 27, 37 |
| abstract_inverted_index.than | 190 |
| abstract_inverted_index.that | 120, 142, 160, 186 |
| abstract_inverted_index.this | 113 |
| abstract_inverted_index.time | 206 |
| abstract_inverted_index.type | 5 |
| abstract_inverted_index.very | 11 |
| abstract_inverted_index.when | 152 |
| abstract_inverted_index.(ANN) | 23 |
| abstract_inverted_index.based | 101 |
| abstract_inverted_index.deals | 151 |
| abstract_inverted_index.found | 141 |
| abstract_inverted_index.input | 166, 177 |
| abstract_inverted_index.large | 12, 57 |
| abstract_inverted_index.makes | 25 |
| abstract_inverted_index.right | 130 |
| abstract_inverted_index.shows | 185 |
| abstract_inverted_index.space | 54 |
| abstract_inverted_index.takes | 203 |
| abstract_inverted_index.terms | 193 |
| abstract_inverted_index.these | 98 |
| abstract_inverted_index.which | 24, 105 |
| abstract_inverted_index.while | 202 |
| abstract_inverted_index.work, | 114 |
| abstract_inverted_index.(CNN), | 3 |
| abstract_inverted_index.Neural | 1, 21 |
| abstract_inverted_index.better | 189 |
| abstract_inverted_index.lesser | 204 |
| abstract_inverted_index.linked | 124 |
| abstract_inverted_index.number | 13, 58, 64, 67, 70 |
| abstract_inverted_index.proved | 119 |
| abstract_inverted_index.random | 103 |
| abstract_inverted_index.reason | 34 |
| abstract_inverted_index.tuning | 39 |
| abstract_inverted_index.volume | 178 |
| abstract_inverted_index.(85.58% | 197 |
| abstract_inverted_index.82.62%) | 201 |
| abstract_inverted_index.Network | 2, 22 |
| abstract_inverted_index.columns | 77 |
| abstract_inverted_index.layers, | 66 |
| abstract_inverted_index.minutes | 208 |
| abstract_inverted_index.padding | 187 |
| abstract_inverted_index.require | 156 |
| abstract_inverted_index.setting | 157 |
| abstract_inverted_index.stride, | 73 |
| abstract_inverted_index.volume. | 167 |
| abstract_inverted_index.accuracy | 196 |
| abstract_inverted_index.analysis | 184 |
| abstract_inverted_index.choosing | 128 |
| abstract_inverted_index.contrast | 17, 199 |
| abstract_inverted_index.cropping | 191 |
| abstract_inverted_index.existing | 89 |
| abstract_inverted_index.kernels, | 72 |
| abstract_inverted_index.lesser). | 209 |
| abstract_inverted_index.neurons, | 69 |
| abstract_inverted_index.padding, | 74 |
| abstract_inverted_index.performs | 188 |
| abstract_inverted_index.practice | 104 |
| abstract_inverted_index.specific | 110 |
| abstract_inverted_index.training | 30, 205 |
| abstract_inverted_index.Learning, | 8 |
| abstract_inverted_index.Moreover, | 85 |
| abstract_inverted_index.datasets. | 111 |
| abstract_inverted_index.depending | 147 |
| abstract_inverted_index.developed | 107 |
| abstract_inverted_index.different | 171 |
| abstract_inverted_index.difficult | 43 |
| abstract_inverted_index.existence | 49 |
| abstract_inverted_index.selection | 96 |
| abstract_inverted_index.Artificial | 20 |
| abstract_inverted_index.algorithm, | 83 |
| abstract_inverted_index.comprising | 55 |
| abstract_inverted_index.demanding. | 32 |
| abstract_inverted_index.literature | 93 |
| abstract_inverted_index.operations | 155 |
| abstract_inverted_index.parameters | 40, 79, 99 |
| abstract_inverted_index.prediction | 195 |
| abstract_inverted_index.techniques | 90 |
| abstract_inverted_index.empirically | 116 |
| abstract_inverted_index.performance | 122, 144 |
| abstract_inverted_index.truncation, | 78 |
| abstract_inverted_index.demonstrated | 169 |
| abstract_inverted_index.hyper-meters | 15 |
| abstract_inverted_index.investigated | 117 |
| abstract_inverted_index.optimization | 41, 53 |
| abstract_inverted_index.Convolutional | 0 |
| abstract_inverted_index.specifically, | 138 |
| abstract_inverted_index.symmetrically | 163 |
| abstract_inverted_index.backpropagation | 82 |
| abstract_inverted_index.implementation. | 136 |
| abstract_inverted_index.hyper-parameters | 60, 131, 159 |
| abstract_inverted_index.implementations, | 172 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.43157545 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |