Visual Sensation and Perception Computational Models for Deep Learning: State of the art, Challenges and Prospects Article Swipe
Bing Wei
,
Yudi Zhao
,
Kuangrong Hao
,
Lei Gao
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2109.03391
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2109.03391
Visual sensation and perception refers to the process of sensing, organizing, identifying, and interpreting visual information in environmental awareness and understanding. Computational models inspired by visual perception have the characteristics of complexity and diversity, as they come from many subjects such as cognition science, information science, and artificial intelligence. In this paper, visual perception computational models oriented deep learning are investigated from the biological visual mechanism and computational vision theory systematically. Then, some points of view about the prospects of the visual perception computational models are presented. Finally, this paper also summarizes the current challenges of visual perception and predicts its future development trends. Through this survey, it will provide a comprehensive reference for research in this direction.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2109.03391
- https://arxiv.org/pdf/2109.03391
- OA Status
- green
- Cited By
- 3
- References
- 115
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3196745875
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3196745875Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2109.03391Digital Object Identifier
- Title
-
Visual Sensation and Perception Computational Models for Deep Learning: State of the art, Challenges and ProspectsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-09-08Full publication date if available
- Authors
-
Bing Wei, Yudi Zhao, Kuangrong Hao, Lei GaoList of authors in order
- Landing page
-
https://arxiv.org/abs/2109.03391Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2109.03391Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2109.03391Direct OA link when available
- Concepts
-
Perception, Computational model, Vision science, Computer science, Visual perception, Sensation, Artificial intelligence, Computational neuroscience, Process (computing), Cognitive science, Cognitive psychology, Psychology, Neuroscience, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1, 2022: 1Per-year citation counts (last 5 years)
- References (count)
-
115Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3196745875 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2109.03391 |
| ids.doi | https://doi.org/10.48550/arxiv.2109.03391 |
| ids.mag | 3196745875 |
| ids.openalex | https://openalex.org/W3196745875 |
| fwci | |
| type | preprint |
| title | Visual Sensation and Perception Computational Models for Deep Learning: State of the art, Challenges and Prospects |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T11605 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9987999796867371 |
| 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 | Visual Attention and Saliency Detection |
| topics[1].id | https://openalex.org/T10689 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9635999798774719 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2214 |
| topics[1].subfield.display_name | Media Technology |
| topics[1].display_name | Remote-Sensing Image Classification |
| topics[2].id | https://openalex.org/T11714 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9452999830245972 |
| 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 | Multimodal Machine Learning Applications |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C26760741 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7917076945304871 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q160402 |
| concepts[0].display_name | Perception |
| concepts[1].id | https://openalex.org/C66024118 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6867536306381226 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1122506 |
| concepts[1].display_name | Computational model |
| concepts[2].id | https://openalex.org/C200220432 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6113878488540649 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q7936208 |
| concepts[2].display_name | Vision science |
| concepts[3].id | https://openalex.org/C41008148 |
| concepts[3].level | 0 |
| concepts[3].score | 0.6038191318511963 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[3].display_name | Computer science |
| concepts[4].id | https://openalex.org/C178253425 |
| concepts[4].level | 3 |
| concepts[4].score | 0.5957452058792114 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q162668 |
| concepts[4].display_name | Visual perception |
| concepts[5].id | https://openalex.org/C130093455 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5674535036087036 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q173253 |
| concepts[5].display_name | Sensation |
| concepts[6].id | https://openalex.org/C154945302 |
| concepts[6].level | 1 |
| concepts[6].score | 0.5233418345451355 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[6].display_name | Artificial intelligence |
| concepts[7].id | https://openalex.org/C15286952 |
| concepts[7].level | 2 |
| concepts[7].score | 0.49874448776245117 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q8037925 |
| concepts[7].display_name | Computational neuroscience |
| concepts[8].id | https://openalex.org/C98045186 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4852452278137207 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q205663 |
| concepts[8].display_name | Process (computing) |
| concepts[9].id | https://openalex.org/C188147891 |
| concepts[9].level | 1 |
| concepts[9].score | 0.4735744893550873 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q147638 |
| concepts[9].display_name | Cognitive science |
| concepts[10].id | https://openalex.org/C180747234 |
| concepts[10].level | 1 |
| concepts[10].score | 0.3611412048339844 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q23373 |
| concepts[10].display_name | Cognitive psychology |
| concepts[11].id | https://openalex.org/C15744967 |
| concepts[11].level | 0 |
| concepts[11].score | 0.3362269997596741 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q9418 |
| concepts[11].display_name | Psychology |
| concepts[12].id | https://openalex.org/C169760540 |
| concepts[12].level | 1 |
| concepts[12].score | 0.08536213636398315 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q207011 |
| concepts[12].display_name | Neuroscience |
| concepts[13].id | https://openalex.org/C111919701 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q9135 |
| concepts[13].display_name | Operating system |
| keywords[0].id | https://openalex.org/keywords/perception |
| keywords[0].score | 0.7917076945304871 |
| keywords[0].display_name | Perception |
| keywords[1].id | https://openalex.org/keywords/computational-model |
| keywords[1].score | 0.6867536306381226 |
| keywords[1].display_name | Computational model |
| keywords[2].id | https://openalex.org/keywords/vision-science |
| keywords[2].score | 0.6113878488540649 |
| keywords[2].display_name | Vision science |
| keywords[3].id | https://openalex.org/keywords/computer-science |
| keywords[3].score | 0.6038191318511963 |
| keywords[3].display_name | Computer science |
| keywords[4].id | https://openalex.org/keywords/visual-perception |
| keywords[4].score | 0.5957452058792114 |
| keywords[4].display_name | Visual perception |
| keywords[5].id | https://openalex.org/keywords/sensation |
| keywords[5].score | 0.5674535036087036 |
| keywords[5].display_name | Sensation |
| keywords[6].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[6].score | 0.5233418345451355 |
| keywords[6].display_name | Artificial intelligence |
| keywords[7].id | https://openalex.org/keywords/computational-neuroscience |
| keywords[7].score | 0.49874448776245117 |
| keywords[7].display_name | Computational neuroscience |
| keywords[8].id | https://openalex.org/keywords/process |
| keywords[8].score | 0.4852452278137207 |
| keywords[8].display_name | Process (computing) |
| keywords[9].id | https://openalex.org/keywords/cognitive-science |
| keywords[9].score | 0.4735744893550873 |
| keywords[9].display_name | Cognitive science |
| keywords[10].id | https://openalex.org/keywords/cognitive-psychology |
| keywords[10].score | 0.3611412048339844 |
| keywords[10].display_name | Cognitive psychology |
| keywords[11].id | https://openalex.org/keywords/psychology |
| keywords[11].score | 0.3362269997596741 |
| keywords[11].display_name | Psychology |
| keywords[12].id | https://openalex.org/keywords/neuroscience |
| keywords[12].score | 0.08536213636398315 |
| keywords[12].display_name | Neuroscience |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2109.03391 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2109.03391 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2109.03391 |
| locations[1].id | doi:10.48550/arxiv.2109.03391 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | cc-by |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | https://openalex.org/licenses/cc-by |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2109.03391 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5064572247 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-2298-1474 |
| authorships[0].author.display_name | Bing Wei |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Bing Wei |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5040397688 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-8444-763X |
| authorships[1].author.display_name | Yudi Zhao |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Yudi Zhao |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5001762132 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-9672-6161 |
| authorships[2].author.display_name | Kuangrong Hao |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Kuangrong Hao |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5002631807 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-4272-9417 |
| authorships[3].author.display_name | Lei Gao |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Lei Gao |
| authorships[3].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://arxiv.org/pdf/2109.03391 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Visual Sensation and Perception Computational Models for Deep Learning: State of the art, Challenges and Prospects |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T11605 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9987999796867371 |
| 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 | Visual Attention and Saliency Detection |
| related_works | https://openalex.org/W2360038484, https://openalex.org/W4252166826, https://openalex.org/W2887972576, https://openalex.org/W2340883203, https://openalex.org/W2062563783, https://openalex.org/W3105486044, https://openalex.org/W2586858905, https://openalex.org/W2064354742, https://openalex.org/W2189483329, https://openalex.org/W3196745875 |
| cited_by_count | 3 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 1 |
| counts_by_year[2].year | 2022 |
| counts_by_year[2].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2109.03391 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2109.03391 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2109.03391 |
| primary_location.id | pmh:oai:arXiv.org:2109.03391 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2109.03391 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2109.03391 |
| publication_date | 2021-09-08 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W3100676321, https://openalex.org/W3021541414, https://openalex.org/W3089079443, https://openalex.org/W3186100195, https://openalex.org/W2955216768, https://openalex.org/W2901801304, https://openalex.org/W2790179710, https://openalex.org/W2344552205, https://openalex.org/W2147527908, https://openalex.org/W2999994890, https://openalex.org/W2978092824, https://openalex.org/W2589930967, https://openalex.org/W3081987387, https://openalex.org/W1979129202, https://openalex.org/W3127550792, https://openalex.org/W2903557836, https://openalex.org/W2144764737, https://openalex.org/W2951049938, https://openalex.org/W3024155585, https://openalex.org/W2072273535, https://openalex.org/W2969831162, https://openalex.org/W2114977008, https://openalex.org/W2419896268, https://openalex.org/W3021151504, https://openalex.org/W1989689139, https://openalex.org/W2145419885, https://openalex.org/W2738724892, https://openalex.org/W3093881247, https://openalex.org/W2963504927, https://openalex.org/W3198106314, https://openalex.org/W2884397646, https://openalex.org/W2604763608, https://openalex.org/W3105757925, https://openalex.org/W2892147425, https://openalex.org/W1984337398, https://openalex.org/W2902963085, https://openalex.org/W2149826048, https://openalex.org/W2964036520, https://openalex.org/W2800603749, https://openalex.org/W3081980877, https://openalex.org/W2963562050, https://openalex.org/W2953061907, https://openalex.org/W2736039329, https://openalex.org/W2950083900, https://openalex.org/W2986815055, https://openalex.org/W2976139116, https://openalex.org/W2550553598, https://openalex.org/W3035328458, https://openalex.org/W2899226533, https://openalex.org/W2909381593, https://openalex.org/W3009352350, https://openalex.org/W3093534379, https://openalex.org/W3020429576, https://openalex.org/W3039975350, https://openalex.org/W2082556599, https://openalex.org/W2900841426, https://openalex.org/W2163485494, https://openalex.org/W3015966228, https://openalex.org/W2950266692, https://openalex.org/W3098280677, https://openalex.org/W3082317190, https://openalex.org/W2953461088, https://openalex.org/W2948510860, https://openalex.org/W2977659243, https://openalex.org/W3138969229, https://openalex.org/W2040306393, https://openalex.org/W2910219960, https://openalex.org/W2805310212, https://openalex.org/W2400568150, https://openalex.org/W2963951231, https://openalex.org/W2172010943, https://openalex.org/W2125663122, https://openalex.org/W2520951797, https://openalex.org/W2979301925, https://openalex.org/W2166150182, https://openalex.org/W2891325393, https://openalex.org/W2221625691, https://openalex.org/W2970736947, https://openalex.org/W2620342231, https://openalex.org/W2163605009, https://openalex.org/W2039546655, https://openalex.org/W3033901474, https://openalex.org/W2766228387, https://openalex.org/W2912395283, https://openalex.org/W2989899308, https://openalex.org/W3109387650, https://openalex.org/W2030031014, https://openalex.org/W2047078145, https://openalex.org/W2811155326, https://openalex.org/W2798658711, https://openalex.org/W2974373385, https://openalex.org/W2803610340, https://openalex.org/W2909624347, https://openalex.org/W2787979513, https://openalex.org/W2333091651, https://openalex.org/W2047226863, https://openalex.org/W2148764920, https://openalex.org/W2466282698, https://openalex.org/W3127386270, https://openalex.org/W2919112843, https://openalex.org/W2165698076, https://openalex.org/W2800256017, https://openalex.org/W2899920172, https://openalex.org/W2745461083, https://openalex.org/W2592598776, https://openalex.org/W1980064688, https://openalex.org/W2971713705, https://openalex.org/W2770604561, https://openalex.org/W2767327192, https://openalex.org/W2460490287, https://openalex.org/W3121623048, https://openalex.org/W3130174139, https://openalex.org/W2804915272, https://openalex.org/W3121112409, https://openalex.org/W2965578190 |
| referenced_works_count | 115 |
| abstract_inverted_index.a | 110 |
| abstract_inverted_index.In | 49 |
| abstract_inverted_index.as | 34, 41 |
| abstract_inverted_index.by | 24 |
| abstract_inverted_index.in | 16, 115 |
| abstract_inverted_index.it | 107 |
| abstract_inverted_index.of | 8, 30, 74, 79, 95 |
| abstract_inverted_index.to | 5 |
| abstract_inverted_index.and | 2, 12, 19, 32, 46, 66, 98 |
| abstract_inverted_index.are | 59, 85 |
| abstract_inverted_index.for | 113 |
| abstract_inverted_index.its | 100 |
| abstract_inverted_index.the | 6, 28, 62, 77, 80, 92 |
| abstract_inverted_index.also | 90 |
| abstract_inverted_index.come | 36 |
| abstract_inverted_index.deep | 57 |
| abstract_inverted_index.from | 37, 61 |
| abstract_inverted_index.have | 27 |
| abstract_inverted_index.many | 38 |
| abstract_inverted_index.some | 72 |
| abstract_inverted_index.such | 40 |
| abstract_inverted_index.they | 35 |
| abstract_inverted_index.this | 50, 88, 105, 116 |
| abstract_inverted_index.view | 75 |
| abstract_inverted_index.will | 108 |
| abstract_inverted_index.Then, | 71 |
| abstract_inverted_index.about | 76 |
| abstract_inverted_index.paper | 89 |
| abstract_inverted_index.Visual | 0 |
| abstract_inverted_index.future | 101 |
| abstract_inverted_index.models | 22, 55, 84 |
| abstract_inverted_index.paper, | 51 |
| abstract_inverted_index.points | 73 |
| abstract_inverted_index.refers | 4 |
| abstract_inverted_index.theory | 69 |
| abstract_inverted_index.vision | 68 |
| abstract_inverted_index.visual | 14, 25, 52, 64, 81, 96 |
| abstract_inverted_index.Through | 104 |
| abstract_inverted_index.current | 93 |
| abstract_inverted_index.process | 7 |
| abstract_inverted_index.provide | 109 |
| abstract_inverted_index.survey, | 106 |
| abstract_inverted_index.trends. | 103 |
| abstract_inverted_index.Finally, | 87 |
| abstract_inverted_index.inspired | 23 |
| abstract_inverted_index.learning | 58 |
| abstract_inverted_index.oriented | 56 |
| abstract_inverted_index.predicts | 99 |
| abstract_inverted_index.research | 114 |
| abstract_inverted_index.science, | 43, 45 |
| abstract_inverted_index.sensing, | 9 |
| abstract_inverted_index.subjects | 39 |
| abstract_inverted_index.awareness | 18 |
| abstract_inverted_index.cognition | 42 |
| abstract_inverted_index.mechanism | 65 |
| abstract_inverted_index.prospects | 78 |
| abstract_inverted_index.reference | 112 |
| abstract_inverted_index.sensation | 1 |
| abstract_inverted_index.artificial | 47 |
| abstract_inverted_index.biological | 63 |
| abstract_inverted_index.challenges | 94 |
| abstract_inverted_index.complexity | 31 |
| abstract_inverted_index.direction. | 117 |
| abstract_inverted_index.diversity, | 33 |
| abstract_inverted_index.perception | 3, 26, 53, 82, 97 |
| abstract_inverted_index.presented. | 86 |
| abstract_inverted_index.summarizes | 91 |
| abstract_inverted_index.development | 102 |
| abstract_inverted_index.information | 15, 44 |
| abstract_inverted_index.organizing, | 10 |
| abstract_inverted_index.identifying, | 11 |
| abstract_inverted_index.interpreting | 13 |
| abstract_inverted_index.investigated | 60 |
| abstract_inverted_index.Computational | 21 |
| abstract_inverted_index.comprehensive | 111 |
| abstract_inverted_index.computational | 54, 67, 83 |
| abstract_inverted_index.environmental | 17 |
| abstract_inverted_index.intelligence. | 48 |
| abstract_inverted_index.understanding. | 20 |
| abstract_inverted_index.characteristics | 29 |
| abstract_inverted_index.systematically. | 70 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |