Adapting a Previously Proposed Open-Set Recognition Method for Time-Series Data: A Biometric User Identification Case Study Article Swipe
András Pál Halász
,
Nawar Al-Hemeary
,
Lóránt Szabolcs Daubner
,
János Juhász
,
Tamás Zsedrovits
,
Kálmán Tornai
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/electronics14203983
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/electronics14203983
Conventional classifiers are generally unable to identify samples from classes absent during the model’s training. However, such samples frequently emerge in real-world scenarios, necessitating the extension of classifier capabilities. Open-Set Recognition (OSR) models are designed to address this challenge. Previously, we developed a robust OSR method that employs generated—“fake”—features to model the space of unknown classes encountered during deployment. Like most OSR models, this method was initially designed for image datasets. However, it is essential to extend OSR techniques to other data types, given their widespread use in practice. In this work, we adapt our model to time-series data while preserving its core efficiency advantage. Thanks to the model’s modular design, only the feature extraction component required modification. We implemented three approaches: a one-dimensional convolutional network for accurate representation, a lightweight method based on predefined statistical features, and a frequency-domain neural network. Further, we evaluated combinations of these methods. Experiments on a biometric time-series dataset, used here as a case study, demonstrate that our model achieves excellent open-set detection and closed-set accuracy. Combining feature extraction strategies yields the best performance, while individual methods offer flexibility: CNNs deliver high accuracy, whereas handcrafted features enable resource-efficient deployment. This adaptability makes the proposed framework suitable for scenarios with varying computational constraints.
Related Topics
Concepts
No concepts available.
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics14203983
- OA Status
- gold
- References
- 22
- OpenAlex ID
- https://openalex.org/W4415166846
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4415166846Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/electronics14203983Digital Object Identifier
- Title
-
Adapting a Previously Proposed Open-Set Recognition Method for Time-Series Data: A Biometric User Identification Case StudyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-10-11Full publication date if available
- Authors
-
András Pál Halász, Nawar Al-Hemeary, Lóránt Szabolcs Daubner, János Juhász, Tamás Zsedrovits, Kálmán TornaiList of authors in order
- Landing page
-
https://doi.org/10.3390/electronics14203983Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3390/electronics14203983Direct OA link when available
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
22Number of works referenced by this work
Full payload
| id | https://openalex.org/W4415166846 |
|---|---|
| doi | https://doi.org/10.3390/electronics14203983 |
| ids.doi | https://doi.org/10.3390/electronics14203983 |
| ids.openalex | https://openalex.org/W4415166846 |
| fwci | 0.0 |
| type | article |
| title | Adapting a Previously Proposed Open-Set Recognition Method for Time-Series Data: A Biometric User Identification Case Study |
| biblio.issue | 20 |
| biblio.volume | 14 |
| biblio.last_page | 3983 |
| biblio.first_page | 3983 |
| topics[0].id | https://openalex.org/T11512 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9998999834060669 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Anomaly Detection Techniques and Applications |
| topics[1].id | https://openalex.org/T12205 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9995999932289124 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1711 |
| topics[1].subfield.display_name | Signal Processing |
| topics[1].display_name | Time Series Analysis and Forecasting |
| topics[2].id | https://openalex.org/T12357 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9896000027656555 |
| 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 | Digital Media Forensic Detection |
| is_xpac | False |
| apc_list.value | 2000 |
| apc_list.currency | CHF |
| apc_list.value_usd | 2165 |
| apc_paid.value | 2000 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 2165 |
| language | en |
| locations[0].id | doi:10.3390/electronics14203983 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210202905 |
| locations[0].source.issn | 2079-9292 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2079-9292 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Electronics |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| 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 | Electronics |
| locations[0].landing_page_url | https://doi.org/10.3390/electronics14203983 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5067482830 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-1741-4528 |
| authorships[0].author.display_name | András Pál Halász |
| authorships[0].countries | HU |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I31882830 |
| authorships[0].affiliations[0].raw_affiliation_string | Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, 1083 Budapest, Hungary |
| authorships[0].institutions[0].id | https://openalex.org/I31882830 |
| authorships[0].institutions[0].ror | https://ror.org/05v9kya57 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I31882830 |
| authorships[0].institutions[0].country_code | HU |
| authorships[0].institutions[0].display_name | Pázmány Péter Catholic University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | András Pál Halász |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, 1083 Budapest, Hungary |
| authorships[1].author.id | https://openalex.org/A5038946677 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-6663-7923 |
| authorships[1].author.display_name | Nawar Al-Hemeary |
| authorships[1].countries | HU |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I31882830 |
| authorships[1].affiliations[0].raw_affiliation_string | Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, 1083 Budapest, Hungary |
| authorships[1].institutions[0].id | https://openalex.org/I31882830 |
| authorships[1].institutions[0].ror | https://ror.org/05v9kya57 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I31882830 |
| authorships[1].institutions[0].country_code | HU |
| authorships[1].institutions[0].display_name | Pázmány Péter Catholic University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Nawar Al Hemeary |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, 1083 Budapest, Hungary |
| authorships[2].author.id | https://openalex.org/A5000408994 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-5436-9370 |
| authorships[2].author.display_name | Lóránt Szabolcs Daubner |
| authorships[2].countries | HU |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I31882830 |
| authorships[2].affiliations[0].raw_affiliation_string | Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, 1083 Budapest, Hungary |
| authorships[2].institutions[0].id | https://openalex.org/I31882830 |
| authorships[2].institutions[0].ror | https://ror.org/05v9kya57 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I31882830 |
| authorships[2].institutions[0].country_code | HU |
| authorships[2].institutions[0].display_name | Pázmány Péter Catholic University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Lóránt Szabolcs Daubner |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, 1083 Budapest, Hungary |
| authorships[3].author.id | https://openalex.org/A5043311933 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-1307-7387 |
| authorships[3].author.display_name | János Juhász |
| authorships[3].countries | HU |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I31882830 |
| authorships[3].affiliations[0].raw_affiliation_string | Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, 1083 Budapest, Hungary |
| authorships[3].affiliations[1].institution_ids | https://openalex.org/I101202996 |
| authorships[3].affiliations[1].raw_affiliation_string | Institute of Medical Microbiology, Semmelweis University, 1089 Budapest, Hungary |
| authorships[3].institutions[0].id | https://openalex.org/I31882830 |
| authorships[3].institutions[0].ror | https://ror.org/05v9kya57 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I31882830 |
| authorships[3].institutions[0].country_code | HU |
| authorships[3].institutions[0].display_name | Pázmány Péter Catholic University |
| authorships[3].institutions[1].id | https://openalex.org/I101202996 |
| authorships[3].institutions[1].ror | https://ror.org/01g9ty582 |
| authorships[3].institutions[1].type | education |
| authorships[3].institutions[1].lineage | https://openalex.org/I101202996 |
| authorships[3].institutions[1].country_code | HU |
| authorships[3].institutions[1].display_name | Semmelweis University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | János Juhász |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, 1083 Budapest, Hungary, Institute of Medical Microbiology, Semmelweis University, 1089 Budapest, Hungary |
| authorships[4].author.id | https://openalex.org/A5011069921 |
| authorships[4].author.orcid | https://orcid.org/0000-0003-0768-1171 |
| authorships[4].author.display_name | Tamás Zsedrovits |
| authorships[4].countries | HU |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I31882830 |
| authorships[4].affiliations[0].raw_affiliation_string | Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, 1083 Budapest, Hungary |
| authorships[4].institutions[0].id | https://openalex.org/I31882830 |
| authorships[4].institutions[0].ror | https://ror.org/05v9kya57 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I31882830 |
| authorships[4].institutions[0].country_code | HU |
| authorships[4].institutions[0].display_name | Pázmány Péter Catholic University |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Tamás Zsedrovits |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, 1083 Budapest, Hungary |
| authorships[5].author.id | https://openalex.org/A5010807777 |
| authorships[5].author.orcid | https://orcid.org/0000-0003-1852-0816 |
| authorships[5].author.display_name | Kálmán Tornai |
| authorships[5].countries | HU |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I31882830 |
| authorships[5].affiliations[0].raw_affiliation_string | Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, 1083 Budapest, Hungary |
| authorships[5].institutions[0].id | https://openalex.org/I31882830 |
| authorships[5].institutions[0].ror | https://ror.org/05v9kya57 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I31882830 |
| authorships[5].institutions[0].country_code | HU |
| authorships[5].institutions[0].display_name | Pázmány Péter Catholic University |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Kálmán Tornai |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, 1083 Budapest, Hungary |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.3390/electronics14203983 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-15T00:00:00 |
| display_name | Adapting a Previously Proposed Open-Set Recognition Method for Time-Series Data: A Biometric User Identification Case Study |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11512 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9998999834060669 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Anomaly Detection Techniques and Applications |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.3390/electronics14203983 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210202905 |
| best_oa_location.source.issn | 2079-9292 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2079-9292 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Electronics |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| 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 |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Electronics |
| best_oa_location.landing_page_url | https://doi.org/10.3390/electronics14203983 |
| primary_location.id | doi:10.3390/electronics14203983 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210202905 |
| primary_location.source.issn | 2079-9292 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2079-9292 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Electronics |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| 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 | Electronics |
| primary_location.landing_page_url | https://doi.org/10.3390/electronics14203983 |
| publication_date | 2025-10-11 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W4323849310, https://openalex.org/W4284895866, https://openalex.org/W4388877293, https://openalex.org/W2039229101, https://openalex.org/W1932198206, https://openalex.org/W1824259148, https://openalex.org/W2119880843, https://openalex.org/W2963149653, https://openalex.org/W3118707261, https://openalex.org/W3150048573, https://openalex.org/W2799709780, https://openalex.org/W4213397551, https://openalex.org/W4410959499, https://openalex.org/W3005601934, https://openalex.org/W1032927584, https://openalex.org/W572355794, https://openalex.org/W3113682125, https://openalex.org/W4385326904, https://openalex.org/W2064675550, https://openalex.org/W2158698691, https://openalex.org/W2116458520, https://openalex.org/W2248269543 |
| referenced_works_count | 22 |
| abstract_inverted_index.a | 42, 122, 129, 138, 151, 158 |
| abstract_inverted_index.In | 89 |
| abstract_inverted_index.We | 118 |
| abstract_inverted_index.as | 157 |
| abstract_inverted_index.in | 20, 87 |
| abstract_inverted_index.is | 73 |
| abstract_inverted_index.it | 72 |
| abstract_inverted_index.of | 26, 53, 146 |
| abstract_inverted_index.on | 133, 150 |
| abstract_inverted_index.to | 5, 35, 49, 75, 79, 96, 106 |
| abstract_inverted_index.we | 40, 92, 143 |
| abstract_inverted_index.OSR | 44, 61, 77 |
| abstract_inverted_index.and | 137, 169 |
| abstract_inverted_index.are | 2, 33 |
| abstract_inverted_index.for | 68, 126, 202 |
| abstract_inverted_index.its | 101 |
| abstract_inverted_index.our | 94, 163 |
| abstract_inverted_index.the | 12, 24, 51, 107, 112, 177, 198 |
| abstract_inverted_index.use | 86 |
| abstract_inverted_index.was | 65 |
| abstract_inverted_index.CNNs | 185 |
| abstract_inverted_index.Like | 59 |
| abstract_inverted_index.This | 195 |
| abstract_inverted_index.best | 178 |
| abstract_inverted_index.case | 159 |
| abstract_inverted_index.core | 102 |
| abstract_inverted_index.data | 81, 98 |
| abstract_inverted_index.from | 8 |
| abstract_inverted_index.here | 156 |
| abstract_inverted_index.high | 187 |
| abstract_inverted_index.most | 60 |
| abstract_inverted_index.only | 111 |
| abstract_inverted_index.such | 16 |
| abstract_inverted_index.that | 46, 162 |
| abstract_inverted_index.this | 37, 63, 90 |
| abstract_inverted_index.used | 155 |
| abstract_inverted_index.with | 204 |
| abstract_inverted_index.(OSR) | 31 |
| abstract_inverted_index.adapt | 93 |
| abstract_inverted_index.based | 132 |
| abstract_inverted_index.given | 83 |
| abstract_inverted_index.image | 69 |
| abstract_inverted_index.makes | 197 |
| abstract_inverted_index.model | 50, 95, 164 |
| abstract_inverted_index.offer | 183 |
| abstract_inverted_index.other | 80 |
| abstract_inverted_index.space | 52 |
| abstract_inverted_index.their | 84 |
| abstract_inverted_index.these | 147 |
| abstract_inverted_index.three | 120 |
| abstract_inverted_index.while | 99, 180 |
| abstract_inverted_index.work, | 91 |
| abstract_inverted_index.Thanks | 105 |
| abstract_inverted_index.absent | 10 |
| abstract_inverted_index.during | 11, 57 |
| abstract_inverted_index.emerge | 19 |
| abstract_inverted_index.enable | 192 |
| abstract_inverted_index.extend | 76 |
| abstract_inverted_index.method | 45, 64, 131 |
| abstract_inverted_index.models | 32 |
| abstract_inverted_index.neural | 140 |
| abstract_inverted_index.robust | 43 |
| abstract_inverted_index.study, | 160 |
| abstract_inverted_index.types, | 82 |
| abstract_inverted_index.unable | 4 |
| abstract_inverted_index.yields | 176 |
| abstract_inverted_index.address | 36 |
| abstract_inverted_index.classes | 9, 55 |
| abstract_inverted_index.deliver | 186 |
| abstract_inverted_index.design, | 110 |
| abstract_inverted_index.employs | 47 |
| abstract_inverted_index.feature | 113, 173 |
| abstract_inverted_index.methods | 182 |
| abstract_inverted_index.models, | 62 |
| abstract_inverted_index.modular | 109 |
| abstract_inverted_index.network | 125 |
| abstract_inverted_index.samples | 7, 17 |
| abstract_inverted_index.unknown | 54 |
| abstract_inverted_index.varying | 205 |
| abstract_inverted_index.whereas | 189 |
| abstract_inverted_index.Further, | 142 |
| abstract_inverted_index.However, | 15, 71 |
| abstract_inverted_index.Open-Set | 29 |
| abstract_inverted_index.accurate | 127 |
| abstract_inverted_index.achieves | 165 |
| abstract_inverted_index.dataset, | 154 |
| abstract_inverted_index.designed | 34, 67 |
| abstract_inverted_index.features | 191 |
| abstract_inverted_index.identify | 6 |
| abstract_inverted_index.methods. | 148 |
| abstract_inverted_index.network. | 141 |
| abstract_inverted_index.open-set | 167 |
| abstract_inverted_index.proposed | 199 |
| abstract_inverted_index.required | 116 |
| abstract_inverted_index.suitable | 201 |
| abstract_inverted_index.Combining | 172 |
| abstract_inverted_index.accuracy, | 188 |
| abstract_inverted_index.accuracy. | 171 |
| abstract_inverted_index.biometric | 152 |
| abstract_inverted_index.component | 115 |
| abstract_inverted_index.datasets. | 70 |
| abstract_inverted_index.detection | 168 |
| abstract_inverted_index.developed | 41 |
| abstract_inverted_index.essential | 74 |
| abstract_inverted_index.evaluated | 144 |
| abstract_inverted_index.excellent | 166 |
| abstract_inverted_index.extension | 25 |
| abstract_inverted_index.features, | 136 |
| abstract_inverted_index.framework | 200 |
| abstract_inverted_index.generally | 3 |
| abstract_inverted_index.initially | 66 |
| abstract_inverted_index.model’s | 13, 108 |
| abstract_inverted_index.practice. | 88 |
| abstract_inverted_index.scenarios | 203 |
| abstract_inverted_index.training. | 14 |
| abstract_inverted_index.advantage. | 104 |
| abstract_inverted_index.challenge. | 38 |
| abstract_inverted_index.classifier | 27 |
| abstract_inverted_index.closed-set | 170 |
| abstract_inverted_index.efficiency | 103 |
| abstract_inverted_index.extraction | 114, 174 |
| abstract_inverted_index.frequently | 18 |
| abstract_inverted_index.individual | 181 |
| abstract_inverted_index.predefined | 134 |
| abstract_inverted_index.preserving | 100 |
| abstract_inverted_index.real-world | 21 |
| abstract_inverted_index.scenarios, | 22 |
| abstract_inverted_index.strategies | 175 |
| abstract_inverted_index.techniques | 78 |
| abstract_inverted_index.widespread | 85 |
| abstract_inverted_index.Experiments | 149 |
| abstract_inverted_index.Previously, | 39 |
| abstract_inverted_index.Recognition | 30 |
| abstract_inverted_index.approaches: | 121 |
| abstract_inverted_index.classifiers | 1 |
| abstract_inverted_index.demonstrate | 161 |
| abstract_inverted_index.deployment. | 58, 194 |
| abstract_inverted_index.encountered | 56 |
| abstract_inverted_index.handcrafted | 190 |
| abstract_inverted_index.implemented | 119 |
| abstract_inverted_index.lightweight | 130 |
| abstract_inverted_index.statistical | 135 |
| abstract_inverted_index.time-series | 97, 153 |
| abstract_inverted_index.Conventional | 0 |
| abstract_inverted_index.adaptability | 196 |
| abstract_inverted_index.combinations | 145 |
| abstract_inverted_index.constraints. | 207 |
| abstract_inverted_index.flexibility: | 184 |
| abstract_inverted_index.performance, | 179 |
| abstract_inverted_index.capabilities. | 28 |
| abstract_inverted_index.computational | 206 |
| abstract_inverted_index.convolutional | 124 |
| abstract_inverted_index.modification. | 117 |
| abstract_inverted_index.necessitating | 23 |
| abstract_inverted_index.one-dimensional | 123 |
| abstract_inverted_index.representation, | 128 |
| abstract_inverted_index.frequency-domain | 139 |
| abstract_inverted_index.resource-efficient | 193 |
| abstract_inverted_index.generated—“fake”—features | 48 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.22094565 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |