Using machine learning to predict artistic styles: an analysis of trends and the research agenda
In the field of art, machine learning models have been used to predict artistic styles in paintings. The foregoing is somewhat advantageous for analysts, as these tools can provide more valuable results and help reduce bias in the results and conclusions provided. Therefore, the objective of this re...
Saved in:
| Published in | The Artificial intelligence review Vol. 57; no. 5; p. 118 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Dordrecht
Springer Netherlands
01.05.2024
Springer Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | In the field of art, machine learning models have been used to predict artistic styles in paintings. The foregoing is somewhat advantageous for analysts, as these tools can provide more valuable results and help reduce bias in the results and conclusions provided. Therefore, the objective of this research was to examine research trends in the use of machine learning to predict artistic styles from a bibliometric review based on the PRISMA methodology. From the search equations, 268 documents were found, out of which, following the application of inclusion and exclusion criteria, 128 documents were analyzed. Through quantitative analysis, a growing research interest in the subject is evident, progressing from user perception approaches to the utilization of tools like deep learning for art studies. Among the main results, it is possible to identify that one of the most used techniques in the field has been neural networks for pattern recognition. Also, a large part of the research focuses on the use of design software for image creation and manipulation. Finally, it is found that the number of studies focused on contemporary modern art is still limited, this is due to the fact that a large part of the investigations has focused on historical artistic styles. |
|---|---|
| AbstractList | In the field of art, machine learning models have been used to predict artistic styles in paintings. The foregoing is somewhat advantageous for analysts, as these tools can provide more valuable results and help reduce bias in the results and conclusions provided. Therefore, the objective of this research was to examine research trends in the use of machine learning to predict artistic styles from a bibliometric review based on the PRISMA methodology. From the search equations, 268 documents were found, out of which, following the application of inclusion and exclusion criteria, 128 documents were analyzed. Through quantitative analysis, a growing research interest in the subject is evident, progressing from user perception approaches to the utilization of tools like deep learning for art studies. Among the main results, it is possible to identify that one of the most used techniques in the field has been neural networks for pattern recognition. Also, a large part of the research focuses on the use of design software for image creation and manipulation. Finally, it is found that the number of studies focused on contemporary modern art is still limited, this is due to the fact that a large part of the investigations has focused on historical artistic styles. |
| ArticleNumber | 118 |
| Audience | Academic |
| Author | Arcila-Diaz, Juan Valencia, Jackeline Valencia-Arias, Alejandro Pineda, Vanessa García Pineda, Geraldine García de la Puente, Renata Teodori |
| Author_xml | – sequence: 1 givenname: Jackeline surname: Valencia fullname: Valencia, Jackeline organization: Instituto de Investigación y Estudios de la Mujer, Universidad Ricardo Palma, Facultad de Artes y Humanidades, Instituto Tecnológico Metropolitano – sequence: 2 givenname: Geraldine García surname: Pineda fullname: Pineda, Geraldine García organization: Facultad de Artes y Humanidades, Instituto Tecnológico Metropolitano – sequence: 3 givenname: Vanessa García surname: Pineda fullname: Pineda, Vanessa García organization: Facultad de Ingenierías, Instituto Tecnológico Metropolitano – sequence: 4 givenname: Alejandro surname: Valencia-Arias fullname: Valencia-Arias, Alejandro email: valenciajho@uss.edu.pe organization: School of Industrial Engineering, Universidad Señor de Sipán – sequence: 5 givenname: Juan surname: Arcila-Diaz fullname: Arcila-Diaz, Juan organization: Escuela de Ingeniería de Sistemas, Universidad Señor de Sipán – sequence: 6 givenname: Renata Teodori surname: de la Puente fullname: de la Puente, Renata Teodori organization: Universidad Ricardo Palma |
| BookMark | eNp9kE1r3DAQhkVJoUnaP9CToGenI1m27N5C6BcEcknO6lga7yp45a1GOey_r1IXWnoIEki8zDMjPRfiLK2JhHiv4EoB2I-swPS6AW0aBVbbBl6Jc9XZtrE1P_vn_kZcMD8CQKdNey5-PHBMO3lAv4-J5EKY03NQVnnMFKIvEnOJXKKXXE4L8SeJqW5cThxZrrMsmVLgGgVZ9iQzcW3i9xJ3Nce34vWMC9O7P-elePjy-f7mW3N79_X7zfVt4w3o0oyDUWNnOyQ1KG8mGDCAUboz82wwmKklHTpUfR90jzR7hb2dJkt29JPyob0UH7a-x7z-fCIu7nF9yvWZ7FpoB9NZ1dtadbVV7XAhF9O8loy-rkCH6KvTOdb82o6grR6hq4DeAJ9X5kyzO-Z4wHxyCtyzerepd1W9-63eQYWG_yAfC5a4pjotLi-j7YZynZN2lP9-4wXqF7E2mo0 |
| CitedBy_id | crossref_primary_10_1016_j_mex_2025_103277 |
| Cites_doi | 10.1007/978-3-030-77004-4_18 10.1002/smr.2486 10.3390/su15118808 10.3389/fevo.2021.64277 10.1016/j.mfglet.2022.07.112 10.1145/3591196.3593341 10.1148/radiol.09090626 10.1109/ICACITE51222.2021.9404606 10.1155/2022/8804711 10.1109/icip.2009.5413338 10.1007/s00500-022-07555-1 10.3390/e24091175 10.1142/S0219843622400023 10.1145/604045.604056 10.1145/642611.642690 10.1016/j.prostr.2022.05.017 10.1016/j.ijnaoe.2020.03.005 10.1109/TVCG.2005.38 10.1002/xrs.3188 10.1109/ro-man47096.2020.9223533 10.1016/j.ijsu.2021.105906 10.1007/978-3-030-32644-9_32 10.1109/icip.2016.7532335 10.1016/j.neucom.2018.05.045 10.1007/s00146-021-01370-2 10.1007/s00500-023-08146-4 10.1016/j.wpi.2022.102106 10.1109/JSTSP.2009.2015077 10.1109/cvpr.2016.566 10.1007/s11263-022-01665-x 10.1007/s42979-022-01529-8 10.1117/12.806245 10.1016/j.sysarc.2020.101766 10.1007/978-981-19-2828-4_21 10.3390/app10103396 10.3390/app8101768 10.6018/riite.548361 10.1109/DTPI52967.2021.9540081 10.1038/s41598-023-39865-1 10.1007/978-3-030-51328-3_14 10.1016/j.jobe.2020.102001 10.1109/msp.2015.2407091 10.1109/cvpr46437.2021.01140 10.1007/s11192-021-04082-y 10.1007/s12652-022-04472-x 10.1117/12.2593838 10.1016/j.eswa.2017.11.056 10.1007/s00521-021-06072-w 10.1016/j.worlddev.2019.04.010 10.1109/IWECAI55315.2022.00029 10.3390/s21061940 10.1109/JEEIT.2019.8717470 10.1038/s41467-020-18566-7 10.3389/fpsyg.2021.723325 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2024 COPYRIGHT 2024 Springer Copyright Springer Nature B.V. May 2024 |
| Copyright_xml | – notice: The Author(s) 2024 – notice: COPYRIGHT 2024 Springer – notice: Copyright Springer Nature B.V. May 2024 |
| DBID | C6C AAYXX CITATION 3V. 7SC 7WY 7WZ 7XB 87Z 8AL 8AO 8FD 8FE 8FG 8FK 8FL ABUWG AFKRA ALSLI ARAPS AZQEC BENPR BEZIV BGLVJ CCPQU CNYFK DWQXO E3H F2A FRNLG F~G GNUQQ HCIFZ JQ2 K60 K6~ K7- L.- L7M L~C L~D M0C M0N M1O P5Z P62 PHGZM PHGZT PKEHL PQBIZ PQBZA PQEST PQGLB PQQKQ PQUKI PRINS PRQQA PSYQQ Q9U |
| DOI | 10.1007/s10462-024-10727-0 |
| DatabaseName | Springer Nature OA Free Journals CrossRef ProQuest Central (Corporate) Computer and Information Systems Abstracts ABI/INFORM Collection ABI/INFORM Global (PDF only) ProQuest Central (purchase pre-March 2016) ABI/INFORM Global (Alumni Edition) Computing Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) ABI/INFORM Collection (Alumni) ProQuest Central (Alumni) ProQuest Central UK/Ireland Social Science Premium Collection Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Central Business Premium Collection ProQuest Technology Collection ProQuest One Library & Information Science Collection ProQuest Central Korea Library & Information Sciences Abstracts (LISA) Library & Information Science Abstracts (LISA) Business Premium Collection (Alumni) ABI/INFORM Global (Corporate) ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection ProQuest Business Collection (Alumni Edition) ProQuest Business Collection Computer Science Database ABI/INFORM Professional Advanced Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional ABI/INFORM Global Computing Database Library Science Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic (New) ProQuest One Academic Middle East (New) ProQuest One Business ProQuest One Business (Alumni) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China ProQuest One Social Sciences ProQuest One Psychology ProQuest Central Basic |
| DatabaseTitle | CrossRef ProQuest Business Collection (Alumni Edition) ProQuest One Psychology Computer Science Database ProQuest Central Student Library and Information Science Abstracts (LISA) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection ProQuest Central China ABI/INFORM Complete ProQuest One Applied & Life Sciences Library & Information Science Collection ProQuest Central (New) Advanced Technologies & Aerospace Collection Business Premium Collection Social Science Premium Collection ABI/INFORM Global ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest Business Collection ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ABI/INFORM Global (Corporate) ProQuest One Business Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest Pharma Collection ProQuest Central ABI/INFORM Professional Advanced ProQuest Library Science ProQuest Central Korea Advanced Technologies Database with Aerospace ABI/INFORM Complete (Alumni Edition) ProQuest Computing ProQuest One Social Sciences ABI/INFORM Global (Alumni Edition) ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest SciTech Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest One Business (Alumni) ProQuest Central (Alumni) Business Premium Collection (Alumni) |
| DatabaseTitleList | CrossRef ProQuest Business Collection (Alumni Edition) |
| Database_xml | – sequence: 1 dbid: C6C name: Springer Nature OA Free Journals url: http://www.springeropen.com/ sourceTypes: Publisher – sequence: 2 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1573-7462 |
| ExternalDocumentID | A790272905 10_1007_s10462_024_10727_0 |
| GroupedDBID | -4Z -59 -5G -BR -EM -~C .4S .86 .DC .VR 06D 0R~ 0VY 1N0 203 23N 2J2 2JN 2JY 2KG 2LR 2~H 30V 4.4 406 408 409 40D 40E 5GY 5VS 67Z 6J9 6NX 77K 7WY 8FL 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AAHNG AAIAL AAJKR AAJSJ AAKKN AANZL AARTL AATVU AAUYE AAWCG AAYIU AAYQN AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABEEZ ABFTD ABFTV ABHLI ABHQN ABIVO ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABSXP ABTEG ABTHY ABTKH ABTMW ABWNU ABXPI ACACY ACGFS ACHSB ACHXU ACIHN ACKNC ACMDZ ACMLO ACOKC ACOMO ACREN ACSNA ACULB ACZOJ ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADYOE ADZKW AEAQA AEFQL AEGAL AEGNC AEJHL AEJRE AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFGXO AFLOW AFQWF AFWTZ AFYQB AFZKB AGAYW AGDGC AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIIXL AILAN AITGF AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALSLI ALWAN AMKLP AMTXH AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN B-. BA0 BENPR BGNMA C24 C6C CS3 CSCUP DDRTE DL5 DNIVK DPUIP EBLON EBS EDO EIOEI ESBYG FEDTE FERAY FFXSO FIGPU FNLPD FRRFC FWDCC GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 GQ8 GROUPED_ABI_INFORM_COMPLETE GXS HCIFZ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IAO IJ- IKXTQ ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ K60 K6~ KDC KOV LLZTM M1O M4Y MA- N9A NB0 NPVJJ NQJWS NU0 O93 O9G O9I O9J OAM P19 P9O PF0 PT5 QOK QOS R89 R9I RHV RNS ROL RPX RSV S16 S27 S3B SAP SCO SDH SDM SHX SISQX SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX VC2 W23 W48 WH7 WK8 YLTOR Z45 Z5O Z7R Z7X Z7Y Z7Z Z81 Z83 Z86 Z88 Z8M Z8N Z8R Z8S Z8T Z8U Z8W Z92 ZMTXR ~A9 ~EX 8AO 8FE 8FG AAFWJ AASML AAYXX ABDBE ABFSG ABUWG ACSTC AEZWR AFHIU AFKRA AHPBZ AHWEU AIXLP AYFIA AZQEC BEZIV BGLVJ BPHCQ CCPQU CITATION CNYFK DWQXO FRNLG GNUQQ ICD K6V K7- M0C MK~ P62 PHGZM PHGZT PQBIZ PQBZA PQQKQ PROAC PSYQQ Q2X AEIIB PMFND 3V. 7SC 7XB 8AL 8FD 8FK E3H F2A JQ2 L.- L7M L~C L~D M0N PKEHL PQEST PQGLB PQUKI PRINS PRQQA Q9U |
| ID | FETCH-LOGICAL-c402t-98419575ae181c4b08ad041254ff4ad4b3e2d5a166d26aefc1a67bb7e79cb1cd3 |
| IEDL.DBID | BENPR |
| ISSN | 1573-7462 0269-2821 |
| IngestDate | Wed Aug 13 06:15:02 EDT 2025 Tue Jun 10 20:55:30 EDT 2025 Tue Jul 01 01:23:28 EDT 2025 Thu Apr 24 23:08:16 EDT 2025 Fri Feb 21 02:40:23 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 5 |
| Keywords | Deep learning PRISMA Image analysis Machine learning Color Artistic styles |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c402t-98419575ae181c4b08ad041254ff4ad4b3e2d5a166d26aefc1a67bb7e79cb1cd3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| OpenAccessLink | https://link.springer.com/10.1007/s10462-024-10727-0 |
| PQID | 3038457167 |
| PQPubID | 36790 |
| ParticipantIDs | proquest_journals_3038457167 gale_infotracacademiconefile_A790272905 crossref_primary_10_1007_s10462_024_10727_0 crossref_citationtrail_10_1007_s10462_024_10727_0 springer_journals_10_1007_s10462_024_10727_0 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2024-05-01 |
| PublicationDateYYYYMMDD | 2024-05-01 |
| PublicationDate_xml | – month: 05 year: 2024 text: 2024-05-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Dordrecht |
| PublicationPlace_xml | – name: Dordrecht |
| PublicationSubtitle | An International Science and Engineering Journal |
| PublicationTitle | The Artificial intelligence review |
| PublicationTitleAbbrev | Artif Intell Rev |
| PublicationYear | 2024 |
| Publisher | Springer Netherlands Springer Springer Nature B.V |
| Publisher_xml | – name: Springer Netherlands – name: Springer – name: Springer Nature B.V |
| References | LcRHuman enough: a space for reconstructions of AI visions in speculative climate futuresCreat Cognit202310.1145/3591196.3593341 Sánchez Santana P, Roman-Rangel E (2021) Quantifying visual similarity for artistic styles. In: Roman-Rangel E, Kuri-Morales ÁF, Martínez-Trinidad JF, Carrasco-Ochoa JA, Olvera-López JA (eds) Pattern recognition. MCPR 2021. Lecture Notes in Computer Science, vol. 12725. Springer, Cham. https://doi.org/10.1007/978-3-030-77004-4_18 MengyaoCYuTIntelligent product art design based on smart equipment and ma-chine learning algorithm: practice effect and trend analysisSoft Comput2023278449845810.1007/s00500-023-08146-4 BoloorAGarimellaKHeXGillCVorobeychikYZhangXAttacking Vi-sion-based perception in end-to-end autonomous driving modelsJ Syst Architect202011010176610.1016/j.sysarc.2020.101766 Li H, Zhang Z (2022) A study of unsupervised networks based on the network prior for the image inpainting. In: Proceedings of the 2022 3rd International Conference on Electronic CommuInication and Artificial Intelligence (IWECAI); IEEE: pp. 07–110 GülzowJMPaetzoldPDeussenORecent developments regarding painting robots for research in automatic painting, artificial creativity, and machine learningAppl Sci202010339610.3390/app10103396 WenjingXCaiZAssessing the best art design based on artificial intelligence and machine learning using GTMASoft Comput20232714915610.1007/s00500-022-07555-1 SafraLChevallierCGrèzesJBaumardNTracking historical changes in perceived trustworthiness in Western Europe using machine learning analyses of facial cues in paintingsNat Commun202010.1038/s41467-020-18566-7 PageMJMcKenzieJEBossuytPMBoutronIHoffmannTCMulrowCDShamseerLTetzlaffJMAklEABrennanSEThe PRISMA 2020 Statement: an updated guideline for reporting systematic reviewsInt J Surg20218810590610.1016/j.ijsu.2021.105906 CasconeLNappiMNarducciFRussoSLClassification of fragments: recognition of artistic styleJ Ambient Intell Humaniz Comput2023144087409710.1007/s12652-022-04472-x ChenAJesusRVilariguesMIdentification and visualization of pure and mixed Paint pigments in heritage artwork using machine learning algorithmsSN Comput Sci2022411510.1007/s42979-022-01529-8 Sun X, Qin J (2021) Deep learning-based creative intention understanding and color suggestions for illustration. In: Ahram T (eds) Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1213. Springer, Cham. https://doi.org/10.1007/978-3-030-51328-3_14 JeongJHWooJHParkJMachine learning methodology for management of shipbuilding master dataInt J Naval Archit Ocean Eng20201242843910.1016/j.ijnaoe.2020.03.005 Fails JA, Olsen DR (2003b) Interactive machine learning. In: Proceedings of the Proceedings of the 8th International Conference on Intelligent User Interfaces; ACM, pp. 39–45 Kumar M, Sharma S, Chaudhary D, Prakash S (2021) Image recognition using artificial intelligence. In: Proceedings of the 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE); IEEE, pp. 760–763 Fails J, Olsen D (2003c) A design tool for camera-based interaction. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems; ACM, 449–456 Achlioptas P, Ovsjanikov M, Haydarov K, Elhoseiny M, Guibas LJ (2021) ArtEmis: affective language for visual art. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and PatternRecognition https://doi.org/10.1109/cvpr46437.2021.01140 RagotSMeasuring the originality of intellectual property assets based on estimated inter-asset distancesWorld Patent Inf20226910210610.1016/j.wpi.2022.102106 Culjak M, Mikus B, Jez K, Hadjic S (2011) Classification of art paintings by genre. In: International Convention on Information and Communication Technology, Electronics and Microelectronics, pp. 1634–1639. https://ieeexplore.ieee.org/document/5967323 SanhudoLCalvettiDMartinsJPRamosNMMMêdaPGonçalvesMCSousaHActivity classification using accelerometers and machine learning for com-plex construction worker activitiesJ Building Eng20213510200110.1016/j.jobe.2020.102001 GengenbachTKerstinSArtificial intelligence raters. neural networks for rating pictorial expressionJ Sci Technol Arts2022144971 Cucci C, Barucci A, Stefani L, Picollo M, Jiménez-Garnica R, Fuster-Lopez L (2021) Reflectance hyperspectral data processing on a set of picasso paintings: which algorithm provides what? A comparative analysis of multivariate, statistical and artificial intel-ligence methods. In R. Groves & H. Liang (Eds.), Proceedings of the Optics for Arts, Architecture, and Archaeology VIII. p. 1 TzengF-YLumEBKwan-Liu Ma An intelligent system approach to higher dimensional classification of volume dataIEEE Trans Vis Comput Graph20051127328410.1109/TVCG.2005.38 FanH-TXiaoGArinezJCoulthardMA case study on first time quality feature investigation for an automotive paint shopManuf Lett20223391992610.1016/j.mfglet.2022.07.112 Polatkan G, Jafarpour S, Brasoveanu A, Hughes SM, Daubechies I (2009) Detection of forgery in paintings using supervised learning. In: Proceedings-International Conference on Image Processing, ICIP https://doi.org/10.1109/icip.2009.5413338 SpeeBTMMikuniJLederHScharnowskiFPelowskiMSteyrlDMachine learning revealed symbolism, emotionality, and imaginativeness as primary predictors of creativity evaluations of Western art paintingsSci Rep202310.1038/s41598-023-39865-1 DobbsTBenedictARasZJumping into the artistic deep end: building the Cata-Logue RaisonnéAI Soc20223787388910.1007/s00146-021-01370-2 Orduña-MaleaECostasRLink-based approach to study scientific software usage: the case of VOSviewerScientometrics20211268153818610.1007/s11192-021-04082-y Lu Y, Guo C, Dai X, Wang F-Y (2021) Image captioning on fine art paintings via virtual paintings. In: Proceedings of the 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence. DTPI, pp. 156–159 XuYNazirSRanking the art design and applications of artificial intelligence and machine learningJ Softw Evol Process202210.1002/smr.2486 Bidgoli A, De Guevara ML, Hsiung C, Oh J, Kang E (2020) Artistic style in robotic painting; a machine learning approach to learning brushstroke from human artists. In: 29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020. https://doi.org/10.1109/ro-man47096.2020.9223533 LürigMDDonougheSSvenssonEIPortoATsuboiMComputer vision, machine learning, and the promise of phenomics in ecology and evolutionary biologyFront Ecol Evol202110.3389/fevo.2021.64277 Vadicherla D, Gadicha V (2022) Supervised machine learning approach for crack detection in digital images. In: Tomar A, Malik H, Kumar P, Iqbal A (eds) Proceedings of 3rd International Conference on Machine Learning, Advances in Computing, Renewable Energy and Communication. Lecture Notes in Electrical Engineering, vol 915. Springer, Singapore. https://doi.org/10.1007/978-981-19-2828-4_21 LiCChenTAesthetic visual quality assessment of paintingsIEEE J Sel Top Signal Process2009323625210.1109/JSTSP.2009.2015077 TianTNanFA multitask convolutional neural network for artwork appreciationMobile Information Systems202210.1155/2022/8804711 KnippenbergEJensenNConstasMQuantifying household resilience with high frequency data: temporal dynamics and methodological optionsWorld Dev201912111510.1016/j.worlddev.2019.04.010 Van Der MaatenLErdmannRGAutomatic thread-level canvas analysis: a machine-learning approach to analyzing the canvas of paintingsIEEE Signal Process Mag2015324384510.1109/msp.2015.2407091 FalomirZMuserosLSanzIAbrilLGCategorizing paintings in art styles based on qualitative color descriptors, quantitative global features and machine learning (QArt-Learn)Expert Syst Appl201897839410.1016/j.eswa.2017.11.056 Jboor NH, Belhi A, Al-Ali AK, Bouras A, Jaoua A (2019) Towards an inpainting framework for visual cultural heritage. In: Proceedings of the 2019 IEEE Jordan Interna-Tional Joint Conference on Electrical Engineering and Information Technology (JEEIT); IEEE, pp. 602–607 MaoWVideo analysis of intelligent teaching based on machine learning and virtual reality technologyNeural Comput Appl2022346603661410.1007/s00521-021-06072-w HuBAnalysis of art therapy for children with autism by using the implemented artificial intelligence systemInt J Humanoid Rob202210.1142/S0219843622400023 Irfan MT, Stork DG (2009) Multiple visual features for the computer authentication of Jackson Pollock’s drip paintings: beyond box counting and fractals. Proceedings Of SPIE. https://doi.org/10.1117/12.806245 Folego G, Gomes O, Rocha A (2016) From impressionism to expressionism: automatically identifying Van Gogh’s paintings. In: Proceedings-International Conference on Image Processing, ICIP https://doi.org/10.1109/icip.2016.7532335 AllahlohASSarfrazMGhalebAMAl-Shamma’aAAFarhHAl-ShaalanAMRevolutionizing IC genset operations with IIoT and AI: a study on fuel savings and predictive maintenanceSustainability202315880810.3390/su15118808 CalifanoAFotiPBertoFBaiesiMBertolinCPredicting damage evolution in panel paintings with machine learningProcedia Struct Integr20224114515710.1016/j.prostr.2022.05.017 LiuYQinZWanTLuoZAuto-painter: cartoon image generation from sketch by using conditional wasserstein generative adversarial networksNeurocomputing2018311788710.1016/j.neucom.2018.05.045 Srinivasa DesikanBShimaoHMitonHWikiArtVectors: style and color repre-sentations of artworks for cultural analysis via information theoretic measuresEntropy202224117510.3390/e24091175 BelhiABourasAFoufouSLeveraging known data for missing label prediction in cultural heritage contextAppl Sci2018810176810.3390/app8101768 MondalBArtificial intelligence: state of the artEn Intell Syst Ref Libr201910.1007/978-3-030-32644-9_32 Alameda-Pineda X, Ricci E, Yan Y, Sebe N (2016) Recognizing emotions from abstract paintings using non-linear matrix completion. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. https:// 10727_CR44 10727_CR48 W Mao (10727_CR35) 2022; 34 R Lc (10727_CR29) 2023 T Gengenbach (10727_CR20) 2022; 14 T Tian (10727_CR49) 2022 S Kogou (10727_CR27) 2020; 50 E Orduña-Malea (10727_CR38) 2021; 126 B Hu (10727_CR22) 2022 J Zhang (10727_CR56) 2021 AS Allahloh (10727_CR3) 2023; 15 10727_CR41 10727_CR33 T Yu (10727_CR55) 2022; 130 A Boloor (10727_CR6) 2020; 110 JH Jeong (10727_CR25) 2020; 12 C Mengyao (10727_CR36) 2023; 27 MJ Page (10727_CR39) 2021; 88 A Califano (10727_CR7) 2022; 41 JM Gülzow (10727_CR21) 2020; 10 BTM Spee (10727_CR46) 2023 MD Lürig (10727_CR34) 2021 X Wenjing (10727_CR53) 2023; 27 A Belhi (10727_CR4) 2018; 8 10727_CR31 I Pedraza-Navarro (10727_CR40) 2022; 2022 10727_CR23 10727_CR24 B Mondal (10727_CR37) 2019 L Van Der Maaten (10727_CR52) 2015; 32 10727_CR28 S Ragot (10727_CR42) 2022; 69 L Sanhudo (10727_CR45) 2021; 35 E Knippenberg (10727_CR26) 2019; 121 L Safra (10727_CR43) 2020 F-Y Tzeng (10727_CR50) 2005; 11 Z Falomir (10727_CR17) 2018; 97 A Chen (10727_CR9) 2022; 4 10727_CR10 Y Xu (10727_CR54) 2022 10727_CR11 10727_CR14 10727_CR15 H-T Fan (10727_CR18) 2022; 33 10727_CR16 Y Liu (10727_CR32) 2018; 311 10727_CR5 10727_CR19 L Cascone (10727_CR8) 2023; 14 T Dobbs (10727_CR12) 2022; 37 10727_CR2 10727_CR1 W Zhao (10727_CR57) 2021 B Srinivasa Desikan (10727_CR47) 2022; 24 V Durieux (10727_CR13) 2010; 255 C Li (10727_CR30) 2009; 3 10727_CR51 |
| References_xml | – reference: TianTNanFA multitask convolutional neural network for artwork appreciationMobile Information Systems202210.1155/2022/8804711 – reference: SafraLChevallierCGrèzesJBaumardNTracking historical changes in perceived trustworthiness in Western Europe using machine learning analyses of facial cues in paintingsNat Commun202010.1038/s41467-020-18566-7 – reference: HuBAnalysis of art therapy for children with autism by using the implemented artificial intelligence systemInt J Humanoid Rob202210.1142/S0219843622400023 – reference: ChenAJesusRVilariguesMIdentification and visualization of pure and mixed Paint pigments in heritage artwork using machine learning algorithmsSN Comput Sci2022411510.1007/s42979-022-01529-8 – reference: MengyaoCYuTIntelligent product art design based on smart equipment and ma-chine learning algorithm: practice effect and trend analysisSoft Comput2023278449845810.1007/s00500-023-08146-4 – reference: SpeeBTMMikuniJLederHScharnowskiFPelowskiMSteyrlDMachine learning revealed symbolism, emotionality, and imaginativeness as primary predictors of creativity evaluations of Western art paintingsSci Rep202310.1038/s41598-023-39865-1 – reference: Srinivasa DesikanBShimaoHMitonHWikiArtVectors: style and color repre-sentations of artworks for cultural analysis via information theoretic measuresEntropy202224117510.3390/e24091175 – reference: ZhangJDuanYGuXResearch on emotion analysis of Chinese Literati painting images based on deep learningFront Psychol202110.3389/fpsyg.2021.723325 – reference: RagotSMeasuring the originality of intellectual property assets based on estimated inter-asset distancesWorld Patent Inf20226910210610.1016/j.wpi.2022.102106 – reference: Pedraza-NavarroISánchez-SerranoSAnálisis De Las Publicaciones presentes En WoS Y Scopus. Posibilidades De Búsqueda Para Evitar Literatura Fugitiva En Las Revisiones SistemáticasRevista Interuniversitaria De Investigación en Tecnología Educativa20222022416110.6018/riite.548361 – reference: KnippenbergEJensenNConstasMQuantifying household resilience with high frequency data: temporal dynamics and methodological optionsWorld Dev201912111510.1016/j.worlddev.2019.04.010 – reference: Sun X, Qin J (2021) Deep learning-based creative intention understanding and color suggestions for illustration. In: Ahram T (eds) Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1213. Springer, Cham. https://doi.org/10.1007/978-3-030-51328-3_14 – reference: Folego G, Gomes O, Rocha A (2016) From impressionism to expressionism: automatically identifying Van Gogh’s paintings. In: Proceedings-International Conference on Image Processing, ICIP https://doi.org/10.1109/icip.2016.7532335 – reference: XuYNazirSRanking the art design and applications of artificial intelligence and machine learningJ Softw Evol Process202210.1002/smr.2486 – reference: Cucci C, Barucci A, Stefani L, Picollo M, Jiménez-Garnica R, Fuster-Lopez L (2021) Reflectance hyperspectral data processing on a set of picasso paintings: which algorithm provides what? A comparative analysis of multivariate, statistical and artificial intel-ligence methods. In R. Groves & H. Liang (Eds.), Proceedings of the Optics for Arts, Architecture, and Archaeology VIII. p. 1 – reference: Bidgoli A, De Guevara ML, Hsiung C, Oh J, Kang E (2020) Artistic style in robotic painting; a machine learning approach to learning brushstroke from human artists. In: 29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020. https://doi.org/10.1109/ro-man47096.2020.9223533 – reference: GülzowJMPaetzoldPDeussenORecent developments regarding painting robots for research in automatic painting, artificial creativity, and machine learningAppl Sci202010339610.3390/app10103396 – reference: Fails J, Olsen D (2003c) A design tool for camera-based interaction. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems; ACM, 449–456 – reference: SanhudoLCalvettiDMartinsJPRamosNMMMêdaPGonçalvesMCSousaHActivity classification using accelerometers and machine learning for com-plex construction worker activitiesJ Building Eng20213510200110.1016/j.jobe.2020.102001 – reference: MaoWVideo analysis of intelligent teaching based on machine learning and virtual reality technologyNeural Comput Appl2022346603661410.1007/s00521-021-06072-w – reference: Achlioptas P, Ovsjanikov M, Haydarov K, Elhoseiny M, Guibas LJ (2021) ArtEmis: affective language for visual art. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and PatternRecognition https://doi.org/10.1109/cvpr46437.2021.01140 – reference: Jboor NH, Belhi A, Al-Ali AK, Bouras A, Jaoua A (2019) Towards an inpainting framework for visual cultural heritage. In: Proceedings of the 2019 IEEE Jordan Interna-Tional Joint Conference on Electrical Engineering and Information Technology (JEEIT); IEEE, pp. 602–607 – reference: Orduña-MaleaECostasRLink-based approach to study scientific software usage: the case of VOSviewerScientometrics20211268153818610.1007/s11192-021-04082-y – reference: LürigMDDonougheSSvenssonEIPortoATsuboiMComputer vision, machine learning, and the promise of phenomics in ecology and evolutionary biologyFront Ecol Evol202110.3389/fevo.2021.64277 – reference: Polatkan G, Jafarpour S, Brasoveanu A, Hughes SM, Daubechies I (2009) Detection of forgery in paintings using supervised learning. In: Proceedings-International Conference on Image Processing, ICIP https://doi.org/10.1109/icip.2009.5413338 – reference: LcRHuman enough: a space for reconstructions of AI visions in speculative climate futuresCreat Cognit202310.1145/3591196.3593341 – reference: LiuYQinZWanTLuoZAuto-painter: cartoon image generation from sketch by using conditional wasserstein generative adversarial networksNeurocomputing2018311788710.1016/j.neucom.2018.05.045 – reference: Culjak M, Mikus B, Jez K, Hadjic S (2011) Classification of art paintings by genre. In: International Convention on Information and Communication Technology, Electronics and Microelectronics, pp. 1634–1639. https://ieeexplore.ieee.org/document/5967323/ – reference: CalifanoAFotiPBertoFBaiesiMBertolinCPredicting damage evolution in panel paintings with machine learningProcedia Struct Integr20224114515710.1016/j.prostr.2022.05.017 – reference: TzengF-YLumEBKwan-Liu Ma An intelligent system approach to higher dimensional classification of volume dataIEEE Trans Vis Comput Graph20051127328410.1109/TVCG.2005.38 – reference: DobbsTBenedictARasZJumping into the artistic deep end: building the Cata-Logue RaisonnéAI Soc20223787388910.1007/s00146-021-01370-2 – reference: BoloorAGarimellaKHeXGillCVorobeychikYZhangXAttacking Vi-sion-based perception in end-to-end autonomous driving modelsJ Syst Architect202011010176610.1016/j.sysarc.2020.101766 – reference: KogouSLeeLShahtahmassebiGLiangHA new approach to the interpretation of XRF spectral imaging data using neural networksX-Ray Spectrom202050431031910.1002/xrs.3188 – reference: Irfan MT, Stork DG (2009) Multiple visual features for the computer authentication of Jackson Pollock’s drip paintings: beyond box counting and fractals. Proceedings Of SPIE. https://doi.org/10.1117/12.806245 – reference: Lu Y, Guo C, Dai X, Wang F-Y (2021) Image captioning on fine art paintings via virtual paintings. In: Proceedings of the 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence. DTPI, pp. 156–159 – reference: ZhaoWZhouDQiuXJiangWHow to represent paintings: a painting classification using artistic comments Sensors202110.3390/s21061940 – reference: WenjingXCaiZAssessing the best art design based on artificial intelligence and machine learning using GTMASoft Comput20232714915610.1007/s00500-022-07555-1 – reference: FanH-TXiaoGArinezJCoulthardMA case study on first time quality feature investigation for an automotive paint shopManuf Lett20223391992610.1016/j.mfglet.2022.07.112 – reference: PageMJMcKenzieJEBossuytPMBoutronIHoffmannTCMulrowCDShamseerLTetzlaffJMAklEABrennanSEThe PRISMA 2020 Statement: an updated guideline for reporting systematic reviewsInt J Surg20218810590610.1016/j.ijsu.2021.105906 – reference: JeongJHWooJHParkJMachine learning methodology for management of shipbuilding master dataInt J Naval Archit Ocean Eng20201242843910.1016/j.ijnaoe.2020.03.005 – reference: LiCChenTAesthetic visual quality assessment of paintingsIEEE J Sel Top Signal Process2009323625210.1109/JSTSP.2009.2015077 – reference: FalomirZMuserosLSanzIAbrilLGCategorizing paintings in art styles based on qualitative color descriptors, quantitative global features and machine learning (QArt-Learn)Expert Syst Appl201897839410.1016/j.eswa.2017.11.056 – reference: YuTLinCZhangSWangCDingXAnHLiuXQuTWanLYouSArtificial intelligence for Dunhuang cultural heritage protection: the project and the datasetInt J Comput Vis20221302646267310.1007/s11263-022-01665-x – reference: DurieuxVGevenoisPABibliometric indicators: quality measurements of scientific publicationRadiology201025534235110.1148/radiol.09090626 – reference: Sánchez Santana P, Roman-Rangel E (2021) Quantifying visual similarity for artistic styles. In: Roman-Rangel E, Kuri-Morales ÁF, Martínez-Trinidad JF, Carrasco-Ochoa JA, Olvera-López JA (eds) Pattern recognition. MCPR 2021. Lecture Notes in Computer Science, vol. 12725. Springer, Cham. https://doi.org/10.1007/978-3-030-77004-4_18 – reference: Vadicherla D, Gadicha V (2022) Supervised machine learning approach for crack detection in digital images. In: Tomar A, Malik H, Kumar P, Iqbal A (eds) Proceedings of 3rd International Conference on Machine Learning, Advances in Computing, Renewable Energy and Communication. Lecture Notes in Electrical Engineering, vol 915. Springer, Singapore. https://doi.org/10.1007/978-981-19-2828-4_21 – reference: AllahlohASSarfrazMGhalebAMAl-Shamma’aAAFarhHAl-ShaalanAMRevolutionizing IC genset operations with IIoT and AI: a study on fuel savings and predictive maintenanceSustainability202315880810.3390/su15118808 – reference: Alameda-Pineda X, Ricci E, Yan Y, Sebe N (2016) Recognizing emotions from abstract paintings using non-linear matrix completion. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2016.566 – reference: CasconeLNappiMNarducciFRussoSLClassification of fragments: recognition of artistic styleJ Ambient Intell Humaniz Comput2023144087409710.1007/s12652-022-04472-x – reference: MondalBArtificial intelligence: state of the artEn Intell Syst Ref Libr201910.1007/978-3-030-32644-9_32 – reference: Li H, Zhang Z (2022) A study of unsupervised networks based on the network prior for the image inpainting. In: Proceedings of the 2022 3rd International Conference on Electronic CommuInication and Artificial Intelligence (IWECAI); IEEE: pp. 07–110 – reference: GengenbachTKerstinSArtificial intelligence raters. neural networks for rating pictorial expressionJ Sci Technol Arts2022144971 – reference: Kumar M, Sharma S, Chaudhary D, Prakash S (2021) Image recognition using artificial intelligence. In: Proceedings of the 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE); IEEE, pp. 760–763 – reference: Fails JA, Olsen DR (2003b) Interactive machine learning. In: Proceedings of the Proceedings of the 8th International Conference on Intelligent User Interfaces; ACM, pp. 39–45 – reference: Fails JA, Olsen DR (2003a) Interactive machine learning. In: Proceedings of the 8th International Conference on Intelligent User Interfaces; ACM, pp. 39–45 – reference: Van Der MaatenLErdmannRGAutomatic thread-level canvas analysis: a machine-learning approach to analyzing the canvas of paintingsIEEE Signal Process Mag2015324384510.1109/msp.2015.2407091 – reference: BelhiABourasAFoufouSLeveraging known data for missing label prediction in cultural heritage contextAppl Sci2018810176810.3390/app8101768 – ident: 10727_CR44 doi: 10.1007/978-3-030-77004-4_18 – year: 2022 ident: 10727_CR54 publication-title: J Softw Evol Process doi: 10.1002/smr.2486 – volume: 15 start-page: 8808 year: 2023 ident: 10727_CR3 publication-title: Sustainability doi: 10.3390/su15118808 – year: 2021 ident: 10727_CR34 publication-title: Front Ecol Evol doi: 10.3389/fevo.2021.64277 – volume: 33 start-page: 919 year: 2022 ident: 10727_CR18 publication-title: Manuf Lett doi: 10.1016/j.mfglet.2022.07.112 – year: 2023 ident: 10727_CR29 publication-title: Creat Cognit doi: 10.1145/3591196.3593341 – volume: 255 start-page: 342 year: 2010 ident: 10727_CR13 publication-title: Radiology doi: 10.1148/radiol.09090626 – ident: 10727_CR28 doi: 10.1109/ICACITE51222.2021.9404606 – year: 2022 ident: 10727_CR49 publication-title: Mobile Information Systems doi: 10.1155/2022/8804711 – ident: 10727_CR41 doi: 10.1109/icip.2009.5413338 – volume: 27 start-page: 149 year: 2023 ident: 10727_CR53 publication-title: Soft Comput doi: 10.1007/s00500-022-07555-1 – volume: 24 start-page: 1175 year: 2022 ident: 10727_CR47 publication-title: Entropy doi: 10.3390/e24091175 – year: 2022 ident: 10727_CR22 publication-title: Int J Humanoid Rob doi: 10.1142/S0219843622400023 – ident: 10727_CR14 doi: 10.1145/604045.604056 – ident: 10727_CR16 doi: 10.1145/642611.642690 – volume: 41 start-page: 145 year: 2022 ident: 10727_CR7 publication-title: Procedia Struct Integr doi: 10.1016/j.prostr.2022.05.017 – volume: 12 start-page: 428 year: 2020 ident: 10727_CR25 publication-title: Int J Naval Archit Ocean Eng doi: 10.1016/j.ijnaoe.2020.03.005 – volume: 11 start-page: 273 year: 2005 ident: 10727_CR50 publication-title: IEEE Trans Vis Comput Graph doi: 10.1109/TVCG.2005.38 – ident: 10727_CR15 doi: 10.1145/604045.604056 – volume: 50 start-page: 310 issue: 4 year: 2020 ident: 10727_CR27 publication-title: X-Ray Spectrom doi: 10.1002/xrs.3188 – ident: 10727_CR5 doi: 10.1109/ro-man47096.2020.9223533 – volume: 88 start-page: 105906 year: 2021 ident: 10727_CR39 publication-title: Int J Surg doi: 10.1016/j.ijsu.2021.105906 – year: 2019 ident: 10727_CR37 publication-title: En Intell Syst Ref Libr doi: 10.1007/978-3-030-32644-9_32 – ident: 10727_CR19 doi: 10.1109/icip.2016.7532335 – volume: 311 start-page: 78 year: 2018 ident: 10727_CR32 publication-title: Neurocomputing doi: 10.1016/j.neucom.2018.05.045 – volume: 37 start-page: 873 year: 2022 ident: 10727_CR12 publication-title: AI Soc doi: 10.1007/s00146-021-01370-2 – volume: 14 start-page: 49 year: 2022 ident: 10727_CR20 publication-title: J Sci Technol Arts – volume: 27 start-page: 8449 year: 2023 ident: 10727_CR36 publication-title: Soft Comput doi: 10.1007/s00500-023-08146-4 – volume: 69 start-page: 102106 year: 2022 ident: 10727_CR42 publication-title: World Patent Inf doi: 10.1016/j.wpi.2022.102106 – volume: 3 start-page: 236 year: 2009 ident: 10727_CR30 publication-title: IEEE J Sel Top Signal Process doi: 10.1109/JSTSP.2009.2015077 – ident: 10727_CR2 doi: 10.1109/cvpr.2016.566 – ident: 10727_CR11 – volume: 130 start-page: 2646 year: 2022 ident: 10727_CR55 publication-title: Int J Comput Vis doi: 10.1007/s11263-022-01665-x – volume: 4 start-page: 115 year: 2022 ident: 10727_CR9 publication-title: SN Comput Sci doi: 10.1007/s42979-022-01529-8 – ident: 10727_CR23 doi: 10.1117/12.806245 – volume: 110 start-page: 101766 year: 2020 ident: 10727_CR6 publication-title: J Syst Architect doi: 10.1016/j.sysarc.2020.101766 – ident: 10727_CR51 doi: 10.1007/978-981-19-2828-4_21 – volume: 10 start-page: 3396 year: 2020 ident: 10727_CR21 publication-title: Appl Sci doi: 10.3390/app10103396 – volume: 8 start-page: 1768 issue: 10 year: 2018 ident: 10727_CR4 publication-title: Appl Sci doi: 10.3390/app8101768 – volume: 2022 start-page: 41 year: 2022 ident: 10727_CR40 publication-title: Revista Interuniversitaria De Investigación en Tecnología Educativa doi: 10.6018/riite.548361 – ident: 10727_CR33 doi: 10.1109/DTPI52967.2021.9540081 – year: 2023 ident: 10727_CR46 publication-title: Sci Rep doi: 10.1038/s41598-023-39865-1 – ident: 10727_CR48 doi: 10.1007/978-3-030-51328-3_14 – volume: 35 start-page: 102001 year: 2021 ident: 10727_CR45 publication-title: J Building Eng doi: 10.1016/j.jobe.2020.102001 – volume: 32 start-page: 38 issue: 4 year: 2015 ident: 10727_CR52 publication-title: IEEE Signal Process Mag doi: 10.1109/msp.2015.2407091 – ident: 10727_CR1 doi: 10.1109/cvpr46437.2021.01140 – volume: 126 start-page: 8153 year: 2021 ident: 10727_CR38 publication-title: Scientometrics doi: 10.1007/s11192-021-04082-y – volume: 14 start-page: 4087 year: 2023 ident: 10727_CR8 publication-title: J Ambient Intell Humaniz Comput doi: 10.1007/s12652-022-04472-x – ident: 10727_CR10 doi: 10.1117/12.2593838 – volume: 97 start-page: 83 year: 2018 ident: 10727_CR17 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2017.11.056 – volume: 34 start-page: 6603 year: 2022 ident: 10727_CR35 publication-title: Neural Comput Appl doi: 10.1007/s00521-021-06072-w – volume: 121 start-page: 1 year: 2019 ident: 10727_CR26 publication-title: World Dev doi: 10.1016/j.worlddev.2019.04.010 – ident: 10727_CR31 doi: 10.1109/IWECAI55315.2022.00029 – year: 2021 ident: 10727_CR57 publication-title: Sensors doi: 10.3390/s21061940 – ident: 10727_CR24 doi: 10.1109/JEEIT.2019.8717470 – year: 2020 ident: 10727_CR43 publication-title: Nat Commun doi: 10.1038/s41467-020-18566-7 – year: 2021 ident: 10727_CR56 publication-title: Front Psychol doi: 10.3389/fpsyg.2021.723325 |
| SSID | ssj0005243 |
| Score | 2.3763113 |
| Snippet | In the field of art, machine learning models have been used to predict artistic styles in paintings. The foregoing is somewhat advantageous for analysts, as... |
| SourceID | proquest gale crossref springer |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 118 |
| SubjectTerms | Artificial Intelligence Bibliometrics Computational linguistics Computer Science Creative ability Deep learning Documents Graphics software Image manipulation Language processing Machine learning Natural language interfaces Neural networks Pattern recognition Trends |
| SummonAdditionalLinks | – databaseName: Springer Nature OA Free Journals dbid: C24 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3LS8MwGA-iFy--xeqUHAQPWugjaRZvYziGoCcHu8W8qofZjbUe_O_9kqWbbxB6apO2fO8f3yMInec2zxWFyK3QJIuJJGUsFaOxBqyQW_APCXPdyHf3xXBEbsd0HJrC6rbavU1Jekv9odmNFFkMPgVMB3jdGID6BgXs7uS673ocloUdJA_tMT_v--SCvhribxlR72gGO2grRIi4t2DpLlqz1R7abk9fwEEZ99Gjz_bjF18NaXE4_uEJN1M8m7v0S4OdWLg5zLhu3ia2vsaygmsxhQRPS9z4gli4ZTDEgTgM_nnGYGMAqR-g0eDmoT-Mw3kJsQYU2MS8S1IO4Ze04LY1UUlXGjdOi5KyJNIQldvMUJkWhckKaUudyoIpxSzjWqXa5IdovZpW9gjhEvReWUmYJdwhTGlNqUuumdHcpkxGKG1JKHQYJu7OtJiI1RhkR3YBZBee7CKJ0OVyz2wxSuPP1ReOM8LpGbxZy9AuAP_nJlaJHuOAqDOe0Ah1WuaJoIC1AM_cJRTAIIvQVcvQ1ePfv3v8v-UnaDNzouVLIDtovZm_2lMIUxp15qXyHeoe3us priority: 102 providerName: Springer Nature |
| Title | Using machine learning to predict artistic styles: an analysis of trends and the research agenda |
| URI | https://link.springer.com/article/10.1007/s10462-024-10727-0 https://www.proquest.com/docview/3038457167 |
| Volume | 57 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1LbxMxEB61zYULjxZEoEQ-VOJALXa9XnvdCwohaVVIQUCkcjJ-LRxKkjbLgX_PeOMlQNVKK6-0D-9qxv5mxp4HwEERisKWqLkJxxnlhtfUWFlSh7ZCEVA-ZDJGI0_PxMmMn56X52nBbZXcKjtMbIHaL1xcI3-JUFvxErV7-Wp5SWPVqLi7mkpobEMPIbhC46v3enz24eNfTh5rvzkmFEXjIk9hMyl4jgtGUUYhFKEUp9k_oul_gL62U9oKoMl9uJs0RzJcs_oBbIX5LtzrqjKQNEn34GvrBUB-tF6SgaSyEN9IsyDLq7gt05A4XGJ-ZrJqfl2E1RExczzW2UnIoiZN6yiLlzxB_ZCkhEDfCWIPWvAPYTYZfx6d0FRHgTq0DhuqKp4rVMtMQHHuuM0q42OarZLXNTee2yIwX5pcCM-ECbXLjZDWyiCVs7nzxSPYmS_m4TGQGvHABsNl4Cpanib42tXKSe9UyKXpQ96RULuUZDzWurjQm_TIkewaya5bsuusDy_-vLNcp9i49ennkTM6zj_s2ZkURoD_FzNZ6aFUaGkzlZV92O-Yp9PEXOnNMOrDYcfQze2bv_vk9t6ewh0Wh1LrCrkPO83Vz_AM1ZXGDmC7mhwPoDd8M333KZ6Pv7wdD9JIxbsjxmMrRthO8_fYztjwN-Id7SE |
| linkProvider | ProQuest |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwEB6VcoBLeYstBXwAcQCLxHHiNRJCFbBs6ePUSr25foUe2uzSDar6p_obmUkcloforVJOeTjRePzNTDzzDcCLIhaFK9Fzq7wUXFpZc-tUyT3GCkVE-5Apqkbe3aumB_LrYXm4ApdDLQylVQ6Y2AF1mHn6R_4WoXYsS_Tu1Yf5d05do2h3dWih0avFdrw4x5Bt8X7rE87vSyEmn_c_TnnqKsA9xkot12OZa3RSbETj5qXLxjYQ6VQp61raIF0RRShtXlVBVDbWPreVck5Fpb3LfShw3BtwUxaFphU1nnz5LaWkz9ITleYYyuSpSCeV6slKcLSICHzoM_DsD0P4tzn4Z1-2M3eTu7CW_FS22SvWPViJzX24M_SAYAkSHsBRl3PATruczMhSE4pvrJ2x-RltArWMlJPYoNmivTiJi3fMNnj0XChsVrO2S8vFU4GhN8oS_dAxQ6Rrgn0IB9ci30ew2sya-BhYjejjopUqSk1xro2h9rX2Kngdc2VHkA8iND5RmlNnjROzJGMmsRsUu-nEbrIRvP71zLwn9Ljy7lc0M4ZWO47sbSpawO8j3iyzqTTG9UJn5Qg2hskzCQYWZqm0I3gzTOjy8v_fu371aM_h1nR_d8fsbO1tP4HbgtSqS8LcgNX27Ed8io5S65512sng6LqXw08XyiHm |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1NTxRBEK0gJsaL-BlXUPug8aANMz093TsmxhBxAyLoQRJuTX9qAsyu7BACP42_4p-xeqbHVYzcOJjsaT96d2dfv6rafvUK4Fnhi8KUmLkJyxnlmgeqjSypxVqh8BgfMhm7kbe2xfoO_7Bb7s7Bed8LE2WVPSe2RO3GNv5HvoJUO-QlZvdyJSRZxOe10dvJdxonSMWT1n6cRgeRTX96guXb9M3GGv7Wzxkbvf_ybp2mCQPUYt3U0GrI8woTFu0x0FlusqF20YCq5CFw7bgpPHOlzoVwTGgfbK6FNEZ6WVmTW1fgutfguuRiGOWEW_mn3-QlnWKPiYpiWZOnhp3UtscFoxgdkQQxf6DZH0HxYmj464y2DX2jBfjRX7RO8bK_fNyYZXt2wU_y_7yqt-FWysjJareF7sCcr-_CQj_tgiTyuwd7rbqCHLbqU0_SuI2vpBmTyVE87mpI3IbR95pMm9MDP31NdI23zvWFjANpWgEy3uUI5t0kGS19I8jptdP3YedKvucDmK_HtX8IJCDPGq-59LyKFb32LthQWels5XOpB5D3AFE2mbfHGSIHamY7HUGlEFSqBZXKBvDy12smnXXJpc9-EXGnIq_hylan9gz8fNEhTK3KKmNYiWXlAJZ6LKlEeFM1A9IAXvVwnT387_d9dPlqT-EGAlJ93NjeXISbLO6ZVm26BPPN0bF_jBlhY560W4_A3lWj8iej-W4P |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Using+machine+learning+to+predict+artistic+styles%3A+an+analysis+of+trends+and+the+research+agenda&rft.jtitle=The+Artificial+intelligence+review&rft.au=Valencia%2C+Jackeline&rft.au=Pineda%2C+Geraldine+Garc%C3%ADa&rft.au=Pineda%2C+Vanessa+Garc%C3%ADa&rft.au=Valencia-Arias%2C+Alejandro&rft.date=2024-05-01&rft.pub=Springer&rft.issn=0269-2821&rft.volume=57&rft.issue=5&rft_id=info:doi/10.1007%2Fs10462-024-10727-0&rft.externalDocID=A790272905 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1573-7462&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1573-7462&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1573-7462&client=summon |