Automatic and intelligent content visualization system based on deep learning and genetic algorithm
Increasing demand in distance education, e-learning, web-based learning, and other digital sectors (e.g., entertainment) has led to excessive amounts of e-content. Learning objects (LOs) are among the most important components of electronic content (e-content) and are preserved in learning object re...
Uloženo v:
| Vydáno v: | Neural computing & applications Ročník 34; číslo 3; s. 2473 - 2493 |
|---|---|
| Hlavní autor: | |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
London
Springer London
01.02.2022
Springer Nature B.V |
| Témata: | |
| ISSN: | 0941-0643, 1433-3058 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Increasing demand in distance education, e-learning, web-based learning, and other digital sectors (e.g., entertainment) has led to excessive amounts of e-content. Learning objects (LOs) are among the most important components of electronic content (e-content) and are preserved in learning object repositories (LORs). LORs produce different types of electronic content. In producing e-content, several visualization techniques are employed to attract users and ensure a better understanding of the provided information. Many of these visualization systems match images with corresponding text using methods such as semantic web, ontologies, natural language processing, statistical techniques, neural networks, and deep neural networks. Unlike these methods, in this study, an automatic and intelligent content visualization system is developed using deep learning and popular artificial intelligence techniques. The proposed system includes subsystems that segment images to panoptic image instances and use these image instances to generate new images using a genetic algorithm, an evolution-based technique that is one of the best-known artificial intelligence methods. This large-scale proposed system was used to test different amounts of LOs for various science fields. The results show that the developed system can be efficiently used to create visually enhanced content for digital use. |
|---|---|
| AbstractList | Increasing demand in distance education, e-learning, web-based learning, and other digital sectors (e.g., entertainment) has led to excessive amounts of e-content. Learning objects (LOs) are among the most important components of electronic content (e-content) and are preserved in learning object repositories (LORs). LORs produce different types of electronic content. In producing e-content, several visualization techniques are employed to attract users and ensure a better understanding of the provided information. Many of these visualization systems match images with corresponding text using methods such as semantic web, ontologies, natural language processing, statistical techniques, neural networks, and deep neural networks. Unlike these methods, in this study, an automatic and intelligent content visualization system is developed using deep learning and popular artificial intelligence techniques. The proposed system includes subsystems that segment images to panoptic image instances and use these image instances to generate new images using a genetic algorithm, an evolution-based technique that is one of the best-known artificial intelligence methods. This large-scale proposed system was used to test different amounts of LOs for various science fields. The results show that the developed system can be efficiently used to create visually enhanced content for digital use. Increasing demand in distance education, e-learning, web-based learning, and other digital sectors (e.g., entertainment) has led to excessive amounts of e-content. Learning objects (LOs) are among the most important components of electronic content (e-content) and are preserved in learning object repositories (LORs). LORs produce different types of electronic content. In producing e-content, several visualization techniques are employed to attract users and ensure a better understanding of the provided information. Many of these visualization systems match images with corresponding text using methods such as semantic web, ontologies, natural language processing, statistical techniques, neural networks, and deep neural networks. Unlike these methods, in this study, an automatic and intelligent content visualization system is developed using deep learning and popular artificial intelligence techniques. The proposed system includes subsystems that segment images to panoptic image instances and use these image instances to generate new images using a genetic algorithm, an evolution-based technique that is one of the best-known artificial intelligence methods. This large-scale proposed system was used to test different amounts of LOs for various science fields. The results show that the developed system can be efficiently used to create visually enhanced content for digital use.Increasing demand in distance education, e-learning, web-based learning, and other digital sectors (e.g., entertainment) has led to excessive amounts of e-content. Learning objects (LOs) are among the most important components of electronic content (e-content) and are preserved in learning object repositories (LORs). LORs produce different types of electronic content. In producing e-content, several visualization techniques are employed to attract users and ensure a better understanding of the provided information. Many of these visualization systems match images with corresponding text using methods such as semantic web, ontologies, natural language processing, statistical techniques, neural networks, and deep neural networks. Unlike these methods, in this study, an automatic and intelligent content visualization system is developed using deep learning and popular artificial intelligence techniques. The proposed system includes subsystems that segment images to panoptic image instances and use these image instances to generate new images using a genetic algorithm, an evolution-based technique that is one of the best-known artificial intelligence methods. This large-scale proposed system was used to test different amounts of LOs for various science fields. The results show that the developed system can be efficiently used to create visually enhanced content for digital use. |
| Author | İnce, Murat |
| Author_xml | – sequence: 1 givenname: Murat orcidid: 0000-0001-5566-5008 surname: İnce fullname: İnce, Murat email: muratince@isparta.edu.tr organization: Vocational School of Technical Sciences, Isparta University of Applied Sciences |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35068702$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kUtv1TAQhS1URG8Lf4AFisSGTcr47W6QqopHpUpsYG05jpO6SuyLnVRqfz3Te0uBLrqyRvOd45k5R-Qg5RQIeUvhhALojxVAMtoCYy0oY3RLX5ANFZy3HKQ5IBs4FdhWgh-So1qvAUAoI1-RQy5RoIFtiD9blzy7JfrGpb6JaQnTFMeQlsZnLPC9iXV1U7xDKKem3tYlzE3naugbrPsQts0UXEkxjTsPFIed3zTmEper-TV5ObiphjcP7zH5-eXzj_Nv7eX3rxfnZ5etF1osrQPNdK8k5Xrgp5KpTkoudCepUOBg0LRTxjuvVRBMKaNUL3TPh8AGHTqn-TH5tPfdrt0ceo_DFzfZbYmzK7c2u2j_76R4Zcd8Y41WgPdDgw8PBiX_WkNd7Byrx4O4FPJaLVOMCW20kIi-f4Je57UkXO-ekoJSwQCpd_9O9DjKn_sjwPaAL7nWEoZHhIK9D9nuQ7YYst2FbCmKzBORj8suHdwqTs9L-V5a8Z80hvJ37GdUvwGYn7xI |
| CitedBy_id | crossref_primary_10_1142_S021812662550152X crossref_primary_10_1155_2022_6901184 crossref_primary_10_1007_s00521_022_07532_7 crossref_primary_10_4018_IJDWM_333518 crossref_primary_10_1002_eng2_12785 crossref_primary_10_1515_comp_2024_0021 crossref_primary_10_1109_ACCESS_2022_3205115 crossref_primary_10_1007_s00521_022_07911_0 crossref_primary_10_1140_epjp_s13360_025_06520_9 crossref_primary_10_1109_ACCESS_2024_3508796 |
| Cites_doi | 10.1038/s41563-020-0678-8 10.1109/CIVEMSA.2018.8439958 10.1109/CVPR.2017.577 10.1109/ACCESS.2017.2710315 10.1007/978-3-030-01267-0_7 10.1038/nature14539 10.3115/980491.980597 10.1109/TAAI.2013.26 10.1007/s00530-014-0371-3 10.1109/CVPR.2019.00719 10.1109/TVCG.2012.264 10.1109/CVPR.2015.7299087 10.1007/s10845-010-0393-4 10.1145/1126004.1126008 10.1109/TPWRS.2013.2238259 10.3991/ijet.v11i04.5574 10.1177/016264340602100201 10.1007/BFb0034824 10.1109/IV.2016.57 10.1002/widm.1187 10.1145/383259.383316 10.1109/72.279181 10.1023/A:1022602019183 10.1016/j.energy.2019.07.134 10.1109/MC.2003.1244540 10.1145/2932710 10.1111/cgf.13852 10.1109/TPAMI.2016.2587640 10.1007/s00521-017-3023-7 10.3115/992424.992482 10.1108/EL-03-2015-0046 10.1145/3173574.3173996 10.1177/0165551519849514 10.1016/S0959-4752(02)00017-8 10.1007/s12193-019-00301-2 10.1109/TVCG.2018.2834341 10.1007/978-94-017-2388-6_5 10.1109/TLT.2013.6 10.3115/1073083.1073135 10.1016/j.learninstruc.2018.01.006 10.4324/9781315471136-6 10.11648/j.ajmse.20190403.12 10.1109/CVPR.2019.00633 10.1016/j.neucom.2017.10.051 10.1145/381641.381653 10.1109/CSNT.2014.183 10.1109/CVPR.2019.00656 10.1109/CEC.2006.1688424 10.1145/2964284.2964299 10.1504/IJCEELL.2006.008917 10.1016/j.sbspro.2014.05.141 10.1016/j.ijer.2018.05.007 10.1007/s10590-009-9050-0 10.1002/widm.1332 10.1613/jair.4900 10.1109/TPAMI.2017.2721945 10.1109/CVPR.2019.00963 10.1109/COMPSAC.2018.00179 10.1021/acs.jchemed.9b00690 10.1016/j.compind.2003.09.002 10.3390/app10010391 10.1145/3020165.3020182 10.1016/j.chb.2005.01.009 10.1109/LRA.2020.2969919 10.7148/2014-0340 10.1080/02680939.2015.1035758 |
| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. Copyright Springer Nature B.V. Feb 2022 |
| Copyright_xml | – notice: The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 – notice: The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. – notice: Copyright Springer Nature B.V. Feb 2022 |
| DBID | AAYXX CITATION NPM 8FE 8FG AFKRA ARAPS BENPR BGLVJ CCPQU DWQXO HCIFZ P5Z P62 PHGZM PHGZT PKEHL PQEST PQGLB PQQKQ PQUKI PRINS 7X8 5PM |
| DOI | 10.1007/s00521-022-06887-1 |
| DatabaseName | CrossRef PubMed ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Technology collection ProQuest One Community College ProQuest Central SciTech Premium Collection Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef PubMed Advanced Technologies & Aerospace Collection Technology Collection ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest One Academic Eastern Edition SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central China ProQuest Central Advanced Technologies & Aerospace Database ProQuest One Applied & Life Sciences ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) MEDLINE - Academic |
| DatabaseTitleList | PubMed MEDLINE - Academic Advanced Technologies & Aerospace Collection |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: P5Z name: Advanced Technologies & Aerospace Database url: https://search.proquest.com/hightechjournals sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1433-3058 |
| EndPage | 2493 |
| ExternalDocumentID | PMC8760887 35068702 10_1007_s00521_022_06887_1 |
| Genre | Journal Article |
| GroupedDBID | -Y2 -~C .4S .86 .DC .VR 06D 0R~ 0VY 123 1N0 1SB 2.D 203 28- 29N 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 30V 4.4 406 408 409 40D 40E 53G 5QI 5VS 67Z 6NX 8FE 8FG 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAOBN AAPKM AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBRH ABBXA ABDBE ABDBF ABDZT ABECU ABFTD ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABLJU ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACUHS ACZOJ ADHHG ADHIR ADHKG ADIMF ADKFA ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFDZB AFEXP AFGCZ AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGQPQ AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHPBZ AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG AVWKF AXYYD AYFIA AYJHY AZFZN B-. B0M BA0 BBWZM BDATZ BENPR BGLVJ BGNMA BSONS CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 EAD EAP EBLON EBS ECS EDO EIOEI EJD EMI EMK EPL ESBYG EST ESX F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNWQR GQ7 GQ8 GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IHE IJ- IKXTQ ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ KDC KOV KOW LAS LLZTM M4Y MA- N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM P19 P2P P62 P9O PF0 PHGZT PT4 PT5 QOK QOS R4E R89 R9I RHV RIG RNI RNS ROL RPX RSV RZK S16 S1Z S26 S27 S28 S3B SAP SCJ SCLPG SCO SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 ZMTXR ~8M ~EX AAYXX ABFSG ABRTQ ACSTC AEZWR AFFHD AFHIU AFOHR AHWEU AIXLP ATHPR CITATION PHGZM PQGLB -4Z -59 -5G -BR -EM ADINQ GQ6 NPM Z5O Z7R Z7S Z7V Z7W Z7X Z7Y Z7Z Z81 Z83 Z86 Z88 Z8M Z8N Z8P Z8Q Z8R Z8S Z8T Z8U Z8W Z92 DWQXO PKEHL PQEST PQQKQ PQUKI PRINS 7X8 PUEGO 5PM |
| ID | FETCH-LOGICAL-c474t-a0727d65137f39526b55347b51460a0f71b68cac76e4266866d47d3fe2f7eba73 |
| IEDL.DBID | RSV |
| ISICitedReferencesCount | 14 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000742823200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0941-0643 |
| IngestDate | Tue Nov 04 02:01:01 EST 2025 Fri Sep 05 10:06:54 EDT 2025 Wed Nov 05 00:44:29 EST 2025 Wed Feb 19 02:26:49 EST 2025 Tue Nov 18 22:36:37 EST 2025 Sat Nov 29 08:04:12 EST 2025 Thu Apr 10 07:42:00 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 3 |
| Keywords | Deep learning e-content visualization Genetic algorithm Panoptic segmentation |
| Language | English |
| License | The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c474t-a0727d65137f39526b55347b51460a0f71b68cac76e4266866d47d3fe2f7eba73 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0001-5566-5008 |
| OpenAccessLink | https://pubmed.ncbi.nlm.nih.gov/PMC8760887 |
| PMID | 35068702 |
| PQID | 2625411420 |
| PQPubID | 2043988 |
| PageCount | 21 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_8760887 proquest_miscellaneous_2622478745 proquest_journals_2625411420 pubmed_primary_35068702 crossref_primary_10_1007_s00521_022_06887_1 crossref_citationtrail_10_1007_s00521_022_06887_1 springer_journals_10_1007_s00521_022_06887_1 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-02-01 |
| PublicationDateYYYYMMDD | 2022-02-01 |
| PublicationDate_xml | – month: 02 year: 2022 text: 2022-02-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England – name: Heidelberg |
| PublicationTitle | Neural computing & applications |
| PublicationTitleAbbrev | Neural Comput & Applic |
| PublicationTitleAlternate | Neural Comput Appl |
| PublicationYear | 2022 |
| Publisher | Springer London Springer Nature B.V |
| Publisher_xml | – name: Springer London – name: Springer Nature B.V |
| References | M Ince (6887_CR9) 2017; 12 R McGreal (6887_CR10) 2001; 2 K Scheiter (6887_CR16) 2006; 22 6887_CR31 6887_CR75 6887_CR30 A Fatemah (6887_CR50) 2020; 97 6887_CR73 6887_CR79 6887_CR34 6887_CR78 B Williamson (6887_CR19) 2016; 31 6887_CR33 S Zhang (6887_CR48) 2004; 54 6887_CR77 T Strzalkowski (6887_CR65) 1999 6887_CR39 6887_CR38 6887_CR37 D De Geus (6887_CR60) 2020; 5 W Cai (6887_CR55) 2020; 10 EY İnce (6887_CR67) 2017; 7 B Akay (6887_CR87) 2012; 23 Z Chang (6887_CR85) 2019; 187 J Sinclair (6887_CR11) 2013; 6 Y Bengio (6887_CR74) 1994; 5 M McClelland (6887_CR12) 2003; 36 D Karaboğa (6887_CR88) 2004; 12 SP Schmidgall (6887_CR22) 2019; 60 R Willrich (6887_CR52) 2019; 46 6887_CR82 6887_CR81 6887_CR80 L Sun (6887_CR3) 2020; 19 M Tam (6887_CR1) 2000; 3 PA Gaona-García (6887_CR25) 2017; 35 6887_CR41 6887_CR84 C Romero (6887_CR7) 2017; 7 M Hamdi (6887_CR8) 2016; 11 6887_CR46 A Ramisa (6887_CR42) 2017; 40 6887_CR45 6887_CR89 Z Sui (6887_CR32) 2019; 4 6887_CR44 DB Fogel (6887_CR69) 2006 M Driscoll (6887_CR5) 2002; 1 O Vinyals (6887_CR72) 2016; 39 6887_CR4 T Yigit (6887_CR14) 2014; 141 Y Jiang (6887_CR40) 2016; 22 J Buckley (6887_CR21) 2018; 90 D Joshi (6887_CR35) 2006; 2 N Gershon (6887_CR26) 2001; 44 T Twyman (6887_CR17) 2006; 21 6887_CR53 J Cushing (6887_CR66) 2009; 25 6887_CR51 Y Yang (6887_CR20) 2017; 1 6887_CR57 AG Karkar (6887_CR47) 2017; 5 6887_CR56 R Ferreira-Mello (6887_CR6) 2019; 9 DE Goldberg (6887_CR70) 1988; 3 6887_CR54 6887_CR15 S Afzal (6887_CR49) 2012; 18 6887_CR59 6887_CR58 L Yao (6887_CR76) 2019; 38 M Braun (6887_CR24) 2019; 13 W Schnotz (6887_CR23) 2003; 13 K Hassani (6887_CR43) 2016; 49 C Wang (6887_CR83) 2013; 28 R Mihalcea (6887_CR36) 2008; 22 R Bernardi (6887_CR18) 2016; 55 RSM De Barros (6887_CR90) 2018; 275 6887_CR64 6887_CR63 A Bozkurt (6887_CR2) 2020; 5 6887_CR62 6887_CR61 6887_CR68 C Brooks (6887_CR13) 2006; 16 M İnce (6887_CR86) 2019; 31 JG Zheng (6887_CR27) 2017 6887_CR29 Y LeCun (6887_CR71) 2015; 521 S Liu (6887_CR28) 2018; 25 |
| References_xml | – volume: 19 start-page: 1 year: 2020 ident: 6887_CR3 publication-title: Nat Mater doi: 10.1038/s41563-020-0678-8 – volume: 1 start-page: 40 issue: 1 year: 2017 ident: 6887_CR20 publication-title: Vis Inf – ident: 6887_CR39 doi: 10.1109/CIVEMSA.2018.8439958 – volume: 12 start-page: 53 issue: 1 year: 2004 ident: 6887_CR88 publication-title: Turkish J Electr Eng Comput Sci – ident: 6887_CR73 doi: 10.1109/CVPR.2017.577 – volume: 5 start-page: 12777 year: 2017 ident: 6887_CR47 publication-title: IEEE Access doi: 10.1109/ACCESS.2017.2710315 – ident: 6887_CR56 doi: 10.1007/978-3-030-01267-0_7 – volume: 521 start-page: 436 issue: 7553 year: 2015 ident: 6887_CR71 publication-title: Nature doi: 10.1038/nature14539 – ident: 6887_CR44 doi: 10.3115/980491.980597 – ident: 6887_CR46 doi: 10.1109/TAAI.2013.26 – volume: 22 start-page: 5 issue: 1 year: 2016 ident: 6887_CR40 publication-title: Multimed Syst doi: 10.1007/s00530-014-0371-3 – ident: 6887_CR38 – ident: 6887_CR63 – ident: 6887_CR57 doi: 10.1109/CVPR.2019.00719 – ident: 6887_CR15 – volume: 18 start-page: 2556 issue: 12 year: 2012 ident: 6887_CR49 publication-title: IEEE Trans Vis Comput Gr doi: 10.1109/TVCG.2012.264 – ident: 6887_CR79 doi: 10.1109/CVPR.2015.7299087 – ident: 6887_CR82 – volume: 23 start-page: 1001 issue: 4 year: 2012 ident: 6887_CR87 publication-title: J Intell Manuf doi: 10.1007/s10845-010-0393-4 – volume: 2 start-page: 68 issue: 1 year: 2006 ident: 6887_CR35 publication-title: ACM Trans Multimed Comput Commun Appl doi: 10.1145/1126004.1126008 – volume: 28 start-page: 3638 issue: 4 year: 2013 ident: 6887_CR83 publication-title: IEEE Trans Power Syst doi: 10.1109/TPWRS.2013.2238259 – volume: 11 start-page: 131 issue: 4 year: 2016 ident: 6887_CR8 publication-title: Int J Emerg Technol Learn doi: 10.3991/ijet.v11i04.5574 – volume: 21 start-page: 5 issue: 2 year: 2006 ident: 6887_CR17 publication-title: J Spec Educ Technol doi: 10.1177/016264340602100201 – ident: 6887_CR64 doi: 10.1007/BFb0034824 – ident: 6887_CR30 doi: 10.1109/IV.2016.57 – ident: 6887_CR29 – volume: 5 start-page: 1 issue: 1 year: 2020 ident: 6887_CR2 publication-title: Asian J Distance Educ – volume: 7 start-page: 1 issue: 1 year: 2017 ident: 6887_CR7 publication-title: Wiley Interdiscip Rev Data Min Knowl Discov doi: 10.1002/widm.1187 – ident: 6887_CR45 doi: 10.1145/383259.383316 – volume: 5 start-page: 157 issue: 2 year: 1994 ident: 6887_CR74 publication-title: IEEE Trans Neural Netw doi: 10.1109/72.279181 – volume: 3 start-page: 95 year: 1988 ident: 6887_CR70 publication-title: Mach Learn doi: 10.1023/A:1022602019183 – volume: 187 start-page: 115804 year: 2019 ident: 6887_CR85 publication-title: Energy doi: 10.1016/j.energy.2019.07.134 – volume: 36 start-page: 107 issue: 11 year: 2003 ident: 6887_CR12 publication-title: Comput doi: 10.1109/MC.2003.1244540 – volume: 7 start-page: 68 issue: 2 year: 2017 ident: 6887_CR67 publication-title: Int J Inf Electron Eng – volume: 49 start-page: 1 issue: 1 year: 2016 ident: 6887_CR43 publication-title: ACM Comput Surv doi: 10.1145/2932710 – volume: 38 start-page: 461 issue: 7 year: 2019 ident: 6887_CR76 publication-title: Comput Gr Forum doi: 10.1111/cgf.13852 – volume: 39 start-page: 652 issue: 4 year: 2016 ident: 6887_CR72 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2016.2587640 – volume: 3 start-page: 50 issue: 2 year: 2000 ident: 6887_CR1 publication-title: Educ Tech Soc – volume: 31 start-page: 671 issue: 1 year: 2019 ident: 6887_CR86 publication-title: Neural Comput Appl doi: 10.1007/s00521-017-3023-7 – volume: 2 start-page: 26 issue: 10 year: 2001 ident: 6887_CR10 publication-title: E Learn – ident: 6887_CR34 doi: 10.3115/992424.992482 – volume: 35 start-page: 69 issue: 1 year: 2017 ident: 6887_CR25 publication-title: Electron Libr doi: 10.1108/EL-03-2015-0046 – ident: 6887_CR33 doi: 10.1145/3173574.3173996 – volume: 46 start-page: 528 issue: 4 year: 2019 ident: 6887_CR52 publication-title: J Inf Sci doi: 10.1177/0165551519849514 – ident: 6887_CR62 – volume: 13 start-page: 141 issue: 2 year: 2003 ident: 6887_CR23 publication-title: Learn Instr doi: 10.1016/S0959-4752(02)00017-8 – volume: 13 start-page: 71 issue: 2 year: 2019 ident: 6887_CR24 publication-title: J Multimodal User Interfaces doi: 10.1007/s12193-019-00301-2 – volume: 25 start-page: 2482 issue: 7 year: 2018 ident: 6887_CR28 publication-title: IEEE Trans Vis Comput Gr doi: 10.1109/TVCG.2018.2834341 – start-page: 113 volume-title: Natural language information retrieval year: 1999 ident: 6887_CR65 doi: 10.1007/978-94-017-2388-6_5 – volume: 6 start-page: 177 issue: 2 year: 2013 ident: 6887_CR11 publication-title: IEEE Trans Learn Technol doi: 10.1109/TLT.2013.6 – ident: 6887_CR77 doi: 10.3115/1073083.1073135 – volume: 60 start-page: 138 year: 2019 ident: 6887_CR22 publication-title: Learn Instr doi: 10.1016/j.learninstruc.2018.01.006 – volume: 12 start-page: 884 year: 2017 ident: 6887_CR9 publication-title: Int J Inf Educ Technol – start-page: 67 volume-title: Global business intelligence year: 2017 ident: 6887_CR27 doi: 10.4324/9781315471136-6 – volume: 4 start-page: 49 issue: 3 year: 2019 ident: 6887_CR32 publication-title: Am J Manag Sci Eng doi: 10.11648/j.ajmse.20190403.12 – ident: 6887_CR78 – volume: 25 start-page: 167 issue: 1 year: 2009 ident: 6887_CR66 publication-title: J Comput Sci Coll – ident: 6887_CR59 doi: 10.1109/CVPR.2019.00633 – volume: 275 start-page: 1954 year: 2018 ident: 6887_CR90 publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.10.051 – volume: 44 start-page: 31 issue: 8 year: 2001 ident: 6887_CR26 publication-title: Commun ACM doi: 10.1145/381641.381653 – ident: 6887_CR41 doi: 10.1109/CSNT.2014.183 – ident: 6887_CR84 – ident: 6887_CR54 doi: 10.1109/CVPR.2019.00656 – ident: 6887_CR61 – ident: 6887_CR89 doi: 10.1109/CEC.2006.1688424 – ident: 6887_CR80 doi: 10.1145/2964284.2964299 – volume: 16 start-page: 50 issue: 1–2 year: 2006 ident: 6887_CR13 publication-title: Int J Contin Eng Educ Life Long Learn doi: 10.1504/IJCEELL.2006.008917 – volume: 141 start-page: 813 year: 2014 ident: 6887_CR14 publication-title: Proced Soc Behav Sci doi: 10.1016/j.sbspro.2014.05.141 – volume: 90 start-page: 64 year: 2018 ident: 6887_CR21 publication-title: Int J Educ Res doi: 10.1016/j.ijer.2018.05.007 – volume: 22 start-page: 153 issue: 3 year: 2008 ident: 6887_CR36 publication-title: Mach Transl doi: 10.1007/s10590-009-9050-0 – volume: 9 start-page: 1 issue: 6 year: 2019 ident: 6887_CR6 publication-title: Wiley Interdiscip Rev Data Min Knowl Discov doi: 10.1002/widm.1332 – volume: 55 start-page: 409 year: 2016 ident: 6887_CR18 publication-title: J Artif Intell Res doi: 10.1613/jair.4900 – ident: 6887_CR58 – volume: 1 start-page: 1 issue: 4 year: 2002 ident: 6887_CR5 publication-title: E Learn – volume: 40 start-page: 1072 issue: 5 year: 2017 ident: 6887_CR42 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2017.2721945 – ident: 6887_CR53 doi: 10.1109/CVPR.2019.00963 – ident: 6887_CR51 doi: 10.1109/COMPSAC.2018.00179 – ident: 6887_CR75 – volume: 97 start-page: 992 issue: 4 year: 2020 ident: 6887_CR50 publication-title: J Chem Educ doi: 10.1021/acs.jchemed.9b00690 – volume: 54 start-page: 1 issue: 1 year: 2004 ident: 6887_CR48 publication-title: Comput Ind doi: 10.1016/j.compind.2003.09.002 – volume: 10 start-page: 391 issue: 1 year: 2020 ident: 6887_CR55 publication-title: Appl Sci doi: 10.3390/app10010391 – ident: 6887_CR31 doi: 10.1145/3020165.3020182 – ident: 6887_CR4 – ident: 6887_CR37 – volume: 22 start-page: 9 issue: 1 year: 2006 ident: 6887_CR16 publication-title: Comput Hum Behav doi: 10.1016/j.chb.2005.01.009 – volume: 5 start-page: 1742 issue: 2 year: 2020 ident: 6887_CR60 publication-title: IEEE Robot Autom Lett doi: 10.1109/LRA.2020.2969919 – ident: 6887_CR81 – ident: 6887_CR68 doi: 10.7148/2014-0340 – volume-title: Evolutionary computation: toward a new philosophy of machine intelligence year: 2006 ident: 6887_CR69 – volume: 31 start-page: 123 issue: 2 year: 2016 ident: 6887_CR19 publication-title: J Educ Pol doi: 10.1080/02680939.2015.1035758 |
| SSID | ssj0004685 |
| Score | 2.410752 |
| Snippet | Increasing demand in distance education, e-learning, web-based learning, and other digital sectors (e.g., entertainment) has led to excessive amounts of... |
| SourceID | pubmedcentral proquest pubmed crossref springer |
| SourceType | Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 2473 |
| SubjectTerms | Artificial Intelligence Artificial neural networks Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Deep learning Distance learning Evolutionary algorithms Genetic algorithms Image Processing and Computer Vision Machine learning Natural language processing Neural networks Original Original Article Probability and Statistics in Computer Science Semantic web Subsystems Visualization |
| SummonAdditionalLinks | – databaseName: Advanced Technologies & Aerospace Database dbid: P5Z link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LT9wwEB5R4NALr1JIC8iVeitR4_i1OVUIgTggxIFKqJcofgRWguzCBn5_PY4TWBBcOEWJH7E1M_bYM_MNwE9jnSqkP-SgSSzlNXdpJbhMragZpdoUrANxPVVnZ6PLy-I8XrjNoltlvyaGhdpODN6R_869os4x8DP7M71LMWsUWldjCo1PsIQoCZi64Vz8exYXGVJy-hMMevdwFoNmQugc3of6rzhKiYJG5zemV9rma6fJF5bTsCEdr350KmuwElVRctDxzjosuGYDVvs0DyRK_RfAa7RJQHYlVWPJeADxbAk6uuPzcTzD4MwupJN06NAEN0hL_Lt1bkpieoqr0Idv7EJ_N1d-YO317Sb8PT66ODxJY26G1HDF27TKvOJjpaBM1awQudRCMK60179kVmW1olqOTGWUdKgDjKS0XFlWu7xWTleKfYXFZtK4bSBSFU5SbS3XmotM-xOOKWhRKeF70yJLgPaEKU0ELsf8GTflALkciFl6YpaBmCVN4NfQZtrBdrxbe6cnVBlFeFY-USmBH0OxFz60qFSNmzyEOjmiG3GRwFbHHsPvmPC9qyxPQM0xzlABgb3nS5rxdQD49jsULv4J7Pcs9jSst2fx7f1ZfIfPeWB3dMXZgcX2_sHtwrJ5bMez-70gOP8B-OAeMQ priority: 102 providerName: ProQuest |
| Title | Automatic and intelligent content visualization system based on deep learning and genetic algorithm |
| URI | https://link.springer.com/article/10.1007/s00521-022-06887-1 https://www.ncbi.nlm.nih.gov/pubmed/35068702 https://www.proquest.com/docview/2625411420 https://www.proquest.com/docview/2622478745 https://pubmed.ncbi.nlm.nih.gov/PMC8760887 |
| Volume | 34 |
| WOSCitedRecordID | wos000742823200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1433-3058 dateEnd: 20241213 omitProxy: false ssIdentifier: ssj0004685 issn: 0941-0643 databaseCode: P5Z dateStart: 20120101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1433-3058 dateEnd: 20241213 omitProxy: false ssIdentifier: ssj0004685 issn: 0941-0643 databaseCode: BENPR dateStart: 20120101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1433-3058 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004685 issn: 0941-0643 databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LT9wwEB5R6KGXAn2mwMqVemsjJfErObYI1ANaraCtVr1EcexAJJpFu4Hf3xnnQRfaSnCKLNuT2J7xeOKZbwA-lNbpTKGRQ1dioaiECwspVGhlxePYlBnvQFxP9HSazufZrA8KWw3e7sOVpN-px2A3-oOJpi_RVSQaaPNsobpLKWHD6dmPP6IhfSJOtFvIp0fwPlTm7zTW1dG9M-Z9V8k796VeDR1vP24AO_C8P3ayzx2f7MKGa17A9pDSgfUS_hLol9nCo7iyorGsHgE7W0ZO7fS8qVcUiNmFb7IOCZqRMrQMy9a5K9anojj3NLCz8_QuzxfLur349Qq-Hx99O_wa9nkYwlJo0YZFhIccq2TMdcUzmSgjJRfa4FlLRUVU6diotCxKrRzp-1QpK7TllUsq7Uyh-WvYbBaNewtM6cyp2FgrjBEyMmjNlFmcFVoiNSOjAOJhOfKyBymnXBmX-Qiv7Gcxx1nM_SzmcQAfxz5XHUTHf1vvD6uc9-K6yhO0AgVFFeMHvB-rUdDo9qRo3OLat0kIyUjIAN50TDG-jkukrqMkAL3GLmMDAvFer2nqCw_mjdqINvoAPg1Mc_tZ_x7Fu4c134Nniec7csPZh812ee0O4Gl509ar5QSe6Hk6ga0vR9PZKZZm8ufEi9RvDvMXwQ |
| linkProvider | Springer Nature |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEB6VggQXyhtDgUWCE1i1va_4gBACqlYNUQ9FqrgY78NtpOKExi3iT_EbmVk_SqjorQdOUeL12rv55rG7M98AvLDO61zhIoeOxGJRCR-XUqjYyYqnqbE5b0lcx3oyGe3v57sr8KvPhaGwyl4nBkXtZpb2yDcydNQFJX4mb-ffY6oaRaerfQmNFhY7_ucPXLIt3mx_wP_3ZZZtftx7vxV3VQViK7Ro4jJBk-2UTLmueC4zZaTkQhv0HFRSJpVOjRrZ0mrlyXqNlHJCO175rNLelJpjv1fgqsA3ISnalV_-yMMMJUBxxUTRRIJ3STohVY_2X_FXmhVFgp0uG8Jz3u35IM2_TmqDAdxc-9-m7hbc7Fxt9q6Vjduw4us7sNaXsWCdVrsLtE04C8y1rKwdmw4kpQ2jQH76PJ0uKPm0TVllLfs1IwfAMfzuvJ-zrvzGQegDb_ahv6MDnIjm8Ns9-HwpQ70Pq_Ws9g-BKZ17lRrnhDFCJgZXcDZP81JL7M3IJIK0B0JhO2J2qg9yVAyU0gE8BYKnCOAp0gheDffMW1qSC1uv98AoOhW1KM5QEcHz4TIqFzoxKms_OwltMmJvEjKCBy0ch8dxib3rJItALwF1aEDE5ctX6ulhIDBHC0zGLYLXPaTPXuvfo3h08SiewfWtvU_jYrw92XkMN7IgahR2tA6rzfGJfwLX7GkzXRw_DULL4OtlQ_03t4p4xw |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1ZT9wwEB5VUCFeui1XA7QYiTeISOJr84hoV62KVkgc4i2KY2eJRLOr3cDvr8c52OWohHiMbI9je0YzY898A3CQaSNjYZ0cfBLzWc6Mn3ImfM1zGoYqi2kN4nomh8P-zU18PpfF76Ld2yfJOqcBUZrK6nii8-Mu8Q1vM60bjHMIFBPr_ywzDKRHf_3iei4z0hXltD4Mxvcw2qTNvExjUTU9szefh00-eTt1KmnQe_9iPsOnxhwlJzX_fIEPplyDXlvqgTSSvw54lTZ26K4kLTUpOiDPijSTkYdihgmadVonqRGiCSpJTey3NmZCmhIVI0fDDjaO3t1oPC2q278bcDX4eXn6y2_qM_gZk6zy08AaP1rwkMqcxjwSinPKpLI2mAjSIJehEv0szaQwaAf0hdBMapqbKJdGpZJuwlI5Ls1XIELGRoRKa6YU44GyXk4Wh3EquaWmeOBB2B5NkjXg5VhD4y7pYJfdLiZ2FxO3i0nowWE3ZlJDd_y392574kkjxrMkst4hw2xj-wP7XbMVQHxVSUszvnd9IkQ4YtyDrZpBuukot9RlEHkgF1in64Dg3ostZXHrQL6tlkIF4MFRy0CPv_X6Krbf1n0PVs5_DJKz38M_O7AaORbESJ1dWKqm9-YbfMweqmI2_e6k6h9WzSAH |
| 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=Automatic+and+intelligent+content+visualization+system+based+on+deep+learning+and+genetic+algorithm&rft.jtitle=Neural+computing+%26+applications&rft.au=%C4%B0nce%2C+Murat&rft.date=2022-02-01&rft.pub=Springer+London&rft.issn=0941-0643&rft.eissn=1433-3058&rft.volume=34&rft.issue=3&rft.spage=2473&rft.epage=2493&rft_id=info:doi/10.1007%2Fs00521-022-06887-1&rft.externalDocID=10_1007_s00521_022_06887_1 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0941-0643&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0941-0643&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0941-0643&client=summon |