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CNN and LSTM-Based Emotion Charting Using Physiological Signals

dc.contributor.authorDar, Muhammad Najam
dc.contributor.authorAkram, Muhammad Usman
dc.contributor.authorKhawaja, Sajid Gul
dc.contributor.authorPujari, Amit N.
dc.date.accessioned2020-08-18T09:20:01Z
dc.date.available2020-08-18T09:20:01Z
dc.date.issued2020-08-14
dc.identifier.citationDar , M N , Akram , M U , Khawaja , S G & Pujari , A N 2020 , ' CNN and LSTM-Based Emotion Charting Using Physiological Signals ' , Sensors , vol. 20 , no. 16 , 4551 . https://doi.org/10.3390/s20164551en
dc.identifier.issn1424-8220
dc.identifier.otherPURE: 173887325
dc.identifier.otherPURE UUID: da23372a-8f91-49d5-b2b7-5ba32290de97
dc.identifier.otherORCID: /0000-0003-1688-4448/work/78819713
dc.identifier.otherScopus: 85089387052
dc.identifier.otherPubMed: 32823807
dc.identifier.urihttps://hdl.handle.net/2164/14940
dc.format.extent26
dc.language.isoeng
dc.relation.ispartofSensorsen
dc.rightsThis is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/4.0/en
dc.subjectConvolutional neural network (CNN)en
dc.subjectlong short-term memory (LSTM)en
dc.subjectemotion recognitionen
dc.subjectEEGen
dc.subjectECGen
dc.subjectGSRen
dc.subjectdeep neural networken
dc.subjectphysiological signalsen
dc.subjectEmotion recognitionen
dc.subjectDeep neural networken
dc.subjectPhysiological signalsen
dc.subjectLong short-term memory (LSTM)en
dc.subjectTA Engineering (General). Civil engineering (General)en
dc.subjectAnalytical Chemistryen
dc.subjectInstrumentationen
dc.subjectAtomic and Molecular Physics, and Opticsen
dc.subjectElectrical and Electronic Engineeringen
dc.subjectBiochemistryen
dc.subject.lccTAen
dc.titleCNN and LSTM-Based Emotion Charting Using Physiological Signalsen
dc.typeJournal articleen
dc.contributor.institutionUniversity of Aberdeen.Engineeringen
dc.description.statusPeer revieweden
dc.description.versionPublisher PDFen
dc.identifier.doihttps://doi.org/10.3390/s20164551
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85089387052&partnerID=8YFLogxKen
dc.identifier.vol20en
dc.identifier.iss16en


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