{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T10:07:14Z","timestamp":1783937234699,"version":"3.55.0"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"14","license":[{"start":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T00:00:00Z","timestamp":1609718400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T00:00:00Z","timestamp":1609718400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2021,7]]},"DOI":"10.1007\/s00521-020-05616-w","type":"journal-article","created":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T11:04:23Z","timestamp":1609758263000},"page":"8669-8685","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["HEU Emotion: a large-scale database for multimodal emotion recognition in the wild"],"prefix":"10.1007","volume":"33","author":[{"given":"Jing","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenhui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2912-8994","authenticated-orcid":false,"given":"Kejun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaoqun","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cong","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziqiang","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meichen","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,1,4]]},"reference":[{"key":"5616_CR1","doi-asserted-by":"crossref","unstructured":"Ahonen T, Hadid A, Pietik\u00e4inen M (2004) Face recognition with local binary patterns. In: European conference on computer vision, pp 469\u2013481. Springer","DOI":"10.1007\/978-3-540-24670-1_36"},{"key":"5616_CR2","doi-asserted-by":"crossref","unstructured":"Ben-Younes H, Cadene R, Thome N, Cord M (2019) Block: Bilinear superdiagonal fusion for visual question answering and visual relationship detection. arXiv preprint arXiv:1902.00038","DOI":"10.1609\/aaai.v33i01.33018102"},{"issue":"4","key":"5616_CR3","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1007\/s10579-008-9076-6","volume":"42","author":"C Busso","year":"2008","unstructured":"Busso C, Bulut M, Lee CC, Kazemzadeh A, Mower E, Kim S, Chang JN, Lee S, Narayanan SS (2008) Iemocap: Interactive emotional dyadic motion capture database. Language resources and evaluation 42(4):335\u2013339","journal-title":"Language resources and evaluation"},{"key":"5616_CR4","unstructured":"Chen SY, Hsu CC, Kuo CC, Ku LW, et\u00a0al (2018) Emotionlines: An emotion corpus of multi-party conversations. arXiv preprint arXiv:1802.08379"},{"key":"5616_CR5","doi-asserted-by":"crossref","unstructured":"Cho K, Van\u00a0Merri\u00ebnboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using rnn encoder-decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp 1724\u20131734. Association for Computational Linguistics","DOI":"10.3115\/v1\/D14-1179"},{"key":"5616_CR6","doi-asserted-by":"crossref","unstructured":"Chou HC, Lin WC, Chang LC, Li CC, Ma HP, Lee CC (2017) Nnime: The nthu-ntua chinese interactive multimodal emotion corpus. In: 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII), pp 292\u2013298. IEEE","DOI":"10.1109\/ACII.2017.8273615"},{"key":"5616_CR7","first-page":"76","volume-title":"First International Workshop on Emotion: Corpora for Research on Emotion and Affect (International conference on Language Resources and Evaluation (LREC 2006))","author":"C Clavel","year":"2006","unstructured":"Clavel C, Vasilescu I, Devillers L, Richard G, Ehrette T, Sedogbo C (2006) The safe corpus: illustrating extreme emotions in dynamic situations. First International Workshop on Emotion: Corpora for Research on Emotion and Affect (International conference on Language Resources and Evaluation (LREC 2006)). Genoa, Italy, pp 76\u201379"},{"key":"5616_CR8","doi-asserted-by":"crossref","unstructured":"Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR\u201905), vol.\u00a01, pp 886\u2013893. IEEE","DOI":"10.1109\/CVPR.2005.177"},{"key":"5616_CR9","doi-asserted-by":"crossref","unstructured":"De\u00a0Silva LC, Miyasato T, Nakatsu R (1997) Facial emotion recognition using multi-modal information. In: Proceedings of ICICS, 1997 International Conference on Information, Communications and Signal Processing. Theme: Trends in Information Systems Engineering and Wireless Multimedia Communications (Cat., vol.\u00a01, pp 397\u2013401. IEEE","DOI":"10.1109\/ICICS.1997.647126"},{"key":"5616_CR10","doi-asserted-by":"crossref","unstructured":"Dhall A, Goecke R, Lucey S, Gedeon T (2011) Static facial expression analysis in tough conditions: Data, evaluation protocol and benchmark. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp 2106\u20132112. IEEE","DOI":"10.1109\/ICCVW.2011.6130508"},{"issue":"3","key":"5616_CR11","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1109\/MMUL.2012.26","volume":"19","author":"A Dhall","year":"2012","unstructured":"Dhall A, Goecke R, Lucey S, Gedeon T (2012) Collecting large, richly annotated facial-expression databases from movies. IEEE MultiMed 19(3):34\u201341. https:\/\/doi.org\/10.1109\/MMUL.2012.26","journal-title":"IEEE MultiMed"},{"key":"5616_CR12","volume-title":"Pictures of facial affect","author":"P Ekman","year":"1976","unstructured":"Ekman P (1976) Pictures of facial affect. Consulting Psychologists Press,"},{"issue":"4","key":"5616_CR13","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1037\/0003-066X.48.4.384","volume":"48","author":"P Ekman","year":"1993","unstructured":"Ekman P (1993) Facial expression and emotion. Am psychol 48(4):384","journal-title":"Am psychol"},{"issue":"8","key":"5616_CR14","first-page":"140","volume":"128","author":"P Ekman","year":"2003","unstructured":"Ekman P (2003) Emotions revealed: Recognizing faces and feelings to improve communication and emotional life. Holt Paperback 128(8):140\u2013140","journal-title":"Holt Paperback"},{"key":"5616_CR15","doi-asserted-by":"crossref","unstructured":"Eyben F, Weninger F, Gross F, Schuller B (2013) Recent developments in opensmile, the munich open-source multimedia feature extractor. In: Proceedings of the 21st ACM international conference on Multimedia, pp 835\u2013838. ACM","DOI":"10.1145\/2502081.2502224"},{"key":"5616_CR16","doi-asserted-by":"crossref","unstructured":"Fan Y, Lam JC, Li VO (2018) Multi-region ensemble convolutional neural network for facial expression recognition. In: International Conference on Artificial Neural Networks, pp. 84\u201394. Springer","DOI":"10.1007\/978-3-030-01418-6_9"},{"key":"5616_CR17","doi-asserted-by":"crossref","unstructured":"Fan Y, Lu X, Li D, Liu Y (2016) Video-based emotion recognition using cnn-rnn and c3d hybrid networks. In: Proceedings of the 18th ACM International Conference on Multimodal Interaction, pp. 445\u2013450. ACM","DOI":"10.1145\/2993148.2997632"},{"key":"5616_CR18","doi-asserted-by":"publisher","first-page":"64827","DOI":"10.1109\/ACCESS.2019.2917266","volume":"7","author":"MI Georgescu","year":"2019","unstructured":"Georgescu MI, Ionescu RT, Popescu M (2019) Local learning with deep and handcrafted features for facial expression recognition. IEEE Access 7:64827\u201364836","journal-title":"IEEE Access"},{"key":"5616_CR19","doi-asserted-by":"crossref","unstructured":"Goodfellow IJ, Erhan D, Carrier PL, Courville A, Mirza M, Hamner B, Cukierski W, Tang Y, Thaler D, Lee DH, et\u00a0al (2013) Challenges in representation learning: A report on three machine learning contests. In: International Conference on Neural Information Processing, pp 117\u2013124. Springer","DOI":"10.1007\/978-3-642-42051-1_16"},{"key":"5616_CR20","doi-asserted-by":"crossref","unstructured":"Gunes H, Piccardi M (2006) A bimodal face and body gesture database for automatic analysis of human nonverbal affective behavior. In: 18th International Conference on Pattern Recognition (ICPR\u201906), vol\u00a01, pp 1148\u20131153. IEEE","DOI":"10.1109\/ICPR.2006.39"},{"key":"5616_CR21","doi-asserted-by":"crossref","unstructured":"Haq S, Jackson PJ (2011) Multimodal emotion recognition. In: Machine audition: principles, algorithms and systems, pp 398\u2013423. IGI Global","DOI":"10.4018\/978-1-61520-919-4.ch017"},{"key":"5616_CR22","doi-asserted-by":"crossref","unstructured":"Hara K, Kataoka H, Satoh Y (2018) Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet? In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp 6546\u20136555","DOI":"10.1109\/CVPR.2018.00685"},{"key":"5616_CR23","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"5616_CR24","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"5616_CR25","doi-asserted-by":"crossref","unstructured":"Hu P, Cai D, Wang S, Yao A, Chen Y (2017) Learning supervised scoring ensemble for emotion recognition in the wild. In: Proceedings of the 19th ACM international conference on multimodal interaction, pp 553\u2013560. ACM","DOI":"10.1145\/3136755.3143009"},{"key":"5616_CR26","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der\u00a0Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"5616_CR27","doi-asserted-by":"crossref","unstructured":"Kazemi V, Sullivan J (2014) One millisecond face alignment with an ensemble of regression trees. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1867\u20131874","DOI":"10.1109\/CVPR.2014.241"},{"key":"5616_CR28","doi-asserted-by":"crossref","unstructured":"Kosti R, Alvarez JM, Recasens A, Lapedriza A (2017) Emotion recognition in context. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1960\u20131968. 10.1109\/CVPR.2017.212","DOI":"10.1109\/CVPR.2017.212"},{"key":"5616_CR29","doi-asserted-by":"crossref","unstructured":"Levi G, Hassner T (2015) Emotion recognition in the wild via convolutional neural networks and mapped binary patterns. In: Proceedings of the 2015 ACM on international conference on multimodal interaction, pp 503\u2013510. ACM","DOI":"10.1145\/2818346.2830587"},{"key":"5616_CR30","doi-asserted-by":"crossref","unstructured":"Li S, Deng W, Du J (2017) Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 2584\u20132593 10.1109\/CVPR.2017.277","DOI":"10.1109\/CVPR.2017.277"},{"issue":"6","key":"5616_CR31","doi-asserted-by":"publisher","first-page":"913","DOI":"10.1007\/s12652-016-0406-z","volume":"8","author":"Y Li","year":"2017","unstructured":"Li Y, Tao J, Chao L, Bao W, Liu Y (2017) Cheavd: a chinese natural emotional audio-visual database. J Amb Intell Human Comput 8(6):913\u2013924","journal-title":"J Amb Intell Human Comput"},{"key":"5616_CR32","doi-asserted-by":"crossref","unstructured":"Li Y, Tao J, Schuller B, Shan S, Jiang D, Jia J (2018) Mec 2017: Multimodal emotion recognition challenge. In: 2018 First Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia), pp 1\u20135. IEEE","DOI":"10.1109\/ACIIAsia.2018.8470342"},{"key":"5616_CR33","doi-asserted-by":"crossref","unstructured":"Liu C, Tang T, Lv K, Wang M (2018) Multi-feature based emotion recognition for video clips. In: Proceedings of the 2018 on International Conference on Multimodal Interaction, pp 630\u2013634. ACM","DOI":"10.1145\/3242969.3264989"},{"issue":"4","key":"5616_CR34","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1109\/TIP.2002.999679","volume":"11","author":"C Liu","year":"2002","unstructured":"Liu C, Wechsler H (2002) Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans Image process 11(4):467\u2013476","journal-title":"IEEE Trans Image process"},{"issue":"5","key":"5616_CR35","doi-asserted-by":"publisher","first-page":"e0196391","DOI":"10.1371\/journal.pone.0196391","volume":"13","author":"SR Livingstone","year":"2018","unstructured":"Livingstone SR, Russo FA (2018) The ryerson audio-visual database of emotional speech and song (ravdess): A dynamic, multimodal set of facial and vocal expressions in north american english. PloS one 13(5):e0196391","journal-title":"PloS one"},{"key":"5616_CR36","doi-asserted-by":"crossref","unstructured":"Lucey P, Cohn JF, Kanade T, Saragih J, Ambadar Z, Matthews I (2010) The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, pp 94\u2013101. IEEE","DOI":"10.1109\/CVPRW.2010.5543262"},{"key":"5616_CR37","doi-asserted-by":"crossref","unstructured":"Martin O, Kotsia I, Macq B, Pitas I (2006) The enterface\u201905 audio-visual emotion database. In: 22nd International Conference on Data Engineering Workshops (ICDEW\u201906), pp 8\u20138. IEEE","DOI":"10.1109\/ICDEW.2006.145"},{"issue":"2","key":"5616_CR38","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1007\/s11031-011-9212-2","volume":"35","author":"D Matsumoto","year":"2011","unstructured":"Matsumoto D, Hwang HS (2011) Evidence for training the ability to read microexpressions of emotion. Motivation Emotion 35(2):181\u2013191","journal-title":"Motivation Emotion"},{"issue":"5588","key":"5616_CR39","doi-asserted-by":"publisher","first-page":"746","DOI":"10.1038\/264746a0","volume":"264","author":"H McGurk","year":"1976","unstructured":"McGurk H, MacDonald J (1976) Hearing lips and seeing voices. Nature 264(5588):746","journal-title":"Nature"},{"issue":"1","key":"5616_CR40","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1109\/T-AFFC.2011.20","volume":"3","author":"G McKeown","year":"2011","unstructured":"McKeown G, Valstar M, Cowie R, Pantic M, Schroder M (2011) The semaine database: annotated multimodal records of emotionally colored conversations between a person and a limited agent. IEEE Trans Affect Comput 3(1):5\u201317","journal-title":"IEEE Trans Affect Comput"},{"key":"5616_CR41","doi-asserted-by":"crossref","unstructured":"Mehrabian A (2008) Communication without words. Communication theory, pp 193\u2013200","DOI":"10.4324\/9781315080918-15"},{"issue":"1","key":"5616_CR42","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/TAFFC.2017.2740923","volume":"10","author":"A Mollahosseini","year":"2019","unstructured":"Mollahosseini A, Hasani B, Mahoor MH (2019) Affectnet: A database for facial expression, valence, and arousal computing in the wild. IEEE Trans Affective Comput 10(1):18\u201331. https:\/\/doi.org\/10.1109\/TAFFC.2017.2740923","journal-title":"IEEE Trans Affective Comput"},{"key":"5616_CR43","doi-asserted-by":"crossref","unstructured":"Ng HW, Nguyen VD, Vonikakis V, Winkler S (2015) Deep learning for emotion recognition on small datasets using transfer learning. In: Proceedings of the 2015 ACM on international conference on multimodal interaction, pp 443\u2013449. ACM","DOI":"10.1145\/2818346.2830593"},{"key":"5616_CR44","doi-asserted-by":"crossref","unstructured":"Peng X, Xia Z, Li L, Feng X (2016) Towards facial expression recognition in the wild: A new database and deep recognition system. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 93\u201399","DOI":"10.1109\/CVPRW.2016.192"},{"key":"5616_CR45","doi-asserted-by":"crossref","unstructured":"Perepelkina O, Kazimirova E, Konstantinova M (2018) Ramas: Russian multimodal corpus of dyadic interaction for affective computing. In: International Conference on Speech and Computer, pp 501\u2013510. Springer","DOI":"10.1007\/978-3-319-99579-3_52"},{"key":"5616_CR46","doi-asserted-by":"crossref","unstructured":"Poria S, Hazarika D, Majumder N, Naik G, Cambria E, Mihalcea R (2018) Meld: A multimodal multi-party dataset for emotion recognition in conversations. arXiv preprint arXiv:1810.02508","DOI":"10.18653\/v1\/P19-1050"},{"key":"5616_CR47","unstructured":"Redmon J, Farhadi A (2018) Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767"},{"key":"5616_CR48","doi-asserted-by":"crossref","unstructured":"Rothe R, Timofte R, Van\u00a0Gool L (2015) Dex: Deep expectation of apparent age from a single image. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp 10\u201315","DOI":"10.1109\/ICCVW.2015.41"},{"key":"5616_CR49","doi-asserted-by":"crossref","unstructured":"Sapi\u0144ski T, Kami\u0144ska D, Pelikant A, Ozcinar C, Avots E, Anbarjafari G (2018) Multimodal database of emotional speech, video and gestures. In: International Conference on Pattern Recognition, pp 153\u2013163. Springer","DOI":"10.1007\/978-3-030-05792-3_15"},{"key":"5616_CR50","doi-asserted-by":"crossref","unstructured":"Schuller B, Steidl S, Batliner A (2009) The interspeech 2009 emotion challenge. In: Tenth Annual Conference of the International Speech Communication Association","DOI":"10.21437\/Interspeech.2009-103"},{"key":"5616_CR51","unstructured":"Simonyan K, Zisserman A (2014) Two-stream convolutional networks for action recognition in videos. In: Advances in neural information processing systems, pp 568\u2013576"},{"key":"5616_CR52","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"issue":"2\u20133","key":"5616_CR53","doi-asserted-by":"publisher","first-page":"311","DOI":"10.3166\/ejc.7.311-327","volume":"7","author":"JA Suykens","year":"2001","unstructured":"Suykens JA (2001) Support vector machines: a nonlinear modelling and control perspective. Eur J Control 7(2\u20133):311\u2013327","journal-title":"Eur J Control"},{"key":"5616_CR54","doi-asserted-by":"crossref","unstructured":"Vasilescu I, Devillers L, Clavel C, Ehrette T (2004) Fiction database for emotion detection in abnormal situations. In: Eighth International Conference on Spoken Language Processing","DOI":"10.21437\/Interspeech.2004-655"},{"key":"5616_CR55","unstructured":"Xie Z (2010) Ryerson multimedia research laboratory (rml). http:\/\/www.rml.ryerson.ca\/rml-emotion-database.html"},{"key":"5616_CR56","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.neucom.2018.03.068","volume":"309","author":"J Yan","year":"2018","unstructured":"Yan J, Zheng W, Cui Z, Tang C, Zhang T, Zong Y (2018) Multi-cue fusion for emotion recognition in the wild. Neurocomputing 309:27\u201335","journal-title":"Neurocomputing"},{"key":"5616_CR57","doi-asserted-by":"crossref","unstructured":"Yu F, Chang E, Xu YQ, Shum HY (2001) Emotion detection from speech to enrich multimedia content. In: Pacific-Rim Conference on Multimedia, pp 550\u2013557. Springer","DOI":"10.1007\/3-540-45453-5_71"},{"issue":"10","key":"5616_CR58","doi-asserted-by":"publisher","first-page":"1499","DOI":"10.1109\/LSP.2016.2603342","volume":"23","author":"K Zhang","year":"2016","unstructured":"Zhang K, Zhang Z, Li Z, Qiao Y (2016) Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process Lett 23(10):1499\u20131503","journal-title":"IEEE Signal Process Lett"},{"key":"5616_CR59","doi-asserted-by":"crossref","unstructured":"Zhu Y, Lan Z, Newsam S, Hauptmann A (2018) Hidden two-stream convolutional networks for action recognition. In: Asian Conference on Computer Vision, pp 363\u2013378. Springer","DOI":"10.1007\/978-3-030-20893-6_23"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05616-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05616-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05616-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,20]],"date-time":"2024-08-20T17:48:55Z","timestamp":1724176135000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05616-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,4]]},"references-count":59,"journal-issue":{"issue":"14","published-print":{"date-parts":[[2021,7]]}},"alternative-id":["5616"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05616-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,4]]},"assertion":[{"value":"6 August 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 December 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standard"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}