{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T22:42:55Z","timestamp":1772750575679,"version":"3.50.1"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T00:00:00Z","timestamp":1682640000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T00:00:00Z","timestamp":1682640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1007\/s00500-023-08230-9","type":"journal-article","created":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T11:03:09Z","timestamp":1682679789000},"page":"8721-8737","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Automated facial expression recognition using exemplar hybrid deep feature generation technique"],"prefix":"10.1007","volume":"27","author":[{"given":"Mehmet","family":"Baygin","sequence":"first","affiliation":[]},{"given":"Ilknur","family":"Tuncer","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9677-5684","authenticated-orcid":false,"given":"Sengul","family":"Dogan","sequence":"additional","affiliation":[]},{"given":"Prabal Datta","family":"Barua","sequence":"additional","affiliation":[]},{"given":"Turker","family":"Tuncer","sequence":"additional","affiliation":[]},{"given":"Kang Hao","family":"Cheong","sequence":"additional","affiliation":[]},{"given":"U. Rajendra","family":"Acharya","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,4,28]]},"reference":[{"key":"8230_CR1","first-page":"200171","volume":"17","author":"N Ahmed","year":"2023","unstructured":"Ahmed N, Al Aghbari Z, Girija S (2023) A systematic survey on multimodal emotion recognition using learning algorithms. Intell Syst Appl 17:200171","journal-title":"Intell Syst Appl"},{"key":"8230_CR2","doi-asserted-by":"publisher","first-page":"1036","DOI":"10.3390\/electronics10091036","volume":"10","author":"MAH Akhand","year":"2021","unstructured":"Akhand MAH, Roy S, Siddique N, Kamal MAS, Shimamura T (2021) Facial emotion recognition using transfer learning in the deep CNN. Electronics 10:1036","journal-title":"Electronics"},{"key":"8230_CR3","doi-asserted-by":"publisher","first-page":"468","DOI":"10.3390\/app13010468","volume":"13","author":"M Arul Vinayakam Rajasimman","year":"2023","unstructured":"Arul Vinayakam Rajasimman M, Manoharan RK, Subramani N, Aridoss M, Galety MG (2023) Robust facial expression recognition using an evolutionary algorithm with a deep learning model. Appl Sci 13:468","journal-title":"Appl Sci"},{"key":"8230_CR4","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1016\/j.ins.2021.10.005","volume":"582","author":"FZ Canal","year":"2022","unstructured":"Canal FZ, M\u00fcller TR, Matias JC, Scotton GG, de Sa Junior AR, Pozzebon E et al (2022) A survey on facial emotion recognition techniques: a state-of-the-art literature review. Inf Sci 582:593\u2013617","journal-title":"Inf Sci"},{"key":"8230_CR5","doi-asserted-by":"crossref","unstructured":"Celniak W, Augustyniak P (2022) Eye-tracking as a component of multimodal emotion recognition systems. In: International conference on information technologies in biomedicine. Springer, pp 66\u201375","DOI":"10.1007\/978-3-031-09135-3_6"},{"key":"8230_CR6","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1007\/s10055-021-00575-6","volume":"26","author":"H-S Cha","year":"2022","unstructured":"Cha H-S, Im C-H (2022) Performance enhancement of facial electromyogram-based facial-expression recognition for social virtual reality applications using linear discriminant analysis adaptation. Virtual Real 26:385\u2013398","journal-title":"Virtual Real"},{"key":"8230_CR207","unstructured":"Chen L-F, Yen Y-S (2007) Taiwanese facial expression image database. Brain Mapp Lab Inst Brain Sci Natl Yang-Ming Univ Taipei, Taiwan"},{"key":"8230_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/S00521-021-06012-8","author":"MK Chowdary","year":"2021","unstructured":"Chowdary MK, Nguyen TN, Hemanth DJ (2021) Deep learning-based facial emotion recognition for human\u2013computer interaction applications. Neural Comput Appl. https:\/\/doi.org\/10.1007\/S00521-021-06012-8","journal-title":"Neural Comput Appl"},{"key":"8230_CR8","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, pp 248\u2013255. IEEE","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"8230_CR9","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1142\/S0219720005001004","volume":"3","author":"C Ding","year":"2005","unstructured":"Ding C, Peng H (2005) Minimum redundancy feature selection from microarray gene expression data. J Bioinform Comput Biol 3:185\u2013205","journal-title":"J Bioinform Comput Biol"},{"key":"8230_CR10","doi-asserted-by":"publisher","first-page":"592","DOI":"10.3390\/s20030592","volume":"20","author":"A Dzedzickis","year":"2020","unstructured":"Dzedzickis A, Kaklauskas A, Bucinskas V (2020) Human emotion recognition: review of sensors and methods. Sensors 20:592","journal-title":"Sensors"},{"key":"8230_CR11","first-page":"169","volume-title":"Darwin and facial expression: a century of research in review","author":"P Ekman","year":"1973","unstructured":"Ekman P (1973) Cross-cultural studies of facial expression. Darwin and facial expression: a century of research in review. Academic Press, New York, pp 169\u2013222"},{"key":"8230_CR12","doi-asserted-by":"crossref","unstructured":"Eng S, Ali H, Cheah A, Chong Y (2091) Facial expression recognition in JAFFE and KDEF Datasets using histogram of oriented gradients and support vector machine. In: IOP conference series: materials science and engineering. IOP Publishing, p 012031","DOI":"10.1088\/1757-899X\/705\/1\/012031"},{"key":"8230_CR13","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1016\/j.ins.2018.05.057","volume":"460","author":"N Farajzadeh","year":"2018","unstructured":"Farajzadeh N, Hashemzadeh M (2018) Exemplar-based facial expression recognition. Inf Sci 460:318\u2013330","journal-title":"Inf Sci"},{"key":"8230_CR14","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.procs.2022.12.109","volume":"216","author":"R Febrian","year":"2023","unstructured":"Febrian R, Halim BM, Christina M, Ramdhan D, Chowanda A (2023) Facial expression recognition using bidirectional LSTM-CNN. Procedia Comput Sci 216:39\u201347","journal-title":"Procedia Comput Sci"},{"key":"8230_CR15","doi-asserted-by":"publisher","first-page":"105651","DOI":"10.1016\/j.engappai.2022.105651","volume":"118","author":"P Foggia","year":"2023","unstructured":"Foggia P, Greco A, Saggese A, Vento M (2023) Multi-task learning on the edge for effective gender, age, ethnicity and emotion recognition. Eng Appl Artif Intell 118:105651","journal-title":"Eng Appl Artif Intell"},{"key":"8230_CR16","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.neunet.2022.11.025","volume":"158","author":"H Gao","year":"2023","unstructured":"Gao H, Wu M, Chen Z, Li Y, Wang X, An S et al (2023) SSA-ICL: multi-domain adaptive attention with intra-dataset continual learning for Facial expression recognition. Neural Netw 158:228\u2013238","journal-title":"Neural Netw"},{"key":"8230_CR17","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/978-3-030-98546-2_4","volume-title":"Digital phenotyping and mobile sensing: new developments in psychoinformatics","author":"M Geiger","year":"2023","unstructured":"Geiger M, Wilhelm O (2023) Computerized facial emotion expression recognition. Digital phenotyping and mobile sensing: new developments in psychoinformatics. Springer, Cham, pp 43\u201356"},{"key":"8230_CR18","doi-asserted-by":"publisher","first-page":"110182","DOI":"10.1016\/j.knosys.2022.110182","volume":"260","author":"S Ghosh","year":"2023","unstructured":"Ghosh S, Priyankar A, Ekbal A, Bhattacharyya P (2023) Multitasking of sentiment detection and emotion recognition in code-mixed Hinglish data. Knowl Based Syst 260:110182","journal-title":"Knowl Based Syst"},{"key":"8230_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-27049-2","volume":"13","author":"S Gil","year":"2023","unstructured":"Gil S, Le Bigot L (2023) Emotional face recognition when a colored mask is worn: a cross-sectional study. Sci Rep 13:1\u201315","journal-title":"Sci Rep"},{"key":"8230_CR20","first-page":"513","volume":"17","author":"J Goldberger","year":"2004","unstructured":"Goldberger J, Hinton GE, Roweis S, Salakhutdinov RR (2004) Neighbourhood components analysis. Adv Neural Inf Process Syst 17:513\u2013520","journal-title":"Adv Neural Inf Process Syst"},{"key":"8230_CR205","doi-asserted-by":"publisher","unstructured":"Goodfellow IJ, Erhan D, Luc Carrier P et al (2015) Challenges in representation learning: a report on three machine learning contests. Neural Networks 64:59\u201363. https:\/\/doi.org\/10.1016\/j.neunet.2014.09.005","DOI":"10.1016\/j.neunet.2014.09.005"},{"key":"8230_CR21","first-page":"1","volume-title":"Mobile and sensor-based technologies in higher education","author":"TK Jupalli","year":"2023","unstructured":"Jupalli TK, Reddy MST, Kondaveeti HK (2023) Artificial intelligence in higher education. Mobile and sensor-based technologies in higher education. IGI Global, pp 1\u201330"},{"key":"8230_CR200","doi-asserted-by":"publisher","unstructured":"Kanade T, Cohn JF, Tian Y (2000) Comprehensive database for facial expression analysis. In: Proc - 4th IEEE Int Conf Autom Face Gesture Recognition, FG 2000, pp 46\u201353. https:\/\/doi.org\/10.1109\/AFGR.2000.840611","DOI":"10.1109\/AFGR.2000.840611"},{"key":"8230_CR22","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.ins.2020.10.065","volume":"549","author":"M Kas","year":"2021","unstructured":"Kas M, Ruichek Y, Messoussi R (2021) New framework for person-independent facial expression recognition combining textural and shape analysis through new feature extraction approach. Inf Sci 549:200\u2013220","journal-title":"Inf Sci"},{"key":"8230_CR23","doi-asserted-by":"publisher","first-page":"689","DOI":"10.32604\/iasc.2023.025437","volume":"35","author":"M Kavitha","year":"2023","unstructured":"Kavitha M, RajivKannan A (2023) Hybrid convolutional neural network and long short-term memory approach for facial expression recognition. Intell Autom Soft Comput 35:689\u2013704","journal-title":"Intell Autom Soft Comput"},{"key":"8230_CR24","doi-asserted-by":"publisher","first-page":"1649","DOI":"10.1007\/s11042-021-11298-w","volume":"81","author":"A Khattak","year":"2022","unstructured":"Khattak A, Asghar MZ, Ali M, Batool U (2022) An efficient deep learning technique for facial emotion recognition. Multimed Tools Appl 81:1649\u20131683","journal-title":"Multimed Tools Appl"},{"key":"8230_CR212","doi-asserted-by":"crossref","unstructured":"Kononenko I (1994) Estimating attributes: analysis and extensions of RELIEF. In:  European conference on machine learning, Springer, pp 171\u2013182","DOI":"10.1007\/3-540-57868-4_57"},{"key":"8230_CR25","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097\u20131105","journal-title":"Adv Neural Inf Process Syst"},{"key":"8230_CR26","doi-asserted-by":"publisher","DOI":"10.21203\/rs.3.rs-866042\/v1","author":"N Kumari","year":"2022","unstructured":"Kumari N, Bhatia R (2022) Efficient facial emotion recognition model using deep convolutional neural network and modified joint trilateral filter. Soft Comput. https:\/\/doi.org\/10.21203\/rs.3.rs-866042\/v1","journal-title":"Soft Comput"},{"key":"8230_CR203","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: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2852\u20132861","DOI":"10.1109\/CVPR.2017.277"},{"key":"8230_CR215","unstructured":"Liu H, Setiono R (1995) Chi2: Feature selection and discretization of numeric attributes. In:  Proceedings of 7th IEEE international conference on tools with artificial intelligence, IEEE, pp 388\u2013391"},{"key":"8230_CR27","doi-asserted-by":"publisher","first-page":"5094","DOI":"10.1109\/TITS.2019.2948596","volume":"21","author":"W-L Liu","year":"2019","unstructured":"Liu W-L, Gong Y-J, Chen W-N, Liu Z, Wang H, Zhang J (2019) Coordinated charging scheduling of electric vehicles: a mixed-variable differential evolution approach. IEEE Trans Intell Transp Syst 21:5094\u20135109","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"8230_CR30","doi-asserted-by":"crossref","unstructured":"Liu Y, Zeng J, Shan S, Zheng Z (2018) Multi-channel pose-aware convolution neural networks for multi-view facial expression recognition. In: 2018 13th IEEE international conference on automatic face & gesture recognition (FG 2018), pp 458\u2013465. IEEE","DOI":"10.1109\/FG.2018.00074"},{"key":"8230_CR28","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1016\/j.ins.2022.11.076","volume":"619","author":"S Liu","year":"2023","unstructured":"Liu S, Gao P, Li Y, Fu W, Ding W (2023) Multi-modal fusion network with complementarity and importance for emotion recognition. Inf Sci 619:679\u2013694","journal-title":"Inf Sci"},{"key":"8230_CR201","doi-asserted-by":"publisher","unstructured":"Lucey P, Cohn JF, Kanade T et al (2010) The extended Cohn-Kanade dataset (CK+): A complete dataset for action unit and emotion-specified expression. In: 2010 IEEE Comput Soc Conf Comput Vis Pattern Recognit - Work CVPRW 2010, pp 94\u2013101. https:\/\/doi.org\/10.1109\/CVPRW.2010.5543262","DOI":"10.1109\/CVPRW.2010.5543262"},{"key":"8230_CR209","doi-asserted-by":"crossref","unstructured":"Lundqvist D, Flykt A, Ohman A (1998) The Karolinska directed emotional faces (KDEF). CD ROM from Dep Clin Neurosci Psychol Sect Karolinska Institutet 2\u20132","DOI":"10.1037\/t27732-000"},{"key":"8230_CR211","doi-asserted-by":"crossref","unstructured":"Lyons MJ (2021) Excavating AI Re-excavated: Debunking a Fallacious Account of the JAFFE Dataset. arXiv preprint http:\/\/arxiv.org\/abs\/2107.13998","DOI":"10.31234\/osf.io\/bvf2s"},{"key":"8230_CR208","unstructured":"Lyons MJ, Kamachi M, Gyoba J (2020) Coding facial expressions with Gabor wavelets (IVC special issue). arXiv preprint http:\/\/arxiv.org\/abs\/arXiv:2009.05938"},{"key":"8230_CR31","doi-asserted-by":"publisher","first-page":"100985","DOI":"10.1016\/j.newideapsych.2022.100985","volume":"68","author":"M Nikolaus","year":"2023","unstructured":"Nikolaus M, Fourtassi A (2023) Communicative feedback in language acquisition. New Ideas Psychol 68:100985","journal-title":"New Ideas Psychol"},{"key":"8230_CR32","doi-asserted-by":"publisher","first-page":"103743","DOI":"10.1016\/j.jvcir.2022.103743","volume":"91","author":"E Othman","year":"2023","unstructured":"Othman E, Werner P, Saxen F, Al-Hamadi A, Gruss S, Walter S (2023) Classification networks for continuous automatic pain intensity monitoring in video using facial expression on the X-ITE Pain Database. J vis Commun Image Represent 91:103743","journal-title":"J vis Commun Image Represent"},{"key":"8230_CR202","doi-asserted-by":"publisher","unstructured":"Pantic M, Valstar M, Rademaker R, Maat L (2005) Web-based database for facial expression analysis. IEEE Int Conf Multimed Expo, ICME 2005:317\u2013321. https:\/\/doi.org\/10.1109\/ICME.2005.1521424","DOI":"10.1109\/ICME.2005.1521424"},{"key":"8230_CR213","doi-asserted-by":"crossref","unstructured":"Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Machine Intell 27:1226\u20131238","DOI":"10.1109\/TPAMI.2005.159"},{"key":"8230_CR33","doi-asserted-by":"publisher","first-page":"1892","DOI":"10.3390\/electronics9111892","volume":"9","author":"S Porcu","year":"2020","unstructured":"Porcu S, Floris A, Atzori L (2020) Evaluation of data augmentation techniques for facial expression recognition systems. Electronics 9:1892","journal-title":"Electronics"},{"key":"8230_CR34","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1023\/A:1025667309714","volume":"53","author":"M Robnik-\u0160ikonja","year":"2003","unstructured":"Robnik-\u0160ikonja M, Kononenko I (2003) Theoretical and empirical analysis of ReliefF and RReliefF. Mach Learn 53:23\u201369","journal-title":"Mach Learn"},{"key":"8230_CR35","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510\u201320","DOI":"10.1109\/CVPR.2018.00474"},{"key":"8230_CR36","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1007\/s00530-021-00854-x","volume":"28","author":"J Shen","year":"2022","unstructured":"Shen J, Yang H, Li J, Cheng Z (2022) Assessing learning engagement based on facial expression recognition in MOOC\u2019s scenario. Multimed Syst 28:469\u2013478","journal-title":"Multimed Syst"},{"key":"8230_CR37","doi-asserted-by":"publisher","first-page":"106124","DOI":"10.1016\/j.knosys.2020.106124","volume":"204","author":"Z Sun","year":"2020","unstructured":"Sun Z, Chiong R, Hu Z-P (2020) Self-adaptive feature learning based on a priori knowledge for facial expression recognition. Knowl Based Syst 204:106124","journal-title":"Knowl Based Syst"},{"key":"8230_CR38","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1109\/TIP.2020.3037467","volume":"30","author":"Y Tang","year":"2020","unstructured":"Tang Y, Zhang X, Hu X, Wang S, Wang H (2020) Facial expression recognition using frequency neural network. IEEE Trans Image Process 30:444\u2013457","journal-title":"IEEE Trans Image Process"},{"key":"8230_CR39","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1007\/978-1-4615-5703-6_3","volume-title":"Nonlinear Modeling: advanced black-box techniques","author":"V Vapnik","year":"1998","unstructured":"Vapnik V (1998) The support vector method of function estimation. Nonlinear Modeling: advanced black-box techniques. Springer, pp 55\u201385"},{"key":"8230_CR40","doi-asserted-by":"publisher","first-page":"21487","DOI":"10.1007\/s11042-020-08901-x","volume":"79","author":"R Vedantham","year":"2020","unstructured":"Vedantham R, Reddy ES (2020) A robust feature extraction with optimized DBN-SMO for facial expression recognition. Multimed Tools Appl 79:21487\u201321512","journal-title":"Multimed Tools Appl"},{"key":"8230_CR41","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2203.06935","author":"Y Wang","year":"2022","unstructured":"Wang Y, Song W, Tao W, Liotta A, Yang D, Li X et al (2022) A systematic review on affective computing: emotion models, databases, and recent advances. Inf Fus. https:\/\/doi.org\/10.48550\/arXiv.2203.06935","journal-title":"Inf Fus"},{"key":"8230_CR42","first-page":"173","volume":"24","author":"AH Wani","year":"2023","unstructured":"Wani AH, Hashmy R (2023) A supervised multinomial classification framework for emotion recognition in textual social data. Int J Adv Intell Paradig 24:173\u2013189","journal-title":"Int J Adv Intell Paradig"},{"key":"8230_CR43","doi-asserted-by":"publisher","first-page":"161","DOI":"10.4304\/jcp.7.1.161-168","volume":"7","author":"W Yang","year":"2012","unstructured":"Yang W, Wang K, Zuo W (2012) Neighborhood component feature selection for high-dimensional data. J Comput 7:161\u2013168","journal-title":"J Comput"},{"key":"8230_CR204","doi-asserted-by":"publisher","unstructured":"Yin L, Wei X, Sun Y et al (2006) A 3D facial expression database for facial behavior research. FGR 2006 Proc 7th Int Conf Autom Face Gesture Recognit 2006:211\u2013216. https:\/\/doi.org\/10.1109\/FGR.2006.6","DOI":"10.1109\/FGR.2006.6"},{"key":"8230_CR206","doi-asserted-by":"publisher","unstructured":"Zhang Z, Luo P, Loy CC, Tang X (2018) From facial expression recognition to interpersonal relation prediction. Int J Comput Vis 126:550\u2013569. https:\/\/doi.org\/10.1007\/s11263-017-1055-1","DOI":"10.1007\/s11263-017-1055-1"},{"key":"8230_CR210","doi-asserted-by":"publisher","unstructured":"Zhao G, Huang X, Taini M et al (2011) Facial expression recognition from near-infrared videos. Image Vis Comput 29:607\u2013619. https:\/\/doi.org\/10.1016\/j.imavis.2011.07.002","DOI":"10.1016\/j.imavis.2011.07.002"},{"key":"8230_CR45","doi-asserted-by":"crossref","unstructured":"Zhao F, Di S, Wang L (2022a) A hyperheuristic with q-learning for the multiobjective energy-efficient distributed blocking flow shop scheduling problem. IEEE Trans Cybern","DOI":"10.1109\/TCYB.2022.3192112"},{"key":"8230_CR44","doi-asserted-by":"publisher","first-page":"107645","DOI":"10.1016\/j.knosys.2021.107645","volume":"235","author":"F Zhao","year":"2022","unstructured":"Zhao F, Hu X, Wang L, Zhao J, Tang J (2022b) A reinforcement learning brain storm optimization algorithm (BSO) with learning mechanism. Knowl Based Syst 235:107645","journal-title":"Knowl Based Syst"},{"key":"8230_CR46","doi-asserted-by":"publisher","first-page":"218","DOI":"10.3390\/electronics12010218","volume":"12","author":"R Zhen","year":"2023","unstructured":"Zhen R, Song W, He Q, Cao J, Shi L, Luo J (2023) Human-computer interaction system: a survey of talking-head generation. Electronics 12:218","journal-title":"Electronics"},{"key":"8230_CR47","doi-asserted-by":"publisher","first-page":"1430","DOI":"10.1109\/TCYB.2019.2939219","volume":"51","author":"S Zhou","year":"2019","unstructured":"Zhou S, Xing L, Zheng X, Du N, Wang L, Zhang Q (2019) A self-adaptive differential evolution algorithm for scheduling a single batch-processing machine with arbitrary job sizes and release times. IEEE Trans Cybern 51:1430\u20131442","journal-title":"IEEE Trans Cybern"}],"updated-by":[{"DOI":"10.1007\/s00500-024-09647-6","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000}}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-023-08230-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-023-08230-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-023-08230-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T12:07:03Z","timestamp":1706789223000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-023-08230-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,28]]},"references-count":61,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["8230"],"URL":"https:\/\/doi.org\/10.1007\/s00500-023-08230-9","relation":{"correction":[{"id-type":"doi","id":"10.1007\/s00500-024-09647-6","asserted-by":"object"}]},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,28]]},"assertion":[{"value":"8 April 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 April 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 February 2024","order":3,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":4,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":5,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1007\/s00500-024-09647-6","URL":"https:\/\/doi.org\/10.1007\/s00500-024-09647-6","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors of this manuscript declare no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}