{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T16:45:14Z","timestamp":1781714714230,"version":"3.54.5"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,8,5]],"date-time":"2022-08-05T00:00:00Z","timestamp":1659657600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,8,5]],"date-time":"2022-08-05T00:00:00Z","timestamp":1659657600000},"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":["Multimed Tools Appl"],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1007\/s11042-022-13567-8","type":"journal-article","created":{"date-parts":[[2022,8,5]],"date-time":"2022-08-05T16:03:08Z","timestamp":1659715388000},"page":"6479-6503","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["A novel enhanced convolution neural network with extreme learning machine: facial emotional recognition in psychology practices"],"prefix":"10.1007","volume":"82","author":[{"given":"Nitesh","family":"Banskota","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2309-3540","authenticated-orcid":false,"given":"Abeer","family":"Alsadoon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P. W. C.","family":"Prasad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed","family":"Dawoud","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tarik A.","family":"Rashid","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Omar Hisham","family":"Alsadoon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,8,5]]},"reference":[{"key":"13567_CR1","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1016\/j.cogsys.2018.06.017","volume":"52","author":"MU Ahmed","year":"2018","unstructured":"Ahmed MU, Woo KJ, Hyeon KY, Bashar MR, Rhee PK (2018) Wild facial expression recognition based on incremental active learning. Cogn Syst Res 52:212\u2013222","journal-title":"Cogn Syst Res"},{"key":"13567_CR2","volume-title":"Proceedings of the tenth Indian conference on computer vision, graphics and image processing - ICVGIP 16","author":"K Bora","year":"2016","unstructured":"Bora K, Chowdhury M, Mahanta LB, Kundu MK, Das AK (2016) Pap smear image classification using convolutional neural network. In: Proceedings of the tenth Indian conference on computer vision, graphics and image processing - ICVGIP 16"},{"key":"13567_CR3","doi-asserted-by":"crossref","unstructured":"Fang B, Zhang Q, Wang H et al (2017) Personality driven task allocation for emotional robot team. Int J Mach Learn Cybern","DOI":"10.1007\/s13042-017-0679-3"},{"key":"13567_CR4","doi-asserted-by":"crossref","unstructured":"Han X, Lei J, Chen Y (2016) HEp-2 cell classification using K-support spatial pooling in deep CNNs. Deep Learning and Data Labeling for Medical Applications Lecture Notes in Computer Science, 3\u201311","DOI":"10.1007\/978-3-319-46976-8_1"},{"issue":"7","key":"13567_CR5","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","volume":"18","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Osindero S, Teh Y (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527\u20131554","journal-title":"Neural Comput"},{"issue":"4","key":"13567_CR6","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1109\/tnn.2006.875977","volume":"17","author":"G Huang","year":"2006","unstructured":"Huang G, Chen L, Siew C (2006) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw 17(4):879\u2013892. https:\/\/doi.org\/10.1109\/tnn.2006.875977","journal-title":"IEEE Trans Neural Netw"},{"key":"13567_CR7","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.patrec.2018.04.010","volume":"115","author":"N Jain","year":"2018","unstructured":"Jain N, Kumar S, Kumar A, Shamsolmoali P, Zareapoor M (2018) Hybrid deep neural network for the emotion recognition. Pattern Recogn Lett 115:101\u2013106","journal-title":"Pattern Recogn Lett"},{"key":"13567_CR8","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.imavis.2017.01.012","volume":"65","author":"H Kaya","year":"2017","unstructured":"Kaya H, G\u00fcrp\u0131nar F, Salah AA (2017) Video-based emotion recognition in the wild using deep transfer learning and score fusion. Image Vision Comput 65:66\u201375","journal-title":"Image Vision Comput"},{"key":"13567_CR9","unstructured":"Li S, Deng W (2018) Deep facial expression recognition: A survey. arXiv preprint arXiv:1804.08348"},{"issue":"12","key":"13567_CR10","doi-asserted-by":"publisher","first-page":"2816","DOI":"10.1109\/TMM.2017.2713408","volume":"19","author":"H Li","year":"2017","unstructured":"Li H, Sun J, Xu Z, Chen L (2017) Multimodal 2D 3D facial expression recognition with deep fusion convolutional neural network. IEEE Trans Multimed 19(12):2816\u20132831","journal-title":"IEEE Trans Multimed"},{"issue":"1","key":"13567_CR11","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1109\/tnnls.2014.2335212","volume":"26","author":"X Liu","year":"2015","unstructured":"Liu X, Lin S, Fang J, Xu Z (2015) Is extreme learning machine feasible? A theoretical assessment (part I). IEEE Trans Neural Netw Learn Syst 26(1):7\u201320. https:\/\/doi.org\/10.1109\/tnnls.2014.2335212","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"13567_CR12","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/j.patcog.2018.07.016","volume":"84","author":"Y Liu","year":"2018","unstructured":"Liu Y, Yuan X, Gong X, Xie Z, Fang F, Luo Z (2018) Conditional convolution neural network enhanced random forest for facial expression recognition. Pattern Recogn 84:251\u2013261","journal-title":"Pattern Recogn"},{"key":"13567_CR13","doi-asserted-by":"publisher","DOI":"10.1109\/cvprw.2010.5543262","volume-title":"2010 IEEE computer society conference on computer vision and pattern recognition - workshops","author":"P Lucey","year":"2010","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. https:\/\/doi.org\/10.1109\/cvprw.2010.5543262"},{"key":"13567_CR14","doi-asserted-by":"publisher","DOI":"10.1109\/afgr.1998.670949","volume-title":"Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition","author":"M Lyons","year":"1998","unstructured":"Lyons M, Akamatsu S, Kamachi M, Gyoba J (1998) Coding facial expressions with Gabor wavelets. In: Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition. https:\/\/doi.org\/10.1109\/afgr.1998.670949"},{"key":"13567_CR15","volume-title":"Nonverbal communication","author":"A Mehrabian","year":"2007","unstructured":"Mehrabian A (2007) Nonverbal communication. Aldine Transaction, New Brunswick, NJ"},{"key":"13567_CR16","doi-asserted-by":"crossref","unstructured":"Peng M, Wang C, Chen T, Liu G, Fu X (2017) Dual temporal scale convolutional neural network for Micro-expression recognition. Front Psychol 8","DOI":"10.3389\/fpsyg.2017.01745"},{"issue":"7","key":"13567_CR17","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1007\/s00521-018-3358-8","volume":"29","author":"A Ruiz-Garcia","year":"2018","unstructured":"Ruiz-Garcia A, Elshaw M, Altahhan A, Palade V (2018) A hybrid deep learning neural appoach emotion recognition from facial expression for sociallu assistive robots. Neural Comput Appl 29(7):359\u2013373","journal-title":"Neural Comput Appl"},{"key":"13567_CR18","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85\u2013117","journal-title":"Neural Netw"},{"issue":"2","key":"13567_CR19","doi-asserted-by":"publisher","first-page":"166","DOI":"10.25103\/jestr.102.20","volume":"10","author":"M Sharif","year":"2017","unstructured":"Sharif M, Naz F, Yasmin M, Shahid MA, Rehman A (2017) Face recognition: a survey. J Eng Sci Technol Rev 10(2):166\u2013177. https:\/\/doi.org\/10.25103\/jestr.102.20","journal-title":"J Eng Sci Technol Rev"},{"key":"13567_CR20","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/j.neucom.2018.05.107","volume":"312","author":"S Wang","year":"2018","unstructured":"Wang S, Li B, Liu Y, Yan W, Ou X, Huang X, Fu X (2018) Micro-expression recognition with small sample size by transferring long-term convolutional neural network. Neurocomputing 312:251\u2013262","journal-title":"Neurocomputing"},{"key":"13567_CR21","doi-asserted-by":"publisher","first-page":"12451","DOI":"10.1109\/ACCESS.2018.2805861","volume":"6","author":"B Wu","year":"2018","unstructured":"Wu B, Lin C (2018) Adaptive feature mapping for customizing deep learning based facial expression recognition model. IEEE Access 6:12451\u201312461","journal-title":"IEEE Access"},{"issue":"6","key":"13567_CR22","doi-asserted-by":"publisher","first-page":"1172","DOI":"10.1007\/s11390-017-1792-1","volume":"32","author":"N Yao","year":"2017","unstructured":"Yao N, Chen H, Guo Q, Wang H (2017) Non-frontal facial expression recognition using a depth-patch based deep neural network. J Comput Sci Technol 32(6):1172\u20131185","journal-title":"J Comput Sci Technol"},{"key":"13567_CR23","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.patcog.2017.12.017","volume":"77","author":"X Yuan","year":"2018","unstructured":"Yuan X, Xie L, Abouelenien M (2018) A regularized ensemble framework of deep learning for cancer detection from multi-class, imbalanced training data. Pattern Recogn 77:160\u2013172","journal-title":"Pattern Recogn"},{"issue":"12","key":"13567_CR24","doi-asserted-by":"publisher","first-page":"2528","DOI":"10.1109\/TMM.2016.2598092","volume":"18","author":"T Zhang","year":"2016","unstructured":"Zhang T, Zheng W, Cui Z, Zong Y, Yan J, Yan K (2016) A deep neural network-driven feature learning method for multi-view facial expression recognition. IEEE Trans Multimed 18(12):2528\u20132536","journal-title":"IEEE Trans Multimed"},{"key":"13567_CR25","doi-asserted-by":"publisher","first-page":"3385","DOI":"10.3150\/16-BEJ850","volume":"23","author":"X Zhao","year":"2017","unstructured":"Zhao X, Yuanshan W, Guosheng Y (2017) Sieve maximum likelihood estimation for a general class of accelerated hazards models with bundled parameters. Bernoulli 23:3385\u20133411. https:\/\/doi.org\/10.3150\/16-BEJ850","journal-title":"Bernoulli"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-13567-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-022-13567-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-13567-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,26]],"date-time":"2023-01-26T08:09:37Z","timestamp":1674720577000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-022-13567-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,5]]},"references-count":25,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["13567"],"URL":"https:\/\/doi.org\/10.1007\/s11042-022-13567-8","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,5]]},"assertion":[{"value":"27 January 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 June 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 July 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 August 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"No conflicts of interests as well.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}