{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T16:04:25Z","timestamp":1762445065164},"reference-count":10,"publisher":"Springer Science and Business Media LLC","issue":"18","license":[{"start":{"date-parts":[[2020,7,27]],"date-time":"2020-07-27T00:00:00Z","timestamp":1595808000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,7,27]],"date-time":"2020-07-27T00:00:00Z","timestamp":1595808000000},"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 & Applic"],"published-print":{"date-parts":[[2020,9]]},"DOI":"10.1007\/s00521-020-05175-0","type":"journal-article","created":{"date-parts":[[2020,7,27]],"date-time":"2020-07-27T17:03:31Z","timestamp":1595869411000},"page":"14241-14245","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Special issue on extreme learning machine and deep learning networks"],"prefix":"10.1007","volume":"32","author":[{"given":"Zhihong","family":"Man","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guang-Bin","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,7,27]]},"reference":[{"key":"5175_CR1","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"G Huang","year":"2006","unstructured":"Huang G, Zhu Q, Siew C (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489\u2013501","journal-title":"Neurocomputing"},{"issue":"4","key":"5175_CR2","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","journal-title":"IEEE Trans Neural Netw"},{"key":"5175_CR3","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s13042-011-0019-y","volume":"2","author":"G Huang","year":"2011","unstructured":"Huang G, Wang D, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Cyber 2:107\u2013122","journal-title":"Int J Mach Cyber"},{"issue":"14","key":"5175_CR4","first-page":"4610","volume":"12","author":"M Albadr","year":"2017","unstructured":"Albadr M, Tiun S (2017) Extreme learning machine: a review. Int J Appl Eng Res 12(14):4610\u20134623","journal-title":"Int J Appl Eng Res"},{"issue":"6","key":"5175_CR5","doi-asserted-by":"publisher","first-page":"1624","DOI":"10.1016\/j.sigpro.2012.07.016","volume":"93","author":"Z Man","year":"2013","unstructured":"Man Z, Lee K, Wang D, Cao Z, Khoo S (2013) An optimal weight learning machine for handwritten digit image recognition. Sig Process 93(6):1624\u20131638","journal-title":"Sig Process"},{"issue":"3","key":"5175_CR6","doi-asserted-by":"publisher","first-page":"946","DOI":"10.1109\/TPWRS.2008.926431","volume":"23","author":"A Nizar","year":"2008","unstructured":"Nizar A, Dong Z, Wang Y (2008) Power utility nontechnical loss analysis with extreme learning machine method. IEEE Trans Power Syst 23(3):946\u2013955","journal-title":"IEEE Trans Power Syst"},{"key":"5175_CR7","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.neucom.2012.11.035","volume":"111","author":"D Lahoz","year":"2013","unstructured":"Lahoz D, Lacruz B, Mateo P (2013) A multi-objective micro genetic ELM algorithm. Neurocomputing 111:90\u2013103","journal-title":"Neurocomputing"},{"issue":"2","key":"5175_CR8","doi-asserted-by":"publisher","first-page":"1490","DOI":"10.1109\/TIE.2017.2733448","volume":"65","author":"L Yao","year":"2018","unstructured":"Yao L, Ge Z (2018) Deep learning of semisupervised process data with hierarchical extreme learning machine and soft sensor application. IEEE Trans Ind Electron 65(2):1490\u20131498","journal-title":"IEEE Trans Ind Electron"},{"issue":"3","key":"5175_CR9","doi-asserted-by":"publisher","first-page":"1174","DOI":"10.1109\/TGRS.2014.2335751","volume":"53","author":"J Tang","year":"2015","unstructured":"Tang J, Deng C, Huang G (2015) Compressed-domain ship detection on spaceborne optical image using deep neural network and extreme learning machine. IEEE Trans Geosci Remote Sens 53(3):1174\u20131185","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"5175_CR10","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1016\/j.neucom.2017.12.062","volume":"283","author":"Z Yin","year":"2018","unstructured":"Yin Z, Zhang J (2018) Task-generic mental fatigue recognition based on neurophysiological signals and dynamical deep extreme learning machine. Neurocomputing 283:266\u2013281","journal-title":"Neurocomputing"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05175-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05175-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05175-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,26]],"date-time":"2021-07-26T23:38:36Z","timestamp":1627342716000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05175-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,27]]},"references-count":10,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2020,9]]}},"alternative-id":["5175"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05175-0","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,27]]},"assertion":[{"value":"27 July 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}