{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:31:51Z","timestamp":1772119911904,"version":"3.50.1"},"reference-count":63,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2021,10,1]],"date-time":"2021-10-01T00:00:00Z","timestamp":1633046400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,10,1]],"date-time":"2021-10-01T00:00:00Z","timestamp":1633046400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,10,1]],"date-time":"2021-10-01T00:00:00Z","timestamp":1633046400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Discovery Projects Funding Scheme","award":["DP190102181"],"award-info":[{"award-number":["DP190102181"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Emerg. Top. Comput. Intell."],"published-print":{"date-parts":[[2021,10]]},"DOI":"10.1109\/tetci.2020.3037918","type":"journal-article","created":{"date-parts":[[2020,11,26]],"date-time":"2020-11-26T20:59:41Z","timestamp":1606424381000},"page":"768-779","source":"Crossref","is-referenced-by-count":12,"title":["Neural Network Training for Uncertainty Quantification Over Time-Range"],"prefix":"10.1109","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3395-1772","authenticated-orcid":false,"given":"H. M. Dipu","family":"Kabir","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6927-0744","authenticated-orcid":false,"given":"Abbas","family":"Khosravi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0360-5270","authenticated-orcid":false,"given":"Saeid","family":"Nahavandi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4877-3478","authenticated-orcid":false,"given":"Dipti","family":"Srinivasan","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2019.2936546"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2013.08.020"},{"key":"ref33","article-title":"Deep autoencoding Gaussian mixture model for unsupervised anomaly detection","author":"zong","year":"2018","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref32","first-page":"1050","article-title":"Dropout as a Bayesian approximation: Representing model uncertainty in deep learning","author":"gal","year":"2016","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref31","article-title":"Uncertainty quantification of molecular property prediction with bayesian neural networks","author":"ryu","year":"2019"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1990.10476225"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2009.07.059"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2014.03.060"},{"key":"ref35","article-title":"Optimal uncertainty-guided neural network training","author":"kabir","year":"2019"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2014.2363873"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2015.2471196"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.08.054"},{"key":"ref61","article-title":"The problem of concept drift: Definitions and related work","volume":"106","author":"tsymbal","year":"2004","journal-title":"Trinity College Dublin Computer Science Department"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP40776.2020.9053788"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1175\/MWR2906.1"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511543494.013"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICNN.1994.374138"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04617-8"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/PROC.1985.13315"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.precisioneng.2017.09.009"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.10.043"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2016.05.095"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.mejo.2017.10.002"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.acha.2014.07.001"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.2478\/v10006-012-0064-z"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557060"},{"key":"ref50","article-title":"Australian energy market operator data","year":"2018"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2018.2866421"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1145\/2523813"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2017.11.004"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1142\/S0219649219500436"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2020.3006319"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TED.2017.2786160"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1049\/iet-rpg.2019.0766"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2017.2778762"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TSTE.2018.2789398"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2836917"},{"key":"ref11","first-page":"5574","article-title":"What uncertainties do we need in Bayesian deep learning for computer vision?","author":"kendall","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/FUZZ-IEEE.2016.7737705"},{"key":"ref12","article-title":"Uncertainty in neural networks: Bayesian ensembling","author":"pearce","year":"2018"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1017\/S0263574719001516"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-33904-3_36"},{"key":"ref15","first-page":"1","article-title":"Uncertainty quantification neural network from similarity and sensitivity","author":"kabir","year":"2020","journal-title":"Proc IEEE Int Joint Conf Neural Netw"},{"key":"ref16","first-page":"6473","article-title":"High-quality prediction intervals for deep learning: A distribution-free, ensembled approach","volume":"9","author":"pearce","year":"2018","journal-title":"Proc 35th Int Conf Mach Learn"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.3390\/s19030721"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2019.2958062"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.cogsys.2020.04.001"},{"key":"ref4","article-title":"Method and system for traffic prediction based on space-time relation","author":"wu","year":"2013"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2009.2036481"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-014-1376-6"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/s10723-015-9359-2"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1038\/nature14956"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2019.8852215"},{"key":"ref49","article-title":"Spinalnet: Deep neural network with gradual input","author":"kabir","year":"2020"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2861573"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.23919\/ACC.1992.4792632"},{"key":"ref45","article-title":"Latent projection BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights","author":"pradier","year":"2018"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1080\/10255840903413565"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.3390\/w11071399"},{"key":"ref42","first-page":"1","article-title":"Fuzzy interval modelling based on joint supervision","author":"mu\u00f1oz-carpintero","year":"2020","journal-title":"Proc IEEE Int Conf Fuzzy Syst"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2964835"},{"key":"ref44","article-title":"Correlated parameters to accurately measure uncertainty in deep neural networks","author":"posch","year":"0","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/s10699-020-09690-0"}],"container-title":["IEEE Transactions on Emerging Topics in Computational Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7433297\/9544073\/09272355.pdf?arnumber=9272355","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:54:07Z","timestamp":1652194447000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9272355\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10]]},"references-count":63,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/tetci.2020.3037918","relation":{},"ISSN":["2471-285X"],"issn-type":[{"value":"2471-285X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10]]}}}