{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T07:43:43Z","timestamp":1782891823694,"version":"3.54.5"},"reference-count":32,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100001659","name":"German Research Foundation (DFG) within the framework of the Heisenberg Professorship Program","doi-asserted-by":"publisher","award":["ES434\/8-1"],"award-info":[{"award-number":["ES434\/8-1"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011699","name":"Siemens Healthineers AG","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100011699","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3106791","type":"journal-article","created":{"date-parts":[[2021,8,23]],"date-time":"2021-08-23T20:39:26Z","timestamp":1629751166000},"page":"117140-117152","source":"Crossref","is-referenced-by-count":24,"title":["System Design for a Data-Driven and Explainable Customer Sentiment Monitor Using IoT and Enterprise Data"],"prefix":"10.1109","volume":"9","author":[{"given":"An","family":"Nguyen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Foerstel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Kittler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrey","family":"Kurzyukov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leo","family":"Schwinn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dario","family":"Zanca","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tobias","family":"Hipp","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sun Da","family":"Jun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Schrapp","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eva","family":"Rothgang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bjoern","family":"Eskofier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330701"},{"key":"ref31","first-page":"2546","article-title":"Algorithms for hyper-parameter optimization","author":"bergstra","year":"2011","journal-title":"Proc 24th Int Conf Neural Inf Process Syst (NIPS)"},{"key":"ref30","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134015"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2016.7840733"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.csi.2020.103442"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.chb.2016.03.008"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/RE.2017.61"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/MS.2019.2923408"},{"key":"ref16","author":"nguyen","year":"2020","journal-title":"Industrial benchmark dataset for customer escalation prediction version 1 0"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0138-9"},{"key":"ref18","first-page":"4765","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"lundberg","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2010.03.014"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/72.279181"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623340"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2019.106587"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.3390\/app9204396"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/78.650093"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.3390\/info11040202"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/PlatCon.2016.7456805"},{"key":"ref7","article-title":"Deep learning for unsupervised insider threat detection in structured cybersecurity data streams","author":"tuor","year":"2017","journal-title":"Proc Workshops 31st AAAI Conf Artif Intell"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2017.11.016"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2020.101739"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1177\/1094670519896422"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-21606-5"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"friedman","year":"2001","journal-title":"Ann Statist"},{"key":"ref26","first-page":"1","article-title":"Imbalanced-learn: A Python toolbox to tackle the curse of imbalanced datasets in machine learning","volume":"18","author":"lema\u00eetre","year":"2017","journal-title":"J Mach Learn Res"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2008.239"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09520354.pdf?arnumber=9520354","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:57:35Z","timestamp":1639771055000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9520354\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":32,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3106791","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}