A Haptics feedback based-LSTM predictive model for pericardiocentesis therapy using public introperative data Khatami, Amin, Tai, Yonghang, Khosravi, Abbas, Wei, Lei, Moradi Dalvand, Mohsen, Peng, Jun and Nahavandi, Saeid 2017, A Haptics feedback based-LSTM predictive model for pericardiocentesis therapy using public introperative data, in ICONIP 2017 : Proceedings of the Neural Information Processing International Conference, Springer, Cham, Switzerland, pp. 810-818, doi: 10.1007/978-3-319-70139-4_82. Attached Files Name Description MIMEType Size Downloads Title A Haptics feedback based-LSTM predictive model for pericardiocentesis therapy using public introperative data Author(s) Khatami, Amin Tai, Yonghang Khosravi, Abbas orcid.org/0000-0001-6927-0744 Wei, Lei orcid.org/0000-0001-8267-0283 Moradi Dalvand, Mohsen Peng, Jun Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Conference name Neural Information Processing. International Conference (24th : 2017 : Guangzhou, China) Conference location Guangzhou, China Conference dates 2017/11/14 - 2017/11/18 Title of proceedings ICONIP 2017 : Proceedings of the Neural Information Processing International Conference Publication date 2017 Series Lecture Notes in Computer Science Start page 810 End page 818 Total pages 9 Publisher Springer Place of publication Cham, Switzerland Keyword(s) Force prediction Haptics predictive models Real-time LSTM ISBN 3319701398 9783319701387 ISSN 0302-9743 1611-3349 Language eng DOI 10.1007/978-3-319-70139-4_82 Field of Research 08 Information And Computing Sciences HERDC Research category E1 Full written paper - refereed ERA Research output type E Conference publication Copyright notice ©2017, Springer International Publishing AG Persistent URL http://hdl.handle.net/10536/DRO/DU:30111639 Document type: Conference Paper Collections: Institute for Intelligent Systems Research and Innovation (IISRI) GTP Research Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Thu, 19 Jul 2018, 17:31:14 EST Thu, 19 Jul 2018, 17:32:11 EST Fri, 20 Jul 2018, 05:01:20 EST Fri, 20 Jul 2018, 06:04:56 EST Fri, 20 Jul 2018, 11:09:08 EST Fri, 20 Jul 2018, 11:15:47 EST Fri, 20 Jul 2018, 11:22:33 EST Fri, 20 Jul 2018, 11:26:36 EST Sat, 07 Nov 2020, 01:30:38 EST Filtered Full Citation counts:   Cited 3 times in TR Web of Science Cited 5 times in Scopus Search Google Scholar Access Statistics: 264 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Thu, 19 Jul 2018, 17:32:11 EST

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