A novel hybrid machine learning algorithm for limited and big data modeling with application in industry 4.0 Khayyam, Hamid, Jamali, Ali, Bab-Hadiashar, Alireza, Esch, Thomas, Ramakrishna, Seeram, Jalili, Mahdi and Naebe, Minoo 2020, A novel hybrid machine learning algorithm for limited and big data modeling with application in industry 4.0, IEEE access, vol. 8, pp. 111381-111393, doi: 10.1109/ACCESS.2020.2999898. Attached Files Name Description MIMEType Size Downloads Title A novel hybrid machine learning algorithm for limited and big data modeling with application in industry 4.0 Author(s) Khayyam, Hamid Jamali, Ali Bab-Hadiashar, Alireza Esch, Thomas Ramakrishna, Seeram Jalili, Mahdi Naebe, Minoo orcid.org/0000-0002-0607-6327 Journal name IEEE access Volume number 8 Start page 111381 End page 111393 Total pages 13 Publisher Institute of Electrical and Electronics Engineers Place of publication Piscataway, N.J. Publication date 2020 ISSN 2169-3536 2169-3536 Keyword(s) Science & Technology Technology Computer Science, Information Systems Engineering, Electrical & Electronic Telecommunications Computer Science Engineering Data models Support vector machines Big Data Machine learning Computational modeling Principal component analysis Industries Industry 40 big data modeling limited data modeling multi-objective optimization Language eng DOI 10.1109/ACCESS.2020.2999898 Indigenous content off Field of Research 091307 Numerical Modelling and Mechanical Characterisation 091202 Composite and Hybrid Materials 091012 Textile Technology 08 Information and Computing Sciences 09 Engineering 10 Technology HERDC Research category C1 Refereed article in a scholarly journal Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30141303 Document type: Journal Article Collections: Institute for Frontier Materials Open Access Collection GTP Research Related Links Link Description Link to full-text (open access)   Connect to Elements publication management system 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. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Tue, 01 Sep 2020, 14:19:48 EST Wed, 02 Sep 2020, 05:01:16 EST Wed, 02 Sep 2020, 06:03:57 EST Tue, 22 Sep 2020, 14:09:31 EST Tue, 22 Sep 2020, 14:11:52 EST Filtered Full Citation counts:   Cited 11 times in TR Web of Science Cited 9 times in Scopus Search Google Scholar Access Statistics: 132 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Tue, 01 Sep 2020, 14:19:48 EST