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dc.contributor.authorTusher, Hasan Mahbub
dc.contributor.authorNazir, Salman
dc.contributor.authorMallam, Steven
dc.contributor.authorMunim, Ziaul Haque
dc.date.accessioned2023-02-17T11:27:51Z
dc.date.available2023-02-17T11:27:51Z
dc.date.created2023-01-03T09:07:46Z
dc.date.issued2022
dc.identifier.citationTusher, H. M., Nazir, S., Mallam, S. & Munim, Z. H. (2022, 7.-10. december). Artificial Neural Network (ANN) for Performance Assessment in Virtual Reality (VR) Simulators: From Surgical to Maritime Training [Paperpresentasjon]. 2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Kuala Lumpur.en_US
dc.identifier.isbn9781665486873
dc.identifier.urihttps://hdl.handle.net/11250/3051904
dc.description.abstractSimulator training is an integral part of seafarer education and training. Maritime Virtual Reality (VR) simulators have added a new dimension to the range of available state-of-the-art training tools in recent years. The lack of appropriate pedagogical intervention including inadequate performance assessment frameworks for the trainees are few of the limitations of maritime VR simulators. In this study, a performance assessment framework utilizing Artificial Neural Network (ANN) in VR training from the healthcare domain is adapted through literature review. This framework could be operationalized in maritime training for aiding the performance assessment of seafarers and in turn increasing the pedagogical efficiency of maritime VR simulators. The implication of such adaption is also discussed considering the human factors and the technical dimensions of maritime training.en_US
dc.language.isoengen_US
dc.relation.ispartof2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) 7-10 Dec 2022
dc.titleArtificial Neural Network (ANN) for Performance Assessment in Virtual Reality (VR) Simulators: From Surgical to Maritime Trainingen_US
dc.typeChapteren_US
dc.description.versionacceptedVersionen_US
dc.rights.holder© 2022 IEEE.en_US
dc.source.pagenumber334-338en_US
dc.identifier.doihttps://doi.org/10.1109/IEEM55944.2022.9989816
dc.identifier.cristin2099285
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.fulltextpostprint
cristin.qualitycode1


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