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dc.contributor.authorPattichis, Constantinos S.en
dc.contributor.authorSchizas, Christos N.en
dc.contributor.authorSergiou, A.en
dc.contributor.authorSchnorrenberg, F.en
dc.creatorPattichis, Constantinos S.en
dc.creatorSchizas, Christos N.en
dc.creatorSergiou, A.en
dc.creatorSchnorrenberg, F.en
dc.date.accessioned2019-11-13T10:41:56Z
dc.date.available2019-11-13T10:41:56Z
dc.date.issued1994
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/54815
dc.description.abstractClinical electromyography (EMG) provides useful information for the diagnosis of neuromuscular disorders. The utility of artificial neural networks trained with the backpropagation, the Kohonen's self-organizing feature maps algorithm, and the genetics based machine learning (GBML) in classifying EMG data has recently been demonstrated. A hybrid diagnostic system was also introduced that combines the above neural network and GBML models. In this paper the WISARD net is applied on the same set of EMG data. The WISARD (Wilkie, Stonham, Aleksander Recognition Device) is an implementation in hardware or software of an n-tuple sampling technique. Results suggest that although the diagnostic performance of the WISARD models is of the order of 80%, that being comparable to the above mentioned three systems, training time has been significantly reduced. In addition, the hardware or software implementation of the WISARD net is simpler than the other three systems.en
dc.publisherIEEEen
dc.sourceIEEE International Conference on Neural Networks - Conference Proceedingsen
dc.sourceProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)en
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-0028710005&partnerID=40&md5=c6ed5b8736feeb9386c22501dd675401
dc.subjectMathematical modelsen
dc.subjectFeature extractionen
dc.subjectVector quantizationen
dc.subjectComputer architectureen
dc.subjectNeural networksen
dc.subjectGenetic algorithmsen
dc.subjectMedical imagingen
dc.subjectBiomedical engineeringen
dc.subjectLearning systemsen
dc.subjectElectromyographyen
dc.subjectResponse time (computer systems)en
dc.subjectHybrid neural networken
dc.subjectSoftware package WISARDen
dc.titleHybrid neural network electromyographic system: Incorporating the WISARD neten
dc.typeinfo:eu-repo/semantics/conferenceObject
dc.description.volume6
dc.description.startingpage3478
dc.description.endingpage3483
dc.author.faculty002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences
dc.author.departmentΤμήμα Πληροφορικής / Department of Computer Science
dc.type.uhtypeConference Objecten
dc.description.notes<p>Sponsors: IEEEen
dc.description.notesConference code: 42367en
dc.description.notesCited By :3</p>en
dc.contributor.orcidPattichis, Constantinos S. [0000-0003-1271-8151]
dc.contributor.orcidSchizas, Christos N. [0000-0001-6548-4980]
dc.gnosis.orcid0000-0003-1271-8151
dc.gnosis.orcid0000-0001-6548-4980


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