dc.contributor.author | Pattichis, Constantinos S. | en |
dc.contributor.author | Schizas, Christos N. | en |
dc.contributor.author | Sergiou, A. | en |
dc.contributor.author | Schnorrenberg, F. | en |
dc.creator | Pattichis, Constantinos S. | en |
dc.creator | Schizas, Christos N. | en |
dc.creator | Sergiou, A. | en |
dc.creator | Schnorrenberg, F. | en |
dc.date.accessioned | 2019-11-13T10:41:56Z | |
dc.date.available | 2019-11-13T10:41:56Z | |
dc.date.issued | 1994 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/54815 | |
dc.description.abstract | Clinical 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.publisher | IEEE | en |
dc.source | IEEE International Conference on Neural Networks - Conference Proceedings | en |
dc.source | Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-0028710005&partnerID=40&md5=c6ed5b8736feeb9386c22501dd675401 | |
dc.subject | Mathematical models | en |
dc.subject | Feature extraction | en |
dc.subject | Vector quantization | en |
dc.subject | Computer architecture | en |
dc.subject | Neural networks | en |
dc.subject | Genetic algorithms | en |
dc.subject | Medical imaging | en |
dc.subject | Biomedical engineering | en |
dc.subject | Learning systems | en |
dc.subject | Electromyography | en |
dc.subject | Response time (computer systems) | en |
dc.subject | Hybrid neural network | en |
dc.subject | Software package WISARD | en |
dc.title | Hybrid neural network electromyographic system: Incorporating the WISARD net | en |
dc.type | info:eu-repo/semantics/conferenceObject | |
dc.description.volume | 6 | |
dc.description.startingpage | 3478 | |
dc.description.endingpage | 3483 | |
dc.author.faculty | 002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Πληροφορικής / Department of Computer Science | |
dc.type.uhtype | Conference Object | en |
dc.description.notes | <p>Sponsors: IEEE | en |
dc.description.notes | Conference code: 42367 | en |
dc.description.notes | Cited By :3</p> | en |
dc.contributor.orcid | Pattichis, Constantinos S. [0000-0003-1271-8151] | |
dc.contributor.orcid | Schizas, Christos N. [0000-0001-6548-4980] | |
dc.gnosis.orcid | 0000-0003-1271-8151 | |
dc.gnosis.orcid | 0000-0001-6548-4980 | |