| dc.contributor.author |
Frossyniotis, D |
en |
| dc.contributor.author |
Anthopoulos, Y |
en |
| dc.contributor.author |
Kintzios, S |
en |
| dc.contributor.author |
Perdikaris, A |
en |
| dc.contributor.author |
Yialouris, CP |
en |
| dc.date.accessioned |
2014-06-06T06:46:57Z |
|
| dc.date.available |
2014-06-06T06:46:57Z |
|
| dc.date.issued |
2006 |
en |
| dc.identifier.issn |
03029743 |
en |
| dc.identifier.uri |
http://62.217.125.90/xmlui/handle/123456789/3323 |
|
| dc.relation.uri |
http://www.scopus.com/inward/record.url?eid=2-s2.0-33749834691&partnerID=40&md5=efd45f651e6415a4342066b513214f18 |
en |
| dc.subject.other |
Bioelectric Recognition Assay (BERA) |
en |
| dc.subject.other |
Gel matrix |
en |
| dc.subject.other |
Multi net system |
en |
| dc.subject.other |
Plant viruses |
en |
| dc.subject.other |
Bioassay |
en |
| dc.subject.other |
Biosensors |
en |
| dc.subject.other |
Neural networks |
en |
| dc.subject.other |
Pattern recognition |
en |
| dc.subject.other |
Plants (botany) |
en |
| dc.subject.other |
Viruses |
en |
| dc.subject.other |
Sensor data fusion |
en |
| dc.title |
A multisensor fusion system for the detection of plant viruses by combining Artificial Neural Networks |
en |
| heal.type |
conferenceItem |
en |
| heal.publicationDate |
2006 |
en |
| heal.abstract |
Several researchers have shown that substantial improvements can be achieved in difficult pattern recognition problems by combining the outputs of multiple neural networks. In this work, we present and test a multi-net system for the detection of plant viruses, using biosensors. The system is based on the Bioelectric Recognition Assay (BERA) method for the detection of viruses, developed by our team. BERA sensors detect the electric response of culture cells suspended in a gel matrix, as a result to their interaction with virus's cells, rendering thus feasible his identification. Currently this is achieved empirically by examining the biosensor's response data curve. In this paper, we use a combination of specialized Artificial Neural Networks that are trained to recognize plant viruses according to biosensors' responses. Experiments indicate that the multi-net classification system exhibits promising performance compared with the case of single network training, both in terms of error rates and in terms of training speed (especially if the training of the classifiers is done in parallel). © Springer-Verlag Berlin Heidelberg 2006. |
en |
| heal.journalName |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
en |
| dc.identifier.volume |
4132 LNCS - II |
en |
| dc.identifier.spage |
401 |
en |
| dc.identifier.epage |
409 |
en |