A multisensor fusion system for the detection of plant viruses by combining Artificial Neural Networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4132 LNCS - II, Issue , 2006, Pages 401-409

  Frossyniotis, Dimitrios ^a   Anthopoulos, Yannis ^a   Kintzios, Spiros ^a   Perdikaris, Antonis ^a   Yialouris, Constantine P ^a  

^a AGRICULTURAL UNIVERSITY OF ATHENS   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

BIOASSAY; BIOSENSORS; NEURAL NETWORKS; PATTERN RECOGNITION; PLANTS (BOTANY); VIRUSES;

BIOELECTRIC RECOGNITION ASSAY (BERA); GEL MATRIX; MULTI NET SYSTEM; PLANT VIRUSES;

SENSOR DATA FUSION;

EID: 33749834691     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11840930_41     Document Type: Conference Paper

References (23)

1. Alpaydin E. Techniques for combining multiple learners. In Proceedings of Engineering of Intelligent Systems, volume 2, pages 6-12, ICSC Press, 1998.
2. Kuncheva L. Combining Classifiers by Clustering, Selection and Decision Templates. Technical report, University of Wales, UK, 2000.
3. Maclin R. and Opitz D. An empirical evaluation of bagging and boosting. In Proceedings of the Fourteenth International Conference on Artificial Intelligence, pages 546-551, AAAI Press/MIT Press, 1997.
4. Freund Y. and Schapire R.E. Experiments with a new boosting algorithm. In Proceedings of the Thirteenth International Conference on Machine Learning, pages 148-156, Morgan Kaufmann, 1996.
5. Kuncheva L. Clustering-and-selection model for classifier combination. In Proceedings of the 4th International Conference on Knowledge-based Intelligent Engineering Systems (KES'2000), Brighton, UK, 2000.
6. Vericas A., Lipnickas A., Malmqvist K., Bacauskiene M. and Gelzinis A. Soft combination of neural classifiers: A comparative study, Pattern Recognition Letters, volume 20, pages 429-444, 1999.
7. Tumer K. and Ghosh J. Classifier combining through trimmed means and order statistics. In Proceedings of the International Joint Conference on Neural Networks, Anchorage, Alaska, 1998.
8. Tumer K. and Ghosh J. Order statistics combiners for neural classifiers. In Proceedings of the World Congress on Neural Networks, pages I:31-34, Washington D.C., INNS Press, 1995.
9. Sharkey A.J.C. Combining Artificial Neural Nets : Ensemble and Modular Multi-Net Systems, Springer-Verlag Press, 1999.
10. Tumer K. and Ghosh J. Limits to performance gains in combined neural classifiers. In Proceedings of the Artificial Neural Networks in Engineering '95, pages 419-424, St. Louis, 1995.
11. Turner K. and Ghosh J. Error correlation and error reduction in ensemble classifiers. Connection Science, Special Issue on Combining Artificial Neural Networks: Ensemble Approaches, volume 8, number 3-5, pages 385-404, 1996.
12. Alpaydin E. Voting over multiple condensed nearest neighbour subsets. Artificial Intelligence Review, volume 11, pages 115-132, 1997.
13. Breiman L. Bagging predictors. Technical report, no. 421, Department of Statistics, University of California, Berkeley, 1994.
14. Chan P.K. and Stolfo S.J. A comparative evaluation of voting and meta-learning on partitioned data. In Proceedings of the Twelfth International Machine Learning Conference, pages 90-98, Morgan Kaufmann, San Mateo, CA, 1995.
15. Kintzios S., E. Pistola, P. Panagiotopoulos, M. Bomsel, N. Alexandropoulos, F. Bem, I. Biselis, R. Levin. Bioelectric recognition assay (BERA). Biosensors and Bioelectronics 16 325-336, 2001.
16. Kintzios S., E. Pistola, J. Konstas, F. Bem, T. Matakiadis, N. Alexandropoulos, I. Biselis, R. Levin. Application of the Bioelectric recognition assay (BERA) for the detection of human and plant viruses: definition of operational parameters. Biosensors and Bioelectronics 16: 467-480, 2001.
17. Kintzios, S., Bem, F., Mangana, O., Nomikou, K., Markoulatos, P., Alexandropoulos, N., Fasseas, C., Arakelyan, V., Petrou, A-L., Soukouli, K., Moschopoulou, G., Yialouris, G., Simonian, A. Study on the mechanism of Bioelectric Recognition Assay: evidence for immobilized cell membrane interactions with viral fragments. Biosensors & Bioelectronics 20:907-916, 2004.
18. Kintzios S., Makrygianni Ef., Pistola E., Panagiotopoulos P., Economou G. Effect of amino acids and amino acid analogues on the in vitro expreesion of glyphosate tolerance in johnsongrass (Sorghum halepense L. pers.) J. Food, Agriculture and Environment 3: 180-184, 2003.
19. Kintzios S., J. Goldstein, A. Perdikaris, G. Moschopoulou, I. Marinopoulou, O. Mangana, K. Nomikou, I. Papanastasiou, A-L. Petrou V. Arakelyan, A. Economou, A. Simonian. The BERA Diagnostic System: An all-purpose cell biosensor for the 21th Century. 5th Biodetection Conference, Baltimore, MD, USA, 9-10/06/05, 2005.
20. Moschopoulou G., Kintzios S. (2005): Membrane engineered Bioelectric Recognition Cell sensors for the detection of subnanomolar concentrations of Superoxide: A novel biosensor principle. International Conference on Instrumental Methods of Analysis (IMA) 2005, Crete, Greece, 1-5/10/2005.
21. Kintzios S., I. Marinopoulou, G. Moschopoulou, O. Mangana, K. Nomikou, K. Endo, I. Papanastasiou, A. Simonian. Construction of a novel, multi-analyte biosensor system for assaying cell division. Biosensors and Bioelectronics.(in press).
22. Tzafestas G.S., Anthopoulos Y., Neural Networks Based Sensorial Signal Fusion: An Application to Material Identification', DSP'97, Santorini, Greece, July 2-4 1997.
23. Dennis J.E., Schnabel R.B., Numerical methods for unconstrained optimization and nonlinear equations. Englewood Cliffs, NJ: Prentice-Hall, 1983.