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A Multisensor Fusion System for the Detection of Plant Viruses by Combining Artificial Neural Networks

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Artificial Neural Networks – ICANN 2006 (ICANN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4132))

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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).

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References

  1. Alpaydin, E.: Techniques for combining multiple learners. In: Proceedings of Engineering of Intelligent Systems, vol. 2, pp. 6–12. ICSC Press (1998)

    Google Scholar 

  2. Kuncheva, L.: Combining Classifiers by Clustering, Selection and Decision Templates. Technical report, University of Wales, UK (2000)

    Google Scholar 

  3. Maclin, R., Opitz, D.: An empirical evaluation of bagging and boosting. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence, pp. 546–551. AAAI Press/MIT Press (1997)

    Google Scholar 

  4. Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Proceedings of the Thirteenth International Conference on Machine Learning, pp. 148–156. Morgan Kaufmann, San Francisco (1996)

    Google Scholar 

  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)

    Google Scholar 

  6. Vericas, A., Lipnickas, A., Malmqvist, K., Bacauskiene, M., Gelzinis, A.: Soft combination of neural classifiers: A comparative study. Pattern Recognition Letters 20, 429–444 (1999)

    Article  Google Scholar 

  7. Tumer, K., Ghosh, J.: Classifier combining through trimmed means and order statistics. In: Proceedings of the International Joint Conference on Neural Networks, Anchorage, Alaska (1998)

    Google Scholar 

  8. Tumer, K., Ghosh, J.: Order statistics combiners for neural classifiers. In: Proceedings of the World Congress on Neural Networks, pp. I31–I34. INNS Press, Washington D.C (1995)

    Google Scholar 

  9. Sharkey, A.J.C.: Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems. Springer, Heidelberg (1999)

    MATH  Google Scholar 

  10. Tumer, K., Ghosh, J.: Limits to performance gains in combined neural classifiers. In: Proceedings of the Artificial Neural Networks in Engineering 1995, pp. 419–424. St. Louis (1995)

    Google Scholar 

  11. Tumer, K., Ghosh, J.: Error correlation and error reduction in ensemble classifiers. Connection Science, Special Issue on Combining Artificial Neural Networks: Ensemble Approaches 8(3-5), 385–404 (1996)

    Google Scholar 

  12. Alpaydin, E.: Voting over multiple condensed nearest neighbour subsets. Artificial Intelligence Review 11, 115–132 (1997)

    Article  Google Scholar 

  13. Breiman, L.: Bagging predictors. Technical report, no. 421, Department of Statistics. University of California, Berkeley (1994)

    Google Scholar 

  14. Chan, P.K., Stolfo, S.J.: A comparative evaluation of voting and meta-learning on partitioned data. In: Proceedings of the Twelfth International Machine Learning Conference, pp. 90–98. Morgan Kaufmann, San Mateo (1995)

    Google Scholar 

  15. Kintzios, S., Pistola, E., Panagiotopoulos, P., Bomsel, M., Alexandropoulos, N., Bem, F., Biselis, I., Levin, R.: Bioelectric recognition assay (BERA). Biosensors and Bioelectronics 16, 325–336 (2001)

    Article  Google Scholar 

  16. Kintzios, S., Pistola, E., Konstas, J., Bem, F., Matakiadis, T., Alexandropoulos, N., Biselis, I., Levin, R.: 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)

    Article  Google Scholar 

  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, C., 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)

    Article  Google Scholar 

  18. Kintzios, S., Makrygianni, E.f., 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)

    Google Scholar 

  19. Kintzios, S., Goldstein, J., Perdikaris, A., Moschopoulou, G., Marinopoulou, I., Mangana, O., Nomikou, K., Papanastasiou, I., Petrou, A.-L., Arakelyan, V., Economou, A., Simonian, A.: The BERA Diagnostic System: An all-purpose cell biosensor for the 21th Century. In: 5th Biodetection Conference, Baltimore, MD, USA, June 9-10 (2005)

    Google Scholar 

  20. Moschopoulou, G., Kintzios, S.: Membrane engineered Bioelectric Recognition Cell sensors for the detection of subnanomolar concentrations of superoxide: A novel biosensor principle. In: International Conference on Instrumental Methods of Analysis (IMA) 2005, Crete, Greece, October 1-5 (2005)

    Google Scholar 

  21. Kintzios, S., Marinopoulou, I., Moschopoulou, G., Mangana, O., Nomikou, K., Endo, K., Papanastasiou, I., Simonian, A.: Construction of a novel, multi-analyte biosensor system for assaying cell division. Biosensors and Bioelectronics (in press)

    Google Scholar 

  22. Tzafestas, G.S., Anthopoulos, Y.: Neural Networks Based Sensorial Signal Fusion: An Application to Material Identification. In: DSP 1997, Santorini, Greece, July 2-4 (1997)

    Google Scholar 

  23. Dennis, J.E., Schnabel, R.B.: Numerical methods for unconstrained optimization and nonlinear equations. Prentice-Hall, Englewood Cliffs (1983)

    MATH  Google Scholar 

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© 2006 Springer-Verlag Berlin Heidelberg

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Frossyniotis, D., Anthopoulos, Y., Kintzios, S., Perdikaris, A., Yialouris, C.P. (2006). A Multisensor Fusion System for the Detection of Plant Viruses by Combining Artificial Neural Networks. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_41

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  • DOI: https://doi.org/10.1007/11840930_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-38871-5

  • Online ISBN: 978-3-540-38873-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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