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@article{IJAMCS_2020_30_3_a4, author = {Plichta, Anna}, title = {Recognition of species and genera of bacteria by means of the product of weights of the classifiers}, journal = {International Journal of Applied Mathematics and Computer Science}, pages = {463--473}, publisher = {mathdoc}, volume = {30}, number = {3}, year = {2020}, language = {en}, url = {http://geodesic.mathdoc.fr/item/IJAMCS_2020_30_3_a4/} }
TY - JOUR AU - Plichta, Anna TI - Recognition of species and genera of bacteria by means of the product of weights of the classifiers JO - International Journal of Applied Mathematics and Computer Science PY - 2020 SP - 463 EP - 473 VL - 30 IS - 3 PB - mathdoc UR - http://geodesic.mathdoc.fr/item/IJAMCS_2020_30_3_a4/ LA - en ID - IJAMCS_2020_30_3_a4 ER -
%0 Journal Article %A Plichta, Anna %T Recognition of species and genera of bacteria by means of the product of weights of the classifiers %J International Journal of Applied Mathematics and Computer Science %D 2020 %P 463-473 %V 30 %N 3 %I mathdoc %U http://geodesic.mathdoc.fr/item/IJAMCS_2020_30_3_a4/ %G en %F IJAMCS_2020_30_3_a4
Plichta, Anna. Recognition of species and genera of bacteria by means of the product of weights of the classifiers. International Journal of Applied Mathematics and Computer Science, Tome 30 (2020) no. 3, pp. 463-473. http://geodesic.mathdoc.fr/item/IJAMCS_2020_30_3_a4/
[1] Abdullah, A., Jing, T., Sie, C., Yusuf, N., Zakaria, A., Omar, M., Shakaff, A.M., Adom, A.H., Kamarudin, L., Juan, Y. and et al. (2014). Rapid identification method of aerobic bacteria in diabetic foot ulcers using electronic nose, Advanced Science Letters 20(1): 37–41.
[2] Alvarez-Ordonez, A., Mouwen, D., Lopez, M. and Prieto, M. (2011). Fourier transform infrared spectroscopy as a tool to characterize molecular composition and stress response in foodborne pathogenic bacteria, Journal of Microbiological Methods 84(3): 369–378.
[3] Arabestani, M.R., Fazzeli, H. and Esfahani, B.N. (2014). Identification of the most common pathogenic bacteria in patients with suspected sepsis by multiplex PCR, Journal of Infection in Developing Countries 8(4): 461–468.
[4] Ates, H. and Gerek, O.N. (2009). An image-processing based automated bacteria colony counter, Proceedings: International Symposium on Computer and Information Sciences ISCIS, Guzelyurt, Cyprus, pp. 18–23.
[5] Blackburn, N., Hagström, Å., Wikner, J., Cuadros-Hansson, R. and Bjórnsen, P.K. (1998). Rapid determination of bacterial abundance, biovolume, morphology, and growth by neural network-based image analysis, Applied and Environmental Microbiology 64: 3246–3255.
[6] Bruyne, D.K., Slabbinck, B., Waegeman, W., Vauterin, P., De Baets, B. and Vandamme, P. (2011). Bacterial species identification from MALDI-TOF mass spectra through data analysis and machine learning, Systematic and Applied Microbiology 34(1): 20–29.
[7] Bulanda,M. and Brzychczy-Włoch,M. (Eds) (2015). Microbiology and Parasitology: Lecture Notes for 2nd Year Students of the Faculty of Medicine of Jagiellonian University Collegium Medicum, Cracow Scientific Publishers Tekst, (in Polish).
[8] Cimpoi, M., Maji, S., Kokkinos, I. and Vedaldi, A. (2016). Deep filter banks for texture recognition, description, and segmentation, International Journal of Computer Vision 118: 65–94.
[9] Cortes, C. and Vapnik, V. (1995). Support-vector networks, Machine Learning 20: 273–297.
[10] Green, G., Chan, A. and Lin, M. (2014). Robust identification of bacteria based on repeated odor measurements from individual bacteria colonies, Sensors and Actuators B: Chemical 190: 16–24.
[11] Hasman, H., Saputra, D., Sicheritz-Ponten, T., Lund, O., Svendsen, C., Frimodt-Móller, N. and Aarestrup, F. (2013). Rapid whole genome sequencing for the detection and characterization of microorganisms directly from clinical samples, Journal of Clinical Microbiology 52(1): 139–146.
[12] Hiremath, P. and Bannigidad, P. (2009). Automated gram-staining characterization of digital bacterial cell images, Procceedings: International Conference on Signal and Image Processing ICSIP, Amsterdam, The Netherlands, pp. 209–211.
[13] Holmberg, M., Gustafsson, F., Hörnsten, G.E., Winquist, F., Nilsson, L.E., Ljung, L. and Lundström, I. (1998). Bacteria classification based on feature extraction from sensor data, Biotechnology Techniques 12(4): 319–324.
[14] Kim, H., Doh, I.-J., Bhunia, A., King, G. and Bae, E. (2015). Scalar diffraction modeling of multispectral forward scatter patterns from bacterial colonies, Optics Express 23(7): 8545–8554.
[15] Krizhevsky, A., Sutskever, I. and Hinton, G. (2012). Imagenet classification with deep convolutional neural networks, in P. Bartlett (Ed.), Advances in Neural Information Processing Systems, NIPS, San Diego, CA, pp. 1097–1105.
[16] Kusic, D., Kampe, B., Rösch, P. and Popp, J. (2014). Identification of water pathogens by Raman microspectroscopy, Water Research 48: 179–189.
[17] Liu, J., Dazzo, F., Glagoleva, O., Yu, B. and Jain, A. (2001). CMEIAS: A computer-aided system for the image analysis of bacterial morphotypes in microbial communities, Microbial Ecology 41: 173–194.
[18] Murray, P., Rosenthal, K. and Pfaller, M. (2015). Medical Microbiology, Elsevier, Amsterdam.
[19] Perner, P. (2001). Classification of hep-2 cells using fluorescent image analysis and data mining, International Symposium on Medical Data Analysis: Medical Data Analysis, Madrid, Spain, pp. 219–224.
[20] Plichta, A. (2019). Methods of classification of the genera and species of bacteria using decision tree, Journal of Telecommunications Information Technology 4: 74–82.
[21] Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition, https://arxiv.org/abs/1409.1556.
[22] Sommer, C. and Gerlich, D. (2013). Machine learning in cell biology—Teaching computers to recognize phenotypes, Journal of Cell Science 126: 18–23.
[23] Suchwałko, A., Buzalewicz, I. and Podbielska, H. (2014). Bacteria identification in an optical system with optimized diffraction pattern registration condition supported by enhanced statistical analysis, Optics Express 22(21): 26312–26327.
[24] Suchwałko, A., Buzalewicz, I., Wieliczko, A. and Podbielska, H. (2013). Bacteria species identification by the statistical analysis of bacterial colonies fresnel patterns, Optics Express 21(9): 11322–11337.
[25] Tadeusiewicz, R. and Wajs,W. (1999). Health Informatics, AGH University of Science and Technology Press, Cracow, (in Polish).
[26] Trattner, S., Greenspan, H., Tepper, G. and Abboud, S. (2004). Automatic identification of bacterial types using statistical imaging methods, IEEE Transactions on Medical Imaging 23: 807–820.
[27] Zieliński, B., Plichta, A., Misztal, K., Spurek, P., Brzychczy-Włoch, M. and Ochońska, D. (2017). Deep learning approach to bacterial colony classification, PloS One 12(9): e0184554.