Open Access
Issue
E3S Web Conf.
Volume 304, 2021
2nd International Conference on Energetics, Civil and Agricultural Engineering (ICECAE 2021)
Article Number 01007
Number of page(s) 9
Section Energetics
DOI https://doi.org/10.1051/e3sconf/202130401007
Published online 21 September 2021
  1. R. Rendondo, V. Marcos Computers and Electronics in Agriculture Pollen segmentation and feature evaluation for automatic classification in bright-field microscopy 110 pp 56–69 (2014) [Google Scholar]
  2. M. Chica Computer Science Standard methods for inexpensive pollen loads authentication by means of computer vision and machine learning URL: https://arxiv.org/abs/1511.04320, (2015) [Google Scholar]
  3. M. Chica, P. Campoy Journal of Food Engineering Discernment of bee pollen loads using computer vision and oneclass classification techniques 112 pp 50–59, (2012) [Google Scholar]
  4. I.I. Jumanov, O.I. Djumanov, R.A. Safarov Chemical Technology, Control and Management Optimization of image processing using characteristics and peculiarities of pollen grains 5 pp 71–78 (2019) [Google Scholar]
  5. I.I. Jumanov, O.I. Djumanov, R.A. Safarov Int. Russian Automation Conf. Optimization of Identification of Images of Micro-Objects Taking Into Account Systematic Error Based on Neural Networks pp. 626–631 (2020) [Google Scholar]
  6. N. Khanzhina, E.B. Zamyatina Perm University Press (Perm) Using classical methods and neural networks for pollen grain recognition vol 4(23) pp 111–119 (2013) [Google Scholar]
  7. A. S. Chernykh, E.B. Zamyatina Perm State Nat. Research University Study of the possibility of using a number of classical methods for the recognition of pollen grains pp 161–169 (2012) [Google Scholar]
  8. D.A. Tarkhov Neural networks. Models and algorithms D.A.Tarkhov Moscow Radio engineering 18 pp 255 (2005) [Google Scholar]
  9. I.I. Jumanov Problems of Informatics and Energy Optimization of image processing of micro-objects based on recurrent learning of a neural network and implicative selection of informative features 4 pp 12 (2016) [Google Scholar]
  10. O.I. Djumanov Sib SUTI Press (Novosibirsk) Adaptive neural network system for image visualization, recognition and classification of micro-objects 2 pp 76–86 (2008) [Google Scholar]
  11. I.I. Jumanov Bulletin of TUIT (Tashkent) Optimization of data processing of non-stationary objects based on fuzzy identification models with setting parameters 1(41) pp 34–47 (2017) [Google Scholar]
  12. O.I. Djumanov X int. scientific method. conf.( Rostov-on-Don) A neural network system for adaptive information processing of a non-stationary nature in the management of a university 6 pp 81–85 (2008) [Google Scholar]
  13. I.I. Jumanov, O.I. Djumanov, R.A. Safarov 11th World Conference “Intelligent System for Industrial Automation” (WCIS-2020) 1323 pp 170–179 (2020) DOI: 10.1007/978-3-030-68004-6_22 [Google Scholar]
  14. Ibragimovich, J.I., Isroilovich, D.O., Abdullayevich, S.R. 2020 International Conference on Information Science and Communications Technologies 9351483 (2020) DOI: 10.1109/ICISCT50599.2020.9351483 [Google Scholar]
  15. I.I. Jumanov, O.I. Djumanov, R.A. Safarov Journal of Physics: Conference Series, 1791(1), 012099 (2021) DOI: 10.1088/1742-6596/1791/1/012099 [Google Scholar]

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