Open Access
BIO Web Conf.
Volume 93, 2024
International Scientific Forestry Forum 2023: Forest Ecosystems as Global Resource of the Biosphere: Calls, Threats, Solutions (Forestry Forum 2023)
Article Number 01010
Number of page(s) 11
Section Forestry, Forest Management and Multipurpose Use of Forests
Published online 20 March 2024
  • B.S. Anami, J.D. Pujari, R. Yakkundimath, International Journal of Computer Applications in Engineering Sciences 1, 3, 356–360 (2011) [Google Scholar]
  • M. El-Helly, A. Rafea, S. El-Gammal, An Integrated Image Processing System for Leaf Disease Detection and Diagnosis, Proceedings of the 1st Indian International Conference on Artificial Intelligence. Hyderabad, India, 2003. – P. 1182–1195. 40 (December 18-20, 2003) [Google Scholar]
  • A.F. Cheshkova, Vavilov Journal of Genetics and Breeding. Novosibirsk, 26, 2, 202-213 (2022). [Google Scholar]
  • V.S. Tutygin, K.M.A. Al-Windi Basim, Engineering Journal of Don 3 (2019) [Google Scholar]
  • N.M. Mirzaev, Vestnik of Ryazan state radioengineering 3, 17-21 (2012) [Google Scholar]
  • N. Mirzaev, E. Saliev, Feature extraction model in systems of diagnostics of plant diseases by the leaf images. Instrumental Engineering, Electronics and Telecommunications –2017. Proceedings of the International forum (November 22–24, 2017, Izhevsk, Russia). Izhevsk: Publishing House of Kalashnikov ISTU. pp. 20-27 (2018) [Google Scholar]
  • Yu.I. Zhuravlev, Selected Scientic Works. Magister, Moscow (1998) [Google Scholar]
  • G.J. McLachlan, Discriminant Analysis and Statistical Pattern Recognition (New York: John Wiley & Sons, 2004) [Google Scholar]
  • V.B. Kudryavtsev, A.E. Andreev, E.E. Gasanov, Theory of test recognition. Fizmatlit, Moscow (2007) [Google Scholar]
  • A.R. Webb, K.D. Copsey, Statistical Pattern Recognition (New York: Wiley, 2011) [Google Scholar]
  • P. Sulewski, Communications in Statistics – Simulation and Computation 52, 6, 2542-2558 (2023). [CrossRef] [Google Scholar]
  • M.A. Ayzerman, E.M. Braverman, L.I. Rozonoer, Method of Potential Functions in the Theory of Machine Learning. Nauka, Moscow (1970) [Google Scholar]
  • E.V. Djukova, G.O. Masliakov, P.A. Prokofyev, Computational Mathematics and Mathematical Physics 59, 9, 1542 –1552 (2019). [CrossRef] [Google Scholar]
  • A.V. Kabulov, E. Urunboev, I. Saymanov, Object recognition method based on logical correcting functions. Proceedings International Conference on Information Science and Communications Technologies (ICISCT 2020): Applications, Trends and Opportunities 1-5. (2020) [Google Scholar]
  • I. Povkhan, Radio Electronics, Computer Science, Control. Zaporizhzhzia 2, 95–105 (2020). [Google Scholar]
  • O.A. Ignat’ev, Computational Mathematics and Mathematical Physics 55, 12, 2094–2099 (2015). [CrossRef] [Google Scholar]
  • A.K. Nishanov, G.P. Djurayev, M.A. Khasanova, COMPUSOFT: an International Journal of Advanced Computer Technology 8, 6, 3158–3165 (2019) [Google Scholar]
  • M. Kamilov, Sh. Fazilov, N. Mirzaev, S. Radjabov, Algorithm of calculation of estimates in condition of features’ correlations, Problems of Cybernetics and Informatics (PCI’2010): Proceedings The Third International Conference, September 6-8, Baku. P. 278-281 (2010) [Google Scholar]
  • Sh.Kh. Fazilov, N.M. Mirzaev, G.R. Mirzaeva, Procedia Computer Science. Amsterdam, 150, 671-678 (2019) [Google Scholar]
  • W. Burger, M.J. Burge, Digital Image Processing. An Algorithmic Introduction. Springer (2021) [Google Scholar]
  • S.N. Ibragimova, S.S. Radjabov, O.N. Mirzaev, S.A. Tavboyev, G.R. Mirzaeva, Recognition Algorithm Models Based on the Selection of Two-Dimensional Preference Threshold Functions, Communications in Computer and Information Science (CCIS). Springer, 1543, 354–366 (2022). [CrossRef] [Google Scholar]
  • G.R. Mirzaeva, Networked Control Systems for Connected and Automated Vehicles 510, 1199–1209 (2023). [Google Scholar]
  • R.C. Gonzalez, R.E. Woods, Digital image processing (New York: Pearson, 2018) [Google Scholar]
  • N.M. Mirzaev, About one model of image recognition, Computer Technology and Applications: Proceedings of The First Russia and Pacific Conference. – Vladivostok. p. 394–398 (2010) [Google Scholar]
  • S. Fazilov, O. Mirzaev, E. Saliev, M. Khaydarova, S. Ibragimova, N. Mirzaev, Model of recognition algorithms for objects specified as images, Proceedings of the 9th International Conference Advanced computer information technologies (ACIT 2019, Ceske Budejovice, Czech Republic, June 5-7, 2019). [Google Scholar]
  • Sh.Kh. Fazilov, N.M. Mirzaev, S.S. Radjabov, O.N. Mirzaev, Determining of Parameters in the Construction of Recognition Operators in Conditions of Features Correlations, Proceedings of the 7th Int. Conf. on Optimization Problems and Their Applications (July 8-14, 2018, Omsk, Russia,). pp. 118-133 (2018) [Google Scholar]
  • B. Wang, S. Zhang, Connection Science. 34:1, 2084-2107 (2022), [CrossRef] [Google Scholar]
  • Sh.Kh. Fazilov, N.M. Mirzaev, S.S. Radjabov, G.R. Mirzaeva, Journal of Physics: Conference Series. London 1260, 1-8 (2019) [Google Scholar]
  • O.N. Mirzaev, Problems of computer science and energy 6, 23–27 (2008) [Google Scholar]
  • U.M. Braga-Neto, E.R. Dougherty, Error Estimation for Pattern Recognition (New York: Springer, 2016) [Google Scholar]
  • S.K. Fazilov, O.N. Mirzaev, S.S. Kakharov, Building a Local Classifier for Component-Based Face Recognition, Intelligent Human Computer Interaction: 14th International Conference, IHCI 2022, Tashkent, Uzbekistan, October 20–22, 2022, Revised Selected Papers. – Cham: Springer Nature Switzerland. – P. 177-187 (2023) [Google Scholar]
  • S.K. Fazilov, et al., Improving image contrast: Challenges and solutions, 2021 International Conference on Information Science and Communications Technologies (ICISCT). – IEEE, 2021. –p. 1-5. (2021) [Google Scholar]

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