Russian Federation
UDC 81
This article analyzes the assessment methods of finished products applied in enterprises using grape raw materials. For this purpose, it is possible to use the random forest algorithm, support vector machine and naive Bayesian methods to predict the evaluation. In addition, it is advisable to use neural network for the regression problem.
evaluation methods, grape raw materials, algorithm, support vector, naive Bayesian method, neural network
1. Dahal, K., Dahal, Dzh., Bandzhade, H., Geyr, S. Prognozirovanie kachestva vina s ispol'zovaniem algoritmov mashinnogo obucheniya // Otkrytyy zhurnal statistiki. – 2021. – №11. – S. 278–289. doi:https://doi.org/10.4236/ojs.2021.112015.
2. Er, E., Atasoy, A. Klassifikaciya belogo vina i krasnogo vina po ih fiziko-himicheskim kachestvam // Intellektual'nye sistemy. Teoriya i prilozheniya. – 2016. – №4. – S. 23-24. doi:https://doi.org/10.18201/ijisae.265954.
3. Sahur, H., Golami, V., Torkman, D., Vazifedan, M., Saidi, S. Sluchaynyy les i algoritmy ekstremal'nogo gradientnogo usileniya dlya modelirovaniya rechnogo potoka s ispol'zovaniem harakteristik sosudov i godichnyh kolec // Ekologicheskie nauki o Zemle. – 2021. – №80. – S. 84–95. doi:https://doi.org/10.1007/s12665-021-10054-5.
4. Li Dzh.E., Hvan G.S., Van Den Berg F., Li Ch., Hong Yu.S. Dokazatel'stva vintazhnyh effektov na vinogradnyh vinah s ispol'zovaniem metabolomnogo issledovaniya na osnove 1H YaMR. // Zhurnal analiticheskoy himii. – 2009. – №648. – S. 71–76. DOI: https://doi.org/10.1016/j.aca.2009.06.039
5. Appalasami, P., Mustafa, A., Rizal, N., Dzhohari, F., Mansor, A. Podhod k intellektual'nomu analizu dannyh na osnove klassifikacii dlya kontrolya kachestva v proizvodstve vina // Zhurnal prikladnyh nauk. – 2012. – №12. – S. 598–601. doi:https://doi.org/10.3923/jas.2012.598.601.
