In this study, an algorithm for artificially expanding the training sample was developed for classifying cotton varieties recommended for planting in the Republic of Uzbekistan by region. The algorithm is based on heuristic logic, and class objects are reformulated based on similarity criteria. Initially, a text, nominal and quantitative character space was formed based on real data from the State Register. Then the characters were converted to a full nominal form, and the similarity levels between objects were determined by scaling. A proximity function and decision rules were developed, and the contribution of class objects to their class was assessed.
| Mualliflar | Axmedov, Oybek Kamarbekovich, Nishanov, Axram |
|---|---|
| Jurnal | Al-Farg'oniy avlodlari |
| Nashr sanasi | 2026-03-13 |
| Son | 1 |
| Betlar | 184-190 |
| Til | Rus |
G ‘o‘za navlari, sinflashtirish, hududlashtirish, o‘xshashlik darajasi, matn, nominal va miqdoriy, ob’ektlar, avtomatik tasniflash, sintetik o‘qitish namunalari, sun’iy intellekt, evristik mezonlar, ma’lumotlar to‘plami, mashinanili o‘qitish., Cotton varieties, classification, regionalization, similarity level, text, nominal and quantitative, objects, automatic classification, synthetic learning models, artificial intelligence, heuristic criteria, dataset, machine learning.
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