OʻZBEK IMO-ISHORA TILINI (UZSL) TANIB OLISHDAGI METOD VA ALGORITMLAR
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Abstract
Hozirgi kunda sun’iy intellekt texnologiyalari asosida eshitish qobiliyati zaif odamlar bilan muloqot qilishni yengillashtiradigan tadqiqotlar anchagina samara bermoqda. Bunda tadqiqotning asosiy mazmuni imo-ishora tilini avtomatik tanib olish va tarjima tizimlarini takomillashtirishga qaratildi. Ushbu tadqiqot Oʻzbek imo-ishora tilini tanib olish uchun yigʻilgan dataset orqali (UzSLDS) yangi yondashuvni taklif qiladi. Bizning uslubimizda tasvirlar ichida har bir barmoq boʻg'imining egallagan pozitsisi asosida koordinatalarini olish va ushbu koordinatalardan burchak xususiyatilari ma'lumotini yaratish uchun tasvir burchagi fozasini baholash classlari yaratiladi. Shundan soʻng, dastur kodi (UzSLViT) modeli uchun ikki fazali kirish bir oʻlchovli massivini yaratish uchun olingan xususiyat ma'lumotlarini vaqtinchalik ketma-ketlikda tartib bilan bajaradi. Eng maqbul yoʻlni aniqlash uchun ushbu tadqiqot (UzSLViT) modelidagi enkoder qatlamlarining sonini ortirish orqali tanib olish aniqligini oshiradi va uning samaradorligini baholash uchun an'anaviy konvolyutsion neyron tarmoq (CNN) modellari bilan solishtirildi. Tadqiqotimizda Oʻzbek imo-ishora tilini tanib olish samaradorligini real vaqtda namoyish etdik.
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References
World Health Organization. Safe Listening Devices and Systems: A WHO-ITU Standard; World Health Organization: Geneva, Switzerland, 2019; p. 10.
Jiang, S.; Sun, B.; Wang, L.; Bai, Y.; Li, K.; Fu, Y. Skeleton Aware Multi-Modal Sign Language Recognition. In Proceedings of the 2021 Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, 19–25 June 2021.
Mirzaev, O., Radjabov, S., Mirzaev, N., Rabbimov, I., & Baratov, J. (2024). Construction of statistical recognition algorithms based on two-dimensional threshold functions. Procedia Computer Science, 234, 123-130.
Hezhen, H.; Wengang, Z.; Houqiang, L. Hand-Model-Aware Sign Language Recognition. In Proceedings of the 35th AAAI Conference on Artificial Intelligence, Virtual, 2–9 February 2021; Volume 35, pp. 1558–1566.
Ahmad, A., Kayumov, O., & Kayumova, N. (2023). Artificial intelligence in the management of intellectual resources of enterprises in the conditions of the digital economy in uzbekistan. Scientific-Theoretical Journal of International Education Research, 1(1), 106-116. https://research-edu.com/index.php/edu/article/view/26
Oybek Kayumov, Nazokat Kayumova, & Aliyeva Rayxona, Yo‘ldosheva Madina. (2023). The strategic significance of human resource management in uzbekistan enterprises on the basis of artificial intelligence. International Journal of Contemporary Scientific and Technical Research, 268–272. Retrieved from https://journal.jbnuu.uz/index.php/ijcstr/article/view/588
Olim Turakulov, Oybek Kayumov, & Nazokat Kayumova. (2023). Management of the intellectual resources of the enterprise based on artificial intelligence during the development of the digital economy in uzbekistan. International Journal of Contemporary Scientific and Technical Research, (Special Issue), 145–154. Retrieved from https://journal.jbnuu.uz/index.php/ijcstr/article/view/338
Мирзаев, Н. М., Мелиев, Ф. Ф., & Баратов, Ж. Р. (2022). ИККИ ЎЛЧОВЛИ БЎСАҒАВИЙ ФУНКЦИЯЛАРГА АСОСЛАНГАН СТАТИСТИК ТАНИБ ОЛИШ АЛГОРИТМЛАРИНИ ҚУРИШ. ББК 22.18 С 56, 210.
Mirzaev, N. M., Meliev, F. F., & Baratov, J. R. (2022). ARTIFICIAL INTELLIGENCE TECHNOLOGIES TO DETECT CANCERS FROM HUMAN COLON HISTOLOGICAL IMAGES. ББK 22.18 C 56, 181.