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INTELLEKTUAL ALGORITMLAR ASOSIDA KOMPYUTER GRAFIKASI JARAYONLARINI BOSHQARISH VA OPTIMALLASHTIRISH

Fan yo'nalishi:Sun'iy intellektHisoblash nazariyasi va matematikaKompyuter grafikasi va kompyuter yordamida dizaynKompyuter tarmoqlari va kommunikatsiyalariAxborot tizimlariSignalni qayta ishlashDasturiy ta'minot
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30._Erkaboyeva_J.S._-_I....pdf

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“Raqamli iqtisodiyot” ilmiy-elektron jurnali

“Raqamli iqtisodiyot” ilmiy-elektron jurnali

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MAQOLA ANNOTATSIYASI

quote
Ushbu maqolada intellektual algoritmlar asosida kompyuter grafikasi jarayonlarini boshqarish va optimallashtirish masalalari tahlil qilingan. Sun’iy intellekt va mashinaviy o‘rganish yondashuvlari yordamida grafik jarayonlarning samaradorligini oshirish, resurslardan oqilona foydalanish va yuqori sifatli vizual tasvirlarni real vaqt rejimida hosil qilish imkoniyatlari ko‘rib chiqiladi. Tadqiqot GPU arxitekturasi va neyron tarmoqlarga asoslangan optimallashtirish usullari, shuningdek vizual sifatni baholash metrikalarini qamrab oladi.

MUALLIFLAR

J.Erkaboyeva

TOSHKENT DAVLAT IQTISODIYOT UNIVERSITETI

Teglar

# intellektual# real# vaqt# modellashtirish# kompyuter# optimallashtirish# GPU# sifat# vizual# algoritmlar# o‘rganish# tarmoqlar# sahna# neyron# grafikasi# mashinaviy# arxitekturasi

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SHU JURNALDAGI BOSHQA MAQOLALAR

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Maqola idintifikatorlari

ROI:

https://eroi.uz/11.142/A026-198271

DOI:

Mavjud emas

Foydalanilgan adabiyotlar

1. Oliy Majlis Qonunchilik palatasi tomonidan 2025-yil 12-avgustda tasdiqlangan “Sunʼiy intellektdan foydalanishni tartibga solish toʻgʻrisida”gi qonun.

2. H. Li, Y. Zhang (2023) “Analysis of computer image processing technology based on intelligent optimization algorithm” In Proceedings of the 2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON). pp. https://doi.org/10.1109/SMARTGENCON60755.2023.10442417 1–6. IEEE. 3. I. Mitrai, P. Daoutidis (2025) “Accelerating process control and optimization via machine learning” Reviews in Chemical Engineering, 41(4), 401-418. https://doi.org/10.1515/revce-2024-0060 4. Xu-Wen Zhao, Xiao-Meng Tong va boshqalar (2025) “Review of empowering computer-aided engineering with artificial intelligence” Advances in Manufacturing. https://doi.org/10.1007/s40436-025-00545-0 5. A. Mohammadi, F. Sheikholeslam (2023) “Intelligent optimization: Literature review and state-of-the-art algorithms (1965-2022)” Engineering Applications of Artificial Intelligence, https://doi.org/10.1016/j.engappai.2023.106959 126, 106959. 6. Y. Du, D. Xu (2022) “Analysis of Graphic Design Based on AI Interaction Technology” (Retraction). Journal of Environmental and Public Health, 2022, 8493528. https://doi.org/10.1155/2022/8493528 7. T. Muller, A. Evans va boshqalar “Instant Neural Graphics Primitives with a Multiresolution Hash Encoding” (2022) https://arxiv.org/abs/2201.05989?utm 8. T. Takikawa, A. Evans va boshqalar “Compact Neural Graphics Primitives with Learned Hash Probing” https://arxiv.org/html/2312.17241v1?utm 9. S. Garbin, M. Kowalski va boshqalar “FastNeRF: High-Fidelity Neural Rendering at 200FPS https://openaccess.thecvf.com/content/ICCV2021/papers/Garbin_FastNeRF_High Fidelity_Neural_Rendering_at_200FPS_ICCV_2021_paper.pdf?utm 10. A. Bakhtiarnia, Qi Zhang va boshqalar (2024) “Efficient High-Resolution Deep Learning: A Survey”, 56 https://dl.acm.org/doi/full/10.1145/3645107?utm (7), 181, pp. 1-35 11. Michael Rubloff (2023) “What are the NeRF Metrics”, Radiance Fields. https://radiancefields.com/what-are-the-nerf-metrics?utm