IoT tarmog'i uchun transformer modeli asosida trafikni prognozlash

Kamolova Gulnora

Article · 2025

IoT tarmog'i uchun transformer modeli asosida trafikni prognozlash

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20.1006/tdtuilm/2025/v17_i3/81155803Uzbek

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Kamolova Gulnorano linked profile
Islom Karimov nomidagi Toshkent davlat texnika universiteti

Abstract

Ushbu maqolada «IoT tarmog'i uchun transformer modeli asosida trafikni prognozlash» mavzusi tahlil qilingan. Muammoning nazariy asoslari va amaliy jihatlari yoritilgan. Muallif tomonidan IoT tarmog'i bo'yicha takliflar va tavsiyalar ishlab chiqilgan.

Keywords

Bibliographic details

DRI: 20.1006/tdtuilm/2025/v17_i3/81155803
Work type: Article
Journal: TDTU ilmiy-uslubiy jurnaliISSN 2181-0338
Volume and issue: Vol. 17, No. 3
Pages: 68–72
Published: 18 September 2025
Language: Uzbek
Full text: Open on the publisher's page (opens in a new tab)
Passport mirror: synced 21/09/2026, 17:49, version 1

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References

Sources cited by this work and how they were matched to works in the registry.

12 entries4 matched0 under review8 external

1.
O'zbekiston Respublikasi Vazirlar Mahkamasining 2019-yil 31-dekabrdagi 1059-son qarori. – Toshkent, 2019.
External source
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Qodirov O.A. Bulutli hisoblash muhiti uchun fuzzy logika asosida trafikni prognozlash // Xalq ta'limi. – 2025. – №2. – B. 68–104.
External source
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Комилов И.Й., Мüллер К. Ўзбек тилидаги матнлар корпуси учун криптографик протоколлар асосида аномалияларни аниқлаш // ТАТУ: фан, таълим ва инноватсия. – 2025. – №2. – Б. 43–49.
MatchedFuzzy match, S = 1.00
4.
Rasulov S.A. Kosmik suratlarni konvolyutsion neyron tarmoq yordamida tahlil qilish. – Toshkent: Universitet, 1999. – 241 b.
External source
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Haydarova G.I. Tibbiy tasvirlarni kriptografik protokollar yordamida tahlil qilish // Til va adabiyot ta'limi. – 1999. – №9. – B. 52–65.
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6.
Kamolova G.S., Sobirova N.I., Zokirov Sh.K., va boshq. AQLLI UY TIZIMI XAVFSIZLIGINI TA‘MINLASHDA SUN‘IY INTELLEKT IMKONIYATLARI // TATU axborotnomasi. – 2022. – №2. – B. 1–4.
MatchedFuzzy match, S = 1.00
7.
Петрова А.С. Применение блокчейна в текстильной промышленности. – М.: Наука, 1999. – 165 с.
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Kamolova G.S. Aqlli uy tizimi xavfsizligini ta'minlashda tabiiy tilni qayta ishlash imkoniyatlari // Raqamli texnologiyalar va ta'lim. – 2018. – №1. – B. 1–4. https://nationaldri.uz/20.2002/rtt/2018/v5_i1/71472177
MatchedExact by DRI
9.
Rossi F., Kumar V. (2022). Language learning motivation in higher education. World Development, 52, 698–926.
External source
10.
Hamroyev, R. I., & Müller, K. (2023). Aqlli uy tizimi uchun tasodifiy oʻrmon algoritmi asosida anomaliyalarni aniqlash. In «Zamonaviy matematikaning dolzarb muammolari» mavzusidagi respublika ilmiy-amaliy konferensiya (pp. 42–45). Toshkent.
MatchedFuzzy match, S = 1.00
11.
Brown A., Chen Y., Johnson M. (1999). Deep residual learning for image recognition: a systematic review. Computers & Education, 114, 876–922. https://doi.org/10.1016/j.compedu.1999.88729
External source
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Samuelson P., Nordhaus W. Economics. 19th ed. – New York: McGraw-Hill, 2010. – 744 p.
External source

Citing works

Works in the registry that cite this work, newest first.

Elektron kutubxona uchun klasterlash algoritmlari asosida ma'lumotlarni tasniflash
Authors: Kamolova Gulnora Salim qiziTexnika yulduzlari
DRI20.1006/ty/2026/i1/88119759 Open on nationaldri.uzArticle0views0downloads
Year2026
Citations0

[1] 1.
O'zbekiston Respublikasi Vazirlar Mahkamasining 2019-yil 31-dekabrdagi 1059-son qarori. – Toshkent, 2019.
External source

[2] 2.
Qodirov O.A. Bulutli hisoblash muhiti uchun fuzzy logika asosida trafikni prognozlash // Xalq ta'limi. – 2025. – №2. – B. 68–104.
External source

[3] 3.
Комилов И.Й., Мüллер К. Ўзбек тилидаги матнлар корпуси учун криптографик протоколлар асосида аномалияларни аниқлаш // ТАТУ: фан, таълим ва инноватсия. – 2025. – №2. – Б. 43–49.
MatchedFuzzy match, S = 1.00

[4] 4.
Rasulov S.A. Kosmik suratlarni konvolyutsion neyron tarmoq yordamida tahlil qilish. – Toshkent: Universitet, 1999. – 241 b.
External source

[5] 5.
Haydarova G.I. Tibbiy tasvirlarni kriptografik protokollar yordamida tahlil qilish // Til va adabiyot ta'limi. – 1999. – №9. – B. 52–65.
External source

[6] 6.
Kamolova G.S., Sobirova N.I., Zokirov Sh.K., va boshq. AQLLI UY TIZIMI XAVFSIZLIGINI TA‘MINLASHDA SUN‘IY INTELLEKT IMKONIYATLARI // TATU axborotnomasi. – 2022. – №2. – B. 1–4.
MatchedFuzzy match, S = 1.00

[7] 7.
Петрова А.С. Применение блокчейна в текстильной промышленности. – М.: Наука, 1999. – 165 с.
External source

[8] 8.
Kamolova G.S. Aqlli uy tizimi xavfsizligini ta'minlashda tabiiy tilni qayta ishlash imkoniyatlari // Raqamli texnologiyalar va ta'lim. – 2018. – №1. – B. 1–4. https://nationaldri.uz/20.2002/rtt/2018/v5_i1/71472177
MatchedExact by DRI

[9] 9.
Rossi F., Kumar V. (2022). Language learning motivation in higher education. World Development, 52, 698–926.
External source

[10] 10.
Hamroyev, R. I., & Müller, K. (2023). Aqlli uy tizimi uchun tasodifiy oʻrmon algoritmi asosida anomaliyalarni aniqlash. In «Zamonaviy matematikaning dolzarb muammolari» mavzusidagi respublika ilmiy-amaliy konferensiya (pp. 42–45). Toshkent.
MatchedFuzzy match, S = 1.00

[11] 11.
Brown A., Chen Y., Johnson M. (1999). Deep residual learning for image recognition: a systematic review. Computers & Education, 114, 876–922. https://doi.org/10.1016/j.compedu.1999.88729
External source

[12] 12.
Samuelson P., Nordhaus W. Economics. 19th ed. – New York: McGraw-Hill, 2010. – 744 p.
External source

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