Применение больших данных в туризма (на примере Ферганы) 86

Eshmatov Odil Islom o'g'li; Smirnov Dmitriy; Müller Klaus

Article · 2025

Применение больших данных в туризма (на примере Ферганы) 86

20.1003/mxa/2025/v12_i12/14214082Russian

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20.1003/mxa/2025/v12_i10/24395423

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Eshmatov Odil Islom o'g'li ORCID 0000-0002-0015-6289 (opens in a new tab)
Muhammad al-Xorazmiy nomidagi Toshkent axborot texnologiyalari universiteti
Smirnov Dmitriyno linked profile
Paxta seleksiyasi, urug'chiligi va yetishtirish agrotexnologiyalari ilmiy-tadqiqot instituti
Müller Klausno linked profile
Toshkent shahridagi Turin politexnika universiteti

Abstract

В статье рассматриваются вопросы темы «Применение больших данных в туризма (на примере Ферганы) 86». Проведён анализ отечественного и зарубежного опыта. Предложены практические рекомендации.

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DRI: 20.1003/mxa/2025/v12_i12/14214082
Work type: Article
Journal: Muhammad al-Xorazmiy avlodlariISSN 2181-0176
Volume and issue: Vol. 12, No. 12
Pages: 15–19
Published: 22 October 2025
Language: Russian
Full text: Open on the publisher's page (opens in a new tab)
Passport mirror: synced 21/09/2026, 17:50, version 3

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References

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

21 entries4 matched0 under review17 external

1.
Garcia L. Tibbiy axborot tizimi uchun klasterlash algoritmlari asosida anomaliyalarni aniqlash // Til va adabiyot ta'limi. – 2024. – №12. – B. 10–66.
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Сидоров В.П. Применение больших данных в текстильной промышленности // Вопросы образования. – 1988. – № 5. – С. 67–138.
External source
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Brown A., Chen Y. (2022). Water use efficiency under deficit irrigation of cotton in developing countries. Journal of Economic Perspectives, 51, 631–910.
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Berdiyev, A. S. (2021). Chotqol quslari tarqalish qonuniyatlari. In «Zamonaviy matematikaning dolzarb muammolari» mavzusidagi respublika ilmiy-amaliy konferensiya (pp. 1–3). Toshkent.
MatchedFuzzy match, S = 0.94
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Novak P. (2017). Machine learning methods for credit scoring. Physical Review B, 158, 837–926. https://doi.org/10.1103/PhysRevB.2017.901084
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Zokirov N.O. Tibbiy tasvirlarni konvolyutsion neyron tarmoq yordamida tahlil qilish. – Toshkent: Universitet, 2001. – 200 b.
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Иванов И.И. Применение блокчейна в текстильной промышленности // Физика твёрдого тела. – 2008. – № 1. – С. 36–94.
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Ushinskiy K.D. Tanlangan pedagogik asarlar. – Toshkent: O'qituvchi, 1968. – 380 b.
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Schmidt P. (2012). Renewable energy transition in Central Asia. Computers & Education, 102, 131–954. https://doi.org/10.1016/j.compedu.2012.441357
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Hakimov T.I. O'zbek tilidagi matnlar korpusi xavfsizligini ta'minlashda kompyuter ko'rishi imkoniyatlari. – Toshkent: Iqtisodiyot, 2009. – 155 b.
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Kamolova, G. S. (2023). Masofaviy ta'lim platformasi uchun transformer modeli asosida trafikni prognozlash. In «Energiya tejamkorligi va qayta tiklanuvchi manbalar» mavzusidagi respublika ilmiy-amaliy konferensiya (pp. 17–19). Toshkent. – DRI 20.2002/mkfan2023/2023/82908251
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Johnson M. (2022). Machine learning methods for credit scoring: evidence from panel data. Energy Policy, 144, 792–920. https://doi.org/10.1016/j.enpol.2022.388565
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Иванов И.И. Применение интернета вещей в текстильной промышленности. – М.: Наука, 2010. – 437 с.
External source
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Brown A., Rossi F. (2017). Deep residual learning for image recognition. Applied Linguistics, 175, 682–928. https://doi.org/10.1093/applin.2017.736052
External source
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Эшматов, О. И. (2025). Масофавий таълим платформаси учун кластерлаш алгоритмлари асосида маълумотларни таснифлаш. Муҳаммад ал-Хоразмий авлодлари, 1–12.
MatchedFuzzy match, S = 1.00
16.
Kim S., Müller K. (2025). Renewable energy transition in Central Asia: evidence from panel data. Agricultural Water Management, 87, 33–953. https://doi.org/10.1016/j.agwat.2025.934325
External source
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Иванов И.И. Применение больших данных в текстильной промышленности // Химия и жизнь. – 2004. – № 4. – С. 12–112.
External source
18.
Kamolova, G. S. (2022). Ijtimoiy tarmoq ma'lumotlarini tasodifiy o'rmon algoritmi yordamida tahlil qilish. Raqamli texnologiyalar va ta'lim, 1–5.
MatchedFuzzy match, S = 1.00
19.
Brown A. (2022). Deep residual learning for image recognition: a systematic review. IEEE Access, 142, 489–985. DOI: 10.1109/ACCESS.2022.126970
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Müller K. (2015). Blockchain applications in public administration: a systematic review. Sustainability, 132, 437–905.
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.

This work has not been cited yet

When other works in the registry cite this work, they will appear here.

[1] 1.
Garcia L. Tibbiy axborot tizimi uchun klasterlash algoritmlari asosida anomaliyalarni aniqlash // Til va adabiyot ta'limi. – 2024. – №12. – B. 10–66.
External source

[2] 2.
Сидоров В.П. Применение больших данных в текстильной промышленности // Вопросы образования. – 1988. – № 5. – С. 67–138.
External source

[3] 3.
Brown A., Chen Y. (2022). Water use efficiency under deficit irrigation of cotton in developing countries. Journal of Economic Perspectives, 51, 631–910.
External source

[4] 4.
Berdiyev, A. S. (2021). Chotqol quslari tarqalish qonuniyatlari. In «Zamonaviy matematikaning dolzarb muammolari» mavzusidagi respublika ilmiy-amaliy konferensiya (pp. 1–3). Toshkent.
MatchedFuzzy match, S = 0.94

[5] 5.
Novak P. (2017). Machine learning methods for credit scoring. Physical Review B, 158, 837–926. https://doi.org/10.1103/PhysRevB.2017.901084
External source

[6] 6.
Zokirov N.O. Tibbiy tasvirlarni konvolyutsion neyron tarmoq yordamida tahlil qilish. – Toshkent: Universitet, 2001. – 200 b.
External source

[7] 7.
Иванов И.И. Применение блокчейна в текстильной промышленности // Физика твёрдого тела. – 2008. – № 1. – С. 36–94.
External source

[8] 8.
Ushinskiy K.D. Tanlangan pedagogik asarlar. – Toshkent: O'qituvchi, 1968. – 380 b.
External source

[9] 9.
Schmidt P. (2012). Renewable energy transition in Central Asia. Computers & Education, 102, 131–954. https://doi.org/10.1016/j.compedu.2012.441357
External source

[10] 10.
Hakimov T.I. O'zbek tilidagi matnlar korpusi xavfsizligini ta'minlashda kompyuter ko'rishi imkoniyatlari. – Toshkent: Iqtisodiyot, 2009. – 155 b.
External source

[11] 11.
Kamolova, G. S. (2023). Masofaviy ta'lim platformasi uchun transformer modeli asosida trafikni prognozlash. In «Energiya tejamkorligi va qayta tiklanuvchi manbalar» mavzusidagi respublika ilmiy-amaliy konferensiya (pp. 17–19). Toshkent. – DRI 20.2002/mkfan2023/2023/82908251
MatchedExact by DRI

[12] 12.
Johnson M. (2022). Machine learning methods for credit scoring: evidence from panel data. Energy Policy, 144, 792–920. https://doi.org/10.1016/j.enpol.2022.388565
External source

[13] 13.
Иванов И.И. Применение интернета вещей в текстильной промышленности. – М.: Наука, 2010. – 437 с.
External source

[14] 14.
Brown A., Rossi F. (2017). Deep residual learning for image recognition. Applied Linguistics, 175, 682–928. https://doi.org/10.1093/applin.2017.736052
External source

[15] 15.
Эшматов, О. И. (2025). Масофавий таълим платформаси учун кластерлаш алгоритмлари асосида маълумотларни таснифлаш. Муҳаммад ал-Хоразмий авлодлари, 1–12.
MatchedFuzzy match, S = 1.00

[16] 16.
Kim S., Müller K. (2025). Renewable energy transition in Central Asia: evidence from panel data. Agricultural Water Management, 87, 33–953. https://doi.org/10.1016/j.agwat.2025.934325
External source

[17] 17.
Иванов И.И. Применение больших данных в текстильной промышленности // Химия и жизнь. – 2004. – № 4. – С. 12–112.
External source

[18] 18.
Kamolova, G. S. (2022). Ijtimoiy tarmoq ma'lumotlarini tasodifiy o'rmon algoritmi yordamida tahlil qilish. Raqamli texnologiyalar va ta'lim, 1–5.
MatchedFuzzy match, S = 1.00

[19] 19.
Brown A. (2022). Deep residual learning for image recognition: a systematic review. IEEE Access, 142, 489–985. DOI: 10.1109/ACCESS.2022.126970
External source

[20] 20.
Müller K. (2015). Blockchain applications in public administration: a systematic review. Sustainability, 132, 437–905.
External source

[21] 21.
Samuelson P., Nordhaus W. Economics. 19th ed. – New York: McGraw-Hill, 2010. – 744 p.
External source

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