AQLLI MUHITDA SUN’IY INTELLEKT TEXNOLOGIYALARINI QO’LLASH SAMARADORLIGI

Authors

  • Usmanova N.B Author
  • Temirova D.X Author
  • G‘ayratov Z.K Author
  • Xiyasova S.R Author

Keywords:

aqlli muhit, sun’iy intellekt, optimallashtirish, qaror qabul qilish, aqlli shahar, avtomatlashtirish, IoT, tahlil, texnologiya, samaradorlik.

Abstract

Ushbu maqolada aqlli muhitda sun’iy intellekt (SI) 
texnologiyalarini qo‘llash samaradorligi tahlil qilinadi. Sun’iy intellekt aqlli 
shaharlar, sog‘liqni saqlash, transport, energetika, xavfsizlik va boshqa 
infratuzilmaviy tizimlarda qaror qabul qilishni avtomatlashtirish, optimallashtirish va 
samaradorligini oshirishda muhim vosita sifatida namoyon bo‘lmoqda. Tadqiqot 
davomida SI texnologiyalarining asosiy yo‘nalishlari, amaliy qo‘llanilishi va ularning 
funksional afzalliklari chuqur o‘rganilib, ularni amalga oshirish jarayonida yuzaga 
keladigan muammolar va ularning yechimlari tahlil qilinadi. Maqola yakunida sun’iy 
intellekt asosida boshqariluvchi aqlli tizimlarning samaradorligini baholash 
mezonlari keltiriladi va kelajakda bu texnologiyalarning rivojlanish istiqbollari 
haqida fikr yuritiladi.

References

1.

Jabareen, Y. (2013). Planning the resilient city: Concepts and strategies for

coping with climate change and environmental risk. Cities, 31, 220–229.

2.

Mohanty, S. P., Choppali, U., & Kougianos, E. (2016). Everything you wanted

to know about smart cities: The Internet of Things is the backbone. IEEE Consumer

Electronics Magazine, 5(3), 60–70.

3.

Bibri, S. E. (2019). The anatomy of the data-driven smart sustainable city:

Instrumentation, datafication, computerization and related applications. Journal of Big

Data, 6(1), 59.

4.

Bhattacharya, S., Somayaji, S. R. K., Gadekallu, T. R., Alazab, M., &

Maddikunta, P. K. R. (2020). A review on deep learning for future smart cities. Internet

Technology Letters, e187.

5.

Kumar, P. M., Gandhi, U., & Varatharajan, R. (2019). Intelligent face

recognition and navigation system using neural learning for smart security in IoT.

Cluster Computing, 22(4), 7733–7744.

6.

Sajjad, M., Nasir, M., Muhammad, K., Khan, S., Jan, Z., & Sangaiah, A. K.

(2020). Raspberry Pi assisted face recognition framework for enhanced law

enforcement services in smart cities. Future Generation Computer Systems, 108, 995

1007.

7.

Jegadeesan, S., Azees, M., Kumar, P. M., Manogaran, G., & Chilamkurti, N.

(2019). An efficient anonymous mutual authentication technique for mobile cloud

computing in smart cities. Sustainable Cities and Society, 49, 101522.8.

Gomathi, P., Baskar, S., & Shakeel, P. M. (2020). Concurrent service access and

management framework for user-centric future IoT in smart cities. Complex &

Intelligent Systems, https://doi.org/10.1007/s40747-020-00160-5

9.

Zhao, L., Wang, J., Liu, J., & Kato, N. (2019). Routing for crowd management

in smart cities: A deep reinforcement learning perspective. IEEE Communications

Magazine, 57(4), 88–93.

10.

Yigitcanlar, T. (2015). Smart cities: An effective urban development and

management model? Australian Planner, 52(1), 27–34.

11.

Jabareen, Y. (2013). Planning the resilient city: Concepts and strategies for

coping with climate change. Cities, 31, 220–229.

12.

Kumar, N., Vasilakos, A. V., & Rodrigues, J. J. (2017). A multi-tenant cloud

based DC nano grid for self-sustained smart buildings in smart cities. IEEE

Communications Magazine, 55(3), 14–21.

13.

Williamson, B. (2017). Computing brains: Learning algorithms and

neurocomputation in the smart city. Information, Communication & Society, 20(1),

81–99.

14.

Aloqaily, M., Otoum, S., Al Ridhawi, I., & Jararweh, Y. (2019). An intrusion

detection system for connected vehicles in smart cities. Ad Hoc Networks, 90, 101842.

15.

Sekaran, K., Meqdad, M. N., Kumar, P., Rajan, S., & Kadry, S. (2020). Smart

agriculture management system using Internet of Things. Telkomnika, 18(3), 1275

1284.

16.

Dieleman, H. (2013). Organizational learning for resilient cities, through

realizing eco-cultural innovations. Journal of Cleaner Production, 50, 171–180.

17.

Macke, J., Casagrande, R. M., Sarate, J. A., & Silva, K. A. (2018). Smart city

and quality of life: Citizens’ perception in a Brazilian case study. Journal of Cleaner

Production, 182, 717–726.

18.

Binz, C., Truffer, B., Li, L., Shi, Y., & Lu, Y. (2012). Conceptualizing

leapfrogging with spatially coupled innovation systems: The case of onsite wastewater

treatment in China. Technological Forecasting and Social Change, 79(1), 155–171. 19.

Cohen, A. J., Brauer, M., Burnett, R., Anderson, H. R., Frostad, J., Estep, K., et

al. (2017). Estimates and 25-year trends of the global burden of disease attributable to

ambient air pollution. The Lancet, 389(10082), 1907–1918.

20.

Van Dalen, A. (2012). The algorithms behind the headlines: How machine

written news redefines the core skills of human journalists. Journalism Practice, 6(5

6), 648–658.

21.

Hanjra, M. A., Blackwell, J., Carr, G., Zhang, F., & Jackson, T. M. (2012).

Wastewater irrigation and environmental health: Implications for water governance.

International Journal of Hygiene and Environmental Health, 215(3), 255–269.

Published

2025-06-19

How to Cite

AQLLI MUHITDA SUN’IY INTELLEKT TEXNOLOGIYALARINI QO’LLASH SAMARADORLIGI. (2025). ОБРАЗОВАНИЕ НАУКА И ИННОВАЦИОННЫЕ ИДЕИ В МИРЕ, 71(5), 374-384. https://scientific-jl.com/obr/article/view/21852