TASVIRLARDAGI OBYEKTLAR VA NAQSHLARNI TANIB OLISH UCHUN MASHINANI O’RGANISH ALGORITMLARDAN FOYDALANISH

Authors

  • Odilbek Sodiqov Andijon Mashinasozlik Instituti talabasi Andijon, O’zbekiston Author

Keywords:

Machine Learning, Object Recognition, Pattern Recognition, CNN, RNN, LSTM, GAN, Image Processing, Healthcare, Automotive, Security, Design.

Abstract

This article explores the use of machine learning algorithms for object and pattern recognition in images, highlighting techniques such as CNN, RNN, LSTM, and GAN. It covers their applications in healthcare, automotive, security, and design, demonstrating their impact in various industries.

References

LeCun, Y., Bengio, Y., & Hinton, G. E. (2015). Deep learning. Nature, 521(7553), 436-444.

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems (pp. 2672-2680).

Published

2025-01-13

How to Cite

TASVIRLARDAGI OBYEKTLAR VA NAQSHLARNI TANIB OLISH UCHUN MASHINANI O’RGANISH ALGORITMLARDAN FOYDALANISH. (2025). Young Scientists, 3(1), 119-121. https://in-academy.uz/index.php/YO/article/view/29019