DRONLARDAGI ALOQA XAVFSIZLIGINI TA’MINLASHDA MASHINALI O‘QITISH ALGORITMLARIDAN FOYDALANISHNING SAMARADORLIGI.
Annotasiya
Mazkur maqolada dronlardagi aloqa xavfsizligini ta’minlashda mashinali o‘qitish algoritmlari SVM, CNN ML , RNN, CRNN yordamida dronni aniqlash. LSTM UAVlar yordamida nosozliklarni aniqlash va UAV ma’lumotlarini tiklash va axborot xavfsizligini ta’minlashni tahlil qilingan.
Kalit so‘zlar:
ML, UAV, drone, SVM, CNN, RNN, CRNN, LSTM.Bibliografik manbalar
Alvarado, Ed (3 May 2021). "237 Ways Drone Applications Revolutionize Business". Drone Industry Insights. Archived from the original on 11 May 2021. Retrieved 11 May 2021.
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M. Bensalem, S. K. Singh, and A. Jukan, “On detecting and preventing jamming attacks with machine learning in optical networks,” in 2019 IEEE Global Communications Conference (GLOBECOM). IEEE, 2019, pp. 1–6.
T. Zeng, O. Semiari, M. Mozaffari, M. Chen, W. Saad, and M. Bennis, “Federated learning in the sky: Joint power allocation and scheduling with uav swarms,” in ICC 2020 - 2020 IEEE International Conference on Communications (ICC), 2020, pp. 1–6.
N. Jeong, H. Hwang, and E. T. Matson, “Evaluation of low-cost lidarvsensor for application in indoor uav navigation,” in 2018 IEEE SensorsvApplications Symposium (SAS). IEEE, 2018, pp. 1–5.
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Litsenziya
Mualliflik huquqi (c) 2024 Rivojiddinov D.B., Qurbanov B.B., Axmedov A.G
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