• M.N.S.Gangadhar
• Dr.T.Suresh

The unanticipated increase of MANETs' portable potential has been very enjoyable. The rapid expansion of wireless network communications infrastructure, improvements in hardware design, and growing user demands for node mobility and local delivery procedures are all contributing to the enormous need for dynamic ad-hoc based networking. Mobile ad-hoc networks have a number of difficult problems. For example, the lack of deep neural layers in machine learning methods makes it impossible to examine parameters such node mobility, channel variance, and channel interference. Owing to the decentralized nature of mobile ad hoc networks, attention must be paid to a number of very important concerns, including security, stability, scalability, and routing-based difficulties including network congestion and optimal path selection.

LSTM, GRUs, DeepChannel, Deep Q-learning Algorithm, Deep Neural Networks, MANETs, Clustering Algorithm

"The Function of Deep Learning in Ad-hoc Mobile Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 10, page no.f962-f972, October-2024, Available :http://www.jetir.org/papers/JETIR2410599.pdf

"The Function of Deep Learning in Ad-hoc Mobile Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 10, page no. ppf962-f972, October-2024, Available at : http://www.jetir.org/papers/JETIR2410599.pdf