We propose a technique for first-order gradient-based optimization of stochastic objective functions called Nesterov-accelerated adaptive moment assessment, which makes use of dynamic evaluations of lower-order moments. The adaptive moment assessment and the Nesterov acceleration gradient are combined. Consequently, it has perks, and this technique is convenient to use, numerically economical, memory-light, and very well-suited for challenges with massive amounts of information and characteristics. Additionally, we investigate the algorithm's convergence characteristics and propose a conservative constraint on the convergence rate. Finally, we employ this technique for the detection and classification of safety helmets.
