An enhanced performance optimization of ATM Networks for Cross-border Money Transfers

This paper provides an improved approach to optimize the performance of ATM networks for move-border money transfers. The proposed technique involves an adaptive disbursed load-balancing strategy that minimizes each aid usage and verbal exchange delays. The proposed gadget makes use of aqueous-optimized neural networks in an ATM community to dynamically perform load balancing and routing responsibilities. Further, the proposed device gives a revolutionary approach to lessen transmission loss and latency by constructing a dedicated topology to mobilize the resources. To assess the effectiveness of the proposed approach, two experimental setups are used: a -level and four-stage simulated ATM community the usage of provider stage settlement (SLA) enabled routers and a real-global ATM network within the UAE. The consequences display that the proposed method outperforms the traditional tactics in terms of aid usage, communication delays, and transmission loss. The proposed method additionally gives higher scalability and fault-tolerance characteristics.