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Active Queue Management (AQM) policies are mechanisms for congestion avoidance, which pro-actively drop packets in order to provide an early congestion notification to the sources. Random Early Detection (RED), the defacto standard and its different flavors have been proposed as simple solutions to the AQM problem. However, these approaches require manual tuning and fail to accurately capture variations in the input traffic, thereby resulting in unstable behavior. α_SNFAQM is a new AQM mechanism that uses a neurofuzzy prediction method (α_SNF) to capture traffic variation and accurately detect the future congestion. It distinguishes (i) severe congestion and (ii) light congestion. We compare the performance of α_SNFAQM with other AQM schemes like RED, PAQM and APACE in a bottleneck link. Simulation results have shown that α_SNFAQM outperforms other AQM schemes in stabilizing the instantaneous queue length, reducing packet loss ratio while keeping a high utilization of the link.