Abstract-I. INTRODUCTION Active Queue Management (AQM) policies attempt to estimate the congestion at a node and signal the incipient congestion by dropping packet(s) before the buffer is full. The main aim of the RED [1] scheme was of providing "congestion avoidance" by dropping packets in anticipation of congestion. The performance of the RED algorithm depends significantly upon the setting of each of its parameters, which appears to be a difficult problem. In [5], Hollot et al. have studied the problem of tuning RED parameters from a control theoretic view point. The aim was to improve the throughput by controlling oscillations in the instantaneous queue. Feng et al. [6] proposed a mechanism for adaptively varying one of the RED parameters, max p , with the aim of reducing the packet loss rates across congested links. Floyd et al. in [7] discuss the algorithmic modifications to the self-configuring RED algorithm [6] for tuning max p adaptively. Despite all these studies, doubts still persist about the useful deployment of RED [8], [9].To address some of the problems of RED, recently there have been some proposals in active queue management. BRED [10] and FRED [11] aim to improve the fairness of RED by maintaining per-active-flow state information. AVQ [3] tries to decouple congestion measure from the performance measure. Predictive AQM (PAQM) [4] tries to exploit traffic predictability in the calculation of the packet dropping probability.In this paper, we propose and analyze a new AQM strategy called APACE and compare its performance with exist-

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Over the years there has been an increasing demand for image recognition as the world is moving towards a digital space. With the increasing demands, the application of Mask RCNN and expanded algorithms based on segmentation and YOLO have seen a major rise in the last 5 years. So the accuracy of the models has improved slightly since most projects take different approaches to the different datasets and have different metrics of Evaluation. By cross-referencing these approaches, a trend is observed that leads to higher success with the implementation of models using the hyperparameters and extra layers that have been added to the Mask RCNN in sequences. In this paper, the different approaches taken by different researchers are explored to understand how the implementations have progressed over the last half of the decade. In our research, we have studied & analyzed 50 papers and found that the majority of papers were using the COCO dataset for training purposes with a specified set of hyper-parameters to measure the accuracy, performance, and memory consumption. The experiment findings were presented to suggest suitable RCNN architecture based on application or hardware attributes.

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