The congestion on the internet is the main issue that affects the performance of transition data over the network. An algorithm for congestion control is required to keep any network efficient and reliable for transfer traffic data of the users. Many Algorithms had been suggested over the years to improve the control of congestion that occurs in the network such as drop tail packets. Recently there are many algorithms have been developed to overcome the drawback of the drop tail procedure. One of the important algorithms developed is active queue management (AQM) that provides efficient congestion control by reducing drop packets, this technique considered as a base for many other congestion control algorithms schema. It works at the network core (router) for controlling the drop and marking of packets in the router's buffer before the congestion inception. In this study, a comprehensive survey is done on the AQM Algorithm schemas that proposed and modification these algorithms to achieve the best performance, the classification of AQM algorithms based on queue length, queue delay, or both. The advantages and limitations of each algorithm have been discussed. Also, debate the intelligent techniques procedure with AQM algorithm to achieve optimization in performance of algorithm operation. Finally, the comparison has been discussed among algorithms to find the weakness and powerful of each one based on different metrics.

Hand gesture recognition is one of communication in which used bodily behavior to transmit several messages. This paper aims to detect hand gestures with the mobile device camera and create a customize dataset that used in deep learning model training to recognize hand gestures. The real-time approach was used for all these objectives: the first step is hand area detection; the second step is hand area storing in a dataset form to use in the future for model training. A framework for human contact was put in place by studying pictures recorded by the camera. It was converted the RGB color space image to the greyscale, the blurring method is used for object noise removing efficaciously. To highlight the edges and curves of the hand, the thresholding method is used. And subtraction of complex background is applied to detect moving objects from a static camera. The objectives of the paper were reliable and favorable which helps deaf and dumb people interact with the environment through the sign language fully approved to extract hand movements. Python language as a programming manner to discover hand gestures. This work has an efficient hand gesture detection process to address the problem of framing from real-time video.