Congestion is an important issue which researchers focus on in the Transmission Control Protocol (TCP) network environment. To keep the stability of the whole network, congestion control algorithms have been extensively studied. Queue management method employed by the routers is one of the important issues in the congestion control study. Active Queue Management (AQM) has been proposed as a router-based mechanism for early detection of congestion inside the network. In this study, we are comparing AQM two popular queue management methods, Random Early Detection (RED) and droptail, in different aspects, such as throughput and fairness Index. The comparison results indicate RED performed slightly better with higher throughput and higher fairness Index than droptail. Simulation is done by using Network Simulator (NS 2 ) and the graphs are drawn using X-graph.

The online attendance system is a project that aims to create a more efficient and secure method for recording attendance at educational institutions, offices, and other organizations. The main objective of the project is to develop a system that can accurately identify individuals based on their facial features, and record their attendance automatically. One of the main problems that the face recognition attendance system aims to solve is the issue of manual attendance recording, which is often time-consuming and prone to errors. By automating the attendance process, we eliminate the need for annual attendance registers and reduce the burden on staff. The online attendance system uses advanced algorithms to analyze and compare facial features, and can accurately identify individuals even if they are wearing masks or have changed their appearance. The system can also be integrated with other software, such as payroll systems, to provide a more streamlined and efficient process for managing attendance and payroll. Overall, the face recognition attendance system is a valuable tool for improving attendance management and security at educational institutions, offices, and other organizations. It offers a more accurate and efficient method for recording attendance, and can significantly reduce the workload and costs associated with manual attendance recording.

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