Navigating Network Traffic: An Exploration of Maze Algorithm Applications in Machine Learning

Abstract

To Determine network performance and highlight the need for integrated and intelligent solutions in response to the growing amount of data generated by smart devices. It launches a link between web browsing and queuing, emphasizing the importance of factors such as time and availability. The proposed method is evaluating some studies on machine learning techniques used for network traffic forecasting, Internet tools, and techniques for analyzing traffic flows. Network traffic is the data flow across a computer network, emphasizing the need for scalable and intelligent solutions due to the rise in data generation from smart devices. Additionally, it draws an analogy between navigating network paths and solving mazes in everyday life based on factors like time, effort, and convenience. Navigating paths resembles solving mazes, as individuals choose routes from origin to destination based on factors like time, effort, and convenience, akin to maze-solving techniques. Algorithms use real-time analytics, machine learning, and historical data to improve network efficiency. Finally, the maze algorithm is measured the network traffic optimization. It is very useful in machine learning, real-time analysis, and historical data to improve network performance.