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

Tue, 02 Apr 2024 00:00:00 +0000

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.

Classification of Genetics Based on Machine Learning Algorithms: A Review

Tue, 02 Apr 2024 00:00:00 +0000

The multifaceted applications of drones in addressing humanitarian challenges, enhancing governance services, medical assistance, and security considerations. Drones are showcased as adaptable and swift responders in conflict or disaster-affected areas, mitigating risks for humanitarian workers and delivering crucial supplies to remote locations. The integration of digital technology in governance services is discussed, emphasizing transparency, efficacy, and reduced corruption. The study also introduces a taxonomy for GPS- guided drones in medical supply delivery, highlighting challenges in accuracy and cost reduction. Drones' wide- ranging potential uses, from police operations to advertising and shipment transportation, are outlined. A comprehensive evaluation of drone security, from consumer drones to military systems, is provided, along with preventive suggestions. Machine learning algorithms for drone detection and classification, showcasing the proposed DDI system's capability to accurately identify intruding drones and their operational modes. machine learning in the context of used drones, focusing on detection and classification. The study assesses various machine learning algorithms, including image processing, sound analysis, and RF signal-based techniques, to identify and classify drones effectively. Data from diverse sensors are utilized for feature extraction, employing algorithms such as Deep Neural Networks, Support Vector Machines, and deep belief networks. The proposed DDI system adopts an RF-based approach and integrates a Deep Learning algorithm for precise detection and identification of used or intruding drones.

IIRJET

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

Classification of Genetics Based on Machine Learning Algorithms: A Review