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Related Concept Videos

Observational Learning01:12

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Supervised Learning of Neural Networks for Active Queue Management in the Internet.

Jakub Szyguła1, Adam Domański1, Joanna Domańska2

  • 1Faculty of Automatic Control, Electronics and Computer Science, Department of Distributed Systems and Informatic Devices, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland.

Sensors (Basel, Switzerland)
|August 10, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Active Queue Management (AQM) mechanism using neural networks to optimize network traffic. The AI-driven AQM effectively manages router queues, reducing packet loss and improving network performance.

Keywords:
Hurst exponentPIα controlleractive queue managementcongestion controldropping packetsinternet trafficneural networksself-similarity

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Area of Science:

  • Computer Science
  • Network Engineering
  • Artificial Intelligence

Background:

  • Active Queue Management (AQM) is crucial for mitigating network congestion.
  • Traditional AQM mechanisms like PIα face challenges with complex network traffic patterns.
  • Self-similar network traffic, often modeled by fractional Gaussian noise, requires advanced management strategies.

Purpose of the Study:

  • To develop a machine learning model that replicates the behavior of the AQM PIα mechanism.
  • To investigate the effectiveness of a neural network-based AQM for managing network traffic.
  • To enhance network performance by proactively dropping packets before buffer overflow.

Main Methods:

  • Utilizing neural networks to model AQM behavior.
  • Generating training data incorporating the self-similarity of network traffic using fractional Gaussian noise.
  • Conducting quantitative analysis through simulations.

Main Results:

  • The proposed neural network-based AQM mechanism was evaluated.
  • Simulations analyzed key performance metrics: queue length, packet rejection rates, and waiting times.
  • The AI-driven AQM demonstrated its utility in managing network resources.

Conclusions:

  • Neural network-based AQM presents a viable and effective approach to network traffic management.
  • The proposed model successfully mimics AQM PIα behavior while handling self-similar traffic.
  • This AI-driven solution offers improved network performance through intelligent queue management.