NexTech 2021 Congress
October 03, 2021 to October 07, 2021 - Barcelona, Spain

  • UBICOMM 2021, The Fifteenth International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies
  • ADVCOMP 2021, The Fifteenth International Conference on Advanced Engineering Computing and Applications in Sciences
  • SEMAPRO 2021, The Fifteenth International Conference on Advances in Semantic Processing
  • AMBIENT 2021, The Eleventh International Conference on Ambient Computing, Applications, Services and Technologies
  • EMERGING 2021, The Thirteenth International Conference on Emerging Networks and Systems Intelligence
  • DATA ANALYTICS 2021, The Tenth International Conference on Data Analytics
  • GLOBAL HEALTH 2021, The Tenth International Conference on Global Health Challenges
  • CYBER 2021, The Sixth International Conference on Cyber-Technologies and Cyber-Systems

SoftNet 2021 Congress
October 03, 2021 to October 07, 2021 - Barcelona, Spain

  • ICSEA 2021, The Sixteenth International Conference on Software Engineering Advances
  • ICSNC 2021, The Sixteenth International Conference on Systems and Networks Communications
  • CENTRIC 2021, The Fourteenth International Conference on Advances in Human-oriented and Personalized Mechanisms, Technologies, and Services
  • VALID 2021, The Thirteenth International Conference on Advances in System Testing and Validation Lifecycle
  • SIMUL 2021, The Thirteenth International Conference on Advances in System Simulation
  • SOTICS 2021, The Eleventh International Conference on Social Media Technologies, Communication, and Informatics
  • INNOV 2021, The Tenth International Conference on Communications, Computation, Networks and Technologies
  • HEALTHINFO 2021, The Sixth International Conference on Informatics and Assistive Technologies for Health-Care, Medical Support and Wellbeing

NetWare 2021 Congress
November 14, 2021 to November 18, 2021 - Athens, Greece

  • SENSORCOMM 2021, The Fifteenth International Conference on Sensor Technologies and Applications
  • SENSORDEVICES 2021, The Twelfth International Conference on Sensor Device Technologies and Applications
  • SECURWARE 2021, The Fifteenth International Conference on Emerging Security Information, Systems and Technologies
  • AFIN 2021, The Thirteenth International Conference on Advances in Future Internet
  • CENICS 2021, The Fourteenth International Conference on Advances in Circuits, Electronics and Micro-electronics
  • ICQNM 2021, The Fifteenth International Conference on Quantum, Nano/Bio, and Micro Technologies
  • FASSI 2021, The Seventh International Conference on Fundamentals and Advances in Software Systems Integration
  • GREEN 2021, The Sixth International Conference on Green Communications, Computing and Technologies

TrendNews 2021 Congress
November 14, 2021 to November 18, 2021 - Athens, Greece

  • CORETA 2021, Advances on Core Technologies and Applications
  • DIGITAL 2021, Advances on Societal Digital Transformation

 


ThinkMind // International Journal On Advances in Internet Technology, volume 6, numbers 3 and 4, 2013 // View article inttech_v6_n34_2013_4


Intelligent Learning Techniques applied to Quality Level in Voice over IP Communications

Authors:
Demóstenes Zegarra Rodríguez
Renata Lopes Rosa
Graça Bressan

Keywords: QoS; VoIP; Machine Learning; MOS; E-Model; PESQ.

Abstract:
This paper presents a method for determining the quality of a Voice over IP communication using machine learning techniques. The solution proposed uses historical values of network parameters and communication quality in order to train the different learning algorithms. After that, these algorithms are able to find the quality of the Voice over IP communication based on network parameters of a specific period of time. Intelligent and other machine learning algorithms take as input a baseline file that contains some values of network parameters and voice coding, associating an index quality for each scenario according to ITU-T Recommendation G.107. The tests were performed in an emulated network environment, totally isolated and controlled with real traffic of voice and realistic IP network parameters. The quality ratings obtained for the learning algorithms in all the scenarios were corroborated with the results of the algorithm of ITU-T Recommendation P.862. The results show the reliability of the four learning algorithms used on the tests: Decision Trees (J.48), Neural Networks (Multilayer Perceptron), Sequential Minimal Optimization (SMO) and Bayesian Networks (Naive). The highest value of reliability for determining the quality of the Voice over IP communications was 0.98 with the use of the Decision Trees Algorithm. These results demonstrate the validity of the method proposed.

Pages: 145 to 155

Copyright: Copyright (c) to authors, 2013. Used with permission.

Publication date: December 31, 2013

Published in: journal

ISSN: 1942-2652

SERVICES CONTACT
2010 - 2017 © ThinkMind. All rights reserved.
Read Terms of Service and Privacy Policy.