Intelligent Bandwidth Management Using Fast Learning Neural Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Ullah:2012:HPCC-ICESS,
  author =       "Fahad Ullah and Gul M. Khan and Sahibzada Ali Mahmud",
  booktitle =    "High Performance Computing and Communication 2012 IEEE
                 9th International Conference on Embedded Software and
                 Systems (HPCC-ICESS), 2012 IEEE 14th International
                 Conference on",
  title =        "Intelligent Bandwidth Management Using Fast Learning
                 Neural Networks",
  year =         "2012",
  month =        "25-27 " # jun,
  address =      "Liverpool",
  pages =        "867--872",
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 Genetic Programming bandwidth allocation, computer
                 network management, learning (artificial intelligence),
                 neural nets, scheduling, telecommunication traffic,
                 video streaming, CGPANN, MPEG-4 video stream traffic,
                 bandwidth efficiency, fast learning neural network
                 algorithm, fast learning neural networks, frame drop
                 rate, frame size prediction error, historical data,
                 intelligent bandwidth management, multiuser MPEG-4
                 traffic, scheduling system, single user MPEG-4 traffic,
                 Artificial neural networks, Bandwidth, Estimation,
                 Multimedia communication, Prediction algorithms,
                 Streaming media, Transform coding, MPEG-4, bandwidth
                 management, evolutionary algorithm, scheduling, traffic
                 estimation",
  URL =          "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6332261",
  DOI =          "doi:10.1109/HPCC.2012.123",
  isbn13 =       "978-1-4673-2164-8",
  abstract =     "A fast learning neural network based scheduling system
                 is presented to predict the frames on a single and
                 multi-user MPEG-4 traffic and to distribute the
                 bandwidth accordingly. MPEG-4 video stream traffic from
                 various sources is used to evaluate the capability of
                 this algorithm. A Fast learning Neural network
                 algorithm also termed as Cartesian Genetic Programming
                 Evolved Artificial Neural Network (CGPANN) is used as a
                 forecaster to predict the size of the next frame based
                 on the historical data consisting of previous 10 frames
                 in the buffer for each individual user. A range of
                 scenarios are exploited and analysed for the frame size
                 prediction error, bandwidth efficiency and the frame
                 drop rate for the whole system as well as every user
                 involved obtaining outstanding results. For the best
                 case, the system - with 50 users using the streaming
                 service - has 35percent of bandwidth efficiency with
                 very low frame drop frequency.",
  notes =        "Also known as \cite{6332261}",
}

Genetic Programming entries for Fahad Ullah Gul Muhammad Khan Sahibzada Ali Mahmud

Citations