Intelligent Bandwidth Management Using Fast Learning Neural Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@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