Engineering Applications of Neural Networks: 14th by Anastasios Tefas, Alexandros Iosifidis, Ioannis Pitas

By Anastasios Tefas, Alexandros Iosifidis, Ioannis Pitas (auth.), Lazaros Iliadis, Harris Papadopoulos, Chrisina Jayne (eds.)

The volumes set, CCIS 383 and 384, constitutes the refereed lawsuits of the 14th overseas convention on Engineering purposes of Neural Networks, EANN 2013, hung on Halkidiki, Greece, in September 2013. The ninety one revised complete papers offered have been conscientiously reviewed and chosen from quite a few submissions. The papers describe the functions of synthetic neural networks and different delicate computing techniques to varied fields akin to trend recognition-predictors, tender computing functions, clinical purposes of AI, fuzzy inference, evolutionary algorithms, class, studying and knowledge mining, keep an eye on techniques-aspects of AI evolution, picture and video research, type, development attractiveness, social media and neighborhood dependent governance, scientific purposes of AI-bioinformatics and learning.

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Extra resources for Engineering Applications of Neural Networks: 14th International Conference, EANN 2013, Halkidiki, Greece, September 13-16, 2013 Proceedings, Part I

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Cartesian Genetic Programming. In: EuroGP, pp. cn Abstract. This paper proposes a hybrid stochastic competitive Hopfield neural network-efficient genetic algorithm (SCH-EGA) approach to tackle the frequency assignment problem (FAP). The objective of FAP is to minimize the cochannel interference between satellite communication systems by rearranging the frequency assignments so that they can accommodate the increasing demands. In fact, as SCH-EGA algorithm owns the good adaptability, it can not only deal with the frequency assignment problem, but also cope with the problems of clustering, classification, the maximum clique problem and so on.

On Artificial Intelligence (2005) 10. 3 Wireless Home Networks. IEEE Transactions on Multimedia 9(3), 642–654 (2007) 11. 3 WPANs. IET Com. 3(1), 1–9 (2009) 12. : Using adaptive linear prediction to support real-time VBR video under RCBR network service model. IEEE Trans. on Networking 6(5), 635–644 (1998) 13. : MPEG-4 Bandwidth Prediction for Broadband Cable Networks. IEEE Transactions on Broadcasting 54(4), 741–751 (2008) 14. : Size matters: Size-Based Scheduling for MEPG-4 over wireless channels.

For example, for a fixed number of nodes in the network, the number of recurrent Fig. 3. R-CGPANN based estimator MPEG-4 Internet Traffic Estimation Using Recurrent CGPANN 27 nodes in the network is varied from 1 to 10 and results are obtained. This process is repeated for a set of 10 different elements representing the number of total nodes in the network. For all the scenarios, the mutation rate is set to 10%. Each neuron (node) has 5 inputs. The evolutionary strategy is set to 1 + with set to 9.

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