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Using Genetic Algorithms in Secured Business Intelligence Mobile Applications

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  • Silvia TRIF

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Abstract

The paper aims to assess the use of genetic algorithms for training neural networks used in secured Business Intelligence Mobile Applications. A comparison is made between classic back-propagation method and a genetic algorithm based training. The design of these algorithms is presented. A comparative study is realized for determining the better way of training neural networks, from the point of view of time and memory usage. The results show that genetic algorithms based training offer better performance and memory usage than back-propagation and they are fit to be implemented on mobile devices.

Suggested Citation

  • Silvia TRIF, 2011. "Using Genetic Algorithms in Secured Business Intelligence Mobile Applications," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 15(1), pages 69-79.
  • Handle: RePEc:aes:infoec:v:15:y:2011:i:1:p:69-79
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    File URL: http://www.revistaie.ase.ro/content/57/06%20-Trif.pdf
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    References listed on IDEAS

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    1. Catalin BOJA & Lorena BATAGAN & Alin ZAMFIROIU, 2010. "Secure Architecture for M-Learning Bluetooth Services," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 14(3), pages 47-59.
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    Cited by:

    1. Silvia TRIF & Adrian VISOIU, 2011. "A Windows Phone 7 Oriented Secure Architecture for Business Intelligence Mobile Applications," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 15(2), pages 119-129.

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