@article{(Open Science Index):https://publications.waset.org/pdf/14515,
	  title     = {Optimal Document Archiving and Fast Information Retrieval},
	  author    = {Hazem M. El-Bakry and  Ahmed A. Mohammed},
	  country	= {},
	  institution	= {},
	  abstract     = {In this paper, an intelligent algorithm for optimal
document archiving is presented. It is kown that electronic archives
are very important for information system management. Minimizing
the size of the stored data in electronic archive is a main issue to
reduce the physical storage area. Here, the effect of different types of
Arabic fonts on electronic archives size is discussed. Simulation
results show that PDF is the best file format for storage of the Arabic
documents in electronic archive. Furthermore, fast information
detection in a given PDF file is introduced. Such approach uses fast
neural networks (FNNs) implemented in the frequency domain. The
operation of these networks relies on performing cross correlation in
the frequency domain rather than spatial one. It is proved
mathematically and practically that the number of computation steps
required for the presented FNNs is less than that needed by
conventional neural networks (CNNs). Simulation results using
MATLAB confirm the theoretical computations.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {3},
	  number    = {3},
	  year      = {2009},
	  pages     = {816 - 829},
	  ee        = {https://publications.waset.org/pdf/14515},
	  url   	= {https://publications.waset.org/vol/27},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 27, 2009},
	}