The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS
Transactions ID Number: 31-256
Full Name: Hazem EL-Bakry
Position: Assistant Professor
Age: ON
Sex: Male
Address: P.O.Box 76, Mansoura
Country: EGYPT
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E-mail address: helbakry50@yahoo.com
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Title of the Paper: A Modified Hopfield Neural Network for Perfect Calculation of Magnetic Resonance Spectroscopy
Authors as they appear in the Paper: Hazem El-Bakry and Nikos Mastorakis
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Number of paper pages: 15
Abstract: In this paper, an automatic determination algorithm for nuclear magnetic resonance (NMR) spectra of the metabolites in the living body by magnetic resonance spectroscopy (MRS) without human intervention or complicated calculations is presented. In such method, the problem of NMR spectrum determination is transformed into the determination of the parameters of a mathematical model of the NMR signal. To calculate these parameters efficiently, a new model called modified Hopfield neural network is designed. The main achievement of this paper over the work in literature [30] is that the speed of the modified Hopfield neural network is accelerated. This is done by applying cross correlation in the frequency domain between the input values and the input weights. The modified Hopfield neural network can accomplish complex dignals perfectly with out any additinal computation steps. This is a valuable advantage as NMR signals are complex-valued. In addition, a technique ca!
lled "modified sequential extension of section (MSES)" that takes into account the damping rate of the NMR signal is developed to be faster than that presented in [30]. Simulation results show that the calculation precision of the spectrum improves when MSES is used along with the neural network. Furthermore, MSES is found to reduce the local minimum problem in Hopfield neural networks. Moreover, the performance of the proposed method is evaluated and there is no effect on the performance of calculations when using the modified Hopfield neural networks.
Keywords: Fast Intrusion Detection, Clustering, Data Mining, E-Government, Cross correlation, Frequency domain, and Neural Networks.
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