**Commenced**in January 2007

**Frequency:**Monthly

**Edition:**International

**Paper Count:**16

# Publications

##### 16 A Novel Adaptive E-Learning Model Based on Developed Learner's Styles

**Authors:**
Hazem M. El-Bakry,
Ahmed A. Saleh,
Taghreed T. Asfour

**Abstract:**

**Keywords:**
Adaptive learning,
Learning styles,
Teaching
strategies.

##### 15 Integrating Fast Karnough Map and Modular Neural Networks for Simplification and Realization of Complex Boolean Functions

**Authors:**
Hazem M. El-Bakry

**Abstract:**

**Keywords:**
Boolean Functions,
Simplification,
KarnoughMap,
Implementation of Logic Functions,
Modular NeuralNetworks.

##### 14 Integrating Fast Karnough Map and Modular Neural Networks for Simplification and Realization of Complex Boolean Functions

**Authors:**
Hazem M. El-Bakry

**Abstract:**

In this paper a new fast simplification method is presented. Such method realizes Karnough map with large number of variables. In order to accelerate the operation of the proposed method, a new approach for fast detection of group of ones is presented. Such approach implemented in the frequency domain. The search operation relies on performing cross correlation in the frequency domain rather than time one. It is proved mathematically and practically that the number of computation steps required for the presented method is less than that needed by conventional cross correlation. Simulation results using MATLAB confirm the theoretical computations. Furthermore, a powerful solution for realization of complex functions is given. The simplified functions are implemented by using a new desigen for neural networks. Neural networks are used because they are fault tolerance and as a result they can recognize signals even with noise or distortion. This is very useful for logic functions used in data and computer communications. Moreover, the implemented functions are realized with minimum amount of components. This is done by using modular neural nets (MNNs) that divide the input space into several homogenous regions. Such approach is applied to implement XOR function, 16 logic functions on one bit level, and 2-bit digital multiplier. Compared to previous non- modular designs, a clear reduction in the order of computations and hardware requirements is achieved.

**Keywords:**
Boolean functions,
simplification,
Karnough map,
implementation of logic functions,
modular neural networks.

##### 13 Fast Forecasting of Stock Market Prices by using New High Speed Time Delay Neural Networks

**Authors:**
Hazem M. El-Bakry,
Nikos Mastorakis

**Abstract:**

**Keywords:**
Fast Forecasting,
Stock Market Prices,
Time Delay NeuralNetworks,
Cross Correlation,
Frequency Domain.

##### 12 A General Framework for Modeling Replicated Real-Time Database

**Authors:**
Hala Abdel hameed,
Hazem M. El-Bakry,
Torky Sultan

**Abstract:**

There are many issues that affect modeling and designing real-time databases. One of those issues is maintaining consistency between the actual state of the real-time object of the external environment and its images as reflected by all its replicas distributed over multiple nodes. The need to improve the scalability is another important issue. In this paper, we present a general framework to design a replicated real-time database for small to medium scale systems and maintain all timing constrains. In order to extend the idea for modeling a large scale database, we present a general outline that consider improving the scalability by using an existing static segmentation algorithm applied on the whole database, with the intent to lower the degree of replication, enables segments to have individual degrees of replication with the purpose of avoiding excessive resource usage, which all together contribute in solving the scalability problem for DRTDBS.

**Keywords:**
Database modeling,
Distributed database,
Real time databases,
Replication

##### 11 Improving Quality of Business Networks for Information Systems

**Authors:**
Hazem M. El-Bakry,
Ahmed Atwan

**Abstract:**

**Keywords:**
Usability Criteria,
Computer Networks,
Fast
Information Processing,
Cross Correlation,
Frequency Domain.

##### 10 Optimal Document Archiving and Fast Information Retrieval

**Authors:**
Hazem M. El-Bakry,
Ahmed A. Mohammed

**Abstract:**

**Keywords:**
Information Storage and Retrieval,
Electronic
Archiving,
Fast Information Detection,
Cross Correlation,
Frequency Domain.

##### 9 A Fast Neural Algorithm for Serial Code Detection in a Stream of Sequential Data

**Authors:**
Hazem M. El-Bakry,
Qiangfu Zhao

**Abstract:**

In recent years, fast neural networks for object/face detection have been introduced based on cross correlation in the frequency domain between the input matrix and the hidden weights of neural networks. In our previous papers [3,4], fast neural networks for certain code detection was introduced. It was proved in [10] that for fast neural networks to give the same correct results as conventional neural networks, both the weights of neural networks and the input matrix must be symmetric. This condition made those fast neural networks slower than conventional neural networks. Another symmetric form for the input matrix was introduced in [1-9] to speed up the operation of these fast neural networks. Here, corrections for the cross correlation equations (given in [13,15,16]) to compensate for the symmetry condition are presented. After these corrections, it is proved mathematically that the number of computation steps required for fast neural networks is less than that needed by classical neural networks. Furthermore, there is no need for converting the input data into symmetric form. Moreover, such new idea is applied to increase the speed of neural networks in case of processing complex values. Simulation results after these corrections using MATLAB confirm the theoretical computations.

**Keywords:**
Fast Code/Data Detection,
Neural Networks,
Cross Correlation,
real/complex values.

##### 8 A New High Speed Neural Model for Fast Character Recognition Using Cross Correlation and Matrix Decomposition

**Authors:**
Hazem M. El-Bakry

**Abstract:**

**Keywords:**
Fast Character Detection,
Neural Processors,
Cross Correlation,
Image Normalization,
Parallel Processing.

##### 7 Fast Complex Valued Time Delay Neural Networks

**Authors:**
Hazem M. El-Bakry,
Qiangfu Zhao

**Abstract:**

**Keywords:**
Fast Complex Valued Time Delay Neural
Networks,
Cross Correlation,
Frequency Domain

##### 6 A New Implementation of PCA for Fast Face Detection

**Authors:**
Hazem M. El-Bakry

**Abstract:**

**Keywords:**
Fast Face Detection,
PCA,
Cross Correlation,
Frequency Domain

##### 5 Fast Painting with Different Colors Using Cross Correlation in the Frequency Domain

**Authors:**
Hazem M. El-Bakry

**Abstract:**

**Keywords:**
Fast Painting,
Cross Correlation,
Frequency Domain,
Parallel Processing

##### 4 Fast Object/Face Detection Using Neural Networks and Fast Fourier Transform

**Authors:**
Hazem M. El-Bakry,
Qiangfu Zhao

**Abstract:**

**Keywords:**
Conventional Neural Networks,
Fast Neural
Networks,
Cross Correlation in the Frequency Domain.

##### 3 A Modified Cross Correlation in the Frequency Domain for Fast Pattern Detection Using Neural Networks

**Authors:**
Hazem M. El-Bakry,
Qiangfu Zhao

**Abstract:**

**Keywords:**
Fast Pattern Detection,
Neural Networks,
Modified Cross Correlation

##### 2 A Novel Hopfield Neural Network for Perfect Calculation of Magnetic Resonance Spectroscopy

**Authors:**
Hazem M. El-Bakry

**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 called “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:**
Hopfield Neural Networks,
Cross Correlation,
Nuclear Magnetic Resonance,
Magnetic Resonance Spectroscopy,
Fast Fourier Transform.

##### 1 Sub-Image Detection Using Fast Neural Processors and Image Decomposition

**Authors:**
Hazem M. El-Bakry,
Qiangfu Zhao

**Abstract:**

In this paper, an approach to reduce the computation steps required by fast neural networksfor the searching process is presented. The principle ofdivide and conquer strategy is applied through imagedecomposition. Each image is divided into small in sizesub-images and then each one is tested separately usinga fast neural network. The operation of fast neuralnetworks based on applying cross correlation in thefrequency domain between the input image and theweights of the hidden neurons. Compared toconventional and fast neural networks, experimentalresults show that a speed up ratio is achieved whenapplying this technique to locate human facesautomatically in cluttered scenes. Furthermore, fasterface detection is obtained by using parallel processingtechniques to test the resulting sub-images at the sametime using the same number of fast neural networks. Incontrast to using only fast neural networks, the speed upratio is increased with the size of the input image whenusing fast neural networks and image decomposition.

**Keywords:**
Fast Neural Networks,
2D-FFT,
CrossCorrelation,
Image decomposition,
Parallel Processing.