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Fast Detection of H1N1 and H1N5 Viruses in DNA Sequence by using High Speed Time Delay Neural Networks


									                                                                    (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                                  Vol. 9, No. 11, November 2011

   Fast Detection of H1N1 and H1N5 Viruses in DNA
   Sequence by using High Speed Time Delay Neural
                              Hazem M. El-Bakry                                                    Nikos Mastorakis

             Faculty of Computer Science & Information Systems,                             Technical University of Sofia,
                         Mansoura University, EGYPT                                                 BULGARIA

Abstract—Fast detection of biological viruses in DNA sequence is very            machinery the host cell would ordinarily use to reproduce its
important for investigation of patients and overcome diseases. First, an         own DNA. Then the host cell is forced to encapsulate this viral
intelligent algorithm to completely retrieve DNA sequence is presented.          DNA into new protein shells; the new viruses created are then
DNA codes that may be missed during the splitting process are retrieved          released, destroying the cell [32-35].
by using Hopfield neural networks. Then, a new approach for fast
detection of biological viruses like H1N1 and H1N5 in DNA
sequence is presented. Such algorithm uses high speed time delay                 All living things are susceptible to viral infections plants,
neural networks (HSTDNNs). The operation of these networks                       animals, or bacteria can all be infected by a virus specific for
relies on performing cross correlation in the frequency domain                   that type of organism. Moreover, within an individual species
between the input DNA sequence and the input weights of neural                   there may be a hundred or more different viruses which can
networks. It is proved mathematically and practically that the                   infect that species alone. There are viruses which infect only
number of computation steps required for the presented                           humans (for example, smallpox), viruses which infect humans
HSTDNNs is less than that needed by conventional time delay                      and one or two additional kinds of animals (for example,
neural networks (CTDNNs). Simulation results using MATLAB                        influenza), viruses which infect only a certain kind of plant
confirm the theoretical computations.
                                                                                 (for example, the tobacco mosaic virus), and some viruses
    Keywords- High Speed Neural Networks; Cross Correlation;                     which infect only a particular species of bacteria (for example,
Frequency Domain; H1N1 and H1N5 Detection                                        the bacteriophage which infects E. coli) [32-35].
                                                                                 Sometimes when a virus reproduces, mutations occur. The
                         I.    INTRODUCTION                                      offspring that have been changed by the mutation may no
                                                                                 longer be infectious. But a virus replicates itself thousands of
A virus is a tiny bundle of genetic material - either DNA or                     times, so there will usually be some offspring that are still
RNA - carried in a shell called a viral coat, or capsid, which is                infectious, but sufficiently different from the parent virus so
made up of protein. Some viruses have an additional layer                        that vaccines no longer work to kill it. The influeza virus can
around this coat called an envelope. When a virus particle                       do this, which is why flu vaccines for last year's flu don't work
enters a cell and begins to reproduce itself, this is called a viral             the next year. The common cold virus changes so quickly that
infection. The virus is usually very, very small compared to                     vaccines are useless; the cold you have today will be a
the size of a living cell. The information carried in the virus's                different strain than the cold you had last month! [31-34]
DNA allows it to take over the operation of the cell,
                                                                                 For efficient treatment of patients in real-time, it is important
converting it to a factory to make more copies of itself. For
                                                                                 to detect biological viruses like H1N1 and H1N5. Recently,
example, the polio virus can make over one million copies of
                                                                                 time delay neural networks have shown very good results in
itself inside a single, infected human intestinal cell [32-35].
                                                                                 different areas such as automatic control, speech recognition,
All viruses only exist to make more viruses. With the possible                   blind equalization of time-varying channel and other
exception of bacterial viruses, which can kill harmful bacteria,                 communication applications. The main objective of this
all viruses are considered harmful, because their reproduction                   research is to reduce the response time of time delay neural
causes the death of the cells which the viruses entered. If a                    networks. The purpose is to perform the testing process in the
virus contains DNA, it inserts its genetic material into the host                frequency domain instead of the time domain. Our approach
cell's DNA. If the virus contains RNA, it must first turn its                    was successfully applied for fast detection of computer viruses
RNA into DNA using the host cell's machinery, before                             as shown in [4]. Sub-image detection by using fast neural
inserting it into the host DNA. Once it has taken over the cell,                 networks (FNNs) was proposed in [5,6]. Furthermore, it was
viral genes are then copied thousands of times, using the                        used for fast face detection [7,10,12], and fast iris detection

                                                                                                            ISSN 1947-5500
                                                             (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                           Vol. 9, No. 11, November 2011
[11]. Another idea to further increase the speed of FNNs                  given even a poor photograph of that person we are quite good
through image decomposition was suggested in [10]. In                     at reconstructing the persons face quite accurately. This is very
addition it was applied for fast prediction of new data as                different from a traditional computer where specific facts are
described in [1,3].                                                       located in specific places in computer memory. If only partial
                                                                          information is available about this location, the fact or
FNNs for detecting a certain code in one dimensional serial
                                                                          memory cannot be recalled at all [35-42].
stream     of    sequential   data    were     described    in
[1,2,3,4,8,14,15,20,23,27,28,29]. Compared with conventional              Theoretical physicists are an unusual lot, acting like
neural networks, FNNs based on cross correlation between the              gunslingers in the old West, anxious to prove themselves
tested data and the input weights of neural networks in the               against a really good problem. And there aren’t that many
frequency domain showed a significant reduction in the                    really good problems that might be solvable. As soon as
number of computation steps required for certain data                     Hopfield pointed out the connection between a new and
detection [1-29]. Here, we make use of the theory of FNNs                 important problem (network models of brain function) and an
implemented in the frequency domain to increase the speed of              old and well-studied problem (the Ising model), many
time delay neural networks for biological virus detection [2].            physicists rode into town, so to speak, with the intention of
The idea of moving the testing process from the time domain               shooting the problem full of holes and then, the brain
to the frequency domain is applied to time delay neural                   understood, riding off into the sunset looking for a newer,
networks. Theoretical and practical results show that the                 tougher problem. (Who was that masked physicist?).
proposed HSTDNNs are faster than CTDNNs. Retrieval of
                                                                          Hopfield made the portentous comment: ‘This case is
missed DNA codes by using Hopfield neural networks is
                                                                          isomorphic with an Ising model,’ thereby allowing a deluge of
introduced in section II. Section III presents HSTDNNs for
                                                                          physical theory (and physicists) to enter neural network
detecting of biological viruses in DNA sequence.
                                                                          modeling. This flood of new participants transformed the field.
Experimental results for fast biological virus detection by
                                                                          In 1974 Little and Shaw made a similar identification of neural
using HSTDNNs are given in section IV.
                                                                          network dynamics with the Ising model, but for whatever
                                                                          reason, their idea was not widely picked up at the time.
      II.   RETRIEVAL OF MISSED DNA CODES BY USING                        Unfortunately, the problem of brain function turned out to be
               HOPFIELD NEURAL NETWORKS                                   more difficult than expected, and it is still unsolved, although
                                                                          a number of interesting results about Hopfield nets were
One of the most important functions of our brain is the laying
                                                                          proved. At present, many of the traveling theoreticians have
down and recall of memories. It is difficult to imagine how we
                                                                          traveled on [38].
could function without both short and long term memory. The
absence of short term memory would render most tasks                      The Hopfield neural network is a simple artificial network
extremely difficult if not impossible - life would be punctuated          which is able to store certain memories or patterns in a manner
by a series of one time images with no logical connection                 rather similar to the brain - the full pattern can be recovered if
between them. Equally, the absence of any means of long term              the network is presented with only partial information.
memory would ensure that we could not learn by past                       Furthermore there is a degree of stability in the system - if just
experience. Indeed, much of our impression of self depends on             a few of the connections between nodes (neurons) are severed,
remembering our past history [36-40].                                     the recalled memory is not too badly corrupted - the network
                                                                          can respond with a "best guess". Of course, a similar
Our memories function in what is called an associative or
                                                                          phenomenon is observed with the brain - during an average
content-addressable fashion. That is, a memory does not exist
                                                                          lifetime many neurons will die but we do not suffer a
in some isolated fashion, located in a particular set of neurons.
                                                                          catastrophic loss of individual memories - our brains are quite
All memories are in some sense strings of memories - you
                                                                          robust in this respect (by the time we die we may have lost 20
remember someone in a variety of ways - by the color of their
                                                                          percent of our original neurons) [44-57].
hair or eyes, the shape of their nose, their height, the sound of
their voice, or perhaps by the smell of a favorite perfume.               The nodes in the network are vast simplifications of real
Thus memories are stored in association with one another.                 neurons - they can only exist in one of two possible "states" -
These different sensory units lie in completely separate parts            firing or not firing. Every node is connected to every other
of the brain, so it is clear that the memory of the person must           node with some strength. At any instant of time a node will
be distributed throughout the brain in some fashion. Indeed,              change its state (i.e start or stop firing) depending on the
PET scans reveal that during memory recall there is a pattern             inputs it receives from the other nodes [44-57].
of brain activity in many widely different parts of the brain
                                                                          If we start the system off with a any general pattern of firing
                                                                          and non-firing nodes then this pattern will in general change
Notice also that it is possible to access the full memory (all            with time. To see this think of starting the network with just
aspects of the person's description for example) by initially             one firing node. This will send a signal to all the other nodes
remembering just one or two of these characteristic features.             via its connections so that a short time later some of these
We access the memory by its contents not by where it is stored            other nodes will fire. These new firing nodes will then excite
in the neural pathways of the brain. This is very powerful;               others after a further short time interval and a whole cascade

                                                                                                      ISSN 1947-5500
                                                                 (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                               Vol. 9, No. 11, November 2011
of different firing patterns will occur. One might imagine that               •    Activation function on each neuron i is:
the firing pattern of the network would change in a
complicated perhaps random way with time. The crucial                                                           ⎧ 1 if net > 0 ⎫
property of the Hopfield network which renders it useful for                                f(net) = sgn(net) = ⎨               ⎬                 (1)
simulating memory recall is the following: we are guaranteed                                                    ⎩- 1 if net < 0 ⎭
that the pattern will settle down after a long enough time to                     where:
some fixed pattern. Certain nodes will be always "on" and                                                           neti = Σwij xj         (2)
others "off". Furthermore, it is possible to arrange that these               •    If net = 0, then the output is the same as before, by
stable firing patterns of the network correspond to the desired                    convention.
memories we wish to store! [44-57].                                           •    There are no separate thresholds or biases. However,
                                                                                   these could be represented by units that have all weights =
The reason for this is somewhat technical but we can proceed
                                                                                   0 and thus never change their output.
by analogy. Imagine a ball rolling on some bumpy surface. We
imagine the position of the ball at any instant to represent the              •    The energy function is defined as:
activity of the nodes in the network. Memories will be                                               E(y1, y2, …, yn) = - Σ Σ wij yiyj            (3)
represented by special patterns of node activity corresponding
to wells in the surface. Thus, if the ball is let go, it will execute               where (y1, y2, …, yn) is outputs, wij is the weight neuron i,
some complicated motion but we are certain that eventually it                                   and the double sum is over i and j.
will end up in one of the wells of the surface. We can think of
the height of the surface as representing the energy of the ball.             Different DNA patterns are stored in Hopfield neural network.
We know that the ball will seek to minimize its energy by                     In the testing process, the missed codes (if any) are retrieved.
seeking out the lowest spots on the surface -- the wells.
Furthermore, the well it ends up in will usually be the one it                      III.   FAST BIOLOGICAL VIRUS DETECTION BY USING
started off closest to. In the language of memory recall, if we                                        HSTDNNS
start the network off with a pattern of firing which
approximates one of the "stable firing patterns" (memories) it                Finding a biological virus like H1N1 or H1N5 in DNA
will "under its own steam" end up in the nearby well in the                   sequence is a searching problem. First neural networks are
energy surface thereby recalling the original perfect memory.                 trained to classify codes which contain viruses from others
The smart thing about the Hopfield network is that there exists               that do not and this is done in time domain. In biological virus
a rather simple way of setting up the connections between                     detection phase, each position in the DNA sequence is tested
nodes in such a way that any desired set of patterns can be                   for presence or absence of biological virus code. At each
made "stable firing patterns". Thus any set of memories can be                position in the input DNA one dimensional matrix, each sub-
burned into the network at the beginning. Then if we kick the                 matrix is multiplied by a window of weights, which has the
network off with any old set of node activity we are                          same size as the sub-matrix. The outputs of neurons in the
guaranteed that a "memory" will be recalled. Not too                          hidden layer are multiplied by the weights of the output layer.
surprisingly, the memory that is recalled is the one which is                 When the final output is 10, this means that the sub-matrix
"closest" to the starting pattern. In other words, we can give                under test contains H1N1. When the final output is 01 this
the network a corrupted image or memory and the network                       means that H1N5 is detected. Otherwise, there is no virus.
will "all by itself" try to reconstruct the perfect image. Of                 Thus, we may conclude that this searching problem is a cross
course, if the input image is sufficiently poor, it may recall the            correlation between the incoming serial data and the weights
incorrect memory - the network can become "confused" - just                   of neurons in the hidden layer.
like the human brain. We know that when we try to remember                    The convolution theorem in mathematical analysis says that a
someone's telephone number we will sometimes produce the                      convolution of f with h is identical to the result of the
wrong one! Notice also that the network is reasonably robust -                following steps: let F and H be the results of the Fourier
if we change a few connection strengths just a little the                     Transformation of f and h in the frequency domain. Multiply F
recalled images are "roughly right". We don't lose any of the                 and H* in the frequency domain point by point and then
images completely [44-57].                                                    transform this product into the spatial domain via the inverse
As with the Linear Associative Memory, the “stored patterns”                  Fourier Transform. As a result, these cross correlations can be
are represented by the weights. To be effective, the patterns                 represented by a product in the frequency domain. Thus, by
should be reasonably orthogonal. The basic Hopfield model                     using cross correlation in the frequency domain, speed up in
can be described as follows [38]:                                             an order of magnitude can be achieved during the detection
                                                                              process [1-29]. Assume that the size of the biological virus
•   N neurons, fully connected in a cyclic fashion:                           code is 1xn. In biological virus detection phase, a sub matrix I
•   Values are +1, -1.                                                        of size 1xn (sliding window) is extracted from the tested
•   Each neuron has a weighted input from all other neurons.                  matrix, which has a size of 1xN. Such sub matrix, which may
•   The weight matrix w is symmetric (wij=wji) and diagonal                   be biological virus code, is fed to the neural network. Let Wi
    terms (self-weights wii = 0).                                             be the matrix of weights between the input sub-matrix and the

                                                                                                           ISSN 1947-5500
                                                             (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                           Vol. 9, No. 11, November 2011
hidden layer. This vector has a size of 1xn and can be                    required for computing the 1D-FFT of the weight matrix at
represented as 1xn matrix. The output of hidden neurons h(i)              each neuron in the hidden layer.
can be calculated as follows [1-7]:                                       2- At each neuron in the hidden layer, the inverse 1D-FFT is
                                                                          computed. Therefore, q backward and (1+q) forward
                            ⎛ n                 ⎞
                      hi = g⎜ ∑ Wi (k)I(k) + bi ⎟            (4)          transforms have to be computed. Therefore, for a given matrix
                            ⎜                   ⎟                         under test, the total number of operations required to compute
                            ⎝ k =1              ⎠
                                                                          the 1D-FFT is (2q+1)Nlog2N.
where g is the activation function and b(i) is the bias of each           3- The number of computation steps required by HSTDNNs is
hidden neuron (i). Equation 4 represents the output of each               complex and must be converted into a real version. It is known
hidden neuron for a particular sub-matrix I. It can be obtained           that, the one dimensional Fast Fourier Transform requires
to the whole input matrix Z as follows [1-6]:                             (N/2)log2N complex multiplications and Nlog2N complex
                      ⎛ n/2                   ⎞                           additions [30]. Every complex multiplication is realized by six
               hi(u)=g⎜ ∑ Wi(k) Z(u + k) +b i ⎟
                      ⎜                       ⎟              (5)          real floating point operations and every complex addition is
                      ⎜k= − n/2               ⎟                           implemented by two real floating point operations. Therefore,
                      ⎝                       ⎠                           the total number of computation steps required to obtain the
Eq.5 represents a cross correlation operation. Given any two              1D-FFT of a 1xN matrix is:
functions f and d, their cross correlation can be obtained by                                   ρ=6((N/2)log2N) + 2(Nlog2N)                  (10)
                                ⎛ ∞            ⎞                          which may be simplified to:
                   d(x)⊗ f(x) = ⎜ ∑f(x + n)d(n)⎟
                                ⎜ n= − ∞       ⎟
                                                                                                          ρ=5Nlog2N                          (11)
                                ⎝              ⎠
Therefore, Eq. 5 may be written as follows [1-7]:                         4- Both the input and the weight matrices should be dot

                                (              )
                                                                          multiplied in the frequency domain. Thus, a number of
                        h i = g Wi ⊗ Z + b i                 (7)          complex computation steps equal to qN should be considered.
                                                                          This means 6qN real operations will be added to the number
where hi is the output of the hidden neuron (i) and hi (u) is the         of computation steps required by HSTDNNs.
activity of the hidden unit (i) when the sliding window is                5- In order to perform cross correlation in the frequency
located at position (u) and (u) ∈ [N-n+1].                                domain, the weight matrix must be extended to have the same
                                                                          size as the input matrix. So, a number of zeros = (N-n) must be
Now, the above cross correlation can be expressed in terms of
                                                                          added to the weight matrix. This requires a total real number
one dimensional Fast Fourier Transform as follows [1-7]:
                                                                          of computation steps = q(N-n) for all neurons. Moreover, after
                   Wi ⊗ Z = F −1 F(Z)• F * Wi   ( ))         (8)          computing the FFT for the weight matrix, the conjugate of this
                                                                          matrix must be obtained. As a result, a real number of
Hence, by evaluating this cross correlation, a speed up ratio             computation steps = qN should be added in order to obtain the
can be obtained comparable to conventional neural networks.               conjugate of the weight matrix for all neurons. Also, a
Also, the final output of the neural network can be evaluated             number of real computation steps equal to N is required to
as follows:                                                               create butterflies complex numbers (e-jk(2Πn/N)), where 0<K<L.
                                                                          These (N/2) complex numbers are multiplied by the elements
                          ⎛ q                       ⎞                     of the input matrix or by previous complex numbers during the
                  O(u) = g⎜ ∑ Wo (i) h i (u ) + b o ⎟
                          ⎜                         ⎟
                                                             (9)          computation of FFT. To create a complex number requires two
                          ⎝ i=1                     ⎠                     real floating point operations. Thus, the total number of
where q is the number of neurons in the hidden layer. O(u) is             computation steps required for HSTDNNs becomes:
the output 2D matrix (corresponding to two output neurons) of                    σ=(2q+1)(5Nlog2N)+6qN+q(N-n)+qN+N                           (12)
the neural network when the sliding window located at the
position (u) in the input matrix Z. Wo is the weight matrix               which can be reformulated as:
between hidden and output layer.
                                                                                       σ=(2q+1)(5Nlog2N)+q(8N-n)+N                           (13)
       IV.   COMPLEXITY ANALYSIS OF HSTDNNS FOR                           6- Using sliding window of size 1xn for the same matrix of
              BIOLOGICAL VIRUS DETECTION                                  1xN pixels, q(2n-1)(N-n+1) computation steps are required
                                                                          when using CTDNNs for biological virus detection or
The complexity of cross correlation in the frequency domain               processing (n) input data. The theoretical speed up factor η
can be analyzed as follows:                                               can be evaluated as follows:
1- For a tested matrix of 1xN elements, the 1D-FFT requires a
number equal to Nlog2N of complex computation steps [30].                                         q(2n - 1)(N- n + 1)
                                                                                    η=                                                       (14)
Also, the same number of complex computation steps is                                    (2q + 1)(5Nlog2 N) + q(8N- n) + N

                                                                                                      ISSN 1947-5500
                                                                        (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                                      Vol. 9, No. 11, November 2011
CTDNNs and HSTDNNs are shown in Figures 1 and 2
                                                                                     [8] Hazem M. El-bakry, and Mohamed Hamada “High speed time delay
respectively.                                                                             Neural Networks for Detecting DNA Coding Regions,” Springer, Lecture
                                                                                          Notes on Artificial Intelligence (LNAI 5711), 2009, pp. 334-342.
Time delay neural networks accept serial input data with fixed                       [9] Hazem M. El-Bakry, "New Faster Normalized Neural Networks for Sub-
size (n). Therefore, the number of input neurons equals to (n).                           Matrix Detection using Cross Correlation in the Frequency Domain and
Instead of treating (n) inputs, the proposed new approach is to                           Matrix Decomposition, " Applied Soft Computing journal, vol. 8, issue 2,
collect all the incoming data together in a long vector (for                              March 2008, pp. 1131-1149.
                                                                                     [10] Hazem M. El-Bakry, "Face detection using fast neural networks and
example 100xn). Then the input data is tested by time delay                               image decomposition," Neurocomputing Journal, vol. 48, 2002, pp. 1039-
neural networks as a single pattern with length L (L=100xn).                              1046.
Such a test is performed in the frequency domain as described                        [11] Hazem M. El-Bakry, "Human Iris Detection Using Fast Cooperative
before.                                                                                   Modular Neural Nets and Image Decomposition," Machine Graphics &
                                                                                          Vision Journal (MG&V), vol. 11, no. 4, 2002, pp. 498-512.
The theoretical speed up ratio for searching short successive                        [12] Hazem M. El-Bakry, "Automatic Human Face Recognition Using
(n) code in a long input vector (L) using time delay neural                               Modular Neural Networks," Machine Graphics & Vision Journal
networks is listed in tables I, II, and III. Also, the practical                          (MG&V), vol. 10, no. 1, 2001, pp. 47-73.
                                                                                     [13] Hazem M. El-Bakry, "A New Neural Design for Faster Pattern Detection
speed up ratio for manipulating matrices of different sizes (L)                           Using Cross Correlation and Matrix Decomposition," Neural World
and different sized weight matrices (n) using a 2.7 GHz                                   journal, Neural World Journal, 2009, vol. 19, no. 2, pp. 131-164.
processor and MATLAB is shown in table IV.                                           [14] Hazem M. El-Bakry, and H. Stoyan, "FNNs for Code Detection in
                                                                                          Sequential Data Using Neural Networks for Communication
An interesting point is that the memory capacity is reduced                               Applications," Proc. of the First International Conference on Cybernetics
when using HSTDNN. This is because the number of variables                                and Information Technologies, Systems and Applications: CITSA 2004,
is reduced compared with CTDNN.                                                      [15] Hazem M. El-Bakry, "New High speed time delay Neural Networks
                                                                                          Using Cross Correlation Performed in the Frequency Domain,"
                            V. CONCLUSION                                                 Neurocomputing Journal, vol. 69, October 2006, pp. 2360-2363.
                                                                                     [16] Hazem M. El-Bakry, "A New High Speed Neural Model For Character
To facilitate investigation of patients and overcome diseases, fast                       Recognition Using Cross Correlation and Matrix Decomposition,"
detection of biological viruses in DNA sequence has been presented.                       International Journal of Signal Processing, vol.2, no.3, 2005, pp. 183-202.
                                                                                     [17] Hazem M. El-Bakry, "New High Speed Normalized Neural Networks for
Missed DNA codes have been retrieved by using Hopfield neural                             Fast Pattern Discovery on Web Pages," International Journal of Computer
networks. After that a new approach for fast detection of                                 Science and Network Security, vol.6, No. 2A, February 2006, pp.142-
biological viruses like H1N1 and H1N5 in DNA sequence has                                 152.
been introduced. Such strategy has been realized by using our                        [18] Hazem M. El-Bakry "Fast Iris Detection for Personal Verification Using
                                                                                          Modular Neural Networks," Lecture Notes in Computer Science,
design for HSTDNNs. Theoretical computations have shown                                   Springer, vol. 2206, October 2001, pp. 269-283.
that HSTDNNs require fewer computation steps than                                    [19] Hazem M. El-Bakry, and Qiangfu Zhao, "Fast Normalized Neural
conventional ones. This has been achieved by applying cross                               Processors For Pattern Detection Based on Cross Correlation
correlation in the frequency domain between the input data and                            Implemented in the Frequency Domain," Journal of Research and Practice
                                                                                          in Information Technology, Vol. 38, No.2, May 2006, pp. 151-170.
the weights of neural networks. Simulation results have                              [20] Hazem M. El-Bakry, and Qiangfu Zhao, "High speed time delay Neural
confirmed this proof by using MATLAB. The proposed                                        Networks," International Journal of Neural Systems, vol. 15, no.6,
algorithm can be applied to detect other biological viruses in                            December 2005, pp.445-455.
DNA sequence perfectly.                                                              [21] Hazem M. El-Bakry, and Qiangfu Zhao, "Speeding-up Normalized
                                                                                          Neural Networks For Face/Object Detection," Machine Graphics &
                              REFERENCES                                                  Vision Journal (MG&V), vol. 14, No.1, 2005, pp. 29-59.
                                                                                     [22] Hazem M. El-Bakry, and Qiangfu Zhao, "A New Technique for Fast
[1] Hazem M. El-Bakry and Wael A. Awad, “A New Hybrid Neural Model                        Pattern Recognition Using Normalized Neural Networks," WSEAS
    for Real-Time Prediction Applications,” International Journal of                      Transactions on Information Science and Applications, issue 11, vol. 2,
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[2] Hazem M. El-Bakry, and Nikos Mastorakis, “An Intelligent Approach for                 Delay Neural Networks," International Journal of Computational
    Fast Detection of Biological Viruses in DNA Sequence,” Proc. of 10th                  Intelligence, vol.2, no.1, pp. 16-26, 2005.
    WSEAS International Conference on APPLICATIONS of COMPUTER                       [24] Hazem M. El-Bakry, and Qiangfu Zhao, "Fast Pattern Detection Using
    ENGINEERING (ACE '11), Spain, March 24-26, 2011, pp. 237-244.                         Neural Networks Realized in Frequency Domain," Enformatika
[3] Hazem M. El-Bakry, and Nikos Mastorakis, “A New Approach for                          Transactions on Engineering, Computing, and Technology, February 25-
    Prediction by using Integrated Neural Networks,” Proc. of 5th WSEAS                   27, 2005, pp. 89-92.
    International Conference on COMPUTER ENGINEERING and                             [25] Hazem M. El-Bakry, and Qiangfu Zhao, "Sub-Image Detection Using
    APPLICATIONS (CEA '11), Puerto Morelos, Mexico, Jan. 29-31, 2011,                     Fast Neural Processors and Image Decomposition," Enformatika
    pp. 17-28.                                                                            Transactions on Engineering, Computing, and Technology, February 25-
[4] Hazem M. El-Bakry, "Fast Virus Detection by using High Speed Time                     27, 2005, pp. 85-88.
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                        I2                                                         Output



                                                                   Cross correlation in time domain
                                        Input                      between the (n) input data and
                                        Layer                      weights of the hidden layer.

                                           Serial input data 1:N in groups of (n) elements
                                           shifted by a step of one element each time.
                                                                         Figure 1. CTDNNs.

                                                                                                                         ISSN 1947-5500
                                                (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                              Vol. 9, No. 11, November 2011


    I2                                              Output




                                       Cross correlation in the frequency
                                       domain between the total (N) input data
                                       and the weights of the hidden layer.

                                             Figure 2. HSTDNNs.

Length of Number of computation steps required for Number of computation steps required Speed up
serial data             CTDNNs                               for HSTDNNs                  ratio
  10000               2.3014e+008                             4.2926e+007                5.3613
  40000               0.9493e+009                             1.9614e+008                4.8397
  90000               2.1478e+009                             4.7344e+008                4.5365
 160000               3.8257e+009                             8.8219e+008                4.3366
 250000               5.9830e+009                             1.4275e+009                4.1912
 360000               8.6195e+009                             2.1134e+009                4.0786
 490000               1.1735e+010                             2.9430e+009                3.9876
 640000               1.5331e+010                             3.9192e+009                3.9119

Length of Number of computation steps required for Number of computation steps required Speed up
serial data             CTDNNs                               for HSTDNNs                  ratio
  10000               3.5132e+008                             4.2919e+007                8.1857
  40000               1.4754e+009                             1.9613e+008                7.5226
  90000               3.3489e+009                             4.7343e+008                7.0737
 160000               0.5972e+010                             8.8218e+008                6.7694
 250000               0.9344e+010                             1.4275e+009                6.5458
 360000               1.3466e+010                             2.1134e+009                6.3717
 490000               1.8337e+010                             2.9430e+009                6.2306
 640000               2.3958e+010                             3.9192e+009                6.1129

                                                                                     ISSN 1947-5500
                                                 (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                               Vol. 9, No. 11, November 2011
Length of Number of computation steps required for Number of computation steps required                Speed up
serial data             CTDNNs                               for HSTDNNs                                 ratio
  10000               4.9115e+008                             4.2911e+007                              11.4467
  40000               2.1103e+009                             1.9612e+008                              10.7600
  90000               4.8088e+009                             4.7343e+008                              10.1575
 160000               0.8587e+010                             8.8217e+008                               9.7336
 250000               1.3444e+010                             1.4275e+009                               9.4178
 360000               1.9381e+010                             2.1134e+009                               9.1705
 490000               2.6397e+010                             2.9430e+009                               8.9693
 640000               3.4493e+010                             3.9192e+009                               8.8009

Length of serial data      Speed up ratio (n=400)      Speed up ratio (n=625)       Speed up ratio (n=900)
      10000                        8.94                        12.97                        17.61
      40000                        8.60                        12.56                        17.22
      90000                        8.33                        12.28                        16.80
      160000                       8.07                        12.07                        16.53
      250000                       7.95                        17.92                        16.30
      360000                       7.79                        11.62                        16.14
      490000                       7.64                        11.44                        16.00
      640000                       7.04                        11.27                        15.89

                                                                                      ISSN 1947-5500

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