]>
2013
6
3
84
A New Neural Network Approach for Face Recognition Based on Conjugate Gradient Algorithms and Principal Component Analysis
A New Neural Network Approach for Face Recognition Based on Conjugate Gradient Algorithms and Principal Component Analysis
en
en
This paper presents a new approach based on conjugate gradient algorithms (CGAs) and principal component analysis (PCA) for face recognition. First, images are decomposed into a set of time-frequency coefficients using discrete wavelet transform (DWT). Basic back propagation (BP) is a well established technique in training a neural network. However, since in this algorithm the steepest descent direction is not the quickest convergence, it is slow for many practical problems and in many cases including face recognition, its performance is not satisfactory. To overcome this problem, four algorithms, namely, Fletcher-Reeves CGA, Polak-Ribikre CGA, Powell-Beale CGA, and scaled CGA have been proposed. Also, in this paper the PCA as a pre-processing step to create the uncorrelated and distinct features of the DWT of images is used. The simulation results show that all of the proposed methods, compared with the basic BP, have greater accuracies.
166
175
Hamed
Azami
Milad
Malekzadeh
Saeid
Sanei
Face recognition
discrete wavelet transform
conjugate gradient algorithm
principal component analysis.
Article.1.pdf
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M. Firdaus, Face recognition using neural networks , International Conference on Intelligent System (ICIS), CD-ROM (2005)
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H. Azami, S. Sanei, H. Alizadeh, GPS GDOP Classification via Advanced Neural Network Training, International Conference on Contemporary Issues in Computer and Information Sciences, Brown Walker press, USA, (2012), 315-320
##[9]
F. Paulin, A. Santhakumaran, Classification of breast cancer by comparing back propagation training algorithms, International Journal on Computer Science and Engineering, 3 (2011), 327-332
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M. H. Shaheed, Performance analysis of 4 types of conjugate gradient algorithms in the nonlinear dynamic modelling of a TRMS using feedforward neural networks, IEEE Conference on Systems, Man and Cybernetics, (2004), 5985-5990
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Z. Zakaria, N. A. M. Isa, S. A. Suandi, A study on neural network training algorithm for multiface detection in static images, International Conference on Computer, Electrical, Systems Science, and Engineering, (2010), 170-173
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M. F. Moller, A scaled conjugate gradient algorithm for fast supervised learning, Neural Networks, 6 (1993), 525-533
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R. Bhati, S. Jain, D. K. Mishra, D. Bhati, A comparative analysis of different neural networks for face recognition using principal component analysis and efficient variable learning rate, International Conference on Mathematical/Analytical Modelling and Computer Simulation, (2010), 354-359
]
The Moments of the Profile in Random Binary Digital Trees
The Moments of the Profile in Random Binary Digital Trees
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en
The purpose of this paper is to provide a precise analysis of the \(t\)-th moment of the profile in random
binary digital trees. We assume that the \(n\) input strings are independent and follow a (binary) Bernoulli
model. In tries, the main difference with the previous analysis is that we have to deal with an
inhomogeneous part in the proper functional equation satisfied by the \(t\)-th moment and in digital search
trees with an inhomogeneous part in a proper functional-differential equation. We show that \(t\)-th moment
of the profile (\(t\geq 2\)) is asymptotically of the same order as the expected value (\(t=1\)). These results are
derived by methods of analytic combinatorics.
176
190
Ramin
Kazemi
Saeid
Delavar
Digital trees
Tries
Digital search trees
Profile
The \(t\)-th moment.
Article.2.pdf
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[1]
R. Cguech, K. Lasmar, H. M. Mahmoud, Distribution of inter-node distances in digital trees, International Conference on Cnalysis of Clgorithms, DMTCS proc. CD, (2005), 1-10
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R. de la. Briandais, File searching using variable length keys, Proceedings of the AFIPS Spring Joint Computer Conference. AFIPS Press, Reston, Va., (1959), 295-298
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J. E. CoGman, T. Eve, File structures using hashing functions, Communications of the ACM, 13 (1970), 427-432
]
Speech Emotion Recognition by Using Combinations of C5.0, Neural Network (nn), and Support Vector Machines (svm) Classification Methods
Speech Emotion Recognition by Using Combinations of C5.0, Neural Network (nn), and Support Vector Machines (svm) Classification Methods
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en
Speech is the fastest and most natural method for human to communicate. This has led several researches to be done in the field of the interaction effects between human and machine. Hence, it is necessary to design machines which can intelligently recognize the emotion of a human voice. However, we are still far from having a natural interaction between the human and machine because machines cannot distinguish the emotion of the speaker. This has established a new field in the literature, namely the speech emotion recognition systems. The accuracy of these systems depends on various factors such as the number and type of the emotion manners as well as the feature selection and the classifier sort. In this paper, classification methods of the Neural Network (NN), Support Vector Machine (SVM), the combination of NN and SVM (NN-SVM), NN and SVM (NN-SVM), NN and C5.0 (NN-C5.0), C5.0 and SVM (SVM-C5.0), and finally the combination of NN, SVM, and C5.0 (NN-SVM-C5.0) have been verified, and their efficiencies in speech emotion recognition have been compared. The utilized features in this research include energy, power, Zero Crossing Rate (ZCR), pitch, and Mel-scale Frequency Cepstral Coefficients (MFCC). The presented results in this paper demonstrate that using the proposed NN-C5.0 classification method is more efficient in recognizing the emotion states-to the extent of 6%- to 30% depending on the number of emotions states-than SVM, NN, and other aforementioned combinations of classification methods.
191
200
Mohammad Masoud
Javidi
Ebrahim Fazlizadeh
Roshan
Emotion recognition
Feature extraction
Mel-scale Frequency Cepstral Coefficients
Neural Network
Support Vector Machines
C5.0
Article.3.pdf
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T. Pao, Y. Chen, J. Yeh, Y. Chang, Emotion recognition and evaluation of mandarin speech using weighted D-KNN classification, Int. Innov. Comput. Info. Control. , 4 (2008), 1695-1709
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M. Ayadi, M. S. Kamel, F. Karray, Survey on speech emotion recognition: features, classification schemes, and databases, Pattern Recognition, 44 (2011), 572-587
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M. Ayadi, S. Kamel, F. Karray, Speech emotion recognition using Gaussian mixture vector autoregressive models, Inproceeding of the international conference on acoustics, speech, and signal processing, 5 (2007), 957-960
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M. Hamidi, M. Mansorizadeh, Emotion recognition from Persian speech with NEURAL NETWORK, International Journal of Artificial Intelligence & Applications (IJAIA), 3, No.5 (2012)
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L.He, M. Lech, N. C. Maddage, N. B. Allen, Study of empirical mode decomposition and spectral analysis for stress and emotion classification in natural speech, Biomed Signal Process Control, 6 (2011), 139-146
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]
Common Fixed Point for Affine Self Maps Invariant Approximation in P-normed Spaces
Common Fixed Point for Affine Self Maps Invariant Approximation in P-normed Spaces
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en
Common fixed point results for new classes of non-commuting self-maps and non-expansive. Within the class of all self-maps \(f\) and \(T\) of a \(W\)−starshaped subset \(M\) of \(X\) where \(f\) is affine or \(W\)−affine. We apply them to obtain several invariant approximation results which unify, extend, and complement well-known results.
201
209
H.
Shojaei
R.
Mortezaei
Best approximation
Common fixed point
affine self-maps
Invariant
\(f\)-nonexpansive
nonexpansive maps.
Article.4.pdf
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]
Effects of Nimodipinee on Cerebral Hemodynamic and Prognosis of Diffuse Axonal Injury Patients with Repeated Measurements Design
Effects of Nimodipinee on Cerebral Hemodynamic and Prognosis of Diffuse Axonal Injury Patients with Repeated Measurements Design
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en
In medical and behavioral researches experimental units, are often the people who are in different social status, physical and other tendency So their responses will be different. In these situations they are required to control the changes in the result of this sources that are potentially variable otherwise the source of variability can increase mean square error significantly and make impossible revealing the real difference between treatments. Appropriate model for such research projects are repeated measurements.
This study is a prospective study that accesses the effects of Nimodipinee on Hemodynamic changes of cerebral vessels and the short time prognosis in DAI patients. The study was done in Trauma word of Imam Reza hospital of Tabriz Iran.
Forty DAI patients were randomized in two equals groups the case group underwent treatment with Nimodipine drug (every 4 hours after admission) Control groups did not received this treatment.
Hemodynamic changes in these patients were measured using transcranial Doppler machine on the first third and tenth days of admission.
Using a repeated measurements design fitting model
\[Y_{ijk}=\mu+\tau_i+\beta_{j(i)}+\gamma_k+(\tau\gamma)_{ik}+(\beta\gamma)_{kj(i)}+\varepsilon_{m(ijk)}\]
In which τi is the drug effect (groups Case and Control) is fixed and is in two levels. \(Β_{j(i)}\)
is the effect Of j-th patient in group i, which is in twenty level. \(\gamma_k\) is TCD stages that is fixed
and it is in three levels. \((\tau\gamma)_{ik}\) and \((\beta\gamma)_{kj(i)}\) are correspond interactions, this finding obtained
that there is no significant differences in Doppler variables between three time TCD and
Nimodipine did not make significant difference between the variables.
In this study using Nimodipine in DAI patients did not make significant effect on Doppler
variables. Therefore, using this drug for this group of patients is not recommended.
210
219
K. Fathi
Vajargah
R.
Mehdizadeh
repeated measurements design
Nimodipine
patients with DAI
Article.5.pdf
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A. H. Hadjahmadi, T. J. Askari, A Decision Support System for Parkinson's Disease Diagnosis using Classification and Regression Tree, , 4(2) (2012), 257-263
]
Numerical Method for Solving a Kind of Volterra Integral Equation Using Differential Transform Method
Numerical Method for Solving a Kind of Volterra Integral Equation Using Differential Transform Method
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en
In this paper we use differential transform method for solving nonlinear and linear Volterra integral equation with the kernel \(ask(x-t)\) by using an efficient technique. We approximate the kernel of integral equation with Taylor series and make integral equation simpler by using some techniques that when we use differential transform method, we do not need difficult computation. Note that without this technique, solving integral equation by DTM method will be hard. Through some examples, we have shown the application of these techniques and differential transform method.
220
229
Hadis
Seiheii
Majid
Alavi
Fatemeh
Ghadami
Differential transform method
Volterra integral equation
Numerical method
Article.6.pdf
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A. Arikoglu, I. Ozkol, , Appl. Math.Comput., 181 (2006), 1-153
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A. Arikoglu, I. Ozkol, , Appl. Math.Comput. , 173 (2006), 1-126
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A New Modified Approach for Solving Seven-order Sawada-kotara Equations
A New Modified Approach for Solving Seven-order Sawada-kotara Equations
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en
Herein, Reconstruction of Variational Iteration Method (RVIM) is used for computing solutions of the seventh-order Sawada-Kotera equation (sSK) and a Lax’s seventh order KdV equations (LsKdV). The results are compared with the Adomian decomposition method (ADM) and the known analytical solutions. Results obtained expose effectiveness and capability of this method to solve the seven-order Sawada-Kotera (sSK) and a Lax's seven-order KdV (LsKdV) equations.
230
237
Masoud
Saravi
Ali
Nikkar
Martin
Hermaan
Javad
Vahidi
Reza
Ahari
Reconstruction of Variational Iteration Method (RVIM)
seven-order Sawada-Kotera (sSK)
Lax's seven-order KdV (LsKdV)
Adomian decomposition method (ADM).
Article.7.pdf
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A. Nikkar, S. Esmaeilzade Toloui, K. Rashedi, H. R. Khalaj Hedayati , Application of energy balance method for a conservative X1/3 force nonlinear oscillator and the Doffing equations, International Journal of Numerical Methods and Applications, 5(1) (2011), 57-66
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A. M. Wazwaz, The variational iteration method: A powerful scheme for handling Linear and nonlinear diffusion equations , Computers and Mathematics with Applications, 54(7) (2007), 933-939
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A. Nikkar, A new approach for solving Gas dynamics equation, Acta Technica Corviniensis–Bulletin of Engineering, 4 (2012), 113-116
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A. Nikkar, J. Vahidi, M. Jafarnejad Ghomi, M. Mighani, Reconstruction of variation Iteration Method for Solving Fifth Order Caudrey-Dodd-Gibbon (CDG) Equation, International journal of Science and Engineering Investigations, 1(6) (2013), 38-41
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S. Ghasempoor, J. Vahidi, A. Nikkar, M. Mighani, Analytical approach to some highly nonlinear equations by means of the RVIM, Res. J. Appl. Sci. Eng. Technol., 5(1) (2013), 296-302
]
Stochastic Online Scheduling with Preemption Penalties
Stochastic Online Scheduling with Preemption Penalties
en
en
This paper considers a stochastic online scheduling problem in which a set of independent jobs are to
be processed on a single machine. Each job has a processing time, which is a random variable with
normal distribution. All the jobs arrive overtime, which means that the existence and the parameters of
each job including its processing time specifications and weight are unknown until its release date.
Moreover, the actual processing time of each job is unknown until its completion. During the
processing, jobs are allowed to be preempted and restarted later. So, the processing time devoted to the
job before the preemption is lost and considered as preemption penalty. The objective is to minimize
the expected value of the total weighted completion time. Since the problem is strongly NP-hard, a
heuristic algorithm is proposed in this paper and is validated using numerical examples. The proposed
method utilizes the properties of the normal distribution but it can be used as a heuristic for other
distributions, as long as their means and variances are available.
238
250
Mehdi
Heydari
Mohammad Mahdavi
Mazdeh
Mohammad
Bayat
Stochastic scheduling
online scheduling
preemption penalty
job preemption
preemption-restart
Article.8.pdf
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