March 11, 2014 To Whom It May Concern, I will be pleased to welcome Mr.Amir Mohamed Nabil Saleh El-ghamry as a visiting

PhD student in my laboratory, with the Egyptian grant supported by the Ministry of

Higher Education and Scientific Research of Egypt to work on his PhD thesis under my

supervision, for the period from January 1, 2015 to January 1, 2016. The visiting period

can be extended for another year, and repeated several times, provided that the funding

from the Ministry of Higher Education and Scientific Research of Egypt can support Mr.

Amir Mohamed Nabil Saleh El-gramry’s extended stay. The proposed topic of his PhD

research is "Ubiquitous Computing", which is an excellent fit within my area of

expertise in computer and information sciences. When he returns to Egypt, he will

submit his PhD thesis to his university, Mansoura University, to earn his PhD degree

from Mansoura University.

Yours Sincerely,

Shi-Kuo Chang, Professor Department of Computer Science University of Pittsburgh

University of Pittsburgh Department of Computer Science

Room 6101

Sennott Square Building

Pittsburgh, PA 15260

(412) 624-8423

Fax: 412-624-8854

Amir Mohamed Nabil Saleh El-ghamry Computer science department Faculty of computers and Information Mansoura University 60 Elgomhoria st, Mansoura, Egypt Tel.: 0502223743 ^Ö 0502234679 Fax: 0502223754 email: fci.mans@gmail.com

amir_nabil@mans.edu.eg Visiting period: Jan 1, 2015 to Jan 1, 2016 Family members accompanying visitor: Wife Aya Anwar Mahmoud ElRaie (B.Sc Faculty OF Computer and Information Sciences) Son Eiad Amir Mohamed Nabil

Amir Mohamed Nabil Saleh El-ghamry

Per

son

al D

ata

Ed

uca

tio

n

● Date of birth : May, 03, 1985 ● Nationality : Egyptian

● Marital status : Married

Military Service : Exempted

B.Sc Faculty OF COMPUTER & INFORMATION SCIENCES.

Mansoura University, May, 2006 "Computer Science department”

Major Grade: Excellent Ranking: First of College

30 B Ahmed Mokhtar Hegazy St, El-Manial , Cairo , Egypt

Mob: +2 010 9 12 44 373

E-mail: amir.elghamery@yahoo.com

amir_nabil@mans.edu.eg

Mas

ter

Deg

ree

Master Degree in computer science – 2011

Paper1:

Signature Identification using Evolutionary Rough Neuron" ,Ain Shams

University, Faculty of computer science and Information- 2011.

Paper2:

Signature Identification using Segmentation Based Rough Neural Model –

Mansoura university -, Faculty of computer science and Information - 2011

Registered at 2012

Currently working Distributed system

Ubiquitous Computing

PH

D

Pro

ject

s

Graduation Project: Biometric Identification – Signature

identification Grade: Excellent

Other projects:

Machine translation System

Text Search Mechanism

Compiler for Pascal programming language

Database and web page for learning center

Demonstrator in Faculty OF COMPUTER & INFORMATION SCIENCES,

Mansoura University "Computer Science department ”, 2007 : 2011

Teaching assistant in Faculty OF COMPUTER & INFORMATION

SCIENCES, Mansoura University "Computer Science department”, 2011 : until now

Wo

rk E

xper

ien

ce

Pro

ject

s

Su

per

visi

on

Human identification using fingerprint patterns

Database and web page for tourism company

Text encoding

Tea

chin

g c

ou

rses

Programming using C#, C++ , Java

Artificial Intelligence

ASP .Net and Ado.Net

SQL Server

DataBase systems

Distributed system

Computer Organization and Architecture

Operating System Concept

Advanced Operating System Concept

Assembly Language

Introduction to IT and Web Applications Introduction to JAVA programming

Neural Networks – Fuzzy logic

Rough Sets

Artificial intelligence

ICDL Courses

Natural Language processing - Arabization

Tra

inin

g

Co

urs

es

● Programming:

C# .Net ( Console APP, Windows APP, Ado.Net, Asp.Net )

● Programming: Java SE7 programming Ed2 , Python ● Programming: C, C++, Visual Basic

● Database: SQL Sever , Oracle development ● Graphics: Photoshop, Flash MX

● Web design: HTML,XML,front page , Jquery , JavaScript, AJAX , LinQ.

● Windows, Office package , Maintenance

Ski

lls

● C# .net: Technology center – Mansoura University

● SQL Server: Technology center – Mansoura University

● Java SE7 programming Ed2 : Oracle university

● ICDL: International Computer Driving License

Ob

ject

ives

Pu

blic

atio

ns

Paper 1: Signature Identification using Evolutionary Rough Neuron –

Published in modern Academy 2009

Paper 2: Signature Identification using Evolutionary Rough Neuron" ,Ain

Shams University, Faculty of computer science and Information- 2011.

Paper 3: Signature Identification using Segmentation Based Rough Neural

Model –Mansoura university - Faculty of computer science and Information - 2011

Hard working and motivated with a strong work ethic. Consistently goes beyond the requirements of the job to achieve company goals,

Seeking a challenging position within a multinational organization which I can develop both my technical and my personal skills to be

a beneficial team member.

Signature, Amir Nabil

REFERENCES FURNISHED UPON REQUEST

Lan

gu

age

● Arabic: Mother tongue.

● English: Very Good.

Signature Identification using

Evolutionary Rough Neuron

Amir.Saleh

1 , Elsayed. Radwan

2 and Taher.Hamza

3

1 amir.elghamery@yahoo.com,

2 elsfradwan@yahoo.com,

3 taher_hamza@yahoo.com

Faculty of Computers and Information sciences,

Computer science Department,

Mansoura University, Egypt

P.D.Box: 35516

Abstract

Hand written signature is the most commonly used way for authentication when dealing with paper documents and forms. Like conventional neural network, many techniques are used for signature identification. Although conventional neural network achieves a good ability to detect all possible interactions between predicted variables, and the availability of multiple training algorithms , it still suffers form different problems such as proneness to overfitting, and the empirical nature of model development. In this paper, a hybrid technique between modified neural network that contains rough neurons and rough set is introduced. Rough Neural Network integrates the rough set that is a good and effective tool to deal with vagueness and uncertainty of information and neural network that is considered a very powerful classifier. This new technique integrates the advantages of the two techniques and avoids their weakness. In contrast with conventional neural network, the details and limitations will be discussed.

Keywords: Rough Set , Neural Network, Handwritten Signature Recognition, Rough Neuron, Reduct.

1. Introduction Within the field of human identification, the usage of biometrics is growing because of its unique properties such as hand geometry, iris scanning, and fingerprint analysis. The verifications, that are the process of confirming the identity of a user, are necessary for many routine activities such as boarding an aircraft, crossing international borders and entering a secure physical location. The higher levels of security and easier interactions to the end user are provided by biometrics for identity verification. The biometrics verifies the person based on feature vectors derived from physiological or behavioral characteristics. Any physiological or behavioral characteristics should posses the following characteristics to serve as a biometric: Uniqueness, Permanence, Acceptability, Collectability and the minimum cost to employ this biometrics [10]. Handwritten signature is one way of verification and is a form of behavioral biometrics. The main advantage that signature verification has over other forms of biometric technologies, such as fingerprints or voice verification, is that handwritten signature is already the most widely accepted biometric for identity verification in our society for years. The long history of trust of signature verification means that people are very willing to accept a signature-based biometric authentication system [11]. Signature identification can be implemented using two approaches: offline method, and online method. This paper implements the offline approach that analyzes the static picture of the signature where it is the most commonly used. Several techniques to perform signature identification have been suggested in the literature: Neural Networks, Neurofuzzy and Wavelet Neural, but these techniques have weakness such as lack of semantics, long training time, proneness to overfitting, and uncertainty of information[10]. Using hybrid technique of ANN that is, considered a powerful classifier because of its Low classification error rate, and Rough sets that is ,a good method for dealing with inconsistencies in information, overcomes these weaknesses because it integrates the advantages of the two techniques [6], [10] ,[18].

* Corresponding Author

Mail: amir_nabil@mans.edu.eg

Tel : 020191244373

2

Signature identification system has specific phases. First acquiring the signature using sensors, then performing some preprocessing on the picture to put it in a specific format. Next phase the core feature extraction is processed where the most significant features are extracted. Finally, a good classifier is needed. Since offline handwritten signature suffers from confusion and inconsistency, a knowledge representation system that represents the data variation, in addition to an innovative classifier is needed. Rough sets[9] have provided an array of tools which turned out to be especially adequate for conceptualization, organization, classification and analysis of various types of data, when dealing with inexact, uncertain, or vague knowledge. Also, rough sets discover hidden pattern and regularities in application. Thus, the main issue tackled in this paper is auto-adaptation occurred in the model of rough neural networks. Rough sets and rough neuron are integrated in the structure of neural network model. Rough sets are used in the preprocessing step to optimum the input features for the network. Rough sets are used for discovering the suitable neural networks structure and removing noise appeared in our picture. Also, by setting the neuron attribute in the form on knowledge representation table, rough set can determine the need to reduce the network structure by deleting one or more neurons. Using this pruning technique, network with smaller number of neurons and a higher accuracy rate is obtained. Finally, classification is immediately implemented using rough neural network. In this paper a new hybrid model of Rough Sets and Rough Neuron will solve the problem of recognizing a handwritten signature. For more accurate result, the the new hybrid model are compared with previous ones such as conventional neuron. The rest of the paper will be organized as follows: section 2 will discuss the most significant topics used in this paper, section 3 discuss the new hybrid model of Rough neuron, where the algorithm also is discussed. Finally, the results of our experiment where the compassion among different techniques take place and conclusions and future works will appear as section 4 and 5 respectively.

2. Preliminaries

2.1 Rough Sets The Rough Sets approach proposed by Pawlak [9] provides mathematical techniques for discovering regularities in data. The principle notion of Rough Sets is that lowering the principle in data representation makes it possible to uncover patterns in the data, which may otherwise be obscured by too many details. At the basis of Rough Sets

theory is the analysis of the limits of discernibility of subsets X of objects from the universe of discourse U .

Let U be a set of objects (universe of discourse), A be a set of attributes, an information system is a

pair AUS ,

. An attribute Aa can be regarded as a function from the domain U to some value set aV. S is

called a decision system if it has the form }){,( dAUS

, where Ad is the decision attribute. The

elements of A are called conditions attributes [16]. An information system may be represented as an attribute value table, in which rows labeled by the objects of universe and columns labeled by the attribute. Similarly, a decision table may represent the decision system.

With every subset of attribute AB one can easily associate an equivalence relation BI on U :

)}()(,every for :),{( yaxaBaUyxIB . Then

.aBaB II If UX , the sets

}][:{ XxUx B and

}][:{ XxUx B where Bx][ denotes the equivalence class of the object

Ux relative to BI, are called the B-lower and B-upper approximation of X in S and denoted by

XBXB ,. It

may be regarded that XB

is the greatest B-definable set contained in X and XB is the smallest B-definable

set containing X . We now define the notions relevant to knowledge reduction. The aim is to obtain irreducible but essential parts of the knowledge encoded by the given information system; these would constitute reducts of the system. So one is, in effect, looking for maximal sets of attributes taken from the initial set (A, say) which induce the same partition on the domain as A. in other words, the essence of the information remains intact, and superfluous attributes are removed. Reducts have nicely characterized in [4] by discernibility matrices and discernibility functions. Consider

3

},...,,{ 21 nxxxU and

},...,,{ 21 maaaA in the information system

AUS ,. But the discernibility matrix

( S ), of S is meant an nn matrix such that

)}()(:{ jiij xaxaAac

A discernibility function Sf is a function of m Boolean variables maa ,...,1 corresponding to the attributes

maa ,...,1 respectively and defined as follows:

},,,1:)({),...,( 1 ijijmS cijnjicaaf

where )( ijc

is disjunction of all variables a with ijca. It is seen in [4] that pii aa ,...,

1 is a reduct in S if and

only if pii aa ...1 is a prime implicit (constituent of the disjunctive normal form) of Sf .

The nation of the Core is:

jiacCaCCore ij , somefor ),(:)(

or

)(Re)( CductCCore

where )(Re Cduct

is the family of all reducts for the set of condition attributes C. The Core corresponding to this part of information cannot be removed without loss in the knowledge that can be derived from it [4][16].

2.2 Rough Neuron

Rough neuron is used for classification. Rough Neural Networks (RNN) consists of rough neurons. Rough neuron defined by representing interval of values where both the upper and lower bounds are used in computations. Each rough neuron is made up of a combination of two individual neurons, namely lower boundary neuron and upper boundary neuron [2], [6]. The lower bound neuron, deals only with the definite or certain part of the input data and generates its output signal called as the lower boundary-signal. The second neuron called the upper boundary neuron processes only that part of the input data which lies in the upper boundary region evaluated based on the concepts of rough sets and generates the output called upper boundary Signal. This interpretation of upper and lower boundary regions is limited only to the learning or training stage of the neural network. The illustration for the aforementioned details is given in Figure 1.

(1)

(2)

(3)

Fig.1.Rough Neuron Structure

4

The lower boundary and the upper boundary neurons have a generalized sigmoid transfer function [10],[6].

xe

xF .1)(

The Input for both the upper and lower neurons will be:

2weightxinput

The output of the lower and upper are attained as show below:

, min upperlowerlower inputFinputFoutput

, max upperlower

upper inputFinputFoutput

The combined output of the rough neuron is:

upperlower outputoutputoutput

Training in rough neural are similar to conventional neural network [1],[17]. During training the network use inductive learning principle to learn from the training set. In supervised training the desired output from output neurons in the training set is known, the weight is modified using learning equation. Neural network use back propagation technique for training, Training using rough Back Propagation perform gradient descent in weight space on an error function.

3. Hybrid Model of Rough Neural The offline handwritten signature is the most widely used technique for verification since it is simple and known for all people. It is used to identify persons depending on their behavioral characteristics. Although the offline handwritten signature is a popular in verification, it still suffers from inconsistency and noise reduction problems. As the result the classification process of offline handwritten signature, which is used to identify genuine members from other forgeries members, is considered to be a difficult problem. According to the previous research [11], conventional neurons in the classification process give good results [1]. Classification by conventional neuron faces the problems of proneness to overfitting because of the big size patterns, and the empirical nature of model development moreover the missing of complete information set which make the variability problem arises. As the result, the classification process depends on irrelevant set of attributes that is not needed in decision making. In this paper, the classification process depends on innovative model of artificial neurons, rough neuron. Using rough sets with ANN helps to get red off these irrelevant set of attributes and relying only on the most significant ones that is so called reduct.

3.1 Rough Sets in Pre-processing In this section, the main problem of classifying node pixel is totally described. A Knowledge representation system is constructed in which neither condition nor decision attribute are distinguished. Therefore we are basically not interested in dependencies among attributes, but in description of some objects in terms of available attributes, in order to find essential differences between objects of interest. The problem of differentiation of varies options is often crucial importance in pattern identification.

For any signature input pattern U of size M × N consists of a set of pixels iju. The knowledge representation table

will be represented as characterization of handwritten signature figures. Each row .iu in the table represent object

of the universe where }, ... ,,{ 321. iNiiii uuuuu where N is the number of pixels in each row in each signature

images. Each column ju. represent the attributes of that object where }, ... ,,{ 321. jKjjjj uuuuu

(4)

(5)

(6)

(7)

5

Where k equals Z x M where Z is the number of training signature images for each person and M represent the numbers of pixels in each column in each signature images for that person.

Rough sets will be used for data analysis and feature selection with neural networks. Rough sets theory provides tools for expressing inexact dependencies within data. A minimum description length principle (MDL principle) gives us the reason of why we will use rough sets to reduce the input feature of the data, since MDL is defined to be the minimum number of rules that describe rough neurons which represents variations in data. According to the rough reduct, the date variation can be characterized and then the difference between crisp and rough neurons can be established.

A reduct is a subset of attributes such that it is enough to consider only the features that belong to this subset and still have the same amount of information. If the decision table S has Z signature images for a particular person so

by )(S we denote an Z x Z matrix)( ijc

called the discernibility matrix of S as shown in equation (1) Such that i,j = 1,2,3 … Z

Where each element in )(S represent pixels that is different from their corresponding in other signature images for that person, this set of pixels represented by rough neurons which represent reduct The main idea of the reduct algorithm is that if a set of attributes satisfies the consistency criterion (i.e. be sufficient to discern all the required objects), it must have a non-empty intersection with non-empty elements of

the discernibility matrix. One can prove that )(CCOREa if and only if there exist two objects, which have the

same value for each attribute from C except a, this statement may be expressed by mean of matrix elements )( ijc

as shown equation (3) The input data to the model will be quantized first, i.e. the feature defining the problem should be identified and labeled. If the input data – are given as real numbers in the preprocessing stage, one has to divide the data into

distinct sets and introduce logical input variable .iusuch that

trueuxIF iji )label(x ( then )X ( i.

The algorithm to compute reduct of S using indiscernibility matrix is reported as

Input: decision table S of signature images in binary form for particular person with set of rules describing

rough neuron Output: S with minimal set of rules describing rough neuron such that the superfluous rules is considered to be conventional neuron (distinguish rough and crisp neurons)

Compute indiscernibility matrix )()( ijcS using equation(1)

Eliminate any empty or non-minimal elements of )(S and create a discernibility list , eKkkk l ),...,,( 21

,

where (S)in element empty non any ofnumber theis e

Build families of sets eRRR ,...,, 10 in the following way

1

),,(:

:S

do e i

1,Set

:

1i

0

1

ii

MSR

trueikRMinTRM

kRT

kRRR

while

iR

iii

ii

kk kRRR

i

ii

i ii

Fig.2.Reduct Algorithm

6

“Min” performs an operation that is analogical to checking for prime implicates of a Boolean function. The returned value is true if the argument R doesn’t contain redundant attributes. With the help of the reduct concept, Rough sets are used in the preprocessing phase. It is an efficient way for data reduction that decreases size of input pattern. Reducing the pattern size allow more speed of classification process, i.e. increase the performance of the signature recognition system. This is done by using Rough sets to determine the structure of the rough neural network. Since neurons in the input layer can be crisp neurons or rough neurons, Rough sets determine the rough neurons depending on the concept of reduct to overcome variability problem which means the vagueness in the human signature identification as follow: if the corresponding pixels in the signatures of one person have the same value then it is crisp neuron otherwise it is considered to be rough neuron [4][14]. The advantage of this approach becomes clear when the pixel corresponding to the crisp neuron mismatch the correct one in some decision making or classification process, i.e. different signature, then the tolerance will be exponentials increase. On the other hand, pixels corresponding to the rough neuron will reduce the associated tolerance or vagueness for human of the correct signature. Also the limit occurred by our method can be defined by determining the perturbation in the output depending on the changes in the feed forward network. Since the output function is linearly piecewise increasing function of the weigh link. Then the perturbation in the output is equivalent to the perturbation the weigh link. we can determine this perturbation if we prove that the transient equation is well posed. The well-posedness of the transient equation, that is considered to be initial value problem, can be proved using the existence and uniqueness theorem by proving that the output function satisfies the Lipschitz condition that states if f(x) is continuous function, it satisfies the equation [4].

2121 xxLxfxf

Where x1 and x2 defined in the function domain. From the mean value theorem

)0()(

)0()()('

ywy

yfwyfwyf

This leads to

)0()()(')0()( ywywyfyfwyf

Where 1,0 Since the trajectory of Back propagation Net is given by

bwxwyn

i

i 1

)(

Where n is the number of neurons connected to the output neuron and b is the bias.

xexf

1

1)(

)1,0(

dw

dx

dx

df

w

wyf

))((

n

i

ixxfxf1

)(1)(

(8)

(9)

(10)

(11)

7

n

i

ixw

wyf

1

))((

Hence

n

i

ixw

wyf

1

))((0

)0()()0()(1

ywyxyfwyfn

i

i i.e.

)0()()0()( ywyLyfwyf ,

n

i

ixL1

From the Existence and Uniqueness Theorm, the transient equation is will posed deferential equation with Lipschitz constant

ALL 1ˆ

Where A defined as follow

n

i

i

n

i

i

x

x

A

1

1

1

Hence the perturbation in the trajectory of transient equation in accordance with the changes in the feed forward cloning template is given by

wkkywy ,0,)0()(

From Eq13,Eq15 and let k=1 we get

n

i

ixwyfwyf1

.)0()(

This shows the maximum error that occurred in the Back propagation Net if the connection is removed. If the maximum error is less than the tolerance value, the weight value on the trajectory of rough neuron has no effect in the classification process and hence can be removed, and so can decrease the amount of processing and increase the efficiency of the classification process.

3.2 Rough Neurons in Post Processing

The reduct set of attributes is computed by determining the pixels that have different values in the corresponding pixels in the signature images of each person, this is done by building a Knowledge representation system for each person that contains the set of pixels in the signature images of each person, then the reduct algorithm in Figure 2 is applied to each Knowledge representation system. The reduct set of attributes that is computed using the rough set theory is corresponding to uncertainty criteria. In other words it is corresponding to the rough neurons in the input pattern. So the rough neuron used to represent the uncertainty pixels in the training set of person's signatures [3][5][15]. The proposed system uses a Backpropagation (BP) NNS for classification process. There is a RNN for each of the trained persons. The structure of each RNN consists of input layer, hidden layer, and output layer. Neurons in the input layer can be crisp neurons or rough neurons according to the reduct theory discussed above; the number of neurons in the input layer is equal to the size of a signature image. The hidden layer consists of crisp neurons; the number of neurons in the hidden layer is approximately half the input layer size. The output layer consists of one crisp neuron that represents the output person corresponding to the current signature input pattern. The computation parameters in the Backpropagation classification algorithm include the learning rate of hidden neurons and its has value of 0.01 and the learning rate of output neuron and its has value of 0.001 , the

(12)

(13)

(14)

(15)

(16)

8

stopping condition during the learning phase include specific numbers of iterations or acceptable error rate. Backpropagation algorithm use sigmoid activation function to compute the output, it also use momentum term to increase the recognition ability. During the test phase; if the output value of the crisp neuron is binary 1, then the input pattern represents that person, if the output value is binary 0, then the input pattern doesn’t represent that person. Back-Propagation propagates the input values through the neural network in such way that calculate the output values, compare the obtained output with the desired output and calculate its error, then it backward redistribute the error to the neurons according to their contributions in the error and modify their synaptic weights. The fitness function of BP utilized is the sum-squared error between the input to the system and the optimized parameters:

n

i

nynycf1

2

)()(

Where n represents the number of input/output sample , )(ny represents the desired output and and )(ny

represents the actual output [1] . The following algorithm summarize the behavior of Rough neural network Identification system

(17)

Input: a set of signature images for the trained persons Output: classification of the signature images for each person Begin Step 1: preprocessing phase (Image processing) Step 1.1: get bound box of image

Step 1.2: convert images to black and white Step 2: determine structure of RNN (Rough set) Step 3: classification phase (rough back-Propagation)

Input: Set of input features of signature images to be classified

Processing:

Step-3.1 for each attribute in the attribute set Do Step-3.2 Compute the upper and lower rough neuron Step-3.3 Build rough neural Networks Step-3.4 Compute the relative error

Step-3.5 Calibrate the rough neural Network

Step-3.6 Repeat 4 and 5 until the error become minimum

Step-3.7 Return Class with minimum error.

Output: The final classification output End

Fig.3.Rough neural Network Identification Algorithm

9

4. Experimental Results The signature database includes signature images of ten trained persons, each person has twenty signature images divided as ten signatures used during training phase and the other used during testing phase . According to our experimental results, the experiments were applied on the of training images for the trained persons, as depicted in Figure 4. The introduced algorithm is applied with random initial weights and the experiment is repeated five times for each training person due to optimizing the weight template.

In contrast with conventional neural network, a comparison between conventional NN and RNN for the ten persons is taken into account. As depicted in Figure 5, RNN achieves more progressive tests for 10 persons than that of the conventional NN and shows that the total identification percentage for conventional NN is 89% and for RNN is approximately 97%

Conventional VS Rough Neural

0

20

40

60

80

100

120

person1

person2

person3

person4

person5

person6

person7

person8

person9

person1

0

Person

Iden

tifi

cati

on

Rate

conventional

rough

The variance analysis of results of Conventional Neural Network and Rough Neural Network is done using ANOVA test. Figure 6 show these results

Fig.5.Conventional NN VS Rough NN

Fig.4.sample images

10

ANOVA test shows that the test statistic (F) that is found by dividing the between group variance by the within group variance for a df of 1 in the numerator that are the degrees of freedom for the between group and 18 in the denominator that are the degrees of freedom for the within group is 7.67 and the critical value (F crit) is 4.41 Since the test statistic is larger than the critical value, we reject the hypothesis of equal means. So there are difference between the two means of Conventional NN and Rough NN which mean that each technique has different effect on the signature recognition system ie ( that the effect is significant between the two cases). Since the average of RNN is larger than for Conventional NN, then RNN is more effective in terms of mean improvement than Conventional NN.

5. Conclusion

This study proposed a new technique to improve the performance of handwritten signature identification process. This technique uses a hybrid model of rough sets and rough neural networks where Rough sets provides mathematical techniques for discovering regularities in data and uncovering patterns in it, which may otherwise be obscured by too many details, it is used for reducing the input pattern size by resolving the variability problem exists in signatures, and to determine which neurons in the input layer considered being rough neurons while Rough neural networks use a combination of rough and conventional neurons. A rough neuron can be viewed as a pair of neurons. One neuron corresponds to the upper bound and the other corresponds to the lower bound. Upper and lower neuron exchange information with each other during the calculation of their outputs, it is used as a classifier for signature identification based on backpropagation learning algorithm. A series of tests were performed to experimentally evaluate our technique. We conclude that this hybrid model give better results compared with other techniques since the errors in estimation from rough neural network models are significantly lower than the conventional neural network model. Moreover, the rough neurons in the input layer seem to improve the prediction performance. In the future we wish to extend the system to deal with more complex types of data and solve inconsistencies found in it using granular computing as an emerging computing paradigm of information processing.

Acknowledgements The authors would like to thank .Prof. Albert Swart for making his signature database available to us.

References [1] Amudha, V.; Venkataramani, B.; Manikandan, J. FPGA Implementation of Isolated Digit Recognition System Using Modified Back Propagation Algorithm. Electronic Design, Proceedings of (ICED). International Conference pp.1 – 6, 1-3 Dec. 2008 [2] Ashwin G. Kothari, Data Mining Tool for Semiconductor Manufacturing Using Rough Neuro Hybrid approach, Proceedings of International Conference on Computer Aided Engineering- CAE-2007, IIT Chennai, pp. 13 - 15 December 2007.

Fig.6.ANOVA Test for Conventional and Rough Neural Network

11

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