Revised Version-final (3)

8/13/2019 Revised Version-final (3)

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BEHAVIOR INVESTIGATION OF ISLAMIC BANK DEPOSIT RETURN

UTILIZING ARTIFICIAL NEURAL NETWORKS MODEL

AbstractPurps! " According to Islamic law, the Islamic banks deliver return to depositors based on

profit and loss sharing principle. Consequently, the return will be uncertain following real

business and economic condition. However, this research investigates the phenomenon that

Indonesian Islamic banking industry seems to mimic interest rate in generating return to

depositors.

M!t#$%&' " This investigation utilies artificial neural networks !A""# model to e$amine

the importance rate of each macroeconomic variables used. The rate indicates the level of

domination or contribution of each variable in determining the volatility of Islamic time

deposit return. The research uses ten years of monthly macroeconomic data as independent

variables. Additionally, the average rate of return from one%month time deposit of all

Indonesian Islamic banks !& is used as dependent variable.

F()$()&s " As a result, the "!'%(%)#, a chosen neural networks architecture, found that *I&T and

I"T& as pro$y of interest rate, dominantly affect && volatility with almost '+ of

importance rate. It shows the very high dominance of interest rate in determining &&

volatility, as indication of mimicking behavior, rather than remaining variables as pro$y of

real economic condition.

Or(&()a%(t' " According to our knowledge, this e$periment is the first study in Islamic

banking and finance in investigating the mimicking interest rate behaviour of Islamic bank

using A"" model.

K!'*r$s- Islamic bank, depositor return, artificial neural networks, macroeconomic

variables, Indonesia.

Pap!r t'p!- &esearch paper

1


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+, I)tr$uct()

Islamic banks commonly have two sources of funds. The first source is the public funds in

bank deposits that comprises of Wadiahdemand deposit, Mudharabahsaving deposit, and

Mudharabah time deposit. The second one is the public funds in non%bank deposits such asreceived financing, securities issued by banks, interbank liabilities, liabilities to the central

bank and other payables !Ismal, /00'#.

1articularly, with Wadiahdemand deposit, Islamic banks may obtain an e$plicit or

implicit authoriation to use the deposit money but they do not pay return or share the profit

to investors. In contrast, with Mudharabah saving deposit, bank may finance the Islamic

pro2ects and share the profit with depositors as deposit return. 3urthermore, Mudharabah

time deposit has two modes called restricted time deposit and unrestricted time deposit. In the

former, Islamic banks may only act as the fund manager, agent, or non%participating

Mudharib !4l%5in, /00(#. The *anks are not authoried to mi$ their own funds with this

account unless permitted by the account holders. Therefore, this account is not considered as

fund providers and is treated as an off balance sheet account. 6n the contrary, the latter

allows the banks to actively occupy the funds and share the risks with depositors without any

voting rights !7rais and 1ellegrini, /008#.

In practice, when depositor opens theMudharabahtime deposit account, both depositor

and the Islamic bank will make an agreement regarding the percentage of profit and loss that

will be shared over the period of deposit. As a result, depositor will receive uncertain amount

of money as return every month that depends on Islamic bank9s profitability. The monthly

return received by depositor is represented by rate of return !& which is calculated by

dividing return received with amount of money deposited. The Islamic banks publish the rate

of return monthly to assist depositors compare it with time deposit interest rate.

The theory says that the monthly rate is fluctuating because it depends on the individual

bank9s profit and predetermined loss and profit ratio offered to depositor !:oubi and 6lson,

/00+#. Consequently, in the macroeconomic turmoil, the bank9s profitability should be

fluctuating, and afterwards the rate of return on time deposit will be fluctuating as well.

However, 4l%7amal !)'';# noticed that the transactions in Islamic bank such as time depositand saving deposit, are themselves not purely Islamic. This is because the return on bank

deposits is seemly rewarded with a high correlation with the market interest rate or in other

word, it is called mimicking interest rate.

The paper discusses particularly about the operational behavior of Islamic bank in

generating return onMudharabahtime deposits due to following reasons-

). *ased on the nature of the product, time deposit account is the product, which only

uses mudharabahcontract. It means that this product is principally created for

investment purpose. 5epositor who opens this account is required to give notice to

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the bank when they want to withdraw the money. In contrast, the saving account

product is principally provided for day%to%day financial needs, which can be

withdrawn without giving any prior notice to the bank, for e$ample, when they

withdraw the money through AT< machine. This product is more fle$ible thantime deposit account and it can be developed either using mudharabahor wadiah

principle.

/. The composition ofMudharabahtime deposit product on total third party fund is

=;.' meanwhile the other product such as saving account and current account are

/'.; and )/.(, respectively. Additionally, the trend of Mudharabah time

deposit products tends to increase otherwise, the Mudharabah saving account

tends to decrease as depicted in the table ).

Tab%! +, T#! p!rc!)ta&! - t(.! $!ps(t accu)t a)$ sa/()& accu)t ) tta% $!ps(t

P!r($ T(.! $!ps(t accu)t Sa/()& accu)t

5ec%/008 =/./= >0./)

5ec%/00; ('.++ >>.;=

5ec%/00+ =/.>0 >>.;(

5ec%/00' =(.(' >).>>

5ec%/0)0 ==.=) /'.+(

?an%/0)) =8.)= /'.;=

@imply saying, there is indication that the intention of depositor to patronie with Islamic

banking is more likely to find a better return rather than as a mean of daily payment.

@pecifically, this research investigates empirically the evidence that the Indonesian

Islamic banking industry mimics interest rate in generating return. This phenomenon cannot

be confirmed directly to the management of Islamic bank due to its confidentiality that

related with the bank9s operational strategy. 3or doing so, the research e$amines the

importance rate of the past ten years of macroeconomic variables in affecting the volatility of

Mudharabahtime deposit return provided by A"" model. The importance rate indicates the

level of domination or contribution of each independent variable in determining changes of

dependent variable.

0, I)$)!s(a) Is%a.(c ba)1 ()$ustr'

The Indonesian Islamic banking industry has been growing very well since the establishment

of the first Islamic bank namely *ank


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integrating )0+> offices around the country as shown in table /.

Tab%! 0, S!%!ct!$ Is%a.(c Ba)1()& P!r-r.a)c! I)$(catrs

Ba)1()& I)$(catr 022

2

022

+

022

0

022

3

0224 0225 0226 0227 0228 0229 02+2

Islamic banks !unit# / / / / > > > > = 8 8

Islamic banking nits !unit# > > 8 + )= )' /0 /= /; /= /=

Islamic rural banks !unit# ;' +) +> +( ++ '/ )0= ))( )>) )>' )(0

Total offices !unit# )(8 )+/ //' >>; ((> ==0 =8; 8+> '=) ''+ )0+>

Total asset !trillion &p# ).;' /.;/ (.0= ;.+8 )=.>

>

/0.+

+

/8.;

/

>8.=

>

('.=

=

88.0

'

8;.(

>

Total financing !trillion &p# )./; /.0= >./+ =.=> )).(

'

)=./

>

)'.=

>

/;.'

(

>+.)

'

(8.+

+

(;.)

(

Total deposit !trillion &p# ).0> ).+) /.'/ =.;/ )).+

8

)=.=

+

/0.8

;

/=.8

=

>8.+

=

=/./

;

=>.)

8

@ource- *ank of Indonesia , data until ?anuary /0)0

trillion with total financings of &p(;.)( trillion, very close with the total

deposits of &p=>.)8 trillion.

Currently, the industry is able to deliver a competitive return to depositors compared

with interest rate due to following reasons. 3irst, the profit grows positively showing the

progressive development of the industry. This also indicates the ability of Islamic banks to

provide a continuous and positive payment of return to their depositors as shown in figure ).

@econdly, the spread between return on financing and return sharing on deposits is mostly

positive as depicted in figure /. Therefore, those reasons enable the Islamic banks to provide

better return on deposits in some particular periods rather than the interest rate of

conventional bank as shown in figure >.

F(&ur! +, I)$)!s(a) Is%a.(c Ba)1:s pr-(t F(&ur! 0, Spr!a$ - I)$)!s(a) Is%a.(c Ba)1:s R!tur)

4

-200,000

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

Dec-00

Jun-01

Dec-01

Jun-02

Dec-02

Jun-03

Dec-03

Jun-04

Dec-04

Jun-05

Dec-05

Jun-06

Dec-06

Jun-07

Dec-07

Jun-08

Dec-08

Jun-09

Dec-09

-25.00

-20.00

-15.00

-10.00

-5.00

0.00

5.00

10.00

15.00

20.00

25.00

Jan-00

Jul-00

Jan-01

Jul-01

Jan-02

Jul-02

Jan-03

Jul-03

Jan-04

Jul-04

Jan-05

Jul-05

Jan-06

Jul-06

Jan-07

Jul-07

Jan-08

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Jul-09


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F(&ur! 3, C.par(s) b!t*!!) r!tur) ;RR< a)$ ()t!r!st rat! ;INTR< - )!".)t# t(.! $!ps(t

3, L(t!ratur! r!/(!*

3.1. Mimicking interest rate issue

According to Islamic law calledsharia, Islamic banks deliver the return to depositors based

on the pre%determined profit and loss sharing !1D@# ratio. nder this principle, the return on

deposits is uncertain. However, this uncertainty should be depending on the profit and

business condition, rather than imitating the volatility of interest rate. 6therwise, it will be

similar with the conventional banks, which pay interest on deposits according to the pre%

determined interest rate. Consequently, along the period of the time deposits, depositors

receive the revenue, which has no relation with bank9s performance.

@ince the last two decades, the difference opinions among Islamic scholars regardingthe mimicking interest rate of Islamic bank have became interesting topic. In the beginning,

"ienhaus !)'+># reported that Islamic banks use interest rate as a benchmark rate to calculate

their profit and loss sharing ratio. 3urthermore, Ahmad !)''/# disagrees with such condition

and wishes to transform the economic system instantaneously to be an Islamic economic. 6n

the other hand, Ehan !)''=# gave difference opinion that mimicking an interest rate based

system is the short%run alternative. This will be gradually replaced with a more Sharia

compliant banking system later on.

However, some current researches reported interesting findings. Haron !/00(#, using

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linear regression, found that Islamic banks still benchmark interest rate to fi$ing their charges

to the creditors as well as the rewards given to depositors.


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of the price of goods that affect business trading and market. The other variable such as stock

indices may lead to a higher growth at the firm, industry and country level !Aburime, /00+#.

This will give more profit to Islamic banks from financing activities. 3urthermore, *ashir

!/000# found that inflation positively affect the banks9 profits if the revenue accrues frombusiness is larger than the arising of overhead cost due to inflation. 6n the other hand,

interest rate has been found to affecting ma2ority of funding and financing activities of

Islamic bank and later on the depositor return. However, the channel of interest affects

depositor return will be investigated further in this research.

3. 3 !rtificial neural networks model

Actually, A"" model is a branch of artificial intelligence that is powerful to solve the

problem especially with regard to pattern classification and recognition. It is a computational

model where its structure and function imitate the biological neuron in the human brain.

A"" consists of a group of artificial neurons, which are interconnected. 4very single neuron

processes information !receiving input and delivering output# using a special algorithm

function.

A"" model is the advance technique, which is commonly used in making prediction

related with business failure and performance !Diou and Gang, /00+#. @er2u !/00/#

mentioned two advantages of A"" technique compared with other techniques to modeling

the relationship between independent and dependent variables. 3irst, they are universal

estimators of function so that they can appro$imate any functional form to represent the

actual data accurately. It means that A"" is considered as a data%driven rather than model%

driven !Argyrou, /008#. Accordingly, A"" is suited for problems, which data are available

but the underlying theoretical model is unknown !:hang et al.)''+#. The A"" is also

superior to other statistical methods because A"" is able to deal with non%linear data and

multi dimensional aspects. @econd, A"" method is suited for the purpose of long%term

forecast horions. However, it is also as good as traditional statistical forecasting methods to

estimate the shorter time horions.

Technically, the A"" process can be seen in figure (. In the figure, there is a neuron 2,

which has a certain number of inputs !$),$/, $>$2# and single output !y2#. 4ach input has aweight !w)2, w/2, w>2wi2# as an indicator of the importance of the incoming signal to the

neuron. The net value !u2# of the neuron is then calculated based on the sum of all inputs

multiplied by their specific weight.

F(&ur! 4, A .$!% - )!ur)

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3urther, the output value !y2# of a neuron refers to the threshold value !t2# and activation

function. 4ach neuron has its own unique threshold value. If the net value !u2# is greater than

the threshold value !t2#, the neuron !2# will send the output !y2# to the other neurons. In

addition, activation function is a function used to transform the activation level of a unit

!neuron# to an output signal. Currently, the most popular activation function is called sigmoid

and logistic.

A single neuron might not be useful enough to solve the problems. It needs a

combination of some neurons into multilayer structured neurons called as neural networks to

learn and answer the pattern classification and recognition problems. 3or this purpose, this

research employs multi layer feed%forward network with back propagation as a learning rule.

In figure =, there is a (%>%) network architecture !in abbreviated form, " !(%>%)## which consists

of one input layer with ( neurodes, one hidden layer with > neurodes and one output layer

with ) neurode. 4very neuron in the layer works with the way as e$plained previously.

F(&ur! 5, A .u%t( %a'!r -!!$"-r*ar$ )!t*r1

@pecifically, the input layer is a layer that directly connects with the e$ternal

information. All data in the input layer will be feed%forward to the hidden layer as the ne$t

layer. This layer functions as a feature detector of the signals and then delivers them to the

output layer. 3inally, the output layer functions as collector of the features detected and then

producing the output as a response. In the networks, output is the function of the linear

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combination of hidden unit9s activation the hidden unit9s activation function is a non%linear

function of the weighted sum of inputs. Aadeh et al., !/00;# e$plained the mathematical

model of A"" as in the following.

+= #,!"f# !)#

Bhere $ is the vector of e$planatory variables, is weights vector !parameters# and J is

the random error component. Then, equation !/# is the unknown function for estimation and

prediction from the available data. As such, the model can be formulated as-

++=

= =

m

$

$

n

i

i$i$ vw"hvf#) )

0 !/#

where-

G K network output

f K output layer activation function

v0K output bias

mK number of hidden units

hK hidden layer activation function

L2K hidden unit biases !2 K ),. . . ,m#

nK number of input units

$iK inputs vector !i K ),. . . ,n#

wi2K weight from input unit i to hidden unit 2

v2K weights from hidden unit 2 to output !2 K ),. . . ,m#

4, M!t#$%&'


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investigate how the Islamic bank responses on macroeconomic fluctuation, which is reflected

on the financial performance. Actually, this paper e$tends the previous research conducted by

Hall et al., !/00+# which also employed A"" model to assess the credit risk of Indonesian

Islamic bank. "onetheless, unlike Hall9s work, this research changes 751 variable as used inhis research with the average of one%month time deposit interest rate !I"T. This is because

the research intends to check I"T&, as pro$y of interest rate, affects the rate of return

volatility as the main purpose of the study.

In the beginning, the research uses five macroeconomic variables, which consist of- !i#

average of one%month time deposit interest rate !I"T !ii# narrow money !


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Additionally, the first model uses /.8; +=.)( %0.08 (./; E%@ dK.)(/>>, pO.0=

@TI" )/0 ),)=/.+= ==/,>+>.)+ ;(>.// 8;.+= 0.;) %0.+> E%@ dK.)='/+, pO.0)

;.(/ /0,)((,)0',>8(.00 )(),'/'.'

=

)/,'=8.>

;

%0.0) %0.+> E%@ dK.0'>+>, pP ./0

,>+'.)

(

/(/,==0,>=+,>'0.0

=

('/,('(.0/ ((,'=+.>= 0.)> %0.=) E%@ dK.0+;+', pP ./0

I"3& )/0 0.;) 0.++ 0.'( 0.0' =.( ((.)+ E%@ dK.)(+>8, pO.0=

I"T& )/0 '.8' ;.>; /.;) 0./= 0.(/ %0.'/ E%@ dK.)>(=', pO.0=

*I&T )/0 )0.;8 )0.= >./( 0.> 0.8= %0.;) E%@ dK.)>'(/, pO.0=

&& )/0 ;.'8 /.;; ).8; 0.)= %0.8+ >.)+ E%@ dK.0;80/, pP ./0

F(&ur! 7, T#! %()!ar(t' t!st

11

Scatterplot of multiple variale! a"ain!t ##

$ata %J&% 9v'120c

()*+

S,%

1

2

%/#

%,#

%#,-2 0 2 4 6 8 10 12

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5, R!su%ts - !.p%'()& A%'u$a )!ur()t!%%(&!)c! s-t*ar!

The followings are the step%by%step e$planation of the utiliation of Alyuda

"eurointelligence software based on the work of Argyrou !/008# which comprise of-

). 5ata preparation

/. A"" learning process

>. Importance rate information

%. 1 &irst !'' model

%. 1. 1 (ata )reparation

The input to Alyuda has similar form with a spreadsheet. At the beginning, all data must be

uncontaminated from data anomalies, because they will give negative impact to neural

network performance. There are two conditions of data anomalies namely- !)# missing values

and !/# outliers. In particular, missing values are values that are not known. 6n the other

hand, outliers are e$treme values that differ from the most data. Then, the && column was set

up as the output or target variables while the macroeconomic variables are categoried as

input variables. 3urthermore, all data are designated as numerical data. Afterwards, the data

are partitioned into three categoried- training data set, validation data set, and testing data

set. This model uses )/0 time series data which )); data are accepted for network

processing. Additionally, for data partitioning, this paper uses random method to determine

+) data for training set, + data for validation set, and )+ data for test set.

The ne$t step is to normaliing the input data to make it appropriate for networks

processing. The normaliation simply converts the input data into a new version before a

neural network is trained. *ishop !)''=# offers the following three reasons for this

normaliation- 3irst, to ensure that the sie of input data reflects their relative importance indetermining the required output. @econd, to facilitate the random initialiation of weights

before training the network and third, different variables possibly have different

measurements unit, therefore their typical values could be different significantly.

%. 1. 2 !'' *earning )rocess

Actually, A"" needs some important stages before go to learning process as following. !)#

finding the best architecture. !/# training the networks. !># evaluating the trained network

through validating and testing the knowledge gained from the training process.

The first stage is to find the best architecture of A"". In this stage, the Alyuda runs

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e$haustive searching to find the best possible architecture under following limitations to

avoid over fitting such as using r%squared as fitness criteria and trials are limited for /0.000

iterations. The process takes considerable time because it intends to search the best network

architecture among all alternatives in the specific range. Among two other top networksresulted from this process, Alyuda chooses "!;%>%)#as the networks architecture used according

to some specific parameters as can be seen in table (.

Tab%! 4, Sp!c(-(c para.!t!rs us!$ t c#s! t#! b!st )!t*r1s arc#(t!ctur!

ID Arc#(t!c"

tur!

= -

W!(ts

F(t)!ss Tra()

Errr

Va%($at()

Errr

T!st

Errr

AIC Crr!%at() R"S>uar!$

) Q;%/%)R )' 0.+;++/+ 0.=(==0) 0.=(+)(; 0.;'/;)8 %>8;.0( 0.+;' 0.;;)()

/ Q;%(%)R >; 0.+;0(+/ 0.=;0='> 0.(;;(+( 0.;/0++) %>/;.>'+ 0.+; 0.;==')

> Q;%>%)R /+ 0.++8(0/ 0.=(+808 0.(;/;(' 0.8;)/8> %>(+.=+) 0.++8 0.;+//(

3urthermore, the research trains the networks to generate the information to be

validated and tested for producing comprehensive knowledge regarding the determination of

macroeconomic variables on && volatility. However, there are three configurations needed

before training. 3irst, the logistic activation function is selected for all the neurodes

regardless of the layer on which they reside. @econd, the sum of squared errors is selected to

minimie the output error function. This is summation of squared differences between the

actual value and model9s output. 3or completeness, the neural network output falls in the

range from 0 to ), because of the logistic activation function used. 3urther, the networks are

trained with specific condition to avoid over fitting such as !)# choosing back propagation

algorithm as learning algorithm, !/# setting the learning and momentum rates at 0.), !>#

stopping the training process when the model9s mean squared error reduces by less than

0.00000) or the model completes /0,000 iterations, whichever condition occurs first.

"e$t stage is to evaluate the trained network through validation and testing. The result

of evaluation is e$pressed in two ways statistical indicator and graph e$amination. The

former is shown by value of correlation !r#, &/, mean of absolute error !A4# and mean of

absolute relative error !A&4# as shown in table =.

Tab%! 5, P!r-r.a)c! - )!t*r1s

Para.!t!r Va%u!

Correlation !r# 0.++=)

&/ 0.;>)8


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According to Alyuda manual, the value of correlation !r# and &/ are the indicators of

multiple correlations between independent or predictor variables and dependent or predicted

variable. The coefficients of r can range from %) to S). Bhen the closer r is to ), the stronger

the positive linear relationship will be between both variables. In contrast, the closer r is to %),the stronger the negative linear relationship will be. *esides, when r is near 0, it means that

there is no linear relationship between both variables. @pecifically, &/is a statistical ratio that

compares model forecasting accuracy with accuracy of the simplest model that 2ust uses

mean of all target values as the forecast for all records. The closer this ratio to ) the better the

model is. , Alyuda statistically

indicates that the performance of "!;%>%)# in learning the relationship among independent and

dependent variables is very good. Another way to evaluate the network is through graphic

e$amination as can be seen in figure +. According to this figure, the networks performance in

making prediction of && is very good. It is shown by the pattern of predicted line of && is

located very near with the actual line of &&.

F(&ur! 8, Actua% /s, pr!$(ct() &rap# -r -(rst ANN M$!%

%. 1. 3 Importance rate information

3inally, the "!;%>%)# reveals the importance rate information as a comprehensive knowledge

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resulted from learning process. The information are shown in table 8. In the table, we found

that I"T& and and >).;) of importance rate, respectively. .8/ and 0.(8, respectively. @ince the networks do not providestrong evidence that the interest rate dominantly affects && volatility !only =).(>#,

therefore the research needs to conduct further e$amination.

Tab%! 6, I.prta)c! rat! - ()$!p!)$!)t /ar(ab%!s

N I)$!p!)$!)t Var(ab%!s I.prta)c! rat! ;?/)'

( 4MCH >.8/=)8(

= I"3& 0.(8;;0)

%. 2 Second Model

7enerally, the second model is treated in the same way as in the previous model. This model

uses the same data as well with two additional variables namely *I&T and


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&/ 0.8(>)

')=

; I"3& 0.0/+>'>

6, I)t!rpr!tat() - t#! r!su%ts

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There are two ways for Islamic banks to smooth the payment of return on deposits. 3irst, the

banks grant some of their profit to ad2ust return on deposit referring to the market interest rate

on deposits directly, !Chong and Diu, /00'#. @econd, the banks benchmark central bank

interest rate in financing process to ad2ust their financing rate instantaneously to competewith market credit rate. This way helps Islamic banks to offer financing to the market

competing with conventional bank. @ubsequently, the bank will be able to deliver the

competitive return on deposit following interest rate without sacrificing their profit, !Haron,

/00(#.

The first model shows that only =) contribution of I"T& affecting && volatility. This

finding is not enough to e$plain that Islamic banks in Indonesia rewarding some of their

profit to ad2ust return on deposit referring to the market interest rate. In accordance with this,

the figure > depicts that Indonesian Islamic banks have delivered better return rather than

Interest rate for three times. In contrast with Chong and Diu !/00'#, this investigation does

not support their finding, which says changes in deposit return of Islamic bank in


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financing rate to generate competitive return for mimicking deposit interest rate.

These results reveal that Islamic banks are not independent in determining the return on

deposits to depositors. To some e$tends, this is acceptable as the country adopts dual banking

system and the share of the industry is still less than >. "evertheless, for the future, Islamicbanking industry is recommended to be more independent and reflecting the true Islamic

banking operations. *esides sharing the actual return from business activities and not being

affected by interest rate, they have to have their own benchmark rate to be used in pricing

their Islamic deposit and financing contracts.

Ac1)*%!$&!.!)t

The authors thank 1rof. 5r. H. 3athurrahman 52amil from @TI4 Ahmad 5ahlan, ?akarta,

2ournal9s reviewers and editor for valuable comments and guidance. The authors gratefully

acknowledged that this research is financed by papers.cfmVabstractWidK)/>)08(, !accessed /0

5ecember, /00'#.

Ahmad, @. !)''/#, ,owards Interest-&ree anking International Islamic 1ublishers, "ew

5elhi, India.

Anwar, @., and


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"orth America and AustraliaU,/ournal of anking and &inance, Fol. )>, pp. 8=%8;.

Chong, *., and %/>.

Ehan, #, 1rofitability of Islamic *anks Competing with Interest *anksU,

/ournal of +esearch in Islamic 0conomics. Fol ) "o. ), pp.>;%(;.

Nin, 5., Nuising, 1., He, M. Diu, @. !/00=#,


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*ank 1erformance 1redictionU,!pplied Soft 5omputing, Fol.+, pp. >0=%>)=.

@er2u, 1. !/00/#,

Documents

Revised Version-final (3)