Volume 41 Number 1 March 2017
Special Issue:
End-user Privacy, Security, and
Copyright issues
Guest Editors:
Nilanjan Dey
Surekha Borra
Suresh Chandra Satapathy
1977
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 Informatica 41 (2017) 1–2 1 
Editors' Introduction to the Special Issue on ‟End-user Privacy, 
Security, and Copyright issues” 
“All artists are protected by copyright...  
and we should be the first to respect copyright" 
― Billy Cannon 
With the rapid growth in the internet technology, end-
user privacy has tremendous impact on the society and a 
firm's business. Technological solutions to minimize the 
digital piracy such as unofficial downloading of E-books, 
product models, songs, movies and commercial software, 
is the need of the hour for marketers. Investigating 
effective techniques and measures for reduction of piracy 
protects human resources and skills, and resolves the 
nightmare of marketers and investors. Further, it ensures 
fairness and accountability, and builds economic growth 
of a country.  
This special issue is organized to promote the 
marketers and investors trust, and to enhance businesses 
by publishing the state-of-art research and developments 
in privacy, security and copyright concerns of 
multimedia content, with emphasis on organizational and 
end user computing. This special issue includes original 
research works; insightful research and practice notes, 
case studies, and surveys on vulnerabilities, 
requirements, attacks, challenges, reviews, mechanisms, 
tools, policies, emerging technologies and technological 
innovations for minimizing the digital piracy. This 
special issue includes six articles as follows:  
In the first article Ahmad et al.  proposed a novel 
hybrid watermarking method based on three different 
transforms: Discrete Wavelet Transform (DWT), 
Discrete Shearlet Transform (DST) and Arnold transform 
one after the other.  To evaluate the proposed method 
authors used six performance measures namely peak 
signal to noise ratio (PSNR), mean squared error (MSE), 
root mean squared error (RMSE), signal to noise ratio 
(SNR), mean absolute error (MAE) and structural 
similarity (SSIM), which indicated a stable outcome 
under any type of attack and performs significantly better 
than state-of-the-art watermarking approaches. 
According to the results achieved, it is recommended to 
consider extending this hybrid method for other 
multimedia data such as video, text and audio.   
Wang et al. in the second article proposed an 
improved gene expression programming algorithm based 
on niche technology of outbreeding fusion (OFN-GEP). 
This algorithm uses population initialization strategy of 
gene equilibrium for improving the quality and diversity 
of population. Further, the algorithm introduced the 
outbreeding fusion mechanism into the niche technology, 
to eliminate the kin individuals, fuse the distantly related 
individuals, and promote the gene exchange between the 
excellent individuals from niches. The authors then 
verified the effectiveness and competitiveness of the 
algorithm through function finding problems, and by 
relating it to literatures. The results being effective in 
convergence speed, quality of solution and in restraining 
the premature convergence phenomenon; the OFN-GEP 
algorithm promises a great application value in solving 
practical problems related to privacy and security related 
issues such as data hiding, hash function generation and 
Boolean function evolution etc. 
Secret key generation and establishment plays main 
role in launching the data sharing sessions, and is 
consequently used for authentication, confidentiality and 
integrity of data. In contrast to traditional key 
establishment protocols, where one user decides the key 
and communicates it to other user, the key agreement 
protocols involve all the users in the communication in 
key establishment process. Sivaranjani and Surekha in 
the third article presented an enhanced ID-based 
authenticated key agreement protocol using hybrid 
mixing of bilinear pairing and Malon-Lee approach for 
secure communication between two users. The results 
proved that the protocol stands secure and satisfies 
desired security properties at minimum time. The authors 
also extended the algorithms for multiple users.   
Traffic and road accident safety are a big issue in 
every country. Data science, being assisting in analyzing 
different factors behind traffic and road accidents, 
Prayag Tiwari et al. in fourth article analyzed different 
clustering and classification techniques such as Decision 
Tree, Lazy classifier, and Multilayer perceptron classifier 
to classify datasets based on casualty class. Further, 
authors proposed clustering techniques which are k-
means and hierarchical to cluster dataset. After analyzing 
dataset without and with clustering, the authors reported 
a noticeable improvement in the accuracy level by using 
clustering techniques on dataset compared to a dataset 
which was classified without clustering. Better results for 
reducing the accident ratio and improving the safety are 
reported after using hierarchical clustering as compared 
to k mode clustering techniques.  
In the fifth article, Siba and Ajanta presented an 
enhanced distributed fault tolerant architecture and the 
related algorithms for connectivity maintenance in 
Wireless Sensor Networks (WSN) of various 
surveillance applications. The algorithms and hence the 
recovery actions are initiated based on fault diagnosis 
notifications, data checkpoints and state checkpoints in a 
distributed manner.   
In the sixth article, Thuong et al. proposed a simple 
and less expensive hybrid approach based on the 
multiscale Curvelet analysis and the Zernike moment for 
detecting image forgeries. The results proved its 
effectiveness in copy-move detection. 
As guest editors, we hope the research work covered 
under this special issue will be effective and valuable for 
multitude of readers/researchers. In addition, the 
technical standard and quality of published content is 
based on the strength and expertise of the submitted 
papers. We are grateful to the authors for their imperative 
research contribution to this issue and their patience 
2 Informatica 41 (2017) 1–2 N. Dey et al. 
 
during the revision stages done by experts in the editorial 
board. We take this opportunity to give our special 
thanks to Prof. Matjaz Gams, Editor-in-chief of the 
Informatica: An International Journal Of Computing And 
Informatics, for all his support, and competence rendered 
to this special issue.  
       
Nilanjan Dey 
Surekha Borra 
Suresh Chandra Satapathy 
 
 
Informatica 41 (2017) 3–24 3
A Hybrid Wavelet-Shearlet Approach to Robust Digital Image Watermarking
Ahmad B. A. Hassanat†, V. B. Surya Prasath∗, Khalil I. Mseidein†, Mouhammd Al-awadi† and Awni Mansoar Hammouri†
†Department of Information Technology, Mutah University, Karak, Jordan
∗Computational Imaging and VisAnalysis (CIVA) Lab, Department of Computer Science, University of Missouri-
Columbia, USA
E-mail: prasaths@missouri.edu
Keywords: watermarking, shearlet transform, Arnold transform, discrete wavelet transform
Received: December 6, 2016
Watermarking systems are one of the most important techniques used to protect digital content. The main
challenge facing most of these techniques is to hide and recover the message without losing much of
information when a specific attack occurs. This paper proposes a novel method with a stable outcome
under any type of attack. The proposed method is a hybrid approach of three different transforms, discrete
wavelet transform (DWT), discrete shearlet transform (DST) and Arnold transform. We call this new
hybrid method SWA (shearlet, wavelet, and Arnold). Initially, DWT applied to the cover image to get
four sub-bands, we selected the HL (High-Low) sub band of DWT, since HL sub-band contains vertical
features of the host image, where these features help maintain the embedded image with more stability.
Next, we apply the DST with HL sub-band, at the same time applying Arnold transform to the message
image. Finally, the output that obtained from Arnold transform will be stored within the Shearlet output. To
evaluate the proposed method we used six performance evaluation measures, namely, peak signal to noise
ratio (PSNR), mean squared error (MSE), root mean squared error (RMSE), signal to noise ratio (SNR),
mean absolute error (MAE) and structural similarity (SSIM). We apply seven different types of attacks on
test images, as well as apply combined multi-attacks on the same image. Extensive experimental results are
undertaken to highlight the advantage of our approach with other transform based watermarking methods
from the literature. Quantitative results indicate that the proposed SWA method performs significantly
better than other transform based state-of-the-art watermarking approaches.
Povzetek: Opisana je robustna metoda digitalnega vodnega tiska, tj. vnosa kode v sliko.
1 Introduction
With the exponential growth of digital contents in the in-
ternet, there is a strong need to protect these contents with
more security automatically. This open online environment
needs more efficient techniques to save original content
creators, and author’s rights. Digital watermarking sys-
tems can significantly contribute to protect information and
files. Generally, people need an easy-to-use model to pro-
tect their files, texts, and images. The availability of au-
tomatic tools that provide such services for documents are
currently restricted and difficult to use. Although, there are
many previous works focused on certain solutions to solve
this security problem, we certainly need more research to
improve the efficiency of these existing methods. This pa-
per provides a new hybrid approach based on some effi-
cient transforms and provides an overview of the relevant
issues and definitions related to digital image watermark-
ing. Copyright nowadays is one of the most important re-
search areas; digital watermarking is considered as one of
the important automatic signal/image processing technique
that enables us to hide our information behind a noisy sig-
nal. This signal may be image, audio or video etc.
An important extension of watermarking area is the im-
age watermarking; here we embedded a watermark image
within a cover image. The produced watermarked image
is a combination of cover image and the watermark. The
watermark is the information to be embedded, on the other
hand the host or cover image is the signal where the wa-
termark is embedded. In general, Watermarks can be clas-
sified depending on several criteria such as: domain that
can be applied by the watermark, type the watermark used
visibility of watermark to user and the application used to
create the watermark. Figure 1 shows these broad classifi-
cations.
1.1 The usage of image watermarking
The growth of digital computing technology in recent times
was the reason beyond the widespread use of digital me-
dia such as video digital, documents and digital images.
As a result of the increase in speed of transmission and
distribution it is easy to obtain digital content. Despite
the abundance of digital products, however this technol-
ogy lacks protection because of illegal use, and imitations.
The protection of intellectual property rights for digital me-
dia has been the attention the focus of many researchers in
the past. Using a digital watermark technology is a suc-
4 Informatica 41 (2017) 3–24 Hassanat et al.
Figure 1: Main types of Watermarking techniques.
Figure 2: Application areas of watermarking techniques.
cessful choice to solve the digital content protection prob-
lem. There are, in general, six types of watermarking ap-
plications are presented; copyright protection, fingerprint-
ing, broadcast monitoring, content authentication, transac-
tion tracking, and tamper detection, these applications are
mentioned in Figure 2.
Recently, the advance of editing capabilities and the
wide availability due to internet penetration in the world,
digital forgery has become easy, and difficult to prevent
in general. Therefore, there is a need to protect end-user
privacy, security, and copyright issues for content genera-
tors [13]. The application areas include biometrics, medi-
cal imagery, telemedicine, etc [4, 38, 39, 40, 41, 42].
Nowadays, digital watermarking is used mainly for the
protection of copyrights. Moreover, it was used early to
send sensitive confidential information across the commu-
nicative signal channels. Thus, applying watermark has
been occupying the attention of researchers in the last
few decades. As a consequence of tremendous growth of
computer networks and data transfer over the web; huge
amounts of multimedia data are vulnerable to unauthorized
access, for e.g., web images which can easily be dupli-
cated and distributed without eligibility. Image watermark-
ing provides copyright protection for documents and mul-
timedia in order to protect intellectual property and data
security.
1.2 Contributions
In this work we will provide an overview five different
transform based methods one of which is a new proposed
hybrid transform approach. We considered the transform
of an image using discrete cosine transform (DCT), dis-
crete wavelet transform (DWT), DWT with DCT, and DWT
with Arnold transform in addition to our proposed hy-
brid method which combines discrete shearlet transform
(DST) with these previous transforms. Also, we consid-
ered adding attacks like crop image, salt and pepper noise,
Gaussian noise, and etc. The schemes we are providing
here are resilient to these types of attacks and novel in this
scenario. In this paper, we will study in details the com-
mon methods that used to protect the original image after
adding attacks. This work also presents a comprehensive
experimental study of these transform based watermarking
algorithms. Another important contribution of this study
is a hybrid method which is based on a fusion of different
transforms taking advantages of each transforms discrim-
inability. Initially, we will design our new method; then we
test it and compare our results with the results of the pre-
vious transform and combination of them. To ensure the
efficiency of our hybrid, we will use different performance
measures to quantitatively benchmark it on various test im-
ages and attacks.
In the digital image watermarking area, efficiency of any
proposed algorithm can be evaluated based on a host of
image and embedded image (message). One of the most
common image is the "Lena" which is traditionally used as
a host. Also, the copyright image has been used widely as a
message. Apart from these images, there are other standard
test images such as Cameraman, Baboon, Peppers, Air-
plane, Boat, Barbara, Elaine, Man, Bird, Couple, House,
and Home which are widely used in benchmarking evalua-
tions. With the development of a new watermarking tech-
nology, it is necessary to protect the images against mul-
A Hybrid Wavelet-Shearlet Approach to. . . Informatica 41 (2017) 3–24 5
tiple types of attack, such as compression, Gaussian filter,
pepper and salt noise, median filter, cropping, resize, and
rotation. The Watermark is said to be robust against some
attack if we can detect the message after that particular at-
tack. In our proposed approach, to ensure and reduce in-
fluence of watermarked image in any attack, shearlet trans-
form is applied at the HL (high-low) sub-band, where this
sub-band retains features of original image. shearlets gen-
erate a series of matrices that obtained from the HL sub-
band. In this way, shearlets applied to specific pixels of the
original image with these features represented in 61 dif-
ferent matrices. As a consequence, any applied attack is
distributed to all of these matrices, and the probability of
changing the value of the pixel that has been embedding
by the attack is low. Based on this observation, our pro-
posed hybrid SWA approach’s results were stable or semi-
static whatever the type of attack and its value. Finally,
the Arnold transform was applied to the message image to
achieve the best protection against the unauthorized change
in pixel values.
We organized the rest of the paper as follows. Sec-
tion 2 provides a brief overview of literature on watermark-
ing with special emphasis on transform domain watermark-
ing techniques. Section 3 provides a detail explanation of
the proposed hybrid shearlet, wavelet, and Arnold (SWA)
method. Section 4 provides detailed experimental results
and discussions with Section 5 concluding the paper.
2 Literature review
Recently, research activities in image watermarking area
has seen a lot of progress, and become more specialized.
Some researchers were focusing on improving the proper-
ties of the watermark or applications, while others were fo-
cusing on improving the efficiency of the techniques used
to embed, and extract the watermarked image, taking into
consideration attacks. Watermarking techniques can be
classified generally in to two broad domains; spatial do-
main, and transform domain.
2.1 Spatial domain watermarking
Classical techniques do watermarking in the spatial do-
main, where the message image is included by adjusting
the pixel values of the original (host) image . Least Signif-
icant bit (LSB) method is considered one of the most im-
portant and most common examples of the use of the spa-
tial domain techniques for the watermark. There are two
main procedures to any watermarking technique model;
embedding procedure and extraction procedure. For tech-
nique of LSB in embedding phase, the host image (original
or cover) and the message image (watermark) being read,
both images must be gray. However, this method suffers
from the problem of an impaired ability to hide informa-
tion [20], therefore it is easy to extract the image hidden
within the original image by unauthorized persons, more-
over, the quality of watermarking is not good when embed-
ding procedure, and extraction procedure are combined. In
other words, the results achieved by spatial domain meth-
ods are not good enough, especially when the intensity of
the pixel changed directly, which affects the value of this
pixel.
Since typical spatial domain methods suffer from much
vulnerability, many authors have tried to improve these.
For example, [50] introduced two different methods to im-
prove the technique of LSB, in the first method LSB sub-
stituted with a pseudo-noise (PN) sequence, and a second
method that adds both together. However, this improve-
ment also gives unsatisfactory results after adding any type
of noise.
2.2 Transform domain watermarking
These techniques rely on hiding images in the transformed
coefficients, thereby giving further information that helps
secreting against any attack. As a consequence, majority
of recent studies in the field of digital image watermark-
ing use the frequency domain. Use of the frequency do-
main gives results better than the spatial domain in terms
of robustness [28] . There are many transforms can be
used for images watermarking based on frequency domain
(FD), such as continuous wavelet transform (CWT), dis-
crete cosine transform (DCT), short time Fourier transform
(STFT), discrete wavelet transforms (DWT), Fourier trans-
form, and combinations of DCT and DWT. We very briefly
review these well-known transforms as they are relevant to
the hybrid method proposed here (Section 3).
2.2.1 Discrete cosine transform (DCT)
One of the most important methods that based on the fre-
quency domain is the discrete cosine transform (DCT).
Typically in DCT based approaches, the image is repre-
sented in the form of a set of sinusoids with changeable
magnitudes and frequencies, where the image is split into
three different sections of frequencies; low frequency (LF),
medium frequency (MF) and high frequency (HF). Data or
message will be hidden in the medium frequency region;
since it is considered to be the best place, whereas if the
message is stored in low frequency regions it will be visi-
ble to the naked human eyes. Thus, for the areas of higher
frequency, if the message is stored in this region, the result-
ing image will be distorted because this frequency spreads
the biggest place of the block on the bottom right corner.
Consequently, this will cause local deformation combined
with the edges, thus the places where the areas are of the
medium frequency, does not affect the quality of the im-
age. The DCT is utilized in a number of earlier studies, see
for e.g. [49] who proposed a new model based DCT tech-
nique within a specific scheme for the watermark, in which
DCT increased the resistance against attacks, mainly JPEG
compression attacks.
6 Informatica 41 (2017) 3–24 Hassanat et al.
2.2.2 Discrete wavelet transform (DWT)
Discrete wavelet transform (DWT) is based on wavelets
and is sampled discretely. The goal of using DWT is to con-
vert an image from the spatial domain to the frequency do-
main in a locality preserving way. In DWT transform, co-
efficients separate the high and low frequency information
in an image on a pixel-to-pixel basis. Original signal is di-
vided into four mini signals - low-low (LL), low-high (LH),
high-low (HL), and high-high(HH), these wavelets are gen-
erated from the original signal by dilations and transla-
tions. It is commonly used in various image processing
applications, and in particular in the application of water-
marking [48]. DWT can find appropriate area to embed
the message efficiently, this means that the message will be
invisible to the naked human eyes.
2.2.3 Joint transforms (DCT and DWT)
According to the advantages of the last two methods,
namely DCT and DWT, we notice that each one is been
characterized by certain positive aspects, however there
are limitations that restrict their application efficiency. To
improve the performances, several studies combined these
two transforms based techniques, see for e.g. [8, 2, 5]. DCT
achieves high results in the robust of hiding data, but it pro-
duces a distorted image, DWT produces high-quality im-
age, but it achieves bad results with the addition of the at-
tack. One of the proposed solutions to resolve this issue
is a hybrid method combined two techniques; this hybrid
method usually called joint DWT-DCT, the joint method is
common use in signal processing application.
3 Proposed hybrid approach
The proposed approach is based on a hybrid approach com-
bining three different transforms namely: discrete wavelet
transform (DWT), discrete shearlet transform (DST) and
Arnold transform. We named this a new hybrid model
SWA, this abbreviation comes from the name of transforms
utilized here; Shearlet, Wavelet, and Arnold transforms re-
spectively. In this section, we will explain the salient points
of these three transforms as well as our method of merging
them together for the purposes of digital image watermark-
ing.
3.1 Discrete wavelet transform (DWT)
As mentioned in Section 2.2.2, DWT is perhaps one of the
most commonly used transform in the field of watermark-
ing, where it is most widely used in image and videos. In
the proposed model, we decompose the host image into
four normal sub-bands according; LL, LH, HL, HH by
level one DWT, the high frequency (LH, HL and HH ) sub-
bands are suitable for watermark embedding, as embedding
watermark in LL sub-band causes the deterioration of im-
age quality [47], the HL sub-band has been adopted for this
model since it contains a mixture of both high and low fre-
quency contents.
3.2 Discrete shearlet transform (DST)
Recently, many of the multi-scale transforms appeared
and became widely applied in image processing, such
as the curvelets, contourlets and Shearlets. These trans-
forms merge between the multi-scale analysis and direc-
tional wavelets to find an optimal directional representa-
tion. Generally these are adopted by building a pyramid
waveform which consists of different directions as well dif-
ferent scales. The nature of transforms architecture enables
us to use directions in multi-scale systems. For this rea-
son, these transforms are widely used in many applications
such as sparse image processing applications, operators de-
composition, inverse problems, edge detection, and image
restoration [19].
In this study, we utilize the discrete shearlet transform
which has many advantages, the most important is that it
is a simple mathematical construct, where it depends on
the affine systems theory, the best solution for sparse mul-
tidimensional representation [35]. Further, Shear trans-
form applied to a fixed function, and exhibit the geometric
and mathematical properties like directionality, elongated
shapes, scales, oscillations [31]. With host image A, the
Shearlet transform for the transformed output image B are
computed by Laplacian pyramid scheme and directional fil-
tering according to equation below,
ShImg → SH{HostImg(a, s, t)}, (1)
where, a is the scale parameter; a > 0, s is the shear pa-
rameter or sometimes it called the direction; s ∈ R, t is the
translation parameter or sometimes it called the location;
t ∈ R; a, s, and t called Shearlet basis functions [33].
Shearlet transform are calculated by dilating, shearing
and translation [33]. It is defined in equations below:
SH{HostImg(a, s, t)}
=
∫
HostImg(y) Ψ(x− y) dy
= HostImg ×Ψ(x), (2)
where Ψ is a generating function is computed by equation,
ψ(x) = |detA(a, s)| − 0.5 Ψ(A(a, s− 1)(x− t)) (3)
Where a and s are geometrical transforms and dilation, and
are 2× 2 matrices calculated by:
a =
[
a 0
0
√
a
]
, s =
[
1 s
0 1
]
, (4)
A(a, s) =
[
a s
√
a
0
√
a
]
. (5)
DST is an appropriate way for image watermarking,
that it achieved high performance for determine the
A Hybrid Wavelet-Shearlet Approach to. . . Informatica 41 (2017) 3–24 7
directional features also the optimal localization [1]. In
this paper, we used Finite Discrete Shearlet Transform1
(FDST) for spread spectrum watermarking [24]. One of
the main advantages of the FDST is that, it is only based
on the Fast Fourier transform (FFT) for which very fast
implementations are available. Further, using band-limited
shearlets one can construct a Parseval frame that provides
a simple and straightforward inverse shearlet transform. In
the notations of the MATLAB software utilized here, the
shearlet transform is applied to the image according to the
following command:
[ST, Psi] = shearletTransformSpect(A,
numOfScales, realCoefficients);
This command returns two most important functions are
ST and Psi, ST is the Shearlet coefficients as a three-
dimensional matrix of size M ×N × η and Psi is the same
size and contains the respective shearlet spectra. Each
one of these functions stores 61 matrices and each matrix
has the same size of the original image. According FDST
toolbox described above, the matrix Psi(18) is considered
good matrix for applying shearlet transform, where it is
defined far from the external boundaries, which reduces
the overlap.
3.3 Arnold transform (AT)
Arnold transform can be used to improve the security of
the logo image that used in many watermarking applica-
tions [16, 55]. Suppose that M is n-dimensional matrix
(n× n) which represents the original image. Arnold trans-
form can be calculated from M as the following formula
by equation,[
x∗
y∗
]
=
[
1 1
1 2
] [
x
y
]
(mod n), (6)
where n is the size of the original image, (x, y) are the orig-
inal pixel coordinates, (x∗, y∗) represents the coordinates
of the pixel after applying Arnold transform. The principle
of Arnold transform is based on modifying the location of
the original pixels many times. This means the possibility
of implementing it on the image periodically [46] which
leads to the improvement of security in the watermarking
image. Moreover, the mathematical characteristics [16] of
the Arnold transform enables it to be used widely in the
area of image watermarking. Note that the pixel location
changes frequently, until it comes back to its original po-
sition after a number of iterations of Arnold transform, so
the original image is recovered. The anti-Arnold transform
which brings back to the original locations suffers from
high time complexity that is needed in the reverse calcu-
lations [55].
1FDST is available as a MATLAB programs presented for applying
DST. This software is available for free: http://www.mathematik.uni-
kl.de/imagepro/software/ffst/
Figure 3: Watermark embedding algorithm of the proposed
SWA method.
3.4 The hybrid SWA watermarking
A general digital image watermarking algorithm includes
two procedures: (i) the first is the embedding, through
this procedure the copyright image (message) hidden in-
side the original image (the host), the resulting image is
called watermarked image [23], (ii) the second is the ex-
traction, through this procedure the copyright image is ex-
tracted from the watermarked image. The proposed SWA
watermarking model uses DWT, DST, and Arnold Trans-
form for embedding procedure, in addition to using these
for extraction procedure as well. Thus, our watermarking
method consists of two major phases, (1) message embed-
ding, and (2) message extraction, which we describe below
in detail.
3.4.1 SWA embedding algorithm
Embedding algorithm procedure in the proposed SWA
method consists of several steps, as shown in Figure 3. In
the beginning, the host image (the original image) is being
read, where its size is (1024 × 1024), also the copyright
image is being read, where its size is (32× 32).
The DWT applied to host image, where four of the sub-
bands produced through applying this transform are; HH,
HL, LH, and LL respectively. After that, the DST per-
formed based on vertical frequency (HL) at level one DWT,
From applying the DST produces two types of parameter
(Ψ, ST) where each type consists of a 61 matrices, with the
size of matrices to be 512× 512.
The Arnold transform applied with copyright image,
then each pixel of the resulting image after applying Arnold
transform hide or embedded in the resulting host image ob-
tained after applying DST. The embedding is performed at
the matrix number (18) which resulted from applying DST
at Ψ parameter. The size of this matrix is 512 × 512, this
matrix divided into 32 blocks (the size of matrix (18) by
copyright image (512/32), the pixel which obtained from
applying Arnold embedded at the last pixel of each block
8 Informatica 41 (2017) 3–24 Hassanat et al.
Figure 4: Watermark extracting algorithm of the proposed
SWA method.
of the matrix (18), pixels 16×16, 16×32, etc. The follow-
ing formula is calculated for all the values in the last Pixel
from the matrix 18,
New_value = SD(LPixl)2/α, (7)
where (SD) is the standard deviation, LPixl is last pixel
in matrix 18 and value of α is set to 10000. From copy-
right image after applying Arnold, when the pixel of the
(New_value) is 1 then New_value is stored as it is, oth-
erwise the negative of (New_value) stored. Finally, in-
verse discrete wavelet transform (IDWT) and inverse dis-
crete shearlet transform (IDST) are performed to return the
image to normal shape.
3.4.2 Extraction algorithm SWA model
The steps to extract the message computed by the inverse
procedure can be described as follows. The proposed ex-
traction algorithm for the SWA watermarking model is de-
picted in Figure 4. Initially, the watermarked image is be-
ing read, where its size is 1024× 1024. DWT applied with
watermarked image, After that, the DST performed based
on horizontal frequency (HL) at level one DWT, the Extrac-
tion performed at the matrix number (18) which resulting
from Ψ parameter. The size of this matrix is 512×512, this
matrix divided into 32 blocks, when the value of the last
pixel of each block of the matrix (18) is positive, then re-
turn 1 otherwise return 0, Thus, the previous Arnold trans-
formed image was generated, and finally inverse Arnold
transform was performed to get the copyright image.
The pseudo codes given in Algorithm 1 and Algorithm 2
summarizes the proposed embedding and extraction algo-
rithms for the proposed SWA watermarking approach.
Algorithm 1 Embedding of SWA model
Input: Host image (1024×1024) and copyright image (32×
32)
Output: Watermarked image of size (1024× 1024)
1: Apply 2D_DWT (Host image)
2: Output: (LL, LH, HL, HH)
3: Apply DST (HL at step 2)
4: Output: 61 matrices (Ψ) of size 512× 512
5: Get matrix Ψ18 (number 18 from matrix Ψ), block size
16× 16
6: Apply Arnold Transform (Copyright image)
7: Let k0= - (std(Host)2)/α
8: Let k1= (std(Host)2)/α
9: Let Ψ18_W (x, y)= Ψ18(x, y)
10: For each pixel from copyright image after Arnold
transform (message vector (pixel)).
11: If (message vector (pixel) is 0) then Ψ18_W (y +
blocksize− 1, x+ blocksize− 1)=k0
12: Else Ψ18_W (y + blocksize − 1, x + blocksize − 1)
=k1
13: If (message vector is last pixel) Go To step 15
14: Next pixel (message vector (pixel++)) Go To step 11
15: Let Ψ18(x, y) = Ψ18_W (x, y)
16: Let C= IDST (Ψ, ST)
17: Let Watermarked image= 2D_IDWT (LL, LH, C, HH)
18: Output Watermarked image of size (1024× 1024)
19: Calculate Performance evaluation measures for (Wa-
termarked image)
4 Experimental results
4.1 Setup and error metrics
Our experimental results were conducted on a Toshiba lap-
top with Intel (R) Core i5-2450M processor with CPU 2.50
GHz, RAM 6.00 GB in MATLAB environment. Most of
the studies in the digital image watermarking literature are
based on standard images such as Lena, Baboon, Boat,
Cameraman, Peppers and Barbara images etc. These im-
ages taken from USC-SIPI miscellaneous database2, it con-
sist of 44 images, 16 colors and 28 monochromes. The
sizes of images are 256×256, 512×512, and 1024×1024.
We gauge the performance of different watermarking
techniques quantitatively by using six most common error
metrics utilized in image processing literature which are
given below.
– Mean Square Error (MSE): MSE [27, 52] between
original image and watermarked image is calculated
using the formula:
MSE =
1
N
∑
i
∑
j
(f(i, j)− g(i, j))2,
where sum j and k is taken over all the pixels in the
image, N is the total number of pixels in the image.
2http://sipi.usc.edu/database/database.php?volume=misc
A Hybrid Wavelet-Shearlet Approach to. . . Informatica 41 (2017) 3–24 9
Algorithm 2 Extraction of SWA model
Input: Watermarked image (1024× 1024)
Output: Message image (32× 32)
1: Apply 2D_DWT (Watermarked image)
2: Output: (LL_W, LH_W, HL_W and HH_W).
3: Apply DST (HL_W at step 2).
4: Output: 61 matrices (Ψ_W ), 61 matrices (ST_W) size
of each matrix is 512× 512
5: Get matrix Ψ18_W (number of 18 from matrix Ψ_W ),
block size 16× 16
6: For each last element from block at Ψ18_W matrix
7: If (Ψ18_W (y + blocksize − 1, x + blocksize −
1)=negativevalue) then (message vector(pixel)=0)
8: Else (message vector(pixel)=1)
9: If (Ψ18_W is last block) Go To step 10.
10: Message_EX= reshape(message vector(pixels))
11: Go To step 6
12: Apply Inverse Arnold Transform (Message_EX) .
13: Output Message_EX image.
14: Calculate Performance evaluation measures for (Mes-
sage_EX image).
15: End
It is worth mentioning here that a good watermarking
system should satisfy minimum MSE.
– Root Mean Square Error (RMSE): RMSE [9]
equals the square root of Mean Square Error
(MSE0.5); it can also be calculated using the formula,
RMSE =
√
1
N
∑
i
∑
j
(f(i, j)− g(i, j))2.
It is worth mentioning here that a good watermarking
system should satisfy minimum RMSE.
– Peak Signal to Noise Ratio (PSNR): PSNR [56] is
used for measuring the quality of the watermarked im-
age and defined using the formula:
PSNR =
10 log10
(max)2
1
m×n
∑
j
∑
k(f(i, j)− g(i, j))2
,
where,m×n is the image size, (max) is the maximum
value of the pixels values in the image f is the host
image, g is the watermarked images. It is worthily
mentioned here that a higher value of PSNR (dB) is
good because it means that the ratio of signal to noise
is higher.
– Signal to Noise Ratio (SNR): SNR [59] is mainly
used to measure the sensitivity of the image. It mea-
sures the power of a signal strength relative to the
background noise. It is calculated by the formula:
SNR =
Psignal
Pnoise
Higher values of SNR (dB) shows better performance.
– Structural Similarity (SSIM): Another popular mea-
sure for similarity comparison between two images is
SSIM [54] with values in the range of [0, 1], where
1 is acquired when two images are identical. Mean
structural similarity index is in the range [0, 1] and
is known to be a better error metric than traditional
signal to noise ratio [54]. It is the mean value of the
structural similarity (SSIM) metric3. The SSIM is cal-
culated between two windows ω1 and ω2 of common
size N ×N , and is given by,
SSIM(ω1, ω2)
=
(2µω1µω2 + c1)(2σω1ω2 + c2)
(µ2ω1 + µ
2
ω2 + c1)(σ
2
ω1 + σ
2
ω2 + c2)
where µωi the average of ωi, σ
2
ωi the variance of ωi,
σω1ω2 the covariance, and c1, c2 stabilization param-
eters. The MSSIM value near 1 implies the optimal
denoising capability of a method and we used the de-
fault parameters.
– Mean Absolute Error (MAE): This measure is used
to compute the average magnitude of the errors in a
set of forecasts, without considering their direction. It
uses to compute the accuracy with continuous values
[25]. MAE is calculated using the formula:
MAE =
1
N
∑
i
∑
j
|f(i, j)− g(i, j)|
Lower values of MAE indicates better performance.
4.2 Attacks
With the development of our proposed SWA watermark-
ing technology, it is necessary to protect the images against
several different types of attacks, which they are exposed
to, such as compression, Gaussian filter, pepper and salt
noise, median filter, cropping, resize, and rotation attacks.
A watermarking method is said to be robust against some
attack if we can recover the original image after that par-
ticular attack. Some of the common types of attack will be
briefly explained below.
– JPEG compression attack: JPEG compression [60]
and [3], is aimed to reduce the size of an image, this
resize operation enables the users to upload and down-
load images efficiently. Moreover, compression re-
duces the complexity time of sending multimedia ma-
terial.
– Salt and pepper noise attack: Another type of at-
tacks is Salt and pepper noise [12] and [30], which af-
fected watermarking images. This kind of noise is not
3We use the default parameters for SSIM and the MATLAB code is
available online at https://ece.uwaterloo.ca/ z70wang/research/ssim/.
10 Informatica 41 (2017) 3–24 Hassanat et al.
only damaging the image through software used for
that purpose, but it can also happen during the process
of image acquisition, by affecting the used camera, or
in the stage of storing in memory. Regarding to 8 bit
grayscale image, salt and pepper noise randomly al-
ter the pixel value to either minimum (0) or maximum
(28 − 1 = 255).
– Cropping attack: Image cropping is the operation of
cutting the outer part of the original image with spe-
cific measurements, to improve the image or get rid
of the excess parts. This operation can be carried out
using different block size ratio [60].
– Median filter attack: Mean filter is an image pro-
cessing technique which changes the center value of a
block (for example 3× 3 block) of an image with the
median value of the pixels, this leads to smooth image
and reduces the variation of the density between any
pixel and its neighbors. The main challenge of median
filter attack [32], is the use of small number of pixels
to reconfigure the original image
– Rotation attack: The principle of image rotation is
the inverse transformation for each pixel of the orig-
inal image, so the image is calculated using the in-
terpolation [43]. Rotation attack could affect the im-
age to various degrees based on the image rotation an-
gle [21].
– Resize (scaling) attack: Image scaling Usually used
to resize digital images for printing purposes, which
does not change the actual pixels in the original im-
age [14], But to keep the image without affected by
scaling, users must take into account not to reduce the
original image to less than half, or not to enlarge it to
more than double as proven in studies [36]. Generally
there two types of scaling attack; down-scaling attack
and up-scaling attack [36].
– Gaussian filter noise: Gaussian filter is a geometric
method which modify the original image by remov-
ing high frequency pixels [3], to reduce the amount
of pixel density variation with the adjacent pixels, this
can be done according to a specific formula depends
on variance and mean [51]. The noise which caused
by Gaussian filter resulted from adding random noise
values to the actual pixel.
4.3 Detailed results
Our main experiments are divided into three parts each con-
sist of multiple sub-experiments. The first part is compari-
son of our proposed SWA watermarking method with four
common transforms based watermarking approaches avail-
able in the literature.
1. DWT - [57, 15, 37, 48, 58], and [7],
2. DCT - [44, 34, 49], and [10].
3. DWT_DCT_ Joint [2, 28, 5], and [6],
4. DWT_ Arnold - [55, 16] and [29].
All these techniques were implemented in MATLAB. In
these experiments, we verify the performance improve-
ments when we applied the proposed algorithm.
The second part is a comparison of quantitative results of
our proposed SWA model, and four different approaches
with seven attacks on Lena image based on PSNR (dB),
MSE, RMSE, SNR (dB), SSIM, and MAE error metrics.
We also provide the ranking of these methods with respect
to each of these error metrics.
The third part is a new way to compare different wa-
termarking methods performances by combining multiple
attacks together. These multi-attacks are applied on Lena
image and we compute the PSNR (dB), and SSIM error
metrics as representative benchmarking of four methods
from the literature with our proposed SWA watermarking
method.
4.3.1 Comparison with other methods on multiple
standard test images
Table 1 shows a comparison between our proposed ap-
proach with four of state-of-the-art watermarking ap-
proaches on multiple USC-SIPI standard test images. In
order to test the advantage of the proposed SWA approach
against these methods from the literature, we utilized six of
the standard test images widely used (as a host image), and
the copyright image which was used as embedded image.
As can be seen by comparing the different error met-
rics reported in Table 1 (with no attacks), the proposed
SWA model achieved the best results across different im-
ages. The MSE measure with Boat image, the percent-
age of squares errors between the original image and the
image, after embedding is (0.0015), and this low percent-
age indicates that our proposed approach obtains good re-
sult for the embedding step. Nevertheless, DWT_DCT
Joint method obtained a decent result, it achieved (6.2047)
whereas DWT achieved (104.8672), which shows that this
method is not satisfactory. Similarly, outcomes of PSNR,
which refers to the ratio of the noise signal of the image, as
when the values of this measure are high, the quality of the
image after embedding is good, the proposed method got
the highest result (76.4063) and the lowest value presented
when applying DWT method, the result was (27.9584).
The MAE, which expresses the absolute error value be-
tween the original image and the embedded image, when-
ever the value of this measure was low the quality water-
marked image is better. The proposed method got least ab-
solute error value (0.0797) for the Lena image, while DWT
method achieved the highest absolute error (8.1849). The
SNR, which indicates confusion of the signals between the
original image and the watermarked image, the perfect re-
sult was obtained with the proposed method (0.000) across
all test images. The SSIM, which refers to the amount of
similarity between the structure of the original image and
A Hybrid Wavelet-Shearlet Approach to. . . Informatica 41 (2017) 3–24 11
Image/Methods MSE RMSE PSNR MAE SNR SSIM
Lena
DWT 105.0586/5 10.2498/5 27.9505/5 8.1849/5 -0.0258/5 0.5839/5
DCT 25.4757/4 5.0473/4 34.1035/4 4.0007/4 -0.0058/4 0.8388/4
DWT_DCT_Joint 5.9893/3 2.4473/3 40.3910/3 0.9294/2 -0.0014/2 0.9532/3
DWT_ Arnold 4.6186/2 2.0903/2 41.7607/2 1.2615/3 0.0018/3 0.9721/2
Our SWA model 0.0085/1 0.0919/1 68.8949/1 0.0797/1 0.0000/1 1.0000/1
Baboon
DWT 104.9922/5 10.2466/5 27.9532/5 8.1817/5 -0.0247/4 0.8225/5
DCT 57.1129/4 7.5573/4 30.5975/4 5.0679/4 -0.0060/3 0.9160/4
DWT_DCT_Joint 11.5060/2 3.3920/2 37.5556/2 1.1783/2 -0.0017/2 0.9915/2
DWT_ Arnold 34.6575/3 5.8871/3 32.7668/3 4.1454/3 0.0348/5 0.9650/3
Our SWA model 0.0199/1 0.1410/1 65.1835/1 0.1152/1 0.0000/1 1.0000/1
Barbara
DWT 103.9729/5 10.1967/5 27.9956/5 8.1256/5 -0.0303/5 0.6931/5
DCT 28.8601/4 5.3722/4 33.5618/4 4.1810/4 -0.0074/3 0.8816/4
DWT_DCT_Joint 10.0737/2 3.1739/2 38.1329/2 1.0764/2 -0.0019/2 0.9694/2
DWT_ Arnold 20.8456/3 4.5657/3 34.9747/3 2.6838/3 0.0269/4 0.9633/3
Our SWA model 0.0079/1 0.0890/1 69.1725/1 0.0702/1 0.0000/1 1.0000/1
Cameraman
DWT 101.1671/5 10.0582/5 28.1144/5 8.0435/5 -0.0245/5 0.5771/5
DCT 23.7020/4 4.8685/4 34.4170/4 3.8271/4 -0.0051/3 0.8375/4
DWT_DCT_ Joint 5.1074/2 2.2599/2 41.0828/ 0.8677/2 -0.0012/2 0.9482/2
DWT_ Arnold 20.5353//3 4.5316/3 35.0398/3 2.2142/3 0.0096/4 0.9299/3
Our SWA model 0.0161/1 0.1268/1 66.0995/1 0.1015/1 0.0000/1 0.9999/1
Peppers
DWT 102.5565/5 10.1270/5 28.0552/5 8.0597/5 -0.0319/5 0.6091/5
DCT 27.3814/4 5.2327/4 33.7902/4 4.1240/4 -0.0075/4 0.8457/4
DWT_DCT_Joint 9.7729/2 3.1262/2 38.2646/2 1.0404/2 -0.0019/2 0.9603/2
DWT_ Arnold 11.4553/3 3.3846/3 37.5747/3 2.0799/3 0.0036/3 0.9392/3
Our SWA model 0.0089/1 0.0943/1 68.6709/1 0.0823/1 0.0000/1 1.0000/1
Boat
DWT 104.8672/5 10.2405/5 27.9584/5 8.1716/5 -0.0214/5 0.6388/5
DCT 27.5275/4 5.2467/4 33.7671/4 4.1335/4 -0.0051/4 0.8586/4
DWT_DCT_Joint 6.2047/2 2.4909/2 40.2376/2 0.9238/2 -0.0011/2 0.9478/3
DWT_ Arnold 8.7440/3 2.9570/3 38.7477/3 1.7856/3 0.0042/3 0.9615/2
Our SWA model 0.0015/1 0.0387/1 76.4063/1 0.0323/1 0.0000/1 1.0000/1
Table 1: Comparison results of our proposed SWA model and four different approaches without attack on embedded
copyright image. We show different error metric values for each method along with ranks. Best results are given in
boldface.
12 Informatica 41 (2017) 3–24 Hassanat et al.
Figure 5: Graph showing the comparison of our proposed
SWA model with other four methods based on PSNR (dB)
values for the value of each type of attack on Lena image
and copyright image.
the image after embedding, the proposed method got the
highest results (1.000) or close to optimal.
4.3.2 Comparison of different attacks on Lena image
with various error metrics
We next compare our proposed SWA model with other ap-
proaches with different attack methods under various er-
ror metrics. The watermarked image is exposed to sev-
eral types of attacks with different parameter values as in-
dicated appropriately. The compression attack expresses
the ratio maintaining the image quality, for instant when
the compression ratio is 10% which means that 90% of the
image quality may be lost, with the maintaining 10% of
the quality of the image, while this may not be discernible
to the naked human eyes. Similarly, when the compres-
sion ratio is 90% means the 90% of the image quality has
been preserved. For Gaussian noise the parameters indicate
the mean and standard deviations indicating the amount of
noise added to the image. Pepper and salt is a multiplicative
noise with the probabilities given. Mean filtering is applied
using the window size. Cropping uses the percentage of
crop applied to the image. Resize is given in terms of the
final size values. Rotation is performed at the angles given.
Figure 5 and Figure 6 shows the comparison of differ-
ent attacks with respect to the PSNR (dB), and SSIM error
metrics respectively. From the figures it is clear that the
proposed SWA model performs the best with DWT_Arnold
performing the next best. The results with other DWT,
DCT, DWT_DCT_Joint perform rather poorly.
Table 2 shows the PSNR (dB) values, which is used to
measure the quality of the image after embedding, com-
paring all transform techniques with our proposed method.
When (10%) compression ratio is applied, the proposed
SWA model achieved a high PSNR = 58.0975 dB value,
and the worst result is achieved by DCT, with PSNR =
30.7130 dB. Except under median filtering our proposed
SWA outperforms the other transform based approaches
Figure 6: Graph showing the comparison of our proposed
SWA model with other four methods based on SSIM val-
ues for the value of each type of attack on Lena image and
copyright image.
with many different types of attacks with various param-
eter settings.
Table 3 examines the impact of applying different meth-
ods on seven types of attack using Lena image with re-
spect to the mean square error (MSE) measure. The results
show that our approach has achieved satisfactory results,
and on average outperformed the rest of the methods with
(0.1016) error value. Although, it is clear from the results
that DWT_ Arnold algorithm was better than our when we
apply compression attack, except 10% compression ratio.
Results also proved the efficiency of our algorithm with
various types of attacks such as Gaussian Noise, Salt and
Peppers, Resizing and Rotation, while the results proved
the efficiency of DWT_ Arnold method with median and
cropping attacks, but, with a little difference.
Similar observations can be made about RMSE metric
on different attacks. Table 4 examines the impact of apply-
ing different methods on seven types of attack using Lena
image with respect to the root mean square error (RMSE)
measure. The results show that our approach has achieved
satisfactory results, and on average outperformed the rest of
the methods with (0.3187) error value. Although, it is clear
from the results that DWT_Arnold algorithm was better
than our when we apply compression attack, except (10%)
compression ratio. Results also proved the efficiency of our
algorithm with various types of attacks such as Gaussian
Noise, Salt and Peppers, Resizing and Rotation, while the
results proved the efficiency of DWT_Arnold method with
median and cropping attacks, but, with a little difference.
Next, Table 5 investigates the impact of applying dif-
ferent methods on seven types of attack using Lena image
with respect to signal to noise ratio (SNR) error metric. The
results show that our approach has achieved satisfactory re-
sults, and on average outperformed the rest of the methods
with (-0.0629) error value. Although, the DWT_Arnold
algorithm was better than ours when we apply compres-
sion attacks in (80%), and (90%) compression ratio, and
A Hybrid Wavelet-Shearlet Approach to. . . Informatica 41 (2017) 3–24 13
Attack/Methods DWT DCT DWT_DCT_Joint DWT_Arnold Our SWA
Compression
[10] 30.7969/5 30.7130/4 30.9133/3 51.7164/2 58.0975/1
[30] 32.4773/4 30.1909/5 34.7766/3 51.6497/2 58.0975/1
[50] 30.4796/5 32.7898/4 35.5173/3 51.6591/2 58.0975/1
[70] 28.0910/5 32.7397/4 35.3425/3 51.6591/2 58.0975/1
[90] 27.4059/5 33.4065/4 38.0909/3 51.6591/2 58.0975/1
Gaussian
[0.03, 0.003] 22.6204/5 23.7255/4 24.0203/3 51.0994/2 58.0975/1
Noise
[0.09, 0.009] 17.4078/5 17.6835/4 17.7568/3 50.6410/2 58.0975/1
[0.1, 0.01] 16.7995/4 11.0168/5 17.1067/3 50.3720/2 58.0975/1
[0.3, 0.03] 8.1811/5 8.1858/4 10.1418/3 50.5888/2 58.0975/1
[0.5, 0.05] 7.2834/5 7.4875/4 7.4862/3 50.7320/2 58.0975/1
Pepper
[0.01] 23.5386/5 24.9051/4 25.3305/3 51.7357/2 58.0975/1
and Salt
[0.05] 18.0867/5 18.4106/4 18.5091/3 51.5284/2 58.0975/1
[0.09] 15.7175/5 15.9571/4 15.9726/3 51.2608/2 58.0975/1
[0.3] 10.7093/5 10.7678/4 10.7559/3 51.1582/2 58.0975/1
[0.5] 8.5105/5 8.5513/3 8.5415/4 51.1078/2 58.0975/1
Median
[1×1] 27.9228/5 34.1035/4 40.3910/3 58.7254/1 58.0975/2
Filter
[3×3] 33.3072/5 34.9873/4 36.1247/3 58.6774/1 58.0975/2
[5×5] 31.3862/5 31.9093/4 32.1208/3 58.4451/1 58.0975/2
[7×7] 29.5287/5 29.8056/4 29.8890/3 56.9006/2 58.0975/1
[9×9] 28.2176/5 28.4383/4 28.4967/3 51.8829/2 58.0975/1
Cropping
[10] 5.7366/4 5.7368/3 5.7368/3 51.8929/2 58.0975/1
[30] 6.11805/5 6.1198/4 6.1205/3 52.4585/2 58.0975/1
[50] 6.9294/5 6.9355/4 6.9379/3 52.4245/2 58.0975/1
[70] 8.4311/5 8.4487/4 8.4543/3 52.1612/2 58.0975/1
[90] 13.0356/5 13.1217/4 13.1446/3 52.2802/2 58.0975/1
Resize
[100,300] 31.0705/5 31.2293/2 31.1997/3 52.3018/2 58.0975/1
[150,450] 33.0241/5 34.1921/3 34.1008/4 53.8430/2 58.0975/1
[200,600] 33.6675/5 36.1633/4 36.2744/3 55.7881//2 58.0975/1
[250,750] 33.9085/5 36.9390/4 37.9065/3 57.1619/2 58.0975/1
[300,900] 33.7695/5 36.8726/4 39.1607/3 57.2298/2 58.0975/1
Rotation
[25] 8.2973/5 8.3022/4 8.3050/3 52.9275/2 58.0975//1
[70] 8.5009/5 8.5064/4 8.5105/3 51.7745/2 58.0975/1
[100] 9.1657/5 9.1750/4 9.1810/3 52.3239/2 58.0975/1
[200] 8.2427/5 8.2487/4 8.2517/3 52.8147/2 58.0975/1
[300] 8.0150/5 8.0195/4 8.0217/3 52.4019/2 58.0975/1
Table 2: Comparison results of our proposed SWA model and four different approaches with seven attacks on Lena image
based on PSNR (dB) metric with ranks. Best results are given in boldface.
14 Informatica 41 (2017) 3–24 Hassanat et al.
Attack/Methods DWT DCT DWT_DCT_Joint DWT_Arnold Our SWA
Compression
[10] 5.2654/5 4.6638/3 5.2194/4 0.1367/2 0.1016/1
[30] 4.5640/4 6.3203/5 3.2932/3 0.0928/1 0.1016/2
[50] 5.8867/5 4.5083/4 3.0358/3 0.0918/1 0.1016/2
[70] 7.9038/5 4.6638/4 3.1663/3 0.0879/1 0.1016/2
[90] 8.7213/5 4.3617/4 2.2306/3 0.0879/1 0.1016/2
Gaussian
[0.03, 0.003] 15.1372/5 13.3999/4 12.9221/3 0.4395/2 0.1016/1
Noise
[0.09, 0.009] 28.2383/5 27.4299/4 27.2864/3 0.6592/2 0.1016/1
[0.1, 0.01] 30.4388/5 29.7588/4 29.5494/3 0.6592/2 0.1016/1
[0.3, 0.03] 70.9105/3 71.0183/5 71.0116/4 0.7363/2 0.1016/1
[0.5, 0.05] 99.7833/3 99.8240/4 99.9451/5 0.7188/2 0.1016/1
Pepper
[0.01] 9.32670/5 5.21240/4 2.2142/3 0.1299/2 0.1016/1
and Salt
[0.05] 14.1145/5 10.1257/4 7.3025/3 0.3057/2 0.1016/1
[0.09] 18.8807/5 14.9787/4 12.2862/3 0.3584/2 0.1016/1
[0.3] 44.0520/5 41.2690/4 38.8275/3 0.4590/2 0.1016/1
[0.5] 67.9147/5 65.6387/4 64.3678/3 0.4834/2 0.1016/1
Median
[1×1] 88.1478/5 4.0007/4 0.9288/3 0.0879/1 0.1016/2
Filter
[3×3] 3.96670/5 3.0514/4 2.2597/3 0.0889/1 0.1016/2
[5×5] 4.18210/5 3.6768/4 3.3332/3 0.0938/1 0.1016/2
[7×7] 4.86260/5 4.4538/4 4.2133/3 0.1338/2 0.1016/1
[9×9] 5.56320/5 5.1595/4 4.9448/3 0.4248/2 0.1016/1
Cropping
[10] 123.8123/5 123.7751/4 123.7279/3 0.2656/2 0.1016/1
[30] 114.3665/5 114.0056/4 113.6263/3 0.0977/1 0.1016/2
[50] 96.12130/5 95.12180/4 94.06350/3 0.0684/1 0.1016/2
[70] 69.48680/5 67.5075/4 65.7319/3 0.0820/1 0.1016/2
[90] 29.8192/5 26.4739/4 23.8796/3 0.0498/1 0.1016/2
Resize
[100,300] 4.1504/5 3.9113/3 3.9644/4 0.3857/2 0.1016/1
[150,450] 3.8638/5 2.9439/3 3.0264/4 0.2705/2 0.1016/1
[200,600] 3.9695/5 2.5879/4 2.5079/3 0.1729/2 0.1016/1
[250,750] 3.9839/5 2.5813/4 2.1687/3 0.1260/2 0.1016/1
[300,900] 4.1283/5 2.7542/4 1.9236/3 0.1240/2 0.1016/1
Rotation
[25] 81.9561/5 81.8842/4 81.8376/3 0.3340/2 0.1016/1
[70] 80.5659/5 80.4934/4 80.4498/3 0.4355/2 0.1016/1
[100] 74.5938/5 74.5079/4 74.4594/3 0.3838/2 0.1016/1
[200] 84.3401/5 84.2893/4 84.2609/3 0.3428/2 0.1016/1
[300] 85.6902/5 85.6161/4 85.5812/3 0.3770/2 0.1016/1
Table 3: Comparison results of our proposed SWA model and four different approaches with seven attacks on Lena image
based on MSE metric with ranks. Best results are given in boldface.
A Hybrid Wavelet-Shearlet Approach to. . . Informatica 41 (2017) 3–24 15
Attack/Methods DWT DCT DWT_DCT_Joint DWT_Arnold Our SWA
Compression
[10] 7.3665/5 5.9055/3 7.2874/4 0.3698/2 0.3187/1
[30] 3.1988/3 7.9195/5 4.6710/4 0.3046/1 0.3187/2
[50] 7.7434/4 5.8715/5 4.2892/3 0.3030/1 0.3187/2
[70] 10.0878/5 5.9055/4 4.3764/3 0.2965/1 0.3187/2
[90] 10.9318/5 5.4691/4 3.1893/3 0.2965/1 0.3187/2
Gaussian
[0.03, 0.003] 18.9023/5 16.7106/4 16.1062/3 0.6629/2 0.3187/1
Noise
[0.09, 0.009] 34.4540/5 33.3613/4 33.1316/3 0.8119/2 0.3187/1
[0.1, 0.01] 36.9761/5 35.9851/4 35.7148/3 0.8149/2 0.3187/1
[0.3, 0.03] 79.7524/5 79.6753/4 79.6042/3 0.8581/2 0.3187/1
[0.5, 0.05] 108.0238/3 108.044/4 108.0808/5 0.8478/2 0.3187/1
Pepper
[0.01] 16.8436/5 14.2325/4 13.8030/3 0.3604/2 0.3187/1
and Salt
[0.05] 31.8880/5 30.5307/4 30.4602/3 0.5529/2 0.3187/1
[0.09] 41.6718/5 40.5566/4 40.6133/4 0.5978/2 0.3187/1
[0.3] 74.6177/5 74.4625/4 74.0317/3 0.6775/2 0.3187/1
[0.5] 95.9893/5 95.6146/4 95.8191/4 0.6953/2 0.3187/1
Median
[1×1] 10.2124/5 5.0473/4 2.4467/3 0.2965/1 0.3187/2
Filter
[3×3] 5.51580/5 4.5591/4 3.9998/3 0.2981/1 0.3187/2
[5×5] 6.90620/5 6.4979/4 6.3416/3 0.3062/1 0.3187/2
[7×7] 8.55710/5 8.2786/4 8.1997/3 0.3658/2 0.3187/1
[9×9] 9.93440/5 9.6901/4 9.6252/3 0.6518/2 0.3187/1
Cropping
[10] 132.2546/5 132.2520/4 132.2506/3 0.5154/2 0.3187/1
[30] 126.5733/5 126.4560/3 126.5357/4 0.3125/1 0.3187/2
[50] 115.2859/5 115.2027/4 115.1709/3 0.2615/1 0.3187/2
[70] 96.98050/5 96.78460/4 96.7225/3 0.2864/1 0.3187/2
[90] 57.07360/5 56.51360/4 56.3652/3 0.2232/1 0.3187/2
Resize
[100,300] 7.1552/5 7.0271/3 7.0514/4 0.6211/2 0.3187/1
[150,450] 5.7099/5 4.9961/3 5.0494/4 0.5201/2 0.3187/1
[200,600] 5.3152/5 3.9818/4 3.9311/3 0.4158/2 0.3187/1
[250,750] 5.1489/5 3.6416/4 3.2583/3 0.3549/2 0.3187/1
[300,900] 5.2512/5 3.6695/4 2.8195/3 0.3522/2 0.3187/1
Rotation
[25] 98.4825/5 98.4311/4 98.3983/3 0.5779/2 0.3187/1
[70] 96.2016/5 96.1435/4 96.0986/3 0.6600/2 0.3187/1
[100] 89.1230/5 89.0206/4 88.9595/3 0.6195/2 0.3187/1
[200] 99.0954/5 99.0390/4 99.0047/3 0.5855/2 0.3187/1
[300] 101.7332/5 101.6871/4 101.6610/3 0.6140/2 0.3187/1
Table 4: Comparison results of our proposed SWA model and four different approaches with seven attacks on Lena image
based on RMSE metric with ranks. Best results are given in boldface.
16 Informatica 41 (2017) 3–24 Hassanat et al.
Attack/Methods DWT DCT DWT_DCT_Joint DWT_Arnold Our SWA
Compression
[10] 0.0068/2 0.0071/4 0.0062/1 -0.2669/5 -0.0629/3
[30] 0.0047/2 0.0096//3 0.00037/1 -0.0224/4 -0.0629/5
[50] 0.0114/3 0.0035/2 -0.00011/1 -0.0179/4 -0.0629/4
[70] 0.0236/4 0.0052/3 0.00180/2 0.0000/1 -0.0629/5
[90] 0.0929/5 0.0059/3 0.00150/2 0.0000/1 -0.0629/4
Gaussian
[0.03, 0.003] 0.5236/4 0.5069/3 0.5027/2 -2.1947/5 -0.0629/1
Noise
[0.09, 0.009] 1.4204/4 1.4076/3 1.4058/2 -4.6177/5 -0.0629/1
[0.1, 0.01] 1.5562/4 1.5474/3 1.5424/2 -4.8161/5 -0.0629/1
[0.3, 0.03] 3.6103/2 3.6132/3 3.6138/4 -5.9191/5 -0.0629/1
[0.5, 0.05] 4.6983/2 4.7012/3 4.7047/4 -5.6487/5 -0.0629/1
Pepper
[0.01] 0.0607/3 0.0403/2 0.0383/1 -0.1870/5 -0.0629/4
and Salt
[0.05] 0.2006/4 0.1825/3 0.1803/2 -1.3078/5 -0.0629/1
[0.09] 0.3399/4 0.3255/3 0.3053/2 -1.6464/5 -0.0629/1
[0.3] 0.9922/4 0.9804/2 0.9850/3 -2.5172/5 -0.0629/1
[0.5] 1.4573/4 1.4037/2 1.5374/4 -2.7211/5 -0.0629/1
Median
[1×1] 0.0254/4 0.0058/3 0.0014/2 0.0000/1 -0.0629/5
Filter
[3×3] -0.0154/4 -0.0147/3 -0.0137/2 -0.0045/1 -0.0629/5
[5×5] -0.0377/4 -0.0333/3 -0.0317/2 -0.0269/1 -0.0629/5
[7×7] -0.0553/4 -0.0485/3 -0.0455/2 -0.2527/5 -0.0629/1
[9×9] -0.6910/4 -0.0606/3 -0.0561/2 -2.0421/5 -0.0629/1
Cropping
[10] -19.0662/3 -19.0817/5 -19.07895/4 -0.9658/2 -0.0629/1
[30] -10.1601/3 -10.1779/4 -10.1840/5 -0.0179/1 -0.0629/2
[50] -5.9778/3 -5.9973/4 -6.00370/5 0.1232/2 -0.0629/1
[70] -3.2358/3 -3.2558/4 -3.2615/5 0.0267/1 -0.0629/2
[90] -0.8318/3 -0.8516/4 -0.8563/5 0.2219/2 -0.0629/1
Resize
[100,300] -0.0202/1 -0.0204/2 -0.0205/3 -1.8058/5 -0.0629/4
[150,450] -0.0096/1 -0.0115/2 -0.0116 -1.0502/5 -0.0629/4
[200,600] -0.0040/1 -0.0071/2 -0.0071/2 -0.4660/4 -0.0629/3
[250,750] -0.0013/1 -0.0044/2 -0.0052/3 -0.2056/5 -0.0629/4
[300,900] 0.0008/1 -0.0027/2 -0.0040/3 -0.2056/5 -0.0629/4
Rotation
[25] -2.4793/3 -2.4844/4 -2.4871/5 -1.4738/2 -0.0629/1
[70] -2.1654/2 -2.1707/3 -2.1742/4 -2.2096/5 -0.0629/1
[100] -1.2720/2 -1.2780/3 -1.2817/4 -1.7788/5 -0.0629/1
[200] -2.1649/3 -2.1708/4 -2.1736//5 -1.5179/2 -0.0629/1
[300] -2.7311/3 -2.7365/4 -2.7391/5 -1.7187/2 -0.0629/1
Table 5: Comparison results of our proposed SWA model and four different approaches with seven attacks on Lena image
based on SNR (dB) metric with ranks. Best results are given in boldface.
A Hybrid Wavelet-Shearlet Approach to. . . Informatica 41 (2017) 3–24 17
Figure 7: Graph showing the comparison of our proposed
SWA model with other four methods based on PSNR (dB)
values for multi-attacks on Lena image and copyright im-
age.
DWT_DCT_Joint in (10%-50%) compression ratios. Re-
sults also proved the efficiency of our algorithm with vari-
ous types of attacks such as Gaussian Noise, Salt and Pep-
pers, Resizing and Rotation, while the results proved the
efficiency of DWT_Arnold method with median and crop-
ping attacks, but, with a little difference. Under Resize at-
tack the DWT performed better under all sizes.
Perhaps the best error metric is SSIM which measures
the performance in terms of preserving structural similar-
ity between original and watermarking images. Table 6
investigates the impact of applying different methods on
seven types of attack using Lena image with SSIM error
metric. The results show that our approach has achieved
satisfactory results, and on average outperformed the rest
of the methods with (0.9950) similarity value. Although, it
is clear from the results that DWT_Arnold algorithm was
similar to our results when we apply compression attackss.
Results also proved the efficiency of our algorithm with
various types of attacks such as Gaussian Noise, Salt and
Peppers, Resizing and Rotation, while the results proved
the efficiency of DWT_Arnold method with median and
cropping attacks, but, with a little difference.
Finally, Table 7 investigates the impact of applying dif-
ferent methods on seven types of attack using Lena image
with respect to the mean absolute error (MAE) metric. The
results show that our approach has achieved satisfactory re-
sults, and on average outperformed the rest of the methods
with (0.1016) error value. Although, the DWT_Arnold al-
gorithm was similar to our results when we apply compres-
sion attack, except (10%) compression ratio as well in Me-
dian Filter, and Cropping attacks. Results also proved the
efficiency of our algorithm with various types of attacks
such as Gaussian Noise, Salt and Peppers, Resizing and
Rotation.
Figure 8: Graph showing the comparison of our proposed
SWA model with other four methods based on SSIM values
for multi-attacks on Lena image and copyright image.
4.3.3 Comparison of multi-attacks on Lena image
with PSNR and SSIM error metrics
When an attack occurs in an image that may lead to the
loss of information or quality of the image (extracted mes-
sage) that will be extracted. The performance gauged by
error metrics which are used to evaluate the efficiency of
the algorithms used in the embedding and extraction pro-
cesses are classified into two types: subjective techniques,
which are based on a view of humans, and objective tech-
niques. We selected the SSIM, and PSNR (dB) metrics as
the subjective and objective representative error measures
for the evaluation next. We expose the image to different
number of the attacks sequentially. This is a new compara-
tive method in the field of digital image watermarking with
multi-attacks.
Table 8 presents these multi-attacks and their corre-
sponding results for different watermarking methods. We
performed different combinations of attacks and we started
with the compression attack (with 50% ratio) applied on
image first. As can be seen, the proposed SWA method
achieved significantly superior results compared to the rest
of methods with PSNR = 58.0975 dB, and SSIM = 0.9950.
The DWT achieved the worst results among others with
PSNR = 30.4512 dB, and SSIM = 0.7207. When the image
was further exposed to noise and filtering attacks with ran-
dom values, along with cropping, resize, rotation attacks,
the proposed SWA method achieved the best results con-
sistently with PSNR = 58.0975 dB, and SSIM = 0.9950.
Note that the DCT, DWT_DCT_Joint methods obtained
the worst results with PSNR = 6.9578, SSIM = 0.0773 and
PSNR = 0.0792, respectively. These results indicate the ro-
bustness of our proposed SWA model against multi-attacks.
Figure 7 and Figure 8 shows the comparison of sequential
multi-attacks with respect to the PSNR (dB), and SSIM er-
ror metrics respectively. From the figures it is clear that the
proposed SWA model performs the best with DWT_Arnold
performing the next best. The results with other DWT,
DCT, DWT_DCT_Joint perform rather poorly.
18 Informatica 41 (2017) 3–24 Hassanat et al.
Attack/Methods DWT DCT DWT_DCT_Joint DWT_Arnold Our SWA
Compression
[10] 0.8271/5 0.8272/4 0.8297/3 0.9951/1 0.9950/2
[30] 0.8204/5 0.6798/4 0.9031/3 0.9950/1 0.9950/1
[50] 0.7204/5 0.8108/4 0.9062/3 0.9950/1 0.9950/1
[70] 0.5985/5 0.7946/4 0.8820/3 0.9950/1 0.9950/1
[90] 0.6004/5 0.8125/4 0.9259/3 0.9950/1 0.9950/1
Gaussian
[0.03, 0.003] 0.3726/5 0.4320/4 0.4508/3 0.9751/2 0.9950/1
Noise
[0.09, 0.009] 0.2376/5 0.2539/4 0.2599/3 0.9457/2 0.9950/1
[0.1, 0.01] 0.2250/5 0.2390/4 0.2446/3 0.9428/2 0.9950/1
[0.3, 0.03] 0.1322/5 0.1343/4 0.1368/3 0.9336/2 0.9950/1
[0.5, 0.05] 0.1403/5 0.1427/4 0.1451/3 0.9338/2 0.9950/1
Pepper
[0.01] 0.4789/5 0.6486/4 0.7248/3 0.9949/2 0.9950/1
and Salt
[0.05] 0.2564/5 0.2937/4 0.3081/3 0.9877/2 0.9950/1
[0.09] 0.1633/5 0.1750/4 0.1820/3 0.9826/2 0.9950/1
[0.3] 0.0479/5 0.0487/4 0.0490/3 0.9704/2 0.9950/1
[0.5] 0.0234/5 0.0238/4 0.0232/3 0.9704/2 0.9950/1
Median
[1×1] 0.5839/5 0.8388/4 0.9532/3 0.9950/1 0.9950/1
Filter
[3×3] 0.8477/5 0.9034/4 0.9264/3 0.9950//1 0.9950/1
[5×5] 0.8525/5 0.8720/4 0.8810/3 0.9951/1 0.9950/2
[7×7] 0.8214/5 0.8338/4 0.8392/3 0.9955/1 0.9950/2
[9×9] 0.7950/5 0.8041/4 0.8087/3 0.9793/2 0.9950 /1
Cropping
[10] 0.0075/5 0.0085/4 0.0091/3 0.9901/2 0.9950/1
[30] 0.0593/5 0.0765/4 0.0871/3 0.9958/1 0.9950/2
[50] 0.1716/5 0.2206/4 0.2514/3 0.9965/1 0.9950/2
[70] 0.3163/5 0.4249/4 0.4829/3 0.9958/1 0.9950/2
[90] 0.4950/5 0.6946/4 0.7881/3 0.9962/1 0.9950/2
Resize
[100,300] 0.8609/5 0.8750/4 0.8722/3 0.9816/2 0.9950/1
[150,450] 0.8621/5 0.9182/4 0.9122/3 0.9896/2 0.9950/1
[200,600] 0.8450/5 0.9316/4 0.9318/3 0.9932/2 0.9950/1
[250,750] 0.8368/5 0.9285/4 0.9444/3 0.9954/2 0.9950/1
[300,900] 0.8248/5 0.9166/4 0.9528/3 0.9948/2 0.9950/1
Rotation
[25] 0.1498/5 0.1723/4 0.1875/3 0.9834/2 0.9950/1
[70] 0.1534/5 0.1751/4 0.1952/3 0.9703/2 0.9950/1
[100] 0.1784/5 0.2130/4 0.2398/3 0.9779/2 0.9950/1
[200] 0.1459/5 0.1692/4 0.1851/3 0.9823/2 0.9950/1
[300] 0.1337/5 0.1534/4 0.1651/3 0.9780/2 0.9950/1
Table 6: Comparison results of our proposed SWA model and four different approaches with seven attacks on Lena image
based on SSIM metric with ranks. Best results are given in boldface.
A Hybrid Wavelet-Shearlet Approach to. . . Informatica 41 (2017) 3–24 19
Attack/Methods DWT DCT DWT_DCT_Joint DWT_Arnold Our SWA
Compression
[10] 5.2654/5 4.6638/3 5.2194/4 0.1367/2 0.1016/1
[30] 4.5640/4 6.3203/5 3.2932/3 0.0928/1 0.1016/2
[50] 5.8867/5 4.5083/4 3.0358/3 0.0918/1 0.1016/2
[70] 7.9038/5 4.6638/4 3.1663/3 0.0879/1 0.1016/2
[90] 8.7213/5 4.3617/4 2.2306/3 0.0879/1 0.1016/2
Gaussian
[0.03, 0.003] 15.1372/5 13.3999/4 12.9221/3 0.4395/2 0.1016/1
Noise
[0.09, 0.009] 28.2383/5 27.4299/4 27.2864/3 0.6592/2 0.1016/1
[0.1, 0.01] 30.4388/5 29.7588/4 29.5494/3 0.6592/2 0.1016/1
[0.3, 0.03] 70.9105/4 71.0183/5 71.0116/3 0.7363/2 0.1016/1
[0.5, 0.05] 99.7833/4 99.8240/5 99.9451/3 0.7188/2 0.1016/1
Pepper
[0.01] 9.32670/5 5.21240/4 2.2142/3 0.1299/2 0.1016/1
and Salt
[0.05] 14.1145/5 10.1257/4 7.3025/3 0.3057/2 0.1016/1
[0.09] 18.8807/5 14.9787/4 12.2862/3 0.3584/2 0.1016/1
[0.3] 44.0520/5 41.2690/4 38.8275/3 0.4590/2 0.1016/1
[0.5] 67.9147/5 65.6387/4 64.3678/3 0.4834/2 0.1016/1
Median
[1×1] 88.1478/5 4.0007/4 0.9288/3 0.0879/1 0.1016/2
Filter
[3×3] 3.96670/5 3.0514/4 2.2597/3 0.0889/1 0.1016/2
[5×5] 4.18210/5 3.6768/4 3.3332/3 0.0938/1 0.1016/2
[7×7] 4.86260/5 4.4538/4 4.2133/3 0.1338/2 0.1016/1
[9×9] 5.56320/5 5.1595/4 4.9448/3 0.4248/2 0.1016/1
Cropping
[10] 123.8123/5 123.7751/4 123.7279/3 0.2656/2 0.1016/1
[30] 114.3665/5 114.0056/4 113.6263/3 0.0977/1 0.1016/2
[50] 96.12130/5 95.12180/4 94.06350/3 0.0684/1 0.1016/2
[70] 69.48680/5 67.5075/4 65.7319/3 0.0820/1 0.1016/2
[90] 29.8192/5 26.4739/4 23.8796/3 0.0498/1 0.1016/2
Resize
[100,300] 4.1504/5 3.9113/4 3.9644/3 0.3857/2 0.1016/1
[150,450] 3.8638/5 2.9439/4 3.0264/3 0.2705/2 0.1016/1
[200,600] 3.9695/5 2.5879/4 2.5079/3 0.1729/2 0.1016/1
[250,750] 3.9839/5 2.5813/4 2.1687/3 0.1260/2 0.1016/1
[300,900] 4.1283/5 2.7542/4 1.9236/3 0.1240/2 0.1016/1
Rotation
[25] 81.9561/5 81.8842/4 81.8376/3 0.3340/2 0.1016/1
[70] 80.5659/5 80.4934/4 80.4498/3 0.4355/2 0.1016/1
[100] 74.5938/5 74.5079/4 74.4594/3 0.3838/2 0.1016/1
[200] 84.3401/5 84.2893/4 84.2609/3 0.3428/2 0.1016/1
[300] 85.6902/5 85.6161/4 85.5812/3 0.3770/2 0.1016/1
Table 7: Comparison results of our proposed SWA model and four different approaches with seven attacks on Lena image
based on MAE metric with ranks. Best results are given in boldface.
20 Informatica 41 (2017) 3–24 Hassanat et al.
Attack DWT DCT DWT_DCT_Joint DWT_Arnold Our SWA
PSNR SSIM PSNR SSIM PSNR SSIM PSNR SSIM PSNR SSIM
Compr.50 30.4512 0.7207 32.7898 0.8108 35.5095 0.9063 51.6591 0.9950 58.0975 0.9950
Gaussian
10.1372 0.1291 10.1321 0.1281 10.1422 0.1298 49.3246 0.9242 58.0975 0.9950Noise
0.3, 0.03
Gaussian
10.0453 0.1198 7.2584 0.0164 8.1910 0.0286 51.0662 0.9662 58.0975 0.9950
Noise
0.3, 0.03
Pepper & Salt
0.3
Gaussian
10.4129 0.2116 10.5051 0.3132 10.5011 0.3169 49.3081 0.9184 58.0975 0.9950
Noise
0.3, 0.03
Pepper & Salt
0.3
Median Filter
1× 1
Gaussian
10.4051 0.2123 10.4917 0.3140 10.5022 0.3140 51.5746 0.9743 58.0975 0.9950
Noise
0.3, 0.03
Pepper & Salt
0.3
Median Filter
1× 1
Cropping 30
Gaussian
10.6769 0.0664 10.6127 0.3886 10.6494 0.3916 50.8800 0.9676 58.0975 0.9950
Noise
0.3, 0.03
Pepper & Salt
0.3
Median Filter
1× 1
Cropping 30
Resize 200,600
Gaussian
7.3280 0.0153 6.9578 0.0773 6.9597 0.0792 55.6671 0.9929 58.0975 0.9950
Noise
0.3, 0.03
Pepper & Salt
0.3
Median Filter
1× 1
Cropping 30
Resize 200,600
Rotation 50
Table 8: Comparison results of our proposed SWA model and four different approaches with multi-attacks on Lena image
based on PSNR (dB), SSIM error metrics. Compression (50%) attack was applied first and the remaining attacks are
applied sequentially. Best results are given in boldface.
A Hybrid Wavelet-Shearlet Approach to. . . Informatica 41 (2017) 3–24 21
Method Embed. Extr. Total
DWT 7.6934 5.1184 12.8118
DCT 1.9716 1.3098 3.2814
DWT_DCT_Joint 5.8207 3.1113 8.932
DWT_Arnold 1.1819 1.4375 2.6194
Our SWA 6.081 7.1644 13.2454
Table 9: Watermarking (embedding/extraction) time con-
sumed (in seconds) by different approaches using Lena im-
age and copyright message.
Ref. Approach PSNR
[53] Arnold 63.25
[45] Arnold + DCT 43.82
[29] DWT + Arnold 62.79
[22] DWT + DCT 57.67
[35] DWT+Shearlet 67.54
[18] Entropy + Hadamard 42.74
[26] Log-average luminance 62.49
[17] DWT + DCT + SVD 57.09
[11] DFT + 2D histogram 49.45
Our DWT + DST + Arnold 68.89
Table 10: Comparison of PSNR (dB) values of the water-
marked Lena image using some recent methods with our
proposed SWA method.
4.4 Timing and other watermarking
methods comparison
In Table 9 we show the total time consumed (in seconds)
for each methods compared here in terms of the embedding
and extraction of the message from a 1024×2014 image. It
can be noted that the proposed SWA consumed the highest
amount of time particularly in the extraction phase, this is
due to the use of three different types of transforms. The
DW_Arnold transform based method takes overall the least
amount of time.
Finally, in Table 10 we show the PSNR (dB) compari-
son results with some more recent studies available in the
literature. Note that these methods also utilize the stan-
dard test Lena image, and the values indicate that our pro-
posed SWA outperforms them with highest PSNR = 68.89
dB, followed by [35] which has PSNR = 67.54 dB, with
pure Arnold transform based approaches fairing better than
other transforms.
5 Conclusions and future works
Digital watermarking is an active area of research within
security and there are many automatic systems that have
been presented to secure the ownership information of the
digital image based on watermarking. These systems uti-
lized available techniques from image processing and data
mining areas and apply them to digital images. In this pa-
per, we studied a new hybrid model called SWA which is
based on shearlet, wavelet, and Arnold transforms for effi-
cient, and robust digital image watermarking. This model
combines three transforms - discrete wavelet (DWT), dis-
crete shearlet transform (DST), and Arnold transform - and
their respective advantages. We utilized standard error im-
age metrics to evaluate the proposed SWA method with
seven types of attacks consists of different values of pa-
rameters; as well as multi-attacks which was used as a new
way for comparing the effects of the attack on the image.
Our results show that the proposed method is not affected
by multi-attacks since applying shearlet at HL sub-band re-
duced the influence of watermarked image in any attack.
Shearlet transform generates a series of matrices that ob-
tained from the HL sub-band, where this sub band contains
various features of the original image. In this way, the pro-
posed SWA algorithm preserves a great deal of informa-
tion. As a consequence, the attack is distributed to all of
the shearlet derived matrices and the probability of chang-
ing the value of the pixel that has been embedding by the
attack is very low. Based on this analysis, the results were
stable or semi-static whatever the type of attack and its val-
ues.
Comparison results proved the robustness and strength
of the proposed SWA method again other transform based
state of the art methods. These results show that the pro-
posed SWA model can be useful in securing image com-
ponent in watermarking area. According to the results
achieved by the proposed hybrid model, we consider that it
is encouraging to apply this hybrid with several multimedia
systems such as Video, text and audio, we expect that this
system will achieve satisfactory results. Since the current
research trends towards the multicore computing systems,
our model may increase protection of videos transmission
ownership.
Although the performance of the proposed method was
the best comparing to state-of-the-art methods, it consumes
more computational time than other approach, reducing
this is one of the important future works. In this work, we
applied shearlet transform at HL sub-band from level one
in the wavelet transform, as another future work, we pro-
pose to go ahead towards applying shearlet transform with
wavelet transform at the second and third level as well. Fur-
ther, we can perform with other sub-bands such as LL, LH
and HH to see if the robustness can be increased when cer-
tain types of attacks are applied. In our proposed method,
we applied shearlet transform on the results of the wavelet
transform, shearlet transform can also be applied with sev-
eral other enhanced transforms like joint DWT with DCT
etc.
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 Informatica 41 (2017) 25–30 25 
An Improved Gene Expression Programming Based on Niche 
Technology of Outbreeding Fusion 
Chao-xue Wang, Jing-jing Zhang, Shu-ling Wu, Fan Zhang 
School of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi'an, China 
E-mail: 13991996237@163.com 
http://www.xauat.edu.cn/en 
 
Jolanda G. Tromp 
Department of Computer Science, State University of New York Oswego, USA 
E-mail: jolanda.tromp@oswego.edu 
 
Keywords: gene expression programming, out breeding fusion, niche technology 
Received: October 11, 2016 
An improved Gene Expression Programming (GEP) based on niche technology of outbreeding fusion 
(OFN-GEP) is proposed to overcome the insufficiency of traditional GEP in this paper. The main 
improvements of OFN-GEP are as follows: (1) using the population initialization strategy of gene 
equilibrium to ensure that all genes are evenly distributed in the coding space as far as possible; (2) 
introducing the outbreeding fusion mechanism into the niche technology, to eliminate the kin 
individuals, fuse the distantly related individuals, and promote the gene exchange between the excellent 
individuals from niches. To validate the superiority of the OFN-GEP, several improved GEP proposed 
in the related literatures and OFN-GEP are compared about function finding problems. The 
experimental results show that OFN-GEP can effectively restrain the premature convergence 
phenomenon, and promises competitive performance not only in the convergence speed but also in the 
quality of solution. 
Povzetek: V prispevku je predstavljena izboljšava genetskih algoritmov na osnovi niš in genetskega 
zapisa. 
1 Introduction 
Gene expression programming (GEP) was invented by 
Candida Ferreira in 2001[1, 2], which is a new 
achievement of evolutionary algorithm. It inherits the 
advantages of Genetic Algorithm (GA) and Genetic 
Programming (GP), and has the simplicity of coding and 
operation of GA and the strong space search ability of 
GP in solving complex problems [3]. GEP has more 
simplify on data set reduction than other intelligent 
computing technologies such as rough set, clustering, and 
abstraction. [4-6]. Currently, GEP becomes a powerful 
tool of function finding and has been widely used in the 
field of mechanical engineering, materials science and so 
on [7, 8], but the problem of low converging speed and 
readily being premature still exists like other 
evolutionary algorithms. 
So far, the domestic and foreign scholars proposed 
different improvements about the traditional GEP in the 
field of function finding. Yi-shen Lin introduced the 
improved K-means clustering analysis into GEP, by 
adjusting the min clustering distance to control the 
number of niches, to improve the global searching ability 
[9]. Tai-yong Li designed adaptive crossover and 
mutation operators, and put forward the measure method 
of population diversity with weighted, to maintain the 
population diversity in the process of evolution [10]. 
Yong-qiang Zhang introduced the superior population 
producing strategy and various population strategy to 
improve the convergence speed of the algorithm and the 
diversity of population [11]. Shi-bin Xuan proposed the 
control of mixed diversity degree (MDC-GEP) to ensure 
the different degree in the process of evolution and avoid 
trapping into local optimal [12]. Hai-fang Mo adopted 
the clonal selection algorithm with GEP code for 
function modelling (CSA-GEP), to maintain the diversity 
of population and increase the convergence rate [13]. 
Yan-qiong Tu proposed an improved algorithm based on 
crowding niche, and the algorithm contributed to push 
out the premature individuals by penalty function and 
made the better individuals have greater probability to 
evolve [14].  
To further improve the performance of the GEP, this 
paper proposes an improved gene expression 
programming based on niche technology of outbreeding 
fusion (OFN-GEP). The main ideas are as follows: (1) 
using the population initialization strategy of gene 
equilibrium to ensure that all genes are evenly distributed 
in the coding space as far as possible; (2) introducing the 
outbreeding fusion mechanism into the niche technology, 
to eliminate the kin individuals, fuse the distantly related 
individuals, and promote the gene exchange between the 
excellent individuals from different sub-populations. The 
experiments compared with other GEP algorithms 
26 Informatica 41 (2017) 25–30 C.Wang et al.  
 
proposed in the related literatures about function finding 
problems are executed，and the results show that OFN-
GEP can overcome the premature convergence 
phenomenon effectively during the evolutionary process, 
and has high solution quality, fast convergence rate.  
2 Standard gene expression 
programming  
Standard gene expression programming (ST-GEP), 
which was firstly put forward by Candida Ferreira in 
2001[1, 2], could be defined as a nine-meta 
group:
0{ , , , , , , , , }GEP C E P M T    , where 
C is the coding means; E is the fitness function; 0P is the 
initial population; M is the size of population;  is the 
selection operator;  is the crossover operator; is the 
point mutation operator;  is the string mutation 
operator; T is the termination condition. In GEP, 
individual is also called chromosome, which is formed 
by gene and linked by the link operator. The gene is a 
linear symbol string which is composed of head and tail. 
The head involves the functions from function set and the 
variables from the terminator set, but the tail merely 
contains the variables from the terminator set. Like GA 
and GP, GEP follows the Darwinian principle of the 
survival of the fittest and uses populations of candidate 
solutions to a given problem to evolve new ones, and the 
basic steps of ST-GEP are as follows [1, 2]: 
(1) Inputting relevant parameters, creating the initial 
population;  
(2) Computing the fitness of each individual; 
(3) If the termination condition is not met, go on the 
next step, otherwise, terminate the algorithm;  
(4) Retaining the best individual; 
(5) Selecting operation;  
(6) Point mutating operation;  
(7) String mutating operation (IS transposition, RIS 
transposition, Gene transposition);  
(8) Crossover operation (1-point recombination, 2-
point recombination, Gene recombination); 
(9) Go to (2). 
3 Gene expression programming 
based on niche technology of 
outbreeding fusion 
The flowchart of the OFN-GEP is schematically 
represented in Fig 1. Its main steps are as follows:  
Step 1: Adopt the population initialization strategy of 
gene equilibrium to generate the population P, and set up 
the maximum MAX and the minimum MIN about the 
number of individuals in the niche. Then divide the 
initial population into several equal niches;  
Step 2: Perform the genetic operators within each 
niche, which including point mutation, string mutation 
(IS, RIS, Gene transposition) and recombination (1-point, 
2-point, gene recombination). Then use the pre-selection 
operator to protect the best individual in every niche; 
 
Start
Initialize and evaluate fitness
Iteration is over? EndYes
Genetic operators
(point mutation,IS、RIS、gene transposition,
1-point、2-point、gene recombination)
No
Divide population into several 
equal niches
Use the dynamic niche 
adjustment strategy to change 
the size of niches adaptively
Adopt the outbreeding fusion 
mechanism to fuse niches
Tournament selection operator 
with elitist strategy
Pre-selection operator
 
Figure 1: The flowchart of OFN-GEP. 
Step 3: Use the outbreeding fusion mechanism to 
eliminate the kin individuals and fuse the distant relatives 
between two niches, and introduce some random 
individuals at the same time; 
Step 4: Adopt the dynamic adjustment strategy to 
change the size of niches according to the maximum 
MAX and the minimum MIN; 
Step 5: Perform the tournament selection operator 
with elitist strategy; 
Step 6: Go to Step 2 until the iteration is over. 
3.1 Population initialization 
This algorithm adopts the population initialization 
strategy of gene equilibrium to increase the initial 
population diversity. The idea of this strategy is to let all 
genes are distributed uniformly in the coding space, so 
that the initial population diversity is rich. This strategy 
can reduce the time of search process and achieve the 
global optimal solution at a rapid speed [15]. 
3.2 Fitness function 
In statistics, the method to assess the relevance degree 
between two groups of data usually uses the correlation 
coefficient. References [16], the fitness function is 
devised as: fitness=R2=1-SSE/SST, where 
             
2
1
ˆ( )
m
j j
j
SSE y y

                                (1) 
       
2
1
( )
m
j
j
SST y y

                                  (2) 
where, jy  is the observation data; ˆ jy is the forecast data 
which is computed with formula and observation data; 
y  is the mean of y ; SSE is the residual sum of squares; 
An Improved Gene Expression Programming Based on... Informatica 41 (2017)25–30 27 
SST is the total sum of squares of deviations; m is the 
size of data. 
3.3 Pre-selection 
The pre-selection operator is: the offspring individual can 
instead of his father and access to the next generation 
only when the fitness of the new individual is bigger than 
his father. Due to the similarity of the offspring 
individual and his father, an individual can be replaced 
by his structure similar individual, which can maintain 
the diversity of population and protect the best individual 
in population. 
3.4 A niche technology of outbreeding 
fusion  
The niche technology of outbreeding fusion includes two 
aspects: one is to use the outbreeding fusion mechanism 
to eliminate the kin individuals, fuse the distant relatives 
between two niches and promote the gene exchange 
between the best individuals, which improving the 
diversity of population and the quality of solutions; the 
other is to use the dynamic niche adjustment strategy to 
change the size of niches adaptively [17], which 
maintaining the genetic diversity of niches.  
Aiming at the judgment of the distant individuals in 
outbreeding fusion, this paper adopts the calculation 
methods of recessive hamming distance (individual 
fitness, the essence differences between individuals), and 
dominant hamming distance (the appearance differences 
between individuals), and the judge rules between kin 
and distant relatives as well in literature [18], to judge the 
kinship between individuals. 
The niche technology of outbreeding fusion 
operators is as follows: 
(1) Select two niches randomly, and merge all 
individuals of the two niches (which are 
supposed as N1 and N2, and before fusion, their 
sizes are S1 and S2 respectively) into niche N1; 
go to (2); 
(2) Adopt the outbreeding fusion strategy 
(Algorithm 1) to eliminate the kin individuals, 
and then obtain the size S1’ of the modified N1; 
go to (3); 
(3) If S1’ is bigger than the maximum MAX, 
corresponding MAX individuals will be selected 
out by tournament selection; then adjust S1’ and 
go to (5); else go to (4); 
(4) If S1’ is smaller than minimum MIN, the new 
individuals will be introduced randomly until the 
smallest size MIN is satisfied; then adjust S1’ and 
go to (5); 
(5) Construct N2 by the individuals generated 
randomly, and the size S2’ of N2 satisfies the 
equation S2’ =S1+S2–S1’. 
Algorithm 1: Outbreeding judgment 
 Sort the fitness of the fusion individuals in 
ascend (or descend); 
 Compute the dominant and recessive hamming 
distance between two adjacent individuals; 
 If the dominant hamming distance between two 
adjacent individuals is less than the setting 
threshold M1, and the recessive hamming 
distance is less than the setting threshold M2, 
then the two individuals are kin relatives; 
otherwise, they are the distant relatives; 
 Eliminate the lower fitness individual between 
the kin relatives, and retain the other one. 
4 Experiments and results 
In this section, two experiments are designed to justify 
the effectiveness and competitiveness of OFN-GEP for 
function finding problems, the general parameters setting 
of experiments are shown as Table 1. The source codes 
are developed by MATLAB 2009a, and run on a PC with 
i7-2600 3.4 GHz CPU, 4.0 GB memory and Windows 7 
professional sp1.  
4.1 Test for the effectiveness of OFN-GEP 
To evaluate the improved effect of OFN-GEP, this paper 
adopts the F function, which was used in literature [1] as 
shown in equation (3), and the 10 groups of training data 
are produced by F. OFN-GEP is compared with the DS-
GEP in literature [19]. The test results are shown in 
Table 2, the evolution curve is shown in Fig 1, Fig 2 
Table 1: The parameter settings of experiments. 
Option Test A Test B 
Times of runs 50 50 
Max evolution generation 200 200 
Size of population 100 100 
Function set +,-,*,/ 
+, -, *, /, ln, exp, S, 
Q, sin, cos, tan, cot 
Terminator set a  
Link operator + + 
The length of head 6 6 
Number of gene 5 5 
Point mutation rate 0.4 0.4 
IS and RIS rate 0.3 0.3 
Crossover rate 0.3 0.3 
Recombination rate 0.3 0.3 
Length of IS element {1,2,3,4,5} {1,2,3,4,5} 
Length of RIS element {1,2,3,4,5} {1,2,3,4,5} 
Size of tournament 3 3 
The number of niches 5 5 
The minimum threshold 
of the size of niche 
10 10 
The maximum threshold 
of the size of niche 
60 60 
The threshold of the 
recessive hamming 
distance 
0.1 0.1 
The threshold of the 
dominant hamming 
distance 
0.5 0.5 
Note: S is Square, Q is Sqrt, and Exp is ex. 
 
28 Informatica 41 (2017) 25–30 C.Wang et al.  
 
separately, where both the optimizing rate and the 
average generations of convergence of OFN-GEP are 
obviously superior to DS-GEP. 
4 3 2
n:5 4 +3 +2 +1n n nF a a a a                   (3) 
Table 2: The results of experiment A. 
Option DS-GEP OFN-GEP 
Times of runs 50 50 
Times of hit 41 48 
Optimizing ratio 82% 96% 
Average generations of 
convergence 
87 41 
 
As is shown in Table2, the average generations of 
convergence that achieves the optimal solution in OFN-
GEP algorithm is less than the one in DS-GEP, so the 
OFN-GEP algorithm can improve convergence speed 
efficiently. From the evolution curves in Fig 2 and Fig 3 
the volatility of average fitness in OFN-GEP is greater 
than the one in DS-GEP, and this says the differences 
between individuals are greater and the population 
diversity is better in OFN-GEP. 
 
Figure 2: The evolution curve of DS-GEP. 
 
Figure 3: The evolution curve of OFN-GEP. 
4.2 Test for the competitiveness of OFN-
GEP 
To test the competitiveness of OFN-GEP, MDC-GEP 
[12] and S-GEP [20] are chosen to compare with OFN-
GEP. Test functions are partly the same with the ones in 
[12] and [20]. They are shown as (4) - (14). The test 
results are shown in Table 3. 
211:8 2 cos(2 ), [0,5]xF e x x                 (4) 
2 22: cos ( ), [0,3]F x x                                  (5) 
1
3: , [0,5 ]
2 sin( )
F x
x


                          (6) 
2 24:5 log(cos (2 ) ), [0,5 ]F x x x     (7) 
3 2
3
3 2 1
5: , [0,5]
5 2
x x
F x
x
 


                       (8) 
 6: cos(10 ), [0,2]xF x                                (9) 
2
2
3 5 1
7 : , , [0,5]
5 3
x x
F x y
y
 


                  (10) 
2
3
sin( 2)
8: , , [ 5,5]
cos( 2.5) 3
x
F x y
y

 
 
        (11) 
2 2
9: , , [ 2,2]x yF xye x y                          (12) 
3
1 2
4 5
sin cos
10 : tan( )
[0,2 ], 1,2,....5
x
i
x x
F x x
e
x i

 
 
              (13) 
2 2: 4.251 ln( ) 7.243 , [ 1,1]aFv a a e a     (14) 
Table 3: Test results of experiment B. 
Function Algorithm Max 
fitness 
Min 
fitness 
Average 
fitness 
F1 
MDC-GEP 0.9675 0.8231 0.9263 
OFN-GEP 0.96825 0.8782 0.9334 
F2 
MDC-GEP 0.9991 0.7865 0.9371 
OFN-GEP 1 0.8112 0.9446 
F3 
MDC-GEP 0.9916 0.8645 0.9843 
OFN-GEP 0.9959 0.8901 0.9505 
F4 
MDC-GEP 0.9812 0.8743 0.9476 
OFN-GEP 0.9898 0.9430 0.9696 
F5 
MDC-GEP 0.9587 0.6856 0.8735 
OFN-GEP 0.9413 0.7641 0.8408 
F6 
MDC-GEP 0.9954 0.8237 0.9465 
OFN-GEP 1 0.9603 0.9913 
F7 
MDC-GEP 0.9462 0.8133 0.8956 
OFN-GEP 0.9792 0.8387 0.9317 
F8 
MDC-GEP 0.9473 0.7012 0.8653 
OFN-GEP 0.9525 0.7464 0.8719 
F9 
MDC-GEP 0.9673 0.6782 0.8750 
OFN-GEP 0.9415 0.7099 0.8777 
F10 
MDC-GEP 0.9771 0.8954 0.9520 
OFN-GEP 0.9909 0.9109 0.9605 
Fv 
S-GEP 0.9991   
OFN-GEP 0.9988 0.9796 0.9926 
An Improved Gene Expression Programming Based on... Informatica 41 (2017)25–30 29 
 
From Table 3, for most functions, the max fitness, 
min fitness and average fitness increase obviously in 
OFN-GEP compared with the MDC-GEP, S-GEP. This 
shows the effectiveness and competitiveness of OFN-
GEP. For relatively simple function F2 and F6, the 
fitness of OFN-GEP can achieve 1, but for F5, F9, Fv, 
their fitness is less than (very close to) the results in 
MDC-GEP. The reasons are that GEP algorithm is a 
random algorithm, the algorithm parameters have great 
influence on the results of experiment, and the 
parameters of every function to obtain the best fitness are 
different. So, this situation exists which few functions 
can’t obtain a better fitness value under the same 
parameters. 
5 Conclusion 
This paper puts forward an improved gene expression 
programming based on niche technology of outbreeding 
fusion (OFN-GEP), and verifies the effectiveness and 
competitiveness of the proposed algorithm about the 
function finding problems. The improvements in the 
paper are that: (1) using the population initialization 
strategy of gene equilibrium to ensure that all genes are 
evenly distributed in the coding space as far as possible; 
(2) introducing the outbreeding fusion mechanism into 
the niche technology, to eliminate the kin individuals, 
fuse the distantly related individuals, and promote the 
gene exchange be-tween the excellent individuals from 
different sub-populations. To validate the effectiveness 
and competitiveness of OFN-GEP, several improved 
GEP proposed in the related literatures and OFN-GEP 
are compared as regards function finding problems. The 
experimental results show that OFN-GEP can effectively 
restrain the premature convergence phenomenon, and 
promises competitive performance not only in the 
convergence speed but also in the quality of solution. 
6 Acknowledgment 
Support from the Natural Science Basic Research 
Plan in Shanxi Province of China (NO.2012JM8023), 
and the Scientific Research Program Funded by Shanxi 
Provincial Education Department (No.12JK0726) are 
gratefully acknowledged. 
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 Informatica 41 (2017) 31–37 31 
Identity-based Signcryption Groupkey Agreement Protocol Using 
Bilinear Pairing 
Sivaranjani Reddi,  
Anil Neerukonda Institute of Technology and Science, Bhimunipatnam, India 
E-mail: sivaranjani.cse@anits.edu.in 
 
Surekha Borra 
K.S. Institute of Technology, Bangalore, India 
E-mail: borrasurekha@gmail.com 
 
Keywords: bilinear pairing, encryption, group key agreement, signcryption 
Received: January 2, 2017 
This paper proposes a key agreement protocol with the usage of pairing and Malon-Lee approach in key 
agreement phase, where users will contribute their key contribution share to other users to compute the 
common key from all the users key contributions and to use it in encryption and decryption phases. 
Initially the key agreement is proposed for two users, later it is extended to three users, and finally a 
generalized key agreement method, which employs the alternate of the signature method and 
authentication with proven security mechanism, is presented. Finally, the proposed protocol is 
compared with the against existing protocols with efficiency and security perspective. 
Povzetek: Razvit je nov varnostni protokol za uporabo več ključev. 
1 Introduction 
Key Establishment is the procedure in which more than 
one user launches the session key, and is consequently 
used in accomplishing the cryptographic services like 
confidentiality or integrity. In general, key establishment 
protocols follow the key transfer approach, where one 
user decides the key and communicate it to other user. In 
contrast, for key agreement protocols all the users in the 
communication are involved in key establishment 
process. Further, these key agreement protocols provides 
the implicit authentication if the user assures that no 
other user or intruder involved in the communication 
knows the confidential key value. Hence, a protocol 
which possesses the implied key authentication to all the 
users involved in the group communication is called 
authenticated group key agreement protocol. Key 
Confirm is one property of the group key agreement 
protocol where one user involved in group 
communication assures that the other user in the group is 
under the control of the confidential key. When a 
protocol possesses both implicit authentication and key 
confirmation, that protocol is called as explicit key 
authentication. More details about key agreement 
protocols are discussed in [1, 21, 22, 23, 24]. 
 This paper emphasis is on an authentic key 
agreement technique. Diffie-Hellman [2] proposed first 
key agreement. However, it is insecure against middle 
attack. Afterwards, many key agreement methodologies 
were published by various authors, but some users 
prerequisite a Public Key Infrastructure (PKI), needs 
more calculation and preserving efforts. Shamir[4] had 
initiated the concept called cryptosystem using user 
identity in which users public key can be  calculated  
using the users unique attributes (e.g. Email, mobile no. 
etc), his private key is estimated by the trustworthy user 
referred as Private Key Generator (PKG).  After that 
public key crypto system is formulated using user 
identity, which had simplified the process of key 
administration thus become a substitute to certificate 
centred PKI. Later, Joux[3] had proposed, Bilinear 
pairing based group key agreement protocol.   
Boneh[5],formally published an ID based encryption 
scheme using bilinear pairings. Many protocols were 
proposed [13, 11, 10, 8, 15], analyzed and some of them 
were broken [14,9,17,12,16]. Few pairing based 
applications use a pairing-friendly elliptic curve of prime 
numbers. There are different coordinate systems that can 
be used to represent points on elliptic curves such as 
Jacobian, Affine and Homogeneous. Inversion to 
multiplication ratio threshold can be used to decide the 
efficiency of coordinate system. In this work timing 
results of pairing is being reported for both affine and 
projective coordinates using BN-curve. All fast 
algorithms to compute pairings on elliptic curves are 
based on so as Miller’s algorithm [26]. In this paper, 
focus is on ID based authenticated key agreement using 
pairings with the two users. It is based on the signature 
scheme suggested Malone-Lee [6]. Furthermore, it is 
elaborated and evaluated against some of the existing 
ones in terms of efficiency and security. Pairing based 
mathematical properties were discussed in section 2, 
Marko Hölbl protocol the existing protocol was 
discussed in section 3, the proposed protocol was 
explained in section 4 and the next talks about 
performance of proposed technique against the existing 
protocols and finally it was concluded. 
32 Informatica 41 (2017) 31–37 S. Reddi et al.  
2 Preliminaries 
This section presents a notation of bilinear pairing 
operations which are to be used next. 
 
Bilinear maps[5] [6]:  Let (G1,+), (G2,+) and (GT, ・) 
are the two additive and one multiplicative group of 
prime order q > 2k for a security  parameter k  N, then 
there exists a bilinear map  ê : G1 × G2 → GT  that has the 
following properties: 
1. Bilinearity: ê (aP, bQ) = ê (P,Q)ab, where  P,Q Є G1, 
and a, b Є Z, can be reformulated as: 
e(P + Q,R) = e(P,R) e(Q,R) and e(P,Q + R) = 
e(P,Q)e(P,R) for P,Q,R Є G1 
2. Non-degeneracy:  ê (P, Q) = 1, if Q Є G2iff P = 1 Є 
G1. 
3. Computability:  ê (P, Q) is efficiently computable 
if P Є G1 and Q Є G2. 
When G1=G2 and P=Q then that group is termed as 
symmetric bilinear map. 
2.1 Signcryption 
Signcryption is a type of crypto mechanism and offers 
security services. It performs encryption and data signing 
in a single operation, and satisfies the requirements of 
smaller bandwidth and less computational cost by doing 
the operations sequentially. In symmetric encryption 
schemes it is computationally impossible to extract the 
plaintext from the signcrypted message without 
receiver’s private key. As in symmetric digital signature, 
creation of signcrypted text without using the private key 
of the sender is computationally infeasible. Some of the 
existing signcryption mechanisms are as follows: 
 
A.  Malone -Lee ID-based encryption scheme[6] 
The detailed description of the Malonee Lee identity 
based encryption is as follows: 
 
Step 1: (Setup): A PKG considers hash functions 
𝐻1: {0,1}
∗ → 𝐺1, 𝐻2: {0,1}
∗ → 𝑍𝑞
∗  , 𝐻3: 𝐺2→ {0,1}
𝑙 and a 
generator P. The PKG can choose a random integer as 
master private key s and calculates 𝑃𝑝𝑢𝑏=sP. Finally 
publishes the parameters <𝑃, ?̂?, 𝑃𝑝𝑢𝑏  , 𝐻1, 𝐻2, 𝐻3>, by 
keeping PKG’s secret keys as secret. 
 
Step 2: (Extract): For given user identification 𝐼𝐷 ∈
{0,1}∗, the PKG calculates the public key 𝑄𝐼𝐷= 𝐻1(𝐼𝐷) 
and secret key 𝑆𝐼𝐷= s*𝑄𝐼𝐷. 
 
Step 3: (sign):  For the given secret key 𝑆𝐼𝐷 and 
message M ∈ {0,1}∗ , the sender selects  random number 
r ∈ 𝑍𝑞
∗ , and U=rP, then computes r=H2(U|| M), W= 
r*𝑃𝑝𝑢𝑏 , V=r*SID+W, y=e(W,QID), x=𝐻3(𝑦) and C=x⨁ 
M, finalizes the signature as (C,U,V) and then send it to 
receiver side. 
 
Step 4:(unsigncrypt):  Upon receiving the signature 
(C,U,V), receiver computes public key of the sender 
using his identity 𝑄𝐼𝐷= 𝐻1((𝐼𝐷), parse the signature 
(C,U,V) then computes y=e(SID,U), x=𝐻3(𝑦), M=x⨁ C, 
r=H2(U|| M), and then accepts M if e(V,P)= 
e(U, 𝑃𝑝𝑢𝑏)*e(𝑄𝐼𝐷, 𝑃𝑝𝑢𝑏)
𝑟 
Advantages: Eliminates distribution of the public 
key. Authentication of the public key is implicitly 
guaranteed as long as individual user kept his private key 
secure. 
Disadvantage: Establishment of the secure channel is 
required between the user and the PKG. 
 
B.  Boneh IBE cryptosystem[5] 
Boneh has proposed an identity based encryption 
technique to encrypt the message using pairing. It mainly 
contains four algorithms described as follows: 
Step 1: (Setup): A PKG considers two hash functions, 
𝐻1 and  𝐻3. The PKG can choose random s ∈ 𝑍𝑞 master 
private key, and calculates 𝑃𝑝𝑢𝑏=sP. Finally, publishes 
the parameters <𝑃, ?̂?, 𝑃𝑝𝑢𝑏  , 𝐻1 , 𝐻3>, by keeping PKG’s 
secret keys as secret. 
Step 2: (Extract): For the given user identity (𝐼𝐷) ∈
{0,1}∗ the PKG calculates publickey 𝑄𝐼𝐷= 𝐻1((𝐼𝐷) and 
secret key 𝑆𝐼𝐷= s*𝑄𝐼𝐷. 
Step 3: (encrypt): An user can choose r, then calculates 
ciphertext(C) for M, be C= (rP, M⨁𝐻3(𝑔𝐼𝐷
𝑟 )) where 
𝑔𝐼𝐷= e (𝑄𝐼𝐷, 𝑃𝑝𝑢𝑏) 
Step 4: (decrypt): from the received C= (U, V) receiver 
computes V ⨁𝐻3(e (𝑆𝐼𝐷, 𝑈)) in order to extract M. 
Advantage: This mechanism is secure against 
forgery under the chosen plaintext attack under Strong 
Diffie Hellman(SDH) assumption without using oracle 
model. 
Disadvantages: All the hash functions are random 
hash functions. Further, as the public keys are directly 
computed, it leads to avoidance of certificate 
maintenance. 
 
C.  Hesse identity based signature[25] 
A signature is computed and enclosed to M before 
sending onto other side. Upon receiving M along with 
the signature; the receiver tries to verify the signature 
before accepting the M. The detailed Hesse mechanism is 
as follows: 
Step 1: (Setup): A PKG considers hash 
function 𝐻1, 𝐻: {0,1}
∗𝑋𝐺2 → 𝑍𝑞
∗. The PKG can choose s 
master private key and calculates 𝑃𝑝𝑢𝑏=sP. Finally 
publish the parameters <𝑃, ?̂?, 𝑃𝑝𝑢𝑏 , 𝐻1, 𝐻>, by keeping 
PKG’s secret key s as secret. 
Step 2: (Extract): For given user with identity (ID), the 
PKG calculates the public key 𝑄𝐼𝐷= 𝐻1(𝐼𝐷) and the 
secret key 𝑆𝐼𝐷= s*𝑄𝐼𝐷. 
Step 3: (Sign):  for the given secret key𝑆𝐼𝐷 and M ∈
{0,1}∗ , the sender selects  𝑃1 ∈ 𝐺1 and  k ∈ 𝑍𝑞
∗, and then 
computes r= e(𝑃1, 𝑃)
𝑘  , v=H(M,r) and u=v*𝑆𝐼𝐷 +
𝑘*𝑃1., finalizes the signature is (u,v). 
Identity-based Signcryption Groupkey... Informatica 41 (2017) 31–37 33 
 
Step 4: (Verify): for a given public key 𝑄𝐼𝐷 , the 
received M and the signature is (u,v). The receiver 
computes r= 𝑒(𝑢, 𝑃)e(𝑄𝐼𝐷, −𝑃𝑝𝑢𝑏)
𝑘 and accept if v=H 
(M, r). 
Advantages: It is secure against adaptive chosen 
message attack in the random oracle model. 
Disadvantages: As PKG is generating the private 
keys of user, there may be a scope to decrypt or sign any 
message without any authorization. Hence it may not be 
fit to attain non repudiation 
2.2 Security analysis 
The protocol mechanism presented in this paper is 
equipped with the following listed attributes: 
(i) Known key Security: For each session, the participant 
randomly selects hi and ri, results separate independent 
group encryption key and decryption keys for other 
sessions. A leakage of group decryption keys in one 
session will not help in derivation of other session group 
decryption keys. 
(ii) Unknown key share:  In proposed protocol, each 
participant Ui generates a signature 𝜌i using xi. 
Therefore, group participants can verify the 𝜌i if it is 
from authorized person or not. Hence, no non group 
participant can be impersonated. 
(iii) Key compromise impersonate: Due to generation of 
unforgeable signature by the participant Ui,, the 
challenger cannot create the valid signature on behalf of 
Ui. Even if participant Uj’s private key is compromised 
by the adversary, he cannot mimic other participant Ui 
with Uj’s private key. Hence, key is not impersonated in 
the proposed protocol. 
3 Marko Hölbl protocol [7] 
This is an ID-based signature technique using the Hess 
algorithm. It is a two party ID-based authenticated key 
agreement protocol requiring PKG. Mainly divided into 
system setup, private key estimation and key agreement 
phase. 
Phase 1 (setup):  In this phase PKG decides the 
parameters called system parameters, which helps in the 
derivation of common group key agreement by all the 
users in the communication. A PKG formulates 𝐺1 , 𝐺2 
and ?̂? and computes the cryptographic function H, P, a 
random integers as  PKG’s private key and 𝑃𝑝𝑢𝑏 as PKGs 
publickey. All elements are of order q. Finally he 
publishes all the parameters <𝐺1, 𝐺2, 𝑃, ?̂?, 𝑃𝑝𝑢𝑏  , 𝐻>, by 
keeping PKG’s secret keys as secret. 
Where  mapping function  ?̂?: 𝐺1 × 𝐺1  → 𝐺2 
Primitive Generator P:  P ∈ 𝐺1 
Random integer s: s ∈ 𝑍𝑞
∗ 
Public Key 𝑃𝑝𝑢𝑏 =sP 
Hash function H: 𝑍𝑞
∗ → 𝐺1 
Phase 2 (Private key extraction):  In this phase PKG 
derives the public key Qi and private key Si of individual 
user by using their identity  IDi and then broad casts the 
public key and firmly send the privatekey to the 
respective user through secured channel, where Qi =
H(IDi)  and Si = s*Qi. 
Phase 3 (Key agreement):  Since signature verification 
will authenticate the data in deciding which user issued 
this, a message generated from this phase will be used 
later to derive the session key. After choosing the 
receiver (B), sender (A) decides the message and then 
signed the message. Later on both message and the 
signature are sent to the receiver. The receivers compute 
the signature from the received message and then 
compare against the received signature, before deriving 
the key sent by sender. Procedure 1 shows the operations 
summary in key agreement phase. 
 
 
Procedure 1: Marko Hölbl protocol. 
 
Marko Hölbl protocol mechanism results in the following 
computational requirements: 
 In order to exchange message, each user has to 
compute two scalar multiplications, exponentiation, 
hash function and summation. 
 In session key computation, 2 pairings and 2 hashing 
operation, scalar multiplication and exponentiation 
are required. 
4 Proposed protocols 
Group key agreement is the mechanism where two or 
more users are involved in the derivation of the group 
key used to encrypt/decrypt the data.  The major phases 
in the proposed algorithm are: setup, extract, signcrypt 
and unsigncrypt phases as shown in Fig.1. This section 
describes the key agreement protocol between two users, 
three users and n numbers of users. 
Global Parameters  <𝐺1, 𝐺2, 𝑃, ?̂?, 𝑃𝑝𝑢𝑏  , 𝐻> 
 
User A Key Generation 
a ∈ 𝑍𝑞
∗ 
𝑇𝐴 = 𝑎𝑃, 𝑈𝐴 = ?̂?(𝑆𝐴 , 𝑃)
𝑎,  
𝑉𝐴 = 𝐻(𝑇𝐴 , 𝑟𝐴), 𝑊𝐴 = H(𝑉𝐴𝑆𝐴 + 𝑎𝑆𝐴) 
 
User B  Key Generation 
 
b ∈ 𝑍𝑞
∗ 
𝑇𝐵 = 𝑏𝑃, 𝑈𝐵 = ?̂?(𝑆𝐵, 𝑃)
𝑏,  
𝑉𝐵 = 𝐻(𝑇𝐵, 𝑟𝐵),  
𝑊𝐵 = H(𝑉𝐵𝑆𝐵 + 𝑏𝑆𝐵) 
 
 Calculation of secret key by User A 
 
𝑈𝐵
′ =?̂?(𝑊𝐵, 𝑃)?̂?(𝑄𝐵, −𝑃𝑝𝑢𝑏)
𝑉𝐵  
𝑉𝐵=H(𝑇𝐵,𝑈𝐵
′ ) 
𝐾𝐴𝐵=a𝑇𝐵=abP 
 
Calculation of secret key by User B 
 
𝑈𝐴
′ =?̂?(𝑊𝐴, 𝑃)?̂?(𝑄𝐴, −𝑃𝑝𝑢𝑏)
𝑉𝐴     
𝑉𝐴=H(𝑇𝐴,𝑈𝐴
′ ) 
𝐾𝐴𝐵=b𝑇𝐴=abP 
 
 
34 Informatica 41 (2017) 31–37 S. Reddi et al.  
4.1 Proposed protocol for two users  
This protocol is designed based on the Malone-Lee [6] 
ID-based crypto system scheme. It is protected against 
chosen random oracle model under BDH. The advantage 
of this algorithm is to perform the message encryption 
and decryption in only one step to attain security services 
more efficiently, instead of first signing and then 
encryption. This scheme is the combination of Boneh 
IBE cryptosystem with the variant of Hesses Identity 
based signature.  
 
Step 1: (Setup): This phase usually finalizes the number 
of users willing to join the group communication. Once 
the number of users is decided, then PKG will finalize 
the common parameters to be used in the derivation of 
other phase parameters. A PKG considers three hash 
functions H1, H2, H3 and P. PKG can choose a random 
integer s, master private key and calculates Ppub=sP. 
Finally publishes the parameters <P, ê, Ppub , H1, H2, H3>, 
by keeping PKG’s secret key s as secret. 
 
Step 2: Extract: PKG employs user's identity 
information in the derivation of secret and public keys. 
The input for this phase is user identity and produces 
QID and D. PKG uses user A Identity (IDA) ∈ {0,1}
∗and 
calculates public key QIDA= H1(IDA) and secret key 
SIDA= s* QIDA. Once generated SIDAis securely sent to 
user A. This process repeats for user B, in calculating 
QIDB and SIDB using the identity(IDB).  
 
Step 3: Signcrypt: Both users A and B can execute this 
phase in parallel, where individual user uses their SID, 
along with other users public key QID and their key 
contribution k in the derivation of ciphertext and the 
signature generation. 
 
Figure 1: Group key agreement protocol. 
The steps for the signcrypt at user A side is as follows: 
a. User A selects ka ∈ {0,1}𝑙, computes 𝑄𝐼𝐷𝐵= 
𝐻1(𝐼𝐷𝐵).    ------(1) 
b. User A chooses a random number 𝑋𝐴 ← 𝑍𝑞
∗  and set 
𝑈𝐴 = 𝑋𝐴P  -----(2) 
c. Calculates 𝑅𝐴= 𝐻1(𝑈𝐴  ||𝑘𝑎), 𝑊𝐴= 𝑋𝐴.𝑃𝑝𝑢𝑏 , 𝑉𝐴= 
𝑅𝐴.𝑆𝐼𝐷𝐴 + 𝑊𝐴, 𝑌𝐴 =e(𝑊𝐴, 𝑄𝐼𝐷𝐵) , 𝑇𝐴=𝐻3(𝑌𝐴).      ---(3) 
d. Finally computes 𝜎𝐴= 𝑇𝐴⨁ ka and then sends 
𝐶𝐴=(𝜎𝐴, 𝑈𝐴, 𝑉𝐴) to B.  ----(4) 
 
Here A chooses the key ka and communicates to B by 
adding a signature for the verification. Parallely B also 
chooses his contribution in key agreement kb, User B 
follows the above steps, uses his private key 𝑆𝐼𝐷𝐵 and 
A’s public key 𝑄𝐼𝐷𝐴 and then sends 𝐶𝐵=(𝜎𝐵 , 𝑈𝐵 , 𝑉𝐵) to 
A. 
 
Figure 2: Key agreement among three users. 
 
Step 4: Unsigncrypt: Key contribution of A can be 
extracted from 𝐶𝐴 after comparing the signature 
validation condition. B uses the following steps in the 
derivation of ka from received 𝐶′𝐴. 
a) Computes the A’s public key  𝑄𝐼𝐷𝐴=𝐻1(𝐼𝐷𝐴)   ---(5) 
b) parse 𝐶′𝐴=(𝜎′𝐴, 𝑈′𝐴, 𝑉′𝐴), compute 𝑌′𝐴 =e(𝑆𝐼𝐷𝐵 , 𝑈′𝐴) 
, 𝑇′𝐴=𝐻3(𝑌′𝐴), 𝑘𝑎′= 𝑇′𝐴⨁𝜎′𝐴 and 𝑅′𝐴= 𝐻1(𝑈′𝐴  ||𝑘𝑎).  
    ---(6) 
c) Accept ka’ when e(𝑉′𝐴 , 𝑃) = 
𝑒(𝑄𝐼𝐷𝐴, 𝑃𝑝𝑢𝑏)
𝑅′𝐴.e(𝑈′𝐴,𝑃𝑝𝑢𝑏)   ------(7) 
 
Limitations of the work: 
 Proposed technique withstands outsider attacks (i.e. 
adversary is not permitted to exhibit the sender's 
private key with which the cipher text was created). 
 Another limitation is due to the procedure used by the 
receiver in non repudiation. The receiver needs to 
prove to the third party that sender is the authorized 
person of a given plaintext. 
4.2 Group key agreement with three users 
The proposed algorithm is extended to three users and 
their arrangement is shown in Figure 2, where, the setup 
and extraction phase is same as described in section 3. 
During the signcrypt phase, user-1 uses other users 
public key with whom he wants to share the key and then 
computes the respective value C1, j where j ∈{3,2}. 
From the diagram, user-1 calculates 𝐶12 and 𝐶13 and send 
to user-2 and user-3 respectively. Similarly user-2 
calculates their contributions 𝐶21 and 𝐶23 and then send 
to user 1 and 3. After signcrypt phase each user will 
receive the encrypted contributions from other users in 
the group. All the keys will be decrypted and then extract 
the individual key user contributions after validating the 
signature. Once all user signatures were satisfied, 
individual user adds his contribution and apply the XOR 
𝐶𝐴=(𝜎𝐴, 𝑈𝐴, 𝑉𝐴  ) 
𝐶𝐵=(𝜎𝐵, 𝑈𝐵 , 𝑉𝐵 ) 
 
User A 
Step 1: Setup 
Step 2:  
Extract (𝐼𝐷𝐴) 
Step3: 
signcrypt(
𝑆𝐼𝐷𝐴, 𝐼𝐷𝐵 , 𝑘𝑎) 
Step 4:  
Unsigncrypt(
𝑆𝐼𝐷𝐴, 𝐼𝐷𝐵 , 𝜎𝐵) 
extract kb 
K=ka ⨁ kb 
User B 
Step 1: Setup 
Step 2:  
Extract (𝐼𝐷𝐵) 
Step3: 
signcrypt(
𝑆𝐼𝐷𝐵, 𝐼𝐷𝐴, 𝑘𝑏) 
Step 4:  
Unsigncrypt(
𝐼𝐷𝐴, 𝑆𝐼𝐷𝐵, 𝜎𝐴) 
extract ka 
K=ka ⨁ kb 
 
Identity-based Signcryption Groupkey... Informatica 41 (2017) 31–37 35 
 
operation on all the users in group in order to derive the 
session group key. 
4.3 Generalized group key agreement 
Step 1: (Setup): This phase usually finalizes the number 
of users willing to join in the group communication. 
Once the users joining task gets completed, then PKG 
will finalize the common parameters to be used in the 
derivation of other phase parameters. A PKG considers 
hash functions 𝐻1, 𝐻2, 𝐻3 and P. PKG can choose a 
random integer s, master private key and calculates 
𝑃𝑝𝑢𝑏=s*P. Finally publish the parameters 
<𝑃, ?̂?, 𝑃𝑝𝑢𝑏 , 𝐻1 , 𝐻2, 𝐻3>, by keeping PKG’s secret key s 
as secret. 
Step 2: Extract: PKG uses individual user's identity 
information in the derivation of secret and public keys. 
The input for this phase is user identity and produces 
𝑄𝐼𝐷  and 𝑆𝐼𝐷 which represents public and private keys 
respectively. PKG uses user i (1≤ i ≤n) identity (𝐼𝐷𝑖)and 
computes 𝑄𝐼𝐷𝑖= 𝐻1(𝐼𝐷𝑖) and secretkey 𝑆𝐼𝐷𝑖= s*𝑄𝐼𝐷𝑖 , 
then sends 𝑆𝐼𝐷𝑖securily to i.  
For i=1 to n 
Calculate 𝑄𝐼𝐷𝑖= 𝐻1(𝐼𝐷𝑖)     ---(8) 
Calculate 𝑆𝐼𝐷𝑖= s* 𝑄𝐼𝐷𝑖      ---(9) 
Step 3: Signcrypt:  Each user derives the parameters 
individually to other participant and communicates. 
User-1 in the group will first decide ka and then 
calculates other variables:X1, U1, 𝑅1, W1, 𝑌1,i,𝑉1 and 𝑇1,i. 
Similarly user-i uses the signcrypt algorithm to securely 
share his key contribution ki. 
a.  A selects ki ∈ {0,1}𝑙, computes 𝑄𝐼𝐷𝑗= 𝐻1(𝐼𝐷𝑗) (1≤ j 
≤n, j≠ i)  ---(10) 
b. Afterwards he chooses a random number 𝑋𝑖 ← 𝑍𝑞
∗ 
and set 𝑈𝑖 = 𝑋𝑖P  ---(11) 
c. Calculates 𝑅𝑖= 𝐻1(𝑈𝑖  ||𝑘𝑖), 𝑊𝑖= 𝑋𝑖.𝑃𝑝𝑢𝑏 , 𝑉𝑖= 
𝑅𝑖.𝑆𝐼𝐷𝑖 + 𝑊𝑖  ---(12) 
d. For each user j ( j≠ i) , user i computes  
𝑌𝑖,𝑗 =e(𝑊𝑖 , 𝑄𝐼𝐷𝑗) , 𝑇𝑖,𝑗=𝐻3(𝑌𝑖). ---(13) 
e. Finally computes 𝜎𝑖,𝑗= 𝑇𝑖,𝑗⨁ ka and then sends 
𝐶𝑖,𝑗=(𝜎𝑖,𝑗 , 𝑈𝐴, 𝑉𝐴) user –j (1≤ j ≤n, j≠ i).---(14) 
Step 4: Unsigncrypt: User-j uses the following 
steps in the derivation of ki from received 𝐶′𝑖,𝑗 . key 
contribution of ith user can be extracted from 𝐶𝑖,𝑗 after 
comparing the signature validation condition 
a. .  Computes the i’s public key  𝑄𝐼𝐷𝑖=𝐻1(𝐼𝐷𝑖)   ---(15) 
b. Parse 𝐶′𝑖,𝑗=(𝜎′𝑖,𝑗 , 𝑈′𝑖 , 𝑉′𝑖), compute 𝑌′𝑖 =e(𝑆𝐼𝐷𝑗 , 𝑈′𝑖), 
𝑇′𝑖=𝐻3(𝑌′𝑖), 𝑘𝑖′= 𝑇′𝑖⨁𝜎′𝑖 and 𝑅′𝑖= 𝐻1((𝑈′𝑖  ||𝑘𝑖).    -(16) 
c. Accept 𝑘𝑖′ when e(𝑉′𝑖 , 𝑃)=𝑒(𝑄𝐼𝐷𝑖 , 𝑃𝑝𝑢𝑏)
𝑅′𝑖 .e(𝑈′𝑖 ,𝑃𝑝𝑢𝑏).  
 
---    (17) 
5 Performance analyses 
Proposed protocol is compared with Wang [16], Yuan-Li 
[18], Chow–Choo without escrow[19], Choie-jeong-
Lee[20] and Marko Hölbl et.al [7].  Tables 1&2 illustrate 
comparison of the suggested protocol against the existing 
protocols. The efficiency is estimated by considering the 
communication cost and the execution cost. 
Communication cost includes number of rounds and the 
length of message transmitted through the network 
during protocol execution. Overall number of rounds in 
protocol 
 
Figure 3: Generalized key agreement Protocol. 
is the primary concern in practical environments where 
the group users are more in number. Yuan-Li has one 
round operation in key agreement phase, used one 
multiplication and exponentiation, one addition. Protocol 
is secured against the key impersonation, backward and 
forward secrecy. Wang's method almost uses the same 
number of operations as yuan's method, but computation 
time is more.  Chow–Choo without escrow key 
agreement protocol mainly contains two rounds: one is 
extract phase and the other is key agreement phase. 
During the extract phase, one hash function and pairing 
function, remaining operations were used during the key 
agreement phase. 
   
Protocol 
Name 
Computation Cost Commu 
-nication 
Cost pairing Mul Exp Add Hash XOR 
[16] 1 3 0 3 3 0 1 
[18] 1 3 0 2 1 0 1 
[19] 1 4 0 2 1 0 2 
[20] 2 4 0 0 2 0 1 
[7] 3 3 2 1 3 0 3 
Proposed 1 5 0 1 4 1 3 
P2P: total point to point communication per user: Pairing:  total 
number of mapping or pairing operations per user:  Add: Total 
number of addition operations per user: Exp : total  
exponentiations performed per user.: Mul: total scalar 
multiplications computed : XoR: total XOR operations 
computed; Hash: total hash functions evaluated per user :  
Rounds: Number of Rounds 
     Table 1: Efficiency Comparison with other protocols. 
User-1 
User-i 
User-n 
User-2 
K==k1⨁𝒌2⨁----⨁𝒌i⨁----⨁𝒌n 
 
𝑪𝟏𝟐 
𝑪𝟏𝒊 
𝑪𝟏𝒏 
36 Informatica 41 (2017) 31–37 S. Reddi et al.  
Protocol 
name 
KKS FoS UKS BS KC KI 
[16] √ √ √ √ √ √ 
[18] √ √ √ √ √ √ 
[19] √ √ √ √ √ √ 
[20] √ √ √ √ √ √ 
[7] √ √ √ √ √ √ 
Proposed √ √ √ √ √ √ 
KI:Key Impersonation  BS:Backward Secrecy UKS:Unknown 
Key Share    FoS:Forward Secrecy; KC:Key control 
  Table 2: Security Analysis with existing protocols. 
 
Marko Hölbl et.al method uses three multiplications, 
three pairings, two exponentiations, one addition, and 
three hashing operations in three rounds for finalizing 
group key using pairing based key agreement.   roposed 
algorithm has three rounds setup, private key extraction 
and common key agreement in the group. The 
computation time for proposed protocol is less compared 
to [7] and [20] protocols because of less number of 
pairing operations. The proposed protocol requires more 
time in scalar multiplication and XOR operation. The 
protocol does not require any exponential operations.  
Inspite of more number of hash functions, the proposed 
protocol requires less computation time because of 
involvement of less expensive operations. 
6 Conclusion 
An enhanced ID-based authenticated key agreement 
protocol is proposed and discussed, which employs 
signatures to authenticate participated user and verifies 
correctness of transferred messages between two users. 
The effectiveness and security of proposed technique 
showed all desired security properties and was compared 
against existing protocols in terms of efficiency and 
security.  The protocol further confirms all the security 
properties with minimum time efficiency. In future, the 
protocol can be extended to hierarchical and cluster 
based network environment for establishing a secured 
communication. Also it can be applied in IoT based 
machine to machine communication, and machine to 
device communication. 
7 References 
[1]  A. Menezes, P.C. Van Oorschot, S. Vanstone,( 
1997) Handbook of Applied Cryptography, CRC 
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38 Informatica 41 (2017) 31–37 S. Reddi et al.  
 
 Informatica 41 (2017) 39–46 39 
Performance Evaluation of Lazy, Decision Tree Classifier and 
Multilayer Perceptron on Traffic Accident Analysis 
Prayag Tiwari 
National University of Science and Technology MISiS 
Department of Computer Science and Engineering, Moscow, Russia 
E-mail: prayagforms@gmail.com 
Huy Dao 
Microsoft Corp, Redmond, Washington, USA 
E-mail: huydao@microsoft.com 
Gia Nhu Nguyen 
Duy Tan University, Danang, VietNam 
E-mail: nguyengianhu@duytan.edu.vn, tel: (+84) 901 964444 
Keywords: decision tree, lazy classifier, multilayer perceptron, K-means, hierarchical clustering 
Received: January 4, 2017 
Traffic and road accident are a big issue in every country. Road accident influence on many things such 
as property damage, different injury level as well as a large amount of death. Data science has such 
capability to assist us to analyze different factors behind traffic and road accident such as weather, 
road, time etc. In this paper, we proposed different clustering and classification techniques to analyze 
data. We implemented different classification techniques such as Decision Tree, Lazy classifier, and 
Multilayer perceptron classifier to classify dataset based on casualty class as well as clustering 
techniques which are k-means and Hierarchical clustering techniques to cluster dataset. Firstly we 
analyzed dataset by using these classifiers and we achieved accuracy at some level and later, we applied 
clustering techniques and then applied classification techniques on that clustered data. Our accuracy 
level increased at some level by using clustering techniques on dataset compared to a dataset which was 
classified with-out clustering.  
Povzetek: Predstavljena je analiza prometnih nesreč z odločitvenimi drevesi in večnivojskimi 
perceptroni. 
1 Introduction 
Traffic and road accident are one of the important 
problems across the world. Diminishing accident ratio is 
most effective way to improve traffic safety. There are 
many types of research has been done in many countries 
in traffic accident analysis by using a different type of 
data mining techniques. Many researchers proposed their 
work in order to reduce the accident ratio by identifying 
risk factors which particularly impact in the accident [1-
5]. There are also different techniques used to analyze 
traffic accident but it’s stated that data mining technique 
is more advance technique and shown better results as 
compared to statistical analysis. However, both methods 
provide an appreciable outcome which is helpful to 
reduce accident ratio [6-13, 28, 29, 37-44].  
From the experimental point of view, mostly studies 
tried to find out the risk factors which affect the severity 
levels. Among most of the studies explained that 
drinking alcoholic beverage and driving influenced more 
in an accident [14]. It identified that drinking an 
alcoholic beverage and driving seriously increase the 
accident ratio. There are various studies which have 
focused on restraint devices like a helmet, seat belts 
influence the severity level of accident and if these 
devices would have been used to accident ratio had 
decreased at a certain level [15]. In addition, few studies 
have focused on identifying the group of drivers who are 
mostly involved in an accident. Elderly drivers whose 
age are more than 60 years, they are identified mostly in 
road accident [16]. Many studies provided a different 
level of risk factors which influenced more in severity 
level of accident.  
Lee C [17] stated that statistical approaches were 
good option to analyze the relation between in various 
risk factors and accident. Although, Chen and Jovanis 
[18] identified that there is some problem like large 
contingency table during analyzing big dimensional 
dataset by using statistical techniques. As well as 
statistical approach also have their own violation and 
assumption which can bring some error results [30-33]. 
Because of this limitation in statistical approach, Data 
techniques came into existence to analyze data of road 
accident. Data mining often called as knowledge or data 
discovery. This is set of techniques to achieve hidden 
valuable knowledge from huge amount of dataset.  There 
are many observed implementations of data mining 
techniques in transportation system like pavement 
analysis, roughness analysis of road and road accident 
analysis.  
40 Informatica 41 (2017) 39–46 P. Tiwari et al.  
 
Data mining methods have been the most widely 
used techniques in a field like agriculture, medical, 
transportation, business, industries, engineering and 
many other scientific fields [21-23]. There are many 
diverse data mining methodologies like clustering, 
association rules, and classification techniques have been 
extensively used for analyzing a dataset of road accident 
[19-20]. Geurts K [24] analyzed dataset by using 
association rule mining to know the, unlike 
circumstances that happen at a very high rate in road 
accident areas on Belgium road. Despair [25] analyzed a 
dataset of a road accident in Belgium by using different 
clustering techniques and stated that clustered based data 
might fetch information at a higher level as compared 
without clustered data. Kwon analyzed dataset by using 
Decision Tree and NB classifiers to factors which are 
affecting more in a road accident. Kashani [27] analyzed 
dataset by using classification and regression algorithm 
to analyze accident ratio in Iran and achieved that there 
are factors such as wrong overtaking, not using seat belts, 
and badly speeding affected the severity level of 
accident. Tiwari [34, 36] used K-modes, Hierarchical 
clustering and Self-Organizing Map (SOM) to cluster 
dataset of Leed City, UK and run classification 
techniques on that clustered dataset, accuracy in-creased 
up to some level around 70% as compared to the 
classified dataset without clustering. Hassan [35] used 
multi-layer perceptron (MLP) fuzzy adaptive resonance 
theory (ART) used a dataset of Central Florida Area and 
his result shown that inter-section in rural areas is more 
dangerous in a situation of injury severity of driver than 
intersection in urban areas. 
2 Methodology 
This research work focuses on casualty class based 
classification of a road accident. The paper describes the 
k-means and Hierarchical clustering techniques for 
cluster analysis. Moreover, Decision Tree, Lazy classifier 
and Multilayer perceptron used in this paper to classify 
the accident data. 
2.1 Clustering techniques 
2.1.1 Hierarchical clustering 
Hierarchical clustering is also known as HCS 
(Hierarchical cluster analysis). It is un-supervised 
clustering techniques which attempt to make clusters 
hierarchy. It is divided into two categories which are 
Divisive and Agglomerative clustering. 
Divisive Clustering: In this clustering technique, we 
allocate all of the inspection to one cluster and later, 
partition that single cluster into two similar clusters. 
Finally, we continue repeatedly on every cluster till there 
would be one cluster for every inspection.  
Agglomerative method: It is bottom up approach. 
We allocate every inspection to their own cluster. Later, 
evaluate the distance between every cluster and then 
amalgamate the most two similar clusters. Repeat steps 
second and third until there could be one cluster left. The 
algorithm is given below 
            X set A of objects {a1, a2,………an} 
            Distance function is d1 and d2 
            For j=1 to n 
           dj={aj} 
           end for  
           D= {d1, d2,…..dn} 
           Y=n+1 
        while D.size>1 do 
- (dmin1, dmin2)=minimum distance (dj, dk) for 
all dj, dk in all D 
- Delete dmin1 and  dmin2  from D 
- Add (dmin1, dmin2) to D 
- Y=Y+1 
          end while 
It is essential to find out proximity matrix consisting 
distance between every point utilizing distance function 
before clustering implementation. There is three methods 
is used to find out the distance between clusters. 
Single Linkage: Distance between two different 
clusters is defined as the minimum distance between two 
points in every cluster. For example a and b is the two 
clusters and distance is given by this formula: 
 L (a, b) = min (D(Yai, Ybj))   (1) 
Complete Linkage: Distance between two different 
clusters is defined as the longest distance between two 
points in every cluster. For example a and b is the two 
clusters and distance is given by this formula: 
 L (a, b) = max (D(Yai, Ybj))  (2) 
Average Linkage: Distance between two different 
clusters is defined as the average distance between two 
points in every cluster. For example a and b is the two 
clusters and distance is given by this formula: 
 L (a, b) =    (3) 
2.1.2 K-modes clustering 
Clustering is a data mining technique which uses 
unsupervised learning, whose major aim is to categorize 
the data features into a distinct type of clusters in such a 
way that features a group would more alike than the 
features in different clusters. K-means technique is an 
extensively used clustering methodologies for large 
numerical dataset analysis. In this, the dataset is grouped 
into k-clusters. In this, there are diverse clustering 
techniques available but the assortment of appropriate 
clustering algorithm rely on the nature and type of data. 
Our major objective of this work is to differentiate the 
accident places on their rate occurrence. Let’s assume 
that X and Y is a matrix of m by n matrix of categorical 
data. The straightforward closeness coordinating measure 
amongst X and Y is the quantity of coordinating quality 
estimations of the two values. The more noteworthy the 
quantity of matches is more the comparability of two 
items. K-modes algorithm can be explained as: 
 d (Xi,Yi)=                          (4) 
Where              (5) 
Performance Evaluation of Lazy, Decision Tree... Informatica 41 (2017) 39–46 41 
2.2 Classification techniques 
2.2.1 Lazy classifier 
Lazy classifier saves the training instances and do no 
genuine work until classification time. Lazy classifier is a 
learning strategy in which speculation past the 
preparation information is postponed until a question is 
made to the framework where the framework tries, to 
sum up the training data before getting queries. The main 
ad-vantage of utilizing a lazy classification strategy is 
that the objective scope will be exacted locally, for 
example, in the k-nearest neighbor. Since the target 
capacity is approximated locally for each question to the 
framework, lazy classifier frameworks can 
simultaneously take care of various issues and 
arrangement effectively with changes in the issue field. 
The burdens with lazy classifier incorporate the extensive 
space necessity to store the total preparing dataset. For 
the most part boisterous pre-paring information expands 
the case bolster pointlessly, in light of the fact that no 
idea is made amid the preparation stage and another 
detriment is that lazy classification strategies are 
generally slower to assess, however, this is joined with a 
quicker preparing stage. 
2.2.2 K-Star 
The K star can be characterized as a strategy for cluster 
examination which fundamentally goes for the partition 
of n perception into k-clusters, where every perception 
has a location with the group to the closest mean. We can 
depict K star as an occurrence based learner which 
utilizes entropy as a separation measure. The advantages 
are that it gives a predictable way to deal with the 
treatment of genuinely esteemed attributes, typical 
attributes, and missing attributes. K star is a basic, 
instance-based classifier, like K-Nearest Neighbor (K-
NN). New data instance, x, are doled out to the class that 
happens most every now and again among the k closest 
information focuses, yj, where j = 1, 2… k. Entropic 
separation is then used to recover the most comparable 
occasions from the informational index. By the method 
for entropic remove as a metric has a number of 
advantages including treatment of genuinely esteemed 
qualities and missing qualities. The K star function can 
be ascertained as: 
 K*(yi, x)=-ln P*(yi, x)       (6) 
Where P* is the likelihood of all transformational 
means from instance x to y. It can be valuable to 
comprehend this as the likelihood that x will touch base 
at y by means of an arbitrary stroll in IC highlight space. 
It will be performed streamlining over the percent mixing 
proportion parameter which is closely resembling K-NN 
‘sphere of influence’, before appraisal with other 
Machine Learning strategies. 
2.2.3 IBK (K - nearest neighbor) 
It’s a k-closest neighbor classifier technique that utilizes 
a similar separation metric. The quantity of closest 
neighbors may be illustrated unequivocally in the object 
editor or determined consequently utilizing blow one 
cross-approval center to a maximum point of 
confinement provided by the predetermined esteem. IBK 
is the knearest-neighbor classifier. A sort of divorce 
pursuit calculations might be used to quicken the errand 
of identifying the closest neighbors. A direct inquiry is a 
default yet promote decision blend ball trees, KD-trees, 
thus called "cover trees". The dissolution work used is a 
parameter of the inquiry strategy. The rest of the thing is 
alike one the basis of IBL—which is called Euclidean 
separation; different alternatives blend Chebyshev, 
Manhattan, and Minkowski separations. Forecasts higher 
than one neighbor may be weighted by their distance 
from the test occurrence and two unique equations are 
implemented for altering over the distance into a weight. 
The quantity of preparing occasions kept by the classifier 
can be limited by setting the window estimate choice. As 
new preparing occasions are included, the most seasoned 
ones are segregated to keep up the quantity of preparing 
cases at this size. 
2.2.4 Decision tree 
Random decision forests or random forest are a package 
learning techniques for regression, classification and 
other tasks, that perform by building a legion of decision 
trees at training time and resulting in the class which 
would be the mode of the mean prediction (regression) or 
classes (classification) of the separate trees. Random 
decision forests good for decision trees' routine of 
overfitting to their training set. In different calculations, 
the classification is executed recursively till each and 
every leaf is clean or pure, that is the order of the data 
ought to be as impeccable as would be prudent. The goal 
is dynamically speculation of a choice tree until it picks 
up the balance of adaptability and exactness. This 
technique utilized the ‘Entropy’ that is the computation 
of disorder data. Here Entropy  is measured by: 
Entropy ( ) = -     (7) 
 Entropy ( ) =      (8) 
Hence so total gain = Entropy ( ) - Entropy ( )   (9) 
Here the goal is to increase the total gain by dividing 
total entropy because of diverging arguments  by value 
i. 
2.2.5 Multilayer perceptron 
An MLP might be observed as a logistic regression 
classifier in which input data is firstly altered utilizing a 
non-linear transformation. In this, alteration deal the 
input dataset into space, and the place where this turn 
into linearly separable. This layer as an intermediate 
layer is known as a hidden layer. One hidden layer is 
enough to create MLPs. 
42 Informatica 41 (2017) 39–46 P. Tiwari et al.  
 
Formally, a single hidden layer Multilayer 
Perceptron (MLP) is a function of f: YI→YO, where I 
would be the input size vector x and O is the size of 
output vector f(x), such that, in matrix notation 
 F(x) = g(θ(2)+W(2)(s(θ(1)+W(1)x)))  (10) 
3 Description of dataset 
The traffic accident data is obtained from the online data 
source for Leeds UK [8]. This data set comprises 13062 
accident which happened since last 5 years from 2011 to 
2015. After carefully analyzed this data, there are 11 
attributes discovered for this study. The dataset consist 
attributes which are Number of vehicles, time, road 
surface, weather conditions, lightening conditions, 
casualty class, sex of casualty, age, type of vehicle, day 
and month and these attributes have different features 
like casualty class has driver, pedestrian, passenger as 
well as same with other attributes with having different 
features which was given in data set. These data are 
shown briefly in table 2. 
Table 2. 
S.NO. Attribute Code Value Total         Casualty Class 
     Driver Passenger Pedestrian 
1. No. of vehicles 1 1 vehicle 3334 763 817 753 
  2 2 vehicle 7991 5676 2215 99 
  3+ >3 vehicle 5214 1218 510 10 
        
2. Time T1 [0-4] 630 269 250 110 
  T2 [4-8] 903 698 133 71 
  T3 [6-12] 2720 1701 644 374 
  T4 [12-16] 3342 1812 1027 502 
  T5 [16-20] 3976 2387 990 598 
  T6 [20-24] 1496 790 498 207 
        
3. Road Surface OTR Other  106 62 30 13 
  DR Dry 9828 5687 2695 1445 
  WT Wet 3063 1858 803 401 
  SNW Snow 157 101 39 16 
  FLD Flood 17 11 5 0 
        
4. Lightening Condition DLGT Day Light 9020 5422 2348 1249 
  NLGT No Light 1446 858 389 198 
  SLGT Street Light 2598 1377 805 415 
        
5. Weather Condition CLR Clear 11584 6770 3140 1666 
  FG Fog 37 26 7 3 
  SNY Snowy 63 41 15 6 
  RNY Rainy 1276 751 350 174 
        
6. Casualty Class DR Driver     
  PSG Passenger     
  PDT Pedestrian     
        
7. Sex of Casualty M Male 7758 5223 1460 1074 
  F Female 5305 2434 2082 788 
        
8. Age Minor <18 years 1976 454 855 667 
  Youth 18-30 years 4267 2646 1158 462 
  Adult 30-60 years 4254 3152 742 359 
  Senior >60 years 2567 1405 787 374 
        
9. Type of Vehicle BS Bus 842 52 687 102 
  CR Car 9208 4959 2692 1556 
  GDV GoodsVehicle 449 245 86 117 
  BCL Bicycle 1512 1476 11 24 
  PTV PTWW 977 876 48 52 
  OTR Other 79 49 18 11 
        
10.  Day WKD Weekday 9884 5980 2499 1404 
  WND Weekend 3179 1677 1043 458 
        
11. Month Q1 Jan-March 3017 1731 803 482 
  Q2 April-June 3220 1887 907 425 
  Q3 July-September 3376 2021 948 406 
  Q4 Oct-December 3452 2018 884 549 
 
Performance Evaluation of Lazy, Decision Tree... Informatica 41 (2017) 39–46 43 
4 Accurecy measurement 
The accuracy is defined by different classifiers of 
provided dataset and that is achieved a percentage of 
dataset tuples which is classified precisely by help of 
different classifiers. The confusion matrix is also called 
as error matrix which is just layout table that enables to 
visualize the behavior of an algorithm. Here confusing 
matrix provides also an important role to achieve the 
efficiency of different classifiers.  There are two class 
labels given and each cell consist prediction by a 
classifier which comes into that cell. 
Table 1. 
Now, there are many factors like Accuracy, 
sensitivity, specificity, error rate, precision, f-measures, 
recall and so on. 
TPR (Accuracy or True Positive Rate) =     (11) 
FPR (False Positive Rate) =           (12) 
Error rate=see Accuracy           (13) 
Precision =                (14) 
Sensitivity or Recall =             (15) 
F-measures=2 (Precision*Recall) / (Precision + Recall) 
            (16) 
And there are also other factors which can find out to 
classify the dataset correctly.  
5 Results and discussion 
Table 2 describe all the attributes available in the road 
accident dataset. There are 11 attributes mentioned and 
their code, values, total and other factors included. We 
divided total accident value on the basis of casualty class 
which is Driver, Passenger, and Pedestrian by the help of 
SQL. 
5.1 Directed classification analysis 
We utilized different approaches to classifying this bunch 
of dataset on the basis of casualty class. We used 
classifier which are Decision Tree, Lazy classifier, and 
Multilayer perceptron. We attained some result to few 
level as shown in table 3. 
Table 3. 
Classifiers Accuracy 
Lazy classifier(K-Star) 67.7324%  
Lazy classifier (IBK) 68.5634% 
Decision Tree 70.7566% 
Multilayer perceptron 69.3031% 
We achieved some results to this given level by 
using these three approaches and then later we utilized 
different clustering techniques which are Hierarchical 
clustering and K-modes. 
66,00% 68,00% 70,00% 72,00%
Lazy classifier (K-Star)
Lazy classifier (IBK)
Decision Tree
Multilayer Perceptron
Accuracy Chart
 
Figure 1:  Direct classified Accuracy. 
5.2 Analysis by using clustering 
techniques 
In this analysis, we utilized two clustering techniques 
which are Hierarchical and K-modes techniques, Later 
we divided the dataset into 9 clusters. We achieved better 
results by using Hierarchical as compared to K-modes 
techniques. 
5.3 Lazy Classifier Output 
K-Star: In this, our classified result increased from 
67.7324 % to 82.352%. It’s sharp improvement in the 
result after clustering.  
Table 4. 
TP 
Rate 
FP 
Rate 
Precis
ion Recall 
F-Me 
asure MCC 
ROC 
Area 
PRC 
Area Class 
0.956 0.320 0.809 0.956 0.876 0.679 0.928 0.947 Driver 
0.529 0.029 0.873 0.529 0.659 0.600 0.917 0.824 Passenger 
0.839 0.027 0.837 0.839 0.838 0.811 0.981 0.906 Pedestrian 
IBK: In this, our classified result increased from 
68.5634% to 84.4729%. It’s sharp improvement in the 
result after clustering.  
Table 5. 
TP 
Rate 
FP 
Rate 
Precis
ion 
Recal
l 
F-Me 
asure MCC 
ROC 
Area 
PRC 
Area Class 
0.945 0.254 0.840 0.945 0.890 0.717 0.950 0.964 Driver 
0.644 0.048 0.833 0.644 0.726 0.651 0.940 0.867 Passenger 
0.816 0.018 0.884 0.816 0.849 0.826 0.990 0.946 Pedestrian 
5.4 Decision Tree Output 
In this study, we used Decision Tree classifier which 
improved the accuracy better than earlier which we 
achieved without clustering. We achieved accuracy 
84.4575 % which is almost more than 15% earlier 
without clustering. 
 
 
Confusion Matrix 
 Correct Labels 
 Negative Positive 
Negative  TN (True negative) FN (False negative) 
Positive FP (False positive) TP (True positive) 
44 Informatica 41 (2017) 39–46 P. Tiwari et al.  
 
Table 6. 
TP 
Rate 
FP 
Rate 
Precis
ion Recall 
F-Me 
asure MCC 
ROC 
Area 
PRC 
Area Class 
0.922 0.220 0.856 0.922 0.888 0.717 0.946 0.961 Driver 
0.665 0.057 0.814 0.665 0.732 0.652 0.936 0.861 Passenger 
0.868 0.027 0.841 0.868 0.855 0.830 0.988 0.939 Pedestrian 
5.5 Multilayer Perceptron Output 
In this study, our accuracy increased from 69.3031% to 
78.8301% after using clustering technique.  
Table 7. 
TP 
Rate 
FP 
Rate 
Precis
ion Recall 
F-Me 
asure MCC 
ROC 
Area 
PRC 
Area Class 
0.929 0.338 0.796 0.929 0.857 0.627 0.892 0.916 Driver 
0.452 0.036 0.824 0.452 0.584 0.520 0.855 0.720 Passenger 
0.849 0.053 0.726 0.849 0.783 0.746 0.955 0.818 Pedestrian 
We achieved error rate, precision, TPR (True 
positive rate), FPR (False positive rate), Precision, recall 
for every classification techniques as shown in given 
tables and also achieved different confusion matrix for 
different classification techniques. We can see the 
performance of different classifier techniques by the help 
of confusion matrix. 
Here in the next table, we have shown the overall 
accuracy of analysis with clustering with the help of table 
8, as we can compare this table from the previous table 
that our accuracy increased in each classification 
techniques after doing clustering. 
Table 8. 
Classifiers Accuracy 
Lazy classifier (K-Star) 82.352%  
Lazy classifier (IBK) 84.4729% 
Decision Tree 84.4575% 
Multilayer perceptron 78.8301% 
We have shown accuracy level of table 8 in given 
figure 2 with the help of chart and we can see from the 
chart that it’s improved after doing clustering in accuracy 
chart also.  
76% 78% 80% 82% 84%
Lazy classifier (K-Star)
Lazy classifier (IBK)
Decision Tree
Multilayer Perceptron
Figure 3: Accuracy after clustering. 
As we can see from table 3 and 8 that our accuracy level 
increased after clustering. We have shown comparison 
chart in fig. 3 without clustering and with clustering. 
6 Conclusion and future suggestions 
In this study, we analyzed dataset without clustering and 
with clustering. Generally, we use clustering techniques 
on accident dataset that could find a homogenous pattern 
of same accident data which could be used to increase 
the accuracy of classifiers. We achieved a better result 
when we used hierarchical clustering as compared to k 
mode clustering techniques.  We used different classifier 
such as Decision Tree, Lazy classifier, and Multilayer 
perceptron to classify our dataset and they have shown 
optimized performance after clustering as well as we 
used different classifiers technique also but they did not 
show better accuracy as these classifiers shown. If 
accuracy would be higher our classified result will be 
better. We achieved better accuracy on the basis of 
casualty class (Driver, Passenger, and Pedestrian) and we 
can see from tables that which factors are affecting more 
in an accident on the basis of casualty class. The future 
work will include a comprehensive evaluation of all 
clusters with an aim to determine the various other 
factors and locations behind traffic accident. 
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 Informatica 41 (2017) 47–58 47 
 
Distributed Fault Tolerant Architecture for Wireless Sensor Network 
Siba Mitra and Ajanta Das 
Department of Computer Science and Engineering, Birla Institute of Technology 
Mesra, Kolkata Campus, India 
E-mail: sibamitra@bitmesra.ac.in, ajantadas@bitmesra.ac.in  
 
Keywords: fault detection, fault recovery, fault tolerance, reliability, wireless sensor network 
Received: December 25, 2016 
Smart applications use wireless sensor network for surveillance of any physical property of that area, to 
realize the vision of ambient intelligence. Since wireless sensor network is resource constrained and for 
unattended deployment scenario faults are quite trivial. Reliability and dependability of the network 
depends on its fault detection, diagnosis and recovery techniques. Detecting faults in wireless sensor 
network is challenging and recovery of faulty nodes is very crucial task. In this research article, a 
distributed fault tolerant architecture is proposed. This paper also proposes fault recovery algorithms. 
Recovery actions are initiated based on fault diagnosis notification. The novelty of this paper is to 
perform recovery actions using data checkpoints and state checkpoints of the node, in a distributed 
manner. Data checkpoint helps to recover the old data and the state checkpoint tells the previous trust 
degree of the node. Moreover, the result section explains, that after replacement of a faulty node, the 
topology and connectivity between rests of the nodes are maintained in WSN. 
Povzetek: Opisana je arhitektura brezžičnega senzorskega omrežja. 
1 Introduction 
The use of wireless sensor network (WSN) nowadays has 
seen a huge growth in the field of ambience intelligence. 
WSN is resource constrained in nature but can be 
integrated with any system by using Dynamic Adaptive 
System Infrastructure (DAiSI) proposed by Klus and 
Niebuhr (2009) in [11]. Component reconfiguration and 
dynamic integration can be done with the help of this. 
Another interesting application that uses WSN to detect 
and track presence of human and human motion in an 
environment is presented in the research of Graham et al. 
(2011) in [7]. It is also shown in the work that 
appropriate device placement scheme can improve 
network performance. 
The unit of WSN is a tiny sensor node, which 
communicates to other sensor nodes through radio 
transmission. Small sensor nodes constituting of sensing 
unit, tiny memory, a microcontroller, a transceiver and an 
omni-directional antenna are deployed in the target area. 
Sensor nodes send relevant data to the nearest base 
station (BS), which is used for some meaningful decision 
making. Fault in WSN is trivial because of its resource 
constraints and unattended deployment scenario. 
Therefore to make the WSN reliable and dependable, 
fault tolerance must be implemented in it.  
Various types of node faults are classified in the 
Figure 1. Faults in WSN can be permanent, transient or 
intermittent in nature. Fault management is the process to 
monitor the nodes, detect and diagnose fault and perform 
necessary recovery tasks to make WSN fault tolerant. 
Permanent failures generally have no option for recovery 
but for transient and intermittent faults the recovery 
actions should prevail. Proper recovery schedules should 
be there for occurred faults to make it fault tolerant and 
help application to make correct decision, even in 
presence of faults. Some of the critical factors in 
recovery process are the available residual energy of the 
sensor node, the network traffic scenario, connectivity 
issues and current topological structure of the WSN.  
A distributed adaptive fault detection scheme for 
WSN is proposed in [28], where each node detect any 
unnatural event by fetching neighbor sensor nodes’ 
reading with queries. A three-bit control packet exchange 
is done during the fault detection phase in order to reduce 
communication overhead.  Here moving average filter 
was employed for implementing fault tolerance in WSN. 
The article claims to have reached high detection 
accuracy and low false alarm rate.  
WSN may comprise of static or mobile sensor nodes. 
Borawake-Satao and Prasad (2017) [4] presents a study 
of effects of sensor node mobility on various 
performance parameters of WSN. A proposal of mobile 
sink with mobile agent mobility model for WSN is also 
presented. In [12] Kumar and Nagarajan (2013) proposed 
Incorporated Network Topological control and Key 
management (INTK) for relay nodes of WSN, for 
privacy and security measures in the network. The 
proposed scheme includes hierarchical routing 
architecture in WSN for better performance and security. 
Another novel research proposal by Mukherjee et al. 
(2016) [22] presents a model for disaster aware mobile 
Unmanned Aerial Vehicle (UAV) for flying Ad-hoc 
network. The nodes can perform collaborative job by 
relaying useful message in a post-disaster situation of 
any ecosystem.  
 
 
48 Informatica 41 (2017) 47–58 S. Mitra et al.  
 
  
 
 
Figure 1: Node Fault Classification.  
The analytical comparison presented in [3] by Bathla 
& Jindal 2016, where two distributed self-healing 
recovery techniques, Recovery by In-ward Motion (RIM) 
and Least Disruptive Topology Repair (LeDiR) are 
analyzed and compared with respect to their efficiency in 
various applications. Both the approaches are distributed 
in nature. 
The RIM method aims to replace a failed node by a 
healthy node, by moving the latter towards the former’s 
location. Here all nodes must have a 1-hop neighbor list 
and should be aware of their neighbor’s locality and 
proximity. Now the goal of LeDiR is to restore the 
connectivity among the sensor nodes. But it also takes 
care that after the recovery action the shortest path length 
among the nodes is not extended compared to the pre-
failure topology.  
A fault recovery algorithm for WSN is proposed in 
[13] by Lakamana et al. 2015, which enhances the 
routing efficiency in the WSN. Battery depletion is a 
major issue and that is taken care of over here by 
reducing the number of node replacements and by 
reusing the historic routing paths. According to the 
authors’ claim the network longevity is increased over 
here.    
In WSN, now sensor node(s) when gets disconnected 
from the network due to some reason may generate 
partitions or isolations in the network, which is not good 
for reliability and dependability of the network. 
Moreover, it is crucial to maintain the connectivity 
throughout its longevity. So the objective of this research 
is to design a distributed fault tolerant architecture for 
WSN, which includes fault detection, diagnosis and 
recovery. However the architecture proposed here is an 
improved version of a fault tolerant framework already 
proposed in our previous research work available in [18] 
by Mitra & De Sarkar (2014). Moreover this article also 
proposes a novel fault recovery model, which is 
integrated with the proposed architecture. This research 
also proposes some algorithms for connectivity 
maintenance, and recovery tasks to be performed. The 
novelty of the proposed recovery technique is to initiate 
the recovery actions after proper diagnosis of the 
detected fault. Recovery tasks are done once after it gets 
notification from the diagnosis layer about the fault-type. 
The recovery model has two phases; the first one being 
set action and start recovery.  
The remainder part of the article is sub-divided into 
sections namely, related work done in the current field, 
followed by proposed Distributed Fault Tolerant 
Architecture and supporting fault recovery architecture 
and algorithms; and then the results and discussions 
section is presented. Finally, the conclusion and the 
references to the article are presented. 
2 Related work 
This section mainly presents some of the valuable 
researches carried out by many scholars in the field of 
WSN. Many existing fault management techniques are 
available, which are used for fault tolerance in WSN. A 
review work of the same is presented in our previous 
research work, Mitra, De Sarkar and Roy (2012) in ref. 
[20] and a few of them are mentioned here also. 
Moreover in this article a study on some of the existing 
recovery schemes is presented. Data communication is 
important factor in WSN hence routing decision is 
significant. Leskovec, et al. (2005) [14] proposed a novel 
link quality estimation model for sensor network, which 
uses link quality map to estimate a link in sensor 
network. This work also optimizes power consumption 
of radio transmission signal, while scheduling the 
communication task and taking routing decision. 
An analytical study and comparison of various 
recovery techniques are presented in our recent research 
work, in [19] (Mitra, Das & Mazumdar 2016). Some of 
those recovery schemes are also discussed briefly here. 
Among them CRAFT (Checkpoint/Recovery-based 
scheme for Fault Tolerance) [26] for WSN, proposed by 
Saleh, Eltoweissy and Agbaria (2007) is studied; another 
scheme proposed by Ma, Lin, Lv & Wang (2009) [16] 
called ABSR, which recovers some compromised sensor 
Node Fault 
Sensing 
Fault 
Communication 
Fault 
Processor 
Fault 
DoS Hardware 
failure 
Software 
failure 
Low 
Residual 
Energy 
High 
Traffic 
Congestion 
Hardware 
failure 
Hardware 
failure 
 
Distributed Fault Tolerant Architecture for... Informatica 41 (2017) 47–58 49 
 
nodes in a heterogeneous sensor network. Various types 
of sensor nodes each playing specific role are used here. 
Reghunath, Kumar & Babu (2014) proposed Fault Node 
Recovery (FNR) algorithm, which is a combination of 
Genetic algorithm with Grade Diffusion algorithm. A 
rank based replacement strategy for the sensor nodes is 
presented in [25].  
In [6] Chen, Kher & Somani (2006) proposed DLFS 
(distributed localized fault sensors) detection algorithm, 
for locating and identifying faulty nodes in WSN. Each 
node can be either in good health or can be faulty 
depending upon the node behavior. The technique here 
uses probabilistic approach. The implemention of the 
algorithm claim that execution complexity of the same is 
much low and detection accuracy is high. Haboush, 
Mohanty, Pattanayak and Al-Tarazi (2014) [8] have 
proposed a faulty node replacement algorithm for hybrid 
WSN. Mobile sensor nodes are considered over here. 
Any node having low residual energy may seek a 
replacement; after replacement maintenance of the 
topology etc. are taken care of. Redundancy is used to 
avoid faulty results and also adaptive threshold policy is 
employed for rectification of the faults and optimizing 
the network lifetime. The research in [2] Akbari et al. 
(2010) presents a survey of faults in WSN due to energy 
crunch and the role of cellular architecture and clustering 
for network sustain purpose. The cluster-based fault 
detection and recovery techniques was observed to be 
quite efficient, robust and fast for WSN sustain and 
longevity. Another cluster maintenance technique is 
designed by them for nodes having energy crunch as 
mentioned in [1] (Akbari, Dana, Khademzadeh & 
Beikmahdavi 2011). First of all, nodes with highest 
residual energy are selected as primary cluster head, and 
nodes second in residual energy becomes the secondary 
cluster head. So the technique is energy aware in nature 
and consequentially selects the cluster head as per the 
nodes’ residual energy.  
An FNR algorithm is proposed by Brahme, 
Gadadare, Kulkarni, Surana & Marathe (2014) in [5], for 
fault recovery in WSN to enhance network lifetime. 
Researchers employed genetic algorithm and grade 
diffusion algorithm for designing the scheme. Moreover 
researchers, Mishal, Narke, Shinde, Zaware & Salve 
(2015) in [17] have worked upon FNR and improved it 
performing lesser number of node replacement for fault 
recovery, and basically old routing paths are reused; 
however better  result is claimed over here. A proposal 
on a distributed fault detection algorithm for detecting 
coverage holes in the WSN is presented in Kang et al. 
2013 [9]. The research do not maintain any node 
coordinates. The critical information of a node can be 
collected from the neighbors and that can be used for 
detection and recovery purpose for WSN. On demand 
checkpoint based recovery technique for WSN is 
proposed in [23] by Nithilan & Renold (2015). In this 
scheme checkpoint coordination and non-blocking 
checkpoint is used for consistency and some backup 
nodes maintains and checks the health of a node by 
monitoring the checkpoints. A localized tree based 
method for fault detection is proposed by Wan, Wu & Xu 
(2008) [27]. The recovery scheme uses elected new 
parent technique for avoiding isolation of children node 
of the tree. This technique enhances the network lifetime.  
The main objective of this research work is to design 
a distributed fault tolerant architecture for WSN, with 
intrinsic parts for fault detection, diagnosis and recovery. 
In this research we mainly concentrate to propose a 
distributed fault recovery model for WSN with a set of 
algorithms, which are employed for performing node, 
data and network recovery. For fault detection, existing 
detection algorithm proposed in our previous work in 
[18] is used. Thereafter the proposed recovery technique 
is employed to maintain the fault tolerance.  The major 
job is to increase the reliability and dependability of the 
WSN for correct decision making. The novelty of this 
research work is to perform recovery actions, using data 
checkpoints and state checkpoints of the node, in a 
distributed manner. Also topology maintenance is being 
performed by each node during the recovery process. 
3 Proposed distributed fault tolerant 
architecture for WSN 
This section details on a proposal of a fault tolerant 
framework for WSN. Event detection is important for 
implementing fault tolerance in WSN, where the event 
can be presence of hole in the network. A distributed, 
lightweight, hole detection algorithm proposed by 
Nguyen et al. (2016) in [24] monitors and reports about 
any hole in the network. The present proposal is an 
improvisation of an already proposed framework 
mentioned in Mitra et al (2014). The architecture as 
mentioned in Figure 2 can be embedded in each sensor 
node of WSN and the node can independently perform 
fault management in a distributed way.  
In a centralized system fault management scheme 
there is a central manager, who monitors and controls the 
network. So each node has to report the central manager 
with relevant data for fault tolerance in the WSN. 
Therefore too much of communication will result and in 
the WSN huge overhead will be incurred in terms of 
energy and bandwidth, which may affect network 
performance. In centralized approach the traffic flow is 
towards a single central manager creating overheads and 
resulting in bottlenecking. However this is not desirable 
in WSN since it is resource constrained and 
infrastructure less. This critical bottleneck problem can 
be avoided in the distributed architecture of fault 
management scheme. In distributed fault management, 
the network is partitioned and self fault management is 
implemented. Moreover in comparison with the 
centralized system the communication cost is less in 
distributed system. Therefore this research work mainly 
aims for distributed architecture. The proposed 
architecture has three main phases viz. Fault Detection, 
Fault Diagnosis and Fault Recovery respectively.  
3.1 Fault detection 
The detection phase has three significant tasks namely 
Node and Link Monitoring, Fault Isolation and Fault 
50 Informatica 41 (2017) 47–58 S. Mitra et al.  
 
Prediction. Fault detection algorithm is already proposed 
in our previous work presented in Mitra et al. (2014). A 
brief discussion is presented hereafter.  All the tasks are 
computed in an energy aware mode. In the monitoring 
stage the sensor node listener carefully monitors and 
examines the health of the sensor node and detects if any 
unnatural event occurs; and then it scans the attributes of 
the event; it also evaluates some useful parameter-value 
required for detecting faults in the node. First of all a 
neighbor table for each node is created and after that 
node performs self-checking. Sensor nodes evaluates 
own tendency by comparing the average of neighbors’ 
reading with the own read value. Again the nodes do a 
similar comparison with its own previous read value and 
current value. The tendency of the node quantizes 
whether it is trustworthy or not. If a node is not 
trustworthy then the trust degree (TD) value is zero and 
if it is trustworthy then the TD value is one. So the TD 
value isolates rather detects the fault in the WSN. 
In the Prediction module the residual energy analysis 
of the node is carried out and any fault-to-be are 
forecasted. The forecast is done on the basis of some 
comparative study of the fault evaluation parameters 
namely residual energy of the node. If the residual 
energy of the node goes below a threshold then the built-
in fault predictor invokes two actions; firstly it broadcast 
the information of its low energy state. Secondly some 
query packets are broadcasted asking for a node with 
high residual energy for offloading its own 
responsibility. Finally the node is sent to sleep mode.   
 
 
 
 
Figure 2: Distributed Fault Tolerant Architecture for WSN.  
 
 
 
 
Node and Link 
Monitoring 
Fault Isolation 
Prediction 
Fault Analysis 
Node Fault Link Fault 
Act as Relay 
Node 
Off/ 
Restart/ 
Replace 
Reconfiguration 
of Routing Path 
Phase 1: Fault 
Detection 
Phase 2: Fault 
Diagnosis 
Phase 3: Fault 
Recovery 
Radio 
Fault 
Radio 
OK 
Distributed Fault Tolerant Architecture for... Informatica 41 (2017) 47–58 51 
 
3.2 Fault diagnosis 
The second phase of the fault tolerant architecture is fault 
diagnosis and it is done after the analysis of the occurred 
event. Fault analysis is a reactive process, and the fault 
category in WSN can be either a node fault or 
communication fault. For diagnosing the node fault, the 
assigned TD value is taken into consideration as 
available in [18]. Evaluation of TD value of a node is 
computed on the basis of self analysis and neighbor 
analysis for a fixed number of iterates. And depending on 
the iteration count the decision of node fault is finalized. 
Now for communication fault diagnosis, two critical 
parameters received signal strength (RSS) and link 
utilization of the sensor nodes are taken into account. 
The average RSS of all the neighbors of the sensor node 
are computed for communication fault analysis. 
Moreover the sensor node also computes the average link 
utilization parameter to check self performance. Once 
self-fault diagnosis is completed then a notification is 
forwarded to the next phase i.e. Fault Recovery. 
4 Proposed fault recovery scheme 
This section presents the fault recovery scheme for WSN. 
This scheme can be integrated in each sensor node such 
that distributed fault recovery is possible. The recovery 
process is invoked when a sensor node is suffering of 
some fault. The next sub-sections present the network 
model and the fault recovery model.  
4.1 Network model  
The WSN model in this research work can be represented 
as a graph structure, )E,S(G , where S is a set of sensor 
nodes
}S,...,S,S{S n21 , which is deployed in random or 
planned way in the target area. The area can be 
represented as a two-dimensional plane whose origin 
is )y,x( 00 . Now }n
E,...,
2
E,
1
E{E  is a set of 
communication links in between a pair of nodes Si and 
Sj, which transmits within its communication range but 
has a sensing range lesser than communication range, in 
[15] given by CS RR  where RS and RC, are sensing 
range and communication range respectively. The 
necessary condition for a node Si to transmit signal to a 
node Sj is the Euclidean distance between the two nodes 
should conform Equation 1. Each node maintains a list of 
neighbors, which may dynamically change with time as 
per availability of the node in the communication 
process. It is very general to perform low power 
transmission in WSN, where node’s transmission power 
is directly proportional to the distance. To send data with 
good quality signal strength a node may have to adjust its 
transmission power. For the current problem scope if Pi,j 
is the power of transmission for communication of  Si 
and Sj. It is quite obvious that Equation 2 will satisfy if 
and only if Equation 3 is true. Moreover the maximum 
value of transmission power is also limited. The 
assumption is that any node Si will perform low power 
transmission for nodes within RC and may sometimes, as 
required, perform high power communication with Sj if 
and only if Equation 4 is true. 
 
Cji RSS 
  Equation (1) 
 
r,kj,i PP 
  Equation (2) 
 
rkji SSSS 
  Equation (3) 
 
CjiS RSSR 
 Equation (4) 
4.2 Connectivity issue 
Now not all the nodes can directly transmit data to the 
sink or BS; so any node unable to do the same will 
employ some intermediary forwarding parent nodes to 
send the data to the BS. At the run time one or more 
sensor nodes may not work properly due to faults and 
then the recovery actions of the nodes may be initiated to 
recover the node, data or network. Any recovering node 
may need to stop its scheduled tasks for self-recovery. In 
that case other affected neighbor nodes may have to 
update their own neighbor list and exclude the recovering 
node from any current activity. This scenario is explained 
in Figure 3.  
It is well understandable from the figure that the 
normal nodes need to maintain the connectivity even in 
absence of the faulty nodes marked as black nodes. 
Hence the normal nodes have to find alternate suitable 
nodes within its communication range for forwarding 
data. But if it is unable to find one then it should perform 
a high power transmission to the nodes, which are in its 
communication range, rather than getting isolated.  In the 
figure the dotted arrows demarcate the unstable or 
sometimes unavailable links. To transmit in high power 
the node should increase its transmission power by a 
multiplicative factor, somewhat proportionate to the 
increment in the distance given by k,ij,i DD  , where Di,j 
and Di,k are explained in Equation 5 and 6. In the 
equations i-th node transmits to k-th node in the place of 
recovering j-th node. 
 
jij,i SSD 
  Equation (5) 
 
kik,i SSD 
  Equation (6) 
4.3 Fault recovery model 
The proposed fault recovery model is depicted in Figure 
4, where there is a Fault Recovery Process, which has 
two main phases viz. Set Action and Start Recovery. The 
Fault Recovery Process gets notification from the fault 
diagnosis layer along with the information on the fault 
type; depending upon that the Set Action decides on what 
kind of recovery activity has to be invoked. Again Start 
Recovery actually begins the specific recovery task. 
Faults can be due to hardware or software failure, bad 
link quality or power depletion of the node. 
 
 
52 Informatica 41 (2017) 47–58 S. Mitra et al.  
 
 
Figure 3: Connectivity Issue in WSN. 
 
Figure 4: Fault Recovery Model for WSN. 
The Recovery Process performs and maintains node 
recovery and network recovery and it also communicates 
with the permanent storage for any kind of query 
checking. The permanent storage contains node status 
and data checkpoint before occurrence of the fault. The 
Recovery Process fetches the necessary information to 
perform the recovery tasks smoothly. It also performs 
various types of communication before the node gets 
reinitialized. Node recovery means data recovery from 
the node and also checking the node state and performing 
activities to preserve the node functionality. Network 
recovery deals with reconfiguring the network by 
performing the path quality estimation already proposed 
by Mitra, Roy & Das (2015) in [21]. The recovery jobs 
are vividly explained later in Algorithm 3. 
5 Proposed algorithms for fault 
recovery 
The proposed fault recovery algorithm consists of 
various parts, where each node carry out some self-
checking task and some of them are already mentioned in 
[18]. Since the total process is being carried out in a 
distributed atmosphere so the nodes perform self-
evaluation and self-recovery. All the symbols and 
notations used in the algorithm are mentioned in Table 1.  
The proposed fault recovery scheme uses some sort of 
check pointing for performing the data recovery task. 
Each node maintains a data checkpoint and a state 
checkpoint by using two variables TD (Trust Degree) 
and DCkpt (Data Checkpoint) respectively, in the 
permanent storage of the node i.e. even if the node is 
restarted the data remains intact for future reference as 
mentioned in Algorithm 1 and presented in Figure 5. TD 
is already proposed, explained and used in our prior 
research mentioned in [18]. This previous work also 
presented a novel fault detection scheme, which is used 
over here to detect and diagnose faults. Now HF means 
sensing unit or hardware failure, where the node is 
unable to sense any ambient signal, or it may be 
transceiver fault that occurs when transmitter or receiver 
is not in working mode and microcontroller fault means 
when a node cannot perform its computations at par.  
Each node has a delivered packet counter as DPC, 
which keeps the count of the delivered packets. 
Whenever a node delivers 200 packets it stores TD, as 
state checkpoint, in the permanent storage and stores the 
current read value of the node in DCkpt, as data 
SET 
ACTION 
START 
RECOVERY 
NODE RECOVERY 
NETWORK RECOVERY 
NOTIFICATION FROM FAULT DIAGNOSER 
RECOVERY 
PROCESS 
PERMANENT 
STORAGE 
 FAULTY NODE 
NORMAL NODE 
Distributed Fault Tolerant Architecture for... Informatica 41 (2017) 47–58 53 
 
checkpoint, in permanent memory. After completion of 
these steps the DPC is reinitialized to zero so that it can 
again count the next set of 100 and 200 delivered packets 
respectively. The checkpoint creation process continues 
for each node until it goes to recovery state. When a node 
gets a notification from the fault diagnosis layer, that a 
fault has occurred, it fetches the fault type and performs 
some internal necessary actions that is mentioned in 
Algorithm 2 and presented in Figure 6. As mentioned the 
fault type can be either hardware fault (HF) or software 
fault (SF). Depending upon fault-type the recovery 
process is initiated as mentioned in Algorithm 3 and 
presented in Figure 7. 
Notation Meaning 
Si i-th node 
Sr Node at Recovery mode 
Sr.NBR Neighbor list of Sr 
Si.CURR-VAL Current reading of Si 
DCkpt Data Checkpoint 
DPC Delivered packet count 
TD Trust degree 
CR Communication range 
Pj,k Power to transmit data from Sj to 
Sk 
PRR Packet reception ratio 
PDR Packet delivery ratio 
Table 1: Symbols and Notations. 
In all these cases the node needs a third party or 
human intervention to get the problem fixed. Software 
failure refers to logical or runtime faults in the software, 
which is again needs third party intervention. If the 
packet reception ratio (PRR) and packet delivery ratio 
(PDR) are much low then there must be some 
disturbances in data transmission and receiving; hence a 
communication failure may occur in near future. So the 
nodes goes for a self-recovery process. Finally the node 
has to be shut down if the residual energy is much less 
than the threshold value, which may be specified as per 
application requirement. The node recovery module for 
any faulty node starts with low-power transmission of 
probe packets to the neighbors, which again go for 
topology maintenance as mentioned in Figure 8 as 
Algorithm 4. The recovery activity takes place by 
reinitializing the sensor nodes so that it releases all its 
resources and take a fresh start. The last state checkpoint 
and data checkpoint is recovered from the permanent 
memory. Data checkpoint helps to recover the old data 
and the state checkpoint tells the previous trust degree of 
the node. 
 
Create Checkpoint ( ) 
{ 
   For each node Si do this 
   { 
        Initialize DPC=0; 
        For each packet delivery 
        { 
             DPC++; 
             If (DPC = 200)   
             { 
                    Store TD in permanent storage;
         Store Si.CURR-VAL in DCkpt;      
         Set DPC=0;  
              } 
         } 
     } 
} 
Figure 5: Algorithm 1. 
For each node Sr with detected fault 
{ 
      Get Notification (fault-type)  
      If (fault-type=HF OR fault-type=SF) 
      { 
           Third party assistance needed 
            Initiate Node Recovery ( ) 
      } 
      If (PRR very low OR PDR very low) 
        Initiate Node Recovery ( ) 
    If (Residual energy << Threshold) 
         Shut down Sensor Node 
} 
Figure 6: Algorithm 2. 
Initiate Node Recovery ( ) 
{ 
        Send probe packets to all of Sr.NBR  
        For each node NBR.SS rj   
        { 
          Maintain Topology ( ) 
        } 
        Start recovery action (Sr) 
        { 
            Reinitialize the sensor node; 
            Fetch the last data from Sr.DCkpt; 
            Get Sr.TD; 
            Perform LQE; 
         }  
}  
Figure 7: Algorithm 3. 
 
54 Informatica 41 (2017) 47–58 S. Mitra et al.  
 
Finally the node performs link quality estimation 
given by LQE, which is again proposed in our work 
mentioned in [21]. In the node recovery algorithm any 
node which is in recovery mode sends some probe 
packets to its neighbor, stating its unavailability for some 
instance of time. The neighbors in turn, update their own 
neighbor tables and get prepared for running the 
topology maintenance schedule. 
Maintain Topology ( ) 
{ 
    Get parent list of Sr 
    For each parent Sk of Sr 
    { 
       If CRSS kj    
       { 
              Update neighbor list 
              Update routing table 
              Transmit through Sk 
        } 
        Else  
        { 
               Estimate transmission power Pj,k 
               If  1k,jk,j PP   
                        Store current Pj,k 
               Else  
                         Keep the previous Pj,k-1 
            } 
    } 
    Select Sk with minimum Pj,k  
    Update neighbor list and routing table 
    Transmit through Sk 
} 
Figure 8: Algorithm 4. 
6 Results and discussions 
In this section the results are displayed and 
corresponding discussion is presented. For simulation 
purpose and preparing the results MATLAB version 
7.11.0.584 (R2010b) and Microsoft Office Excel 2003 
was used. The sensor node specifications considered and 
the simulation environment is mentioned in the Tables 2 
and Table 3 respectively. Table 4 next shows the 
computed energy consumption for each task done by 
each node. Initially the nodes are deployed randomly and 
then they are initialized and they start to do their normal 
task. In an area of 100×100 m2 thirty sensor nodes were 
randomly deployed considering a uniform 
communication range of 25 meters.  
6.1 Fault detection and diagnosis 
The nodes were deployed randomly and then gradually 
nodes become faulty in the WSN. The faults are detected 
and consequentially the recovery is carried out by the 
nodes.  
Just after the nodes are deployed, the scenario is 
presented in the first quadrant of the Figure 9, which is 
followed by the edge development of the nodes, 
depending upon the transmission radius of the sensor 
nodes and presented in second quadrant of Figure 9. Now 
for the detection of faults the fault detection algorithm 
proposed by Mitra and De Sarkar (2014) is used and the 
nodes demarcated by red colors in the third and fourth 
quadrant of Figure 9. It was observed that five out thirty 
nodes were detected to be faulty. Moreover in the Figure 
10 especially the faulty nodes with the affected links are 
represented. 
 
Parameter  Value  
Frequency Range 2.4 – 2.48 GHz 
Data Rate 250 Kbps  
Current Draw  16 mA @ Receive mode 
17 mA @ Transmit mode 
8 mA @ Active mode 
8 µA @ Sleep mode 
Table 2: Sensor Node Specifications. 
Parameter  Value  
No. of Nodes Deployed 30 
Area Covered 100×100 m2 
Communication Range 25 meter 
Node Density (ρ) 0.003nodes/ m2 
Table 3: Simulation Environment. 
Task Performed Energy 
Consumed (in mJ) 
Data Sensing 0.0018 
Data Processing 0.0513 
Data Transmission 0.1864152 
Data Receiving  0.0627456 
Self Evaluation  0.12 
Table 4: Energy Consumption for various tasks 
performed by Sensor Nodes. [18] 
6.2 Fault recovery 
After the faults are detected then the recovery activities 
are started. Faulty nodes go for recovery and here they 
are named as recovering node (RN) and the affected 
nodes (AN) are their neighbors. Now as in Figure 10 the 
red nodes are RN and red links are affected links, which 
will get defunct later on. A list of susceptible parents for 
each set of ANs is mentioned in Table 5. However the 
ANs have to select a suitable node to maintain the 
connectivity even in absence of the corresponding RN.  
 
 
Distributed Fault Tolerant Architecture for... Informatica 41 (2017) 47–58 55 
 
 
Figure 9: Node Deployments and Fault Scenario. 
Case 1 (Recovery for Node 1): Here the node ID 1 is 
RN after getting faulty goes to recovery mode; now this 
node sends probe packets to its neighboring nodes, which 
are actually in its communication range. The IDs of the 
ANs are 9, 12 and 20. Now these nodes will update their 
neighbor list and each of them will try to find out a node, 
which will act as their new parent. There are multiple 
susceptible parents for nodes 9, 12 and 20 and they select 
node IDs 18, 23 and 6 respectively, as their new parent. 
Node ID 9 have 5 susceptible parents and out of which it 
selects node ID 18 since the transmission power factor is 
minimum among all other possible parents (as presented 
in Table 5). Similarly node ID 12 and 20 has 5 and 6 
susceptible parents respectively but node IDs 23 and 6 
are selected as actual parents because of low 
transmission factor. So in all the 3 situation the tasks are 
carried out to minimize the power consumption. The 
necessary results to support new parent selection, from 
the each node’s susceptible parent list, is elaborately 
presented in Table 5. 
Case 2 (Recovery for Node 4): In the second case 
node ID 4 is RN and its ANs are 7, 11, 28 and 29. Just 
similarly like case 1 here all ANs find a suitable parent 
for transmitting data. In this case there are multiple 
susceptible parents out of which nodes 7, 11, 28 and 29 
(mentioned in Table 5) but each node selects some 
56 Informatica 41 (2017) 47–58 S. Mitra et al.  
 
specific node as their new parent. Moreover they update 
their neighbor list also.  
Node ID 7 and 11 have 6 susceptible parents and out 
of which node ID 7 selects node ID 10 as its immediate 
parent and node 11 selects 22 as its new parent. This 
selection is done on the basis of the minimum power 
factor for these nodes. Lower power factor means lower 
power consumption for transmission. Similarly node ID 
28 and 29 selects node ID 2 and 30 as their new parent 
respectively. So in all the 4 situations specific selections 
are made to keep the power consumption of the node 
low, in comparison to others. The necessary results to 
support new parent selection, from the each node’s 
susceptible parent list, is elaborately presented in Table 
5.  
7 Discussion 
Moreover in Table 6 all the ANs are mentioned along 
with their transmission power and distance from the 
current parent. After simulation it is inferred that the 
ANs select those nodes as their new parent, in absence of 
the RN, through which they can forward data towards 
BS. 
Node 28 has to raise its multiplication factor as high 
as 2.82, in order to avoid isolation. As in the cases of 
nodes 11, 28 and 29 the distance with the new parent is 
greater than their distance with node 4 so they have to 
raise their transmission power. 
Here the activities for two of the nodes with ID 1 and 
4 are shown the same activities are carried out for other 
RNs. All RNs send the probe packets to their neighbors, 
and the ANs in turn perform the topology maintenance 
tasks and then the RNs are reinitialized and the values 
from system variable DCkpt and TD are fetched, since 
they contain the last data checkpoint and state checkpoint 
of the node. After that the link quality estimation is done 
to check the vicinity traffic situation and finally the node 
comes back to the network. 
 
 
 
Figure 10: Affected Links due to Node Fault. 
 
 
 
 
Distributed Fault Tolerant Architecture for... Informatica 41 (2017) 47–58 57 
 
AN ID Susceptible Parent IDs Power Factor For Each Parent 
9 3, 5, 13, 18, 19 1.42, 1.20, 2.15, 1.08, 1.86 
12 5, 6, 14, 18, 23 2.89, 2.53, 2.30, 2.30, 1.79 
20 3, 5, 6, 13, 17,19 2.10, 1.51, 1.44, 2.69, 2.85, 2.71 
7 2, 10, 15, 16, 26, 30 2.72, 1.93, 3.10, 3.83, 3.25, 3.99 
11 2, 3, 13, 17, 22, 29 2.47, 2.46, 2.15, 2.16, 1.91, 2.15 
28 2, 3, 15, 16, 26 2.82, 3.01, 3.11, 2.96, 3.14 
29 11, 16, 28, 30 1.51, 1.65, 1.46, 1.19 
Table 5: Susceptible Parent List for Affected Nodes. 
RN ID AN ID New  Parent 
Node ID 
Power 
Factor 
Distance of AN 
with new Parent 
(in meters) 
1 9 18 1.08 24.56 
12 23 1.79 28.39 
20 6 1.44 26.41 
4 7 10 1.93 17.45 
11 22 1.91 30.54 
28 2 2.82 38.95 
29 30 1.19 27.02 
Table 6: New Parent Selections. 
8 Conclusion 
WSN is used widely nowadays for various field 
surveillance and distributed fault tolerance in necessary 
in the same for reliability and dependability of WSN. 
Novel fault recovery architecture is designed and 
proposed in this paper; the recovery architecture is 
destined to be integrated with a fault tolerant framework 
for wireless sensor network. This paper also presented 
proposed algorithms for fault recovery and connectivity 
maintenance in WSN. This algorithm explains details of 
recovery tasks are carried out. A brief discussion is 
presented to identify the detection of faults and then 
different cases for recovery are done.  
This proposed recovery technique takes care of 
recovery actions related to the faults due to hardware or 
software failure. It also improves link quality or 
connectivity among the nodes during recovery phases. 
However, the noise-related measurement or error due to 
presence of noise is not scope of this paper. This research 
will enhance the recovery scheme with self-organization 
and noise-related measurement based recovery in future. 
As a future work this research will also present a result- 
interpretation based comparative study of recovery 
schemes. 
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 Informatica 41 (2017) 59–70 59 
Combined Zernike Moment and Multiscale Analysis for Tamper 
Detection in Digital Images  
Thuong Le-Tien, Tu Huynh-Kha, Long Pham-Cong-Hoan, An Tran-Hong 
Dept of Electronics and Electrical Eng., Bach Khoa University of Ho Chi Minh City, Vietnam 
E-mail: thuongle@hcmut.edu.vn, hktu@hcmiu.edu.vn, phamcong.hoanlong@gmail.com, an.tranhong94@gmail.com 
Nilanjan Dey 
Techno India College of Technology, Rajarhat, Kolkata, India 
E-mail: neelanjandey@gmail.com 
Marie Luong 
University Paris 13, France  
E-mail: marie.luong@univ-paris13.fr 
Keywords: wavelet transform, curvelet transform, multiscale analysis, zernike moment, block-based technique, 
morphological technique, copy-move images 
Received: January 12, 2017 
The paper proposes a new approach as a combination of the multiscale analysis and the Zernike 
moment based for detecting tampered image with the formation of copy – move forgeries. Although the 
traditional Zernike moment based technique has proved its ability to detect image forgeries in block 
based method, it causes large computational cost. In order to overcome the weakness of the Zernike 
moment, a combination of multiscale and Zernike moments is applied. In this paper, the wavelets and 
curvelets are candidates for multiscale role in the proposed method. The Wavelet transform has 
successful in balancing the running time and precision while the precision of the algorithm applied the 
Curvelets does not meet the expectation. The comparison and evaluation of the performance between the 
Curvelets analysis and the Wavelets analysis combining with the Zernike moments in a block based 
forgery detection technique not only prove the effective of the combination of feature extraction and 
multiscale but also confirm Wavelets to be the best multiscale candidate in copy-move detection. 
Povzetek: Razvita je metoda za pospešitev ugotavljanja prekopiranih ponarejenih slik. 
1 Introduction 
In the world we are living in, most information is 
created, captured, transmitted and stored in digital form. 
It is really the flourished time of digital information. 
However, the digital information can easily be interfered 
and forged without being recognized by naked eyes, and 
thus create a significant challenge when analyzing digital 
evidences such as images. There are many kinds of 
counterfeiting images which are classified in two groups: 
active and passive. The active techniques are known as 
watermarking or digital signature in which the 
information of original image is given while there is no 
any prior information in the passive ones. In recent years, 
the passive, also called the blind, methods are more 
interested and challenge the researchers and scientists in 
the field of image processing and image forensics. And 
among the manipulations creating faked images in the 
passive/blind kind, copy-move is the most popular. A 
copy-move tampered image is created by copying and 
pasting content within the same image. Nowadays, 
various software for images processing are getting more 
sophisticated and easily accessed by anybody. That is 
why the studies on digital image forgeries are getting 
more attention lately. Actually, image forgery detection 
not only belongs to image processing but also is a field of 
image security. Standing out from other studies, the 
researches about Zernike moments had shown its 
advance in detecting forged region in copy-move digital 
image forgery. The Zernike moment has proved to be 
superior compare to other types of moment; however the 
Zernike moment based algorithm requires a large 
computational cost [. In addition to the moments, studies 
about the multiscale analyses have long been applied to 
digital signal processing due to their advantages. A 
proposed method using a combination of the Zernike 
moment and the Wavelet transform has been studied and 
evaluated [1] in which the Wavelets analysis has been 
used in order to reduce the computational cost of the 
Zernike moment based technique. The combination of 
the Zernike moment and the Wavelets has shown its 
feasibility in detecting the copy-move forgeries and the 
reduction in running time of the algorithm. An example 
of a typical copy-move forgery image is shown in Figure 
1. 
60 Informatica 41 (2017) 59–70 T. Le-Tien et al. 
 
Figure 1: Example image of a typical copy-move 
forgery; (a). the original image; (b). the tampered 
image [2]. 
In this study, the Curvelets is also used to combine 
with the Zernike moment based technique instead of the 
Wavelets because of the ability to represent the curves of 
the objects. A block based technique using the Zernike 
moment to extract feature for the image forgery detection 
is used. However, instead of the original testing images, 
the images which pre-processed by the multiscale 
analysis is going to be used to calculate the Zernike 
moments. The goal of this paper is to evaluate the 
performance of the Wavelets and Curvelets in different 
perspectives and to examine which multiscale 
representation is more suitable with the Zernike moment 
based for detecting copy-move tampered images. The 
experiments are simulated on MATLAB R2013a with.  
2 Related works 
In which images’ feature extraction techniques are 
divided into two groups: extracting features directly with 
and without transformation. And from which, we also 
chose Zernike moments based techniques for further 
research. Suggested by Seung – Jin Ryu et al. in [3], 
Zernike moments technique has advance in feature 
representation capability, rotation invariance, fast 
computation, multi-level representation for describing 
shapes of patterns and low noise sensitivity. The 
disadvantage of this technique is that it’s weak against 
scaling and other tampering type based on affine 
transform and it has high computational cost. 
Generally, copy – move forgeries detection using 
block based techniques requires 7 steps [4]; the steps go 
from dividing the input image into overlapping blocks 
then calculate features of blocks and final steps are 
comparing blocks for forgery detection. From [1], a 
combination between Zernike moments and Wavelet 
transform was applied to reduce the running time while 
keeping precision of the original algorithm. Another 
approach from [5] uses neighborhood sorting, in which 
G. Li et al. used the Discrete Wavelet Transform (DWT) 
and Singular Value Decomposition (SVD). As discussed 
in the introduction, Wavelet transform based techniques 
has weakness in processing objects in detailed image 
with lines and curves, and to work-around that 
disadvantage, the Curvelet transform was propose [6, 7] 
by Candès and Donoho in 2000 as a Multi-resolution 
geometric analysis. The base theory of the Curvelet 
transform is The Ridgelets transform, proposed by the 
same authors. The Curvelet transform was born to 
analyze local line or curve singularities, which were a 
difficulty for original Ridgelets. The Curvelet transform 
allows an almost optimal sparse representation of objects 
with singularities along smooth curves. Recently, Ying et 
al. [8] has extended the Curvelet transform to three 
dimensions. Comparing with Wavelets, the Curvelets in 
image processing is still new and also a new orientation 
[6–15].  
In this paper, we will carry out and comparison and 
evaluation of two combinations that are: Zernike 
moments based technique and Wavelet transform, the 
other is Zernike moments based technique and Curvelet 
transform; in order to examine which multiscale 
representation is more suitable with the Zernike moment 
based for detecting copy-move tampered images. 
3 Methodology 
The main purpose of paper is developing an algorithm to 
balance the running time and exactness using a 
combination of multiscale and feature extraction, from 
which the performances of multiscale methods are 
evaluated to propose the most suitable multiscale method 
in image forgery detection. The workflow of the copy-
move image forgery detection algorithm is shown in 
Figure 2. In the pre-processing step, the tampered image 
is going through some morphological techniques after 
converted into the grayscale. The foreground component 
is extracted and divided into overlapping blocks of the 
same size before going to the next step. The Curvelet 
transform or the Wavelet transform will be applied to 
analyze the image before extracting features by Zernike 
moments. The detection results are concluded using the 
Euclidean distances of each pair of blocks if they are 
lower than a threshold. Taking account to the similarity 
between neighbour blocks, the actual distances of a pair 
of blocks must higher than a threshold before the 
algorithm can conclude them as the copy-move regions. 
3.1 Pre-processing 
The tampered images will be converted into gray scale 
before enhancing by the morphological techniques and 
extracting the foreground components. Moreover, by 
converting to gray scale, the computational cost of the 
Zernike moment will be reduced since we only need to 
calculate the Zernike moment in one dimension instead 
of three color dimensions in the color images. 
Morphological processing for gray scale images 
requires more sophisticated mathematical development. 
For binary image, the pixel has only two values are bit 1 
and bit 0, black and white; in gray scale image, pixels 
have their scale from 0 to 255. Therefore, the operation 
requires more works. Structuring elements in grayscale 
morphology come in two categories: non-flat and flat 
[16]. To reduce the redundant information in background 
and concentrate in searching the information of the 
objects belonging to foreground only, a foreground 
extraction is applied. Among the morphological 
functions that used for extracting the foreground 
Combined Zernike Moment and Multiscale... Informatica 41 (2017) 59–70 61 
 
Figure 3: Example of creating the mask from an 
input image; (a). Input image; (b). Binary gradient 
mask; (c). Mask that used to extract foreground 
component [2]. 
 
Figure 4: Wavelets transform block diagram. 
 
Figure 2: Block diagram of the algorithm. 
components such as dilation, erosion, image filling, etc, a 
combination of dilation and erosion can build all other 
operations [11]. 
The dilation of f by a flat structuring element (SE) b 
at any location (x, y) is defined as the maximum value of 
the image in the window outlined by b̂ when the origin of  
is b̂ at (x, y). That is: 
 ),(max),]([
),(
tysxfyxbf
bts


       (1) 
When we used that 
),(ˆ yxbb                         (2) 
Where, similarly to the correlation, x and y are 
incremented through all values required so that the origin 
of b visits every pixel in f . That is, to find the dilation of 
f by b, the structuring element is reflected about the 
origin. The dilation is the maximum value of f from all 
values of f in the region of f coincident with b. 
For a non-flat structuring element 
Nb , the dilation of 
f is defined as: 
 ),(),(max),]([
),(
tsbtysxfyxbf N
bts
N
N


(3) 
Since we add values to f , the dilation with a non-flat 
SE is not bounded by the values of f, which may present 
a problem while interpreting results. Grayscale SEs are 
rarely used. 
The erosion of f by a flat structuring element b at any 
location (x, y) is defined as the minimum value of the 
image in the window outlined by b̂  when the origin of b̂  
is at (x, y). Therefore, the erosion at (x, y) of an image f 
by a structuring element b is given by: 
 ),(max),]([
),(
tysxfyxbf
bts


      (4) 
The explanation is similar to one for dilation except 
for using minimum instead of maximum and that we 
place the origin of the structuring element at every pixel 
location in the image. 
Similarly, the erosion of image f by non-flat 
structuring elements
Nb is defined as the following 
equation: 
 ),(),(max),]([
),(
tsbtysxfyxbf N
bts
N
N


 
(5) 
Erosion and dilation, separately, are not very useful 
in gray-scale image processing. These operations become 
powerful when used in combination to develop high-
level algorithms. 
In the image, the components differ greatly from the 
background image can be segmented. Operators that can 
calculate the gradient of the image can be used to detect 
the changes in contrast. Therefore, the edges of the input 
can be extracted easily and are then processed by dilation 
and filling holes to create the mask, which is call binary 
gradient mask, for extracting the foreground component 
(see Figure 3). 
3.2 Wavelets analysis 
According to [11, 17-31], the Wavelet transform is easier 
to compress, transmit and analyze in image processing 
compared to the Fourier transform. The Wavelets 
analysis can be used in order to divide the information of 
the image into approximation and detail sub-signal. The 
general trend of pixel values is shown in the 
approximation sub-band while the three detail sub-bands 
are so small that they can be neglected. Therefore, it is 
feasible to use just the approximation sub-band to detect 
the image forgery. Figure 4 shows the steps to get the 
approximate image from an original image. 
According to [11], the two-dimensional images which 
requires a two-dimensional scaling function, φ(x, y), and 
three two-dimensional wavelets, ψH (x, y), ψV (x, y) and 
ψD (x, y). They are the product of one-dimensional 
scaling function φ and corresponding wavelet ψ, 
 
)()(),( yxyx                            (6) 
 )()(),( yxyxH                         (7) 
)()(),( yxyxV                         (8) 
)()(),( yxyxD                        (9) 
 
In which ψH measures variations along horizontal 
62 Informatica 41 (2017) 59–70 T. Le-Tien et al. 
 
Figure 5: Image analyzed using Wavelet transform level 
1; (a). Original image, (b). The approximate and detail 
images after applying the Wavelet transform [2]. 
 
Figure 6: Local Ridgelet transform on bandpass filtered 
image. The red ellipse is significant coefficient while 
the blue one is insignificant coefficient [6]. 
edges, ψv measures variations along vertical edges and 
ψD measures variations along diagonals. After finding 
two-dimensional scaling and wavelet functions, based on 
one-dimensional discrete wavelet transform, we define 
the scaled and translated basis functions: 
      )2,2(2),( 2/,, nymxyx
jjj
nmj        (10) 
)2,2(2),( 2/,, nymxyx
jjji
nmj          (11) 
),(),(
1
),,(
1
0
1
0
,,00 yxyxf
MN
nmjW
M
x
N
y
nmj




          (12) 
),(),(
1
),,(
1
0
1
0
,, yxyxf
MN
nmjW
M
x
N
y
i
nmj
i 




 
  (13) 
i = {H, V, D}, where j0  is an arbitrary starting scale 
and the Wφ(j0,m,n) coefficients define an approximation 
of f(x, y)at scale j0. The Wiψ(J, m, n) coefficients add 
horizontal, vertical, and diagonal details for scales j≥ j0. 
Normally we let j0 = 0 and select N =M = 2J so that we 
can get j = 0, 1, 2..,J – 1 and m, n = 0, 1, 2,…, 2J – 1. 
Given the Wφ and Wiψ of 2 equations (12) (13), f(x, y) is 
obtained by inverse discrete wavelet transform [11]. 
),(),,(
1
),(),,(
1
),(
,,0
0,,
,,00
yxnmjW
MN
yxnmjW
MN
yxf
nmij
n
i
jj mDVHi
n
nmj
m









           (14) 
The wavelet transform used in the algorithm is 
selected from the Wavelet families, such as “db1”, 
“haar”, “db2”,…with similar results. Optionally, the 
wavelets “haar” is used to demonstrate wavelets 
transform level 1 in Figure 5.   
From the Figure 5, the reduction in size of the 
approximate image which analyzed by the Wavelet 
transform can be clearly seen. The approximate image is 
only a quarter of the original image. 
3.3 Curvelets analysis 
In this research, the first generation of the Curvelet 
transform is used to analyze the image. From [6, 7, 32], 
the idea of the first generation Curvelet transform is to 
decompose the image into a set of wavelet bands and 
analyze each band by a local Ridgelet transform as 
shown on Figure 6. Different sub-bands of a filter bank 
output are represented by different levels of Ridgelet 
pyramid. Moreover, a relationship between the width and 
length of the crucial frame elements is contained in this 
sub-band decomposition. The first generation discrete 
Curvelet transform of a continuous function f(x) is 
making use of a dyadic sequence of scales and a bank of 
filters with characteristic that the band bass filter ∆j is 
concentrated near the frequencies of [22j,22j+2], as follow: 
ff jj *)( 2                         (15) 
)2(ˆ)(ˆ 22 vv
j
j
                      (16) 
Following the research in [33,34], the decomposition 
of the first generation discrete Curvelet transform is a 
sequence of the following steps: 
Sub-band decomposition: Decomposing the object f 
into sub-bands. 
,...),,( 210 fffPf                 (17) 
Each layer contains details of different frequencies. 
P0 is low pass filter, ∆1, ∆2,… are high-pass (band-pass) 
filters. The sub-band decomposition can be approximated 
using the well-known wavelet transform; f is 
decomposed into S0, D1, D2, D3, etc. P0f is partially 
constructed from S0 and D1, and may include also D2 and 
D3. ∆sf is constructed from D2s and D2s+1. 
Smooth partitioning: The sub-bands are smoothly 
windowed into squares of a suitable scale. The scale is 
optional, but in the proposed method, the scale is 2 to 
reduce the size of image by a half corresponding to a 
wavelets decomposition level 1. A grid of dyadic squares 
is defined as. 
ssssskks
Q
kkkk
Q 




 





 

2
1
,
22
1
,
2
211
),,(
2
21
    (18) 
Where, Qs is all the dyadic squares of the grid. Let w 
be a smooth windowing function with “main” support of 
size 2-sx2-s. For each square, wQ is a displacement of w 
localized near Q. Multiplying ∆sf with wQ (QQs) 
produces a smooth dissection of the function into 
“squares”. 
fwh sQQ                            (19) 
Renormalization: is centering each dyadic square to unit 
square. For each Q, the operator TQ is defined as: 
)2,2(2),)(( 221121 kxkxfxxfT
sss
Q     (20) 
Each square is renormalized: 
QQQ hTg
1
                             (21) 
Ridgelet analysis: DRT is used to analyze each square. 
Combined Zernike Moment and Multiscale... Informatica 41 (2017) 59–70 63 
 
Figure 7: Image analyzed using the Curvelet transform. 
(a), (b) Original image and its edges; (c), (d) Image 
processed with scale of 2 and its edges [2]. 
  ,),( QQ g                       (22) 
In which the Ridgelet element has a formula in the 
frequency domain as: 
 
(23) 
Where, i,l are periodic wavelets for [-,). i is the 
angular scale and l[0,2i-1-l] is the angular location. j,k 
are Meyer wavelets for . j is the Ridgelet scale and k is 
the Ridgelet location. 
Below is an example result for the Curvelet 
transform with scale of 2 and the image’s edges are 
shown (see Figure 7). 
3.4 Zernike moment’s properties 
According to the relative research paper about Zernike 
moments in [3, 35], we can summarize the Zernike 
moments through some mathematical background. In this 
section, we describe the Zernike moments as following 
functions. 
The 2D Zernike moment of order n with repetition of 
m for a continuous image function f(x,y) that vanishes 
outside the unit circle is: 




1
*
22
),(
1
yx
nmnm Vyxf
n
Z

          (24) 
Where n is a non-negative integer and m is an integer 
such that (n-|m|) is non-negative and even. The 2D 
Zernike moment, ( , )nmV    is defined in polar 
coordinate ),(  inside the unit circle as: 
)exp()(),(  jmRV nmnm            (25) 
Where ( , )nmR   is the n-th order of Zernike radial 
polynomial given by: 
 
   
( )/2
2
0
( )!
( ) 1
2 2
! ! !
2 2
n m
k n k
nm
k
n k
R
n k m n k m
k
 




 
      
   
   

  
  (26) 
Zernike moments have rotational invariance, and can 
be made scale and translational invariant, making them 
suitable for many applications. Zernike moments are 
accurate descriptors even with relatively few data points. 
Reconstruction of Zernike moments can be used to 
determine the amount of moments necessary to make an 
accurate descriptor. Though the complexity of the 
computation process compared to geometric and 
Legendre moments, Zernike moments have proved to be 
better in terms of their feature representation capability, 
rotation invariance, multi-level representation for 
describing the shapes of patterns and low noise 
sensitivity [3]. 
3.5 Forgery Detection Conclusion 
After calculating Zernike moments, to remove the similar 
features due to neighbor blocks, the Euclidean distance 
of each pair of Zernike will be calculated and compare to 
a threshold D1 [3]. This threshold is often set equal to the 
size of a block. In addition to Euclidean distance, the 
actual distance of each pair of blocks is also calculated in 
order to avoid the miss-detection between neighbor 
blocks. 
11)( DZZ pp                    (27) 
    2
22
Dljki                (28) 
Where Zp = Vij and Zp+1 = Vkl. Using these equations, 
the testing blocks are determined if they are tampered 
region or not. 
4 Proposed system 
The paper develops an algorithm to detect the forged 
regions in term of copy-move in images by using the 
combination of the multiscale analysis and the Zernike 
moment based technique. Using this algorithm, the 
performance of the Wavelet transform and the Curvelet 
transform will be compared and evaluated at pixel level. 
The test image is firstly converted to grayscale 
before extracting foreground using a binary gradient 
mask which is created by a combination of dilation and 
erosion. A wavelet transform decomposition level 1 or a 
fast discrete curvelet transform (FDCT) is applied to the 
image which is extracted foreground. In case of 1 level 
DWT, only the approximation subband is considered. 
The approximation or an image with FDCT is then 
divided into many overlapping blocks. The blocks’ 
features are collected by calculating their Zernike 
moments. The Euclidean and actual distance are also 
calculated to make sure that they are similar, but not 
neighbors. Vectors satisfying the constraints in two 
above distances will be considered the suspicious 
vectors. Blocks corresponding to these vectors are 
candidates of copied regions. The flowchart is shown in 
Figure 8. 
From a personal collection and database in [2], we 
choose 12 images with different characteristics and sizes 
to conduct our experiments. Figure 9 shows the images 
used in the experiments. The images have different sizes 
and features. The smallest images have size of 128x128 
while the largest have sizes of 1440x1440. Some of them 
have very little detail on background while the others 
have detailed background. The purpose of this is that we 
can conduct experiments on different types of images for 
diversity. 
Error measurement 
At pixel level, the important measures are the number of 
correctly detected forged pixels, TP, the number of pixels 
that have been erroneously detected as forged, FP, and 
the falsely missed forged pixels FN. From these 
         

likjlikj ,,,,
2
1
ˆˆ
2
1
)(ˆ
64 Informatica 41 (2017) 59–70 T. Le-Tien et al. 
 
Figure 8: Flowchart of the proposed method. 
 
 
Figure 9: Images used in the experiments [2]. 
parameters, we can compute the measure precision p and 
recall r. They are defined as:  
)/( ppp FTTp                      (29) 
)/( Npp FTTr                      (30) 
Precision shows the probability that a detected forgery is 
truly a forgery, while the recall or true positive rate 
expresses the probability that a forged pixel is detected. 
The trade-off between the precision and recall exists; 
hence, to consider both precision and recall together, F is 
the combination of precision and recall in a single value.  
)/(2 rpprF                      (31) 
The three measures are used to evaluate the performance 
of the copy-move image forgery detection method. 
 
5 Simulations and evaluations 
In the experiments, MATLAB program (version 2013a) 
is used with Window 7 Ultimate 64-bit, CPU Intel Core 
i5 @ 1.8GHz and 4GB RAM in order to run the 
simulations.  
Using the images in Figure 9, we conducted different 
experiments by changing the order of Zernike moments, 
block size, the Curvelet transform’s order, etc. Every test 
image will go through the proposed method and 
consequently Precision, Recall and F – measure will be 
evaluated. 
Based on the fact that the investigating images are 
usually color images, there are two options for proposed 
method. First is to calculate Zernike moments from each 
color channel and subsequently link the moment values. 
The other option is to convert the RGB image into a 
gray-scale image. We choose the latter method because 
the same copy-move forgery is applied to each color 
channel similarly. Hence, by only calculating the Zernike 
moments on one dimension rather than for three 
dimensions of color image, the computation time of 
Zernike moments can be minimized. 
A. Zernike moment’s order 
According to [35], a relatively small set of Zernike 
moments can characterize the global shape of a pattern 
effectively. The low order moments represent the global 
shape of a pattern and the higher orders represent the 
detail. However, the higher the order the more 
computational cost it takes which result in the increasing 
in running time. In Figure 10 below show the average 
running time of proposed method for images with 
Combined Zernike Moment and Multiscale... Informatica 41 (2017) 59–70 65 
 
Figure 10: Computation time for the Zernike 
moment on multiscale analyzed images. 
 
Figure 11: Detection results for forgery images of 
different Zernike moment’s orders with Wavelets 
analysis. 
 
Figure 12: Detection results for forgery images of 
different Zernike moment’s orders with Curvelets 
analysis. 
different Zernike moments’ orders in which the higher 
order is, the more computational time takes and 
dramatically increases for Curvelets. 
In this research, the complex Zernike moments based 
on complex Zernike polynomials as the moment basis set 
are used in the processing. As we can see from Figure 10, 
the higher the order, the longer it takes to process. For 
the Wavelets transform method which only uses the 
approximation of images to compute the Zernike 
moments, the running time is much shorter comparing 
with the Curvelet transform method. 
From the Figure 11 and Figure 12, the effect of 
different Zernike moment’s orders on parameter F which 
shows the relationship between precision and recall was 
displayed. In this experiment, all images in the dataset 
will be tested with different Zernike moment’s orders 
and different multiscale analysis. Even though 
theoretically the Zernike moment with higher order 
provides better precision compare to the lower ones 
which means that most part of detected region is correct, 
the recall which presents the percentage of forgery region 
detected will relatively reduce as a trade-off for the 
precision. 
Therefore, according to Figure 10 and Figure 11, the 
F – measure of higher Zernike moment’s order detection 
test is not always higher than the lower orders although it 
needs more computation time as in Figure 9 shown. 
Comparing the results of the order of 5, 10, 15 and 20, 
we can see that the F – measure of 5th order and 10th 
order is relatively higher than others. For both the 
Wavelet transform test and the Curvelet transform test, 
10th order of Zernike moment is decided to be used as it 
yields higher F – measure compare to other orders which 
means the trade-off between the precision and recall of it 
is better than other order. Hence, for the next 
experiments, we will use Zernike moment of order 10 to 
analyze the performance of the Curvelet transform and 
the Wavelet transform in forgery detection in other 
different perspectives. 
B. Block size 
In this experiment, we tested the 13 images in dataset 
with different dividing block sizes from 8 pixels to 32 
pixels in order to analyze the effect of the block size to 
the detectability and to choose a suitable block size for 
further experiments. After analyzed by a multiscale 
method, the testing image will be divided into square 
blocks with same size before calculating the Zernike 
moments. 
The different in block size effect greatly on the 
detectability. Figure 12 and Figure 13 show the detection 
result of forgery images with block sizes of 8, 16, 24 and 
32. As we can see from the figures, most of the detection 
results when dividing the images into block sizes of 24 
and 32 pixels fail to detect the forgery regions. This 
shows that the algorithm is effective if the copied region 
is comprised by many smaller blocks. This also means if 
the block size is bigger than the copy-move region, the 
detectability will drop dramatically since the forgery part 
in one block is not large enough to provide adequate 
information for detection. The popular size of divided 
blocks is 8x8 or 16x16. 
From the results shown in Figure 12 and Figure 13, 
for both the Wavelets analysis and the Curvelets analysis, 
the image with highest F – measure is G with 99.9% and 
98.8% respectively with block size of 16x16. With block 
size of 24x24, for the Wavelets analysis, the image A, B, 
F, J and K  have visually lower F–measure compare to 
the highest values, in which the F – values reached 0% in 
image B and K where the algorithm returns “real image” 
results. With block size of 32x32, except for the image 
G, H and I which are large original image with big copy-
move region, the other image have 0% for precision and 
recall. For the Curvelet transform combining with 
Zernike moment algorithm, the processed images have a 
bigger size compare to the images analyzed by the 
66 Informatica 41 (2017) 59–70 T. Le-Tien et al. 
 
Figure 13: Detection results for forgery images of 
different block sizes with Wavelets analysis. 
 
Figure 14: Detection results for forgery images of 
different block sizes with Curvelets analysis. 
 
Figure 15: Negative detection results for a forgery 
image analyzed by the Curvelet transform with 
different number of scale; (a). Forged image,                         
(b).b n =2,(c) n =3,(d) n =4 [2]. 
Wavelet transform. Therefore, with the block size of 
24x24, only image B have visually lower F – measure (at 
0%) compare to other block size and with block size of 
32x32, only images B, E, F, J and K have extreme low F 
– parameters (10% for image F and 0% for the rest). 
For Wavelets analysis, the images’ size is reduced to 
a quarter of the original forgery images which make the 
copy-move region is shrink to a quarter of original copy-
move region. Therefore, compare to the Curvelet analysis 
detection results, the detectability of bigger block size of 
Wavelet analysis is lower. 
The block size of 16 pixels is chosen since we found 
this to be a good trade-off between detected image details 
and the feature robustness. This block size will be fixed 
across the different analysis, when possible, to allow for 
a fairer comparison of the feature performance. Note that 
the majority of the previous research also proposed a 
block size of 16 pixels. 
 
Scale of the Curvelet transform 
For our experiments, the first-generation Curvelet 
transform was used to enhance the images. The 
applications of the first-generation Curvelets are image 
denoising, image contrast enhancement, etc. The default 
number of scales including the coarsest wavelet level is 
equal to log2(N)-3, in which N is the size of the NxN 
testing image. In this experiment we will observe the 
detection results by changing the number of scale from 
smallest to equal or less than the default value.  
Different images will have different default values of 
scale if they have different sizes. A larger image can be 
analyzed by larger scale of the Curvelet transform. 
Therefore, for consistency in this experiment, all 12 
images in the dataset will be tested with different number 
of scale according to the smallest size of the images in 
dataset which is 128 x 128. With N = 128, we have the 
number of scale varies from 2, 3 or 4. Hence, the 12 
images will be applied Curvelets analysis with one of 
these scales before going through further steps in the 
algorithm such as calculating the Zernike moments or 
computing Euclidean distances.  
From the detection results of image J in Figure 15, 
we can see that with higher number of scale (n = 4) for 
Curvelet analysis, the percentage of detected forgery 
region is higher compare to the lower number of scale 
detection results. 
The results of the experiments are shown in Figure 
16. Although the experiments are conducted with 
different number of scales, the F – measure of the 
detection result is almost the same for most of the testing 
images except the image A, B, I, J and K. The results of 
the Curvelets analysis with scale of 2 are slightly higher 
compare to other scales as we can see from the images A, 
I, J and K. Although the scale of 4 of the Curvelet 
analysis algorithm has a higher detection result in image 
B, the scale of 2 has slightly higher average F – measure 
compare to other scales. The Curvelet transform with 
scale of 2 is chosen from the experiments we conducted 
in this section. Moreover, from [30], the authors also 
proved that the small scale is more robust. For the next 
section, we will perform a throughout comparison 
against the Wavelet transform with the results from the 
previous experiments. 
Combined Zernike Moment and Multiscale... Informatica 41 (2017) 59–70 67 
 
Figure 16: Detection results for forgery images 
analyzed by the Curvelet transform with different 
number of scales. 
 
Figure 17: Performance comparison between the 
Wavelets analysis and the Curvelets analysis 
combining with the Zernike moment based technique. 
 
Image 
Measure (%) 
Image 
Measure (%) 
P R F P R F 
(a) 87.6 96.4 91.8 (g) 99.8 100.0 99.9 
(b) 100.0 78.0 87.6 (h) 78.6 100.0 88.0 
(c) 74.4 94.3 83.2 (i) 34.7 99.6 51.5 
(d) 90.8 92.8 91.8 (j) 93.8 74.1 82.8 
(e) 84.2 88.5 86.3 (k) 78.1 81.9 80.0 
(f) 91.5 100.0 95.6 (l) 81.1 100.0 89.6 
Average 82.9 92.1 85.7 
Table 1: Detection rates for 12 images analyzed by the 
Wavelet transform combining with Zernike moment 
Image 
Measure (%) 
Image 
Measure (%) 
P R F P R F 
(a) 89.4 96.0 92.6 (g) 98.1 100.0 99.0 
(b) 7.03 57.1 63.0 (h) 73.5 100.0 84.7 
(c) 79.8 99.4 88.5 (i) 44.3 94.0 60.2 
(d) 76.7 62.7 69.0 (j) 42.5 78.3 55.1 
(e) 78.5 88.1 83.0 (k) 30.7 82.2 44.7 
(f) 26.5 62.2 37.2 (l) 80.8 100.0 89.4 
Average 65.9 85.0 72.2 
Table 2: Detection rates for 12 images analyzed by the 
Curvelet transform combining with Zernike moment. 
C. Performance comparison between the Curvelet 
transform and the Wavelet transform combining with 
the Zernike moment based technique 
In Figure 17, the F – measure of both Curvelets analysis 
and Wavelets analysis is shown. Except for image (C) 
and (I) where the F – measure of the Curvelet transform 
method is higher than the Wavelet transform method, the 
performance of the Curvelet transform is lower for most 
the testing images. Especially for image (F), the Curvelet 
transform method’s F – measure is lower than 40% while 
the Wavelet transform method has approximately 95%. 
In order to analyze the different between two multiscale 
analyses, we will investigate some of the testing images 
that have great dissimilarity. The precision and recall of 
each testing images will also be analyze for further 
discussions. 
For image B, D, F, J and K, they are not the simple 
images compared to others in the dataset, but they have 
detailed background which may be the cause for the drop 
in performance of the Curvelet transform method. Detail 
statistics about the precision and recall for the images can 
be analyzed from the Table 1 and 2. 
For practical use, the most important aspect is the 
ability to distinguish tampered and original images. 
However, the power of an algorithm to correctly annotate 
the tampered region is also significant, especially when a 
human expert is visually inspecting a possible forgery. 
Thus, when evaluating the copy-move algorithm with 
different multiscale analyses, we analyze their 
performance at pixel level, where we evaluate how 
accurately tampered regions can be identified through 
three parameters: Precision, Recall and F – measure. 
The experiments conducted are all “plain copy-
move” which means we evaluate the performance of each 
method under ideal conditions. We used the 12 original 
images and spliced 12 images without any additional 
modification. We chose per-method optimal thresholds 
for classifying these 24 images. Although the sizes of the 
images and the manipulation regions vary on this test set, 
both tested analyses solved this copy-move problem with 
a recall rate of above 85% (see Table 1 and 2). However, 
only the Wavelet transform method has a precision of 
more than 80%. This means that for the Curvelet 
transform algorithm, even under these ideal conditions, 
generate false alarms.  
With Curvelet analysis, the algorithm has a difficult 
time to distinguish the background and detect them 
falsely as the forgery regions. Through the experiments, 
it proves that the proposed method which uses the 
forgery images analyzed by the Curvelet transform is not 
suitable for the block based method combining with 
Zernike moment to detect the copy-move forgery images. 
Disscusion 
Through these experiments, different perspectives of 
block based technique using a combination of the 
Zernike moment and multiscale analysis for digital image 
forgeries are studied. The Zernike moment’s orders, 
block sizes, the number of scale for multiscale analysis 
and different kinds of multiscale analyses are considered.  
For higher Zernike moment’s order, the 
computational time will increase proportionally along 
with the increasing in orders. Although the complexity in 
computation increases, the detection results are not worth 
it. In another word, the F – measure parameter is not 
going up with higher orders but for higher orders; some 
68 Informatica 41 (2017) 59–70 T. Le-Tien et al. 
testing images proved that its detectability performance 
is worse than a lower order.  
According the experiments’ results and other block 
based techniques, the block size of 16x16 is more 
favorable than the others. From the detection results with 
different block sizes, we can see that the trade-off 
between precision and recall of 16x16 size is the most 
suitable for the block based method for detecting image 
forgeries. Although the block size of 8x8 can also 
provide a comparable F – measure, for such a small 
blocks, the information it holds may not enough to be 
distinctive to other blocks. Therefore, the block size of 
16x16 is used for different experiments in this research. 
From the test by changing the number of scale of the 
Curvelet transform, the detection results between 
different numbers of scale is quite similar to each other. 
Nevertheless, the scale of two some provides better 
detection results compare to other number of scale. As 
shown above, the trade-off between the precision and 
recall for the scale of two is slightly superior compare to 
other number of scales. 
Although the Curvelets analysis has many superior 
characteristics compare to the Wavelets analysis, in this 
block based technique combining with Zernike moment 
for digital image forgery detection, the detectability of 
the Curvelets analysis method shown is worse than the 
Wavelets analysis method’s for most of the cases. The 
performance of the Curvelets analysis has yet reached the 
expectation of a multiscale analysis which allowed an 
almost optimal sparse representation of objects with 
singularities along smooth curves. The detection results 
have shown that in this digital image forgery detection 
method, the characteristics of the Curvelet transform 
have not utilized. Moreover, the precision of the 
detection results is also lower than the Wavelets analysis 
which leads to the lower F – measure.  
In this research, the proposed idea of using the 
Curvelets analysis combining with Zernike moments by 
my study is not suitable. This method not only failed to 
utilize the characteristics of the Curvelet transform, but 
also reduced the efficiency of detecting a tampered 
image. Comparing to the Wavelets analysis, the 
Curvelets analysis is not feasible for combining with 
Zernike moments in the block based techniques. 
6 Simulations and evaluations 
In previous method [1], the combination between the 
Zernike moments and the Wavelet transform for 
detecting digital image forgeries has successfully achieve 
the desired goal. The computational cost reduces 
significantly while the precision is still acceptable. 
However, the Wavelets transform has disadvantage when 
analyzing edges or curves details. Continuing that 
research paper, another multi-resolution, the Curvelet 
transform, is used to combine with the Zernike moment 
in the block based technique for detecting image 
forgeries in order to solve that problems and increase the 
precision of the proposed method in the research paper 
although the computational cost will increase as the 
compensation for the additional information of the 
images. The efficiency of the algorithm will be evaluated 
through three parameters: precision, recall and F – which 
is combination of precision and recall in one parameter. 
Other parameter such as Zernike moment’s order and the 
block size are chosen after conducting the experiments to 
evaluate the effect of them on the performance. From 
real experiments, we get a conclusion: 
The Wavelets transform gives a desired result with 
low computational time and high precision, its 
advantages is simplicity, easy to implement and require 
low computational resources. Most images preprocess by 
the Wavelets transform have expectation results, 
however, for some images where the details on edge and 
curve that neighbor to each other is blended in after the 
Wavelets preprocessing, therefore having a low 
precision. 
The Curvelet transform provides lower results, 
contrary to the expectation. Although the edge and curve 
details are clearer, images preprocessed by the Curvelet 
transform have lower performance and much higher 
Zernike moments calculation time than the Wavelets 
transform combination. From the simulation results, the 
Curvelet transform is not suitable for the proposed 
method which used the combination of Zernike moment 
based and the Curvelet analysis in the block based 
technique for detecting image forgeries. 
The combination of the multiscale analysis and 
Zernike moment based technique is still not tested with 
different transformation attack such as rotation and 
scaling. Although the Zernike moment is known for the 
rotation invariant, in this block based technique, we have 
yet conducted experiments for rotation attacks.  
For future research, in the problem of identifying the 
copied areas of a digital image, we may explore the 
rotation invariant characteristic of the Zernike moments 
which can robust against rotation attacks. Moreover, by 
combining with SIFT or SURF, the running time of 
Zernike moments can be enhanced.  
For the Curvelet transform, a new method is needed 
to utilize its features. There are some feasible algorithms 
such as keypoint-based algorithms or block based 
algorithms which do not use Zernike moments but 
intensity-based or frequency based. 
7 Acknowledgement 
This research is funded by Vietnam National University 
Ho Chi Minh City (VNU-HCM) under grant number 
B2015-20-02. 
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 Informatica 41 (2017) 71–86 71 
Aggregation Methods in Group Decision Making: A Decade Survey 
Wan Rosanisah Wan Mohd and Lazim Abdullah 
School of Informatics and Applied Mathematics 
Universiti Malaysia Terengganu, 21030, Kuala Terengganu, Terengganu, Malaysia 
E-mail: wanrosanisahwm@gmail.com, lazim_m@umt.edu.my 
 
Overview paper 
 
Keywords: aggregation phase, Choquet integral, MCDM, interacting, survey, interdependent 
 
Received: August 3, 2016 
A number of aggregation method has been proposed to solve various selected problems.  In this work, 
we make a survey of the existing aggregation method which were used in various fields from 2006 until 
2016. The information of the aggregation method retrieved from some academic databases and 
keywords that appeared through international journals from 2006 to 2016 are gathered and analyzed. It 
is observed that eighteen over ninety five of journal articles or nineteen percent applied the Choquet 
integral to the selection process. This survey shows this method most prominent compared to the other 
aggregation method and it is a good indication to the researchers to expand the appropriate 
aggregation method in MCDM. Besides that, this paper will give the useful information for other 
researches since the information given in this survey provides the latest evidence about the aggregation 
operator 
Povzetek: Predstavljena je analiza metod za združevanje odločitev skupine.  
1 Introduction 
Decision making is one of the most widely used 
management processes in dealing with real world 
problems which is typically characterized by complex 
and difficult task.  Multiple criteria decision making 
(MCDM) has been one of the fastest growing 
knowledge areas in decision sciences and has been 
used extensively in many disciplines. For example, 
Roy [1] developed a multi criteria decision analysis for 
renewable energy sources where it deals with the 
process of making decisions in the presence of multiple 
objective. Many researchers used other diverse 
methods of MCDM to solve the decision problem [2]. 
Basically, MCDM is a branch of operation research 
models and a well-known field of decision making. 
Both quantitative as well as qualitative criteria and 
attributes can be solved using the methods and it can 
analyze conflict in criteria and decision makers as well 
[3]. MCDM problems usually divided into two types: 
continuous and discrete. MCDM problems have two 
categories: multi-objective decision making (MODM) 
and multi-attribute decision making (MADM). By 
MODM methods, the decision variable values can be 
determined in a continuous or integer domain as it has 
a large number of alternative choices. Thus, MADM 
methods are generally discrete, with a limited number 
of specified alternatives. Each matrix has four main 
parts, namely: (a) alternatives, (b) attributes, (c) weight 
or relative importance of each attribute and (d) 
measures of performance of alternatives with respect to 
the attributes [4]. 
MCDM deals with the problem of helping the 
decision maker to choose the best alternative according 
to several criteria. In order to meet this objective, an 
MCDM method has basically four steps that support 
making the most efficient and rational decisions. 
According to Pohekar and Ramachandran [3], the first 
basic step is structuring the decision process, 
alternative selection and criteria formulation. The 
second step is displaying tradeoff among criteria and 
determine criteria weights. Applying value judgment 
concerning acceptable tradeoffs and evaluation is the 
third step in most MCDM. Methods. The fourth step is 
calculating the final aggregation prior to make a 
decision.  Some literature also suggests that MCDM 
can be divided into two phase process. The first phase 
is called as the rating phase where aggregation of the 
values of criteria for each alternative is made.  Ranking 
or ordering between the alternatives with respect to the 
global consensus degree of satisfaction is another 
phase in MCDM. It can be seen that aggregation is one 
of the fundamental phases in MCDM. Many MCDM 
methods such as ELECTRE I, II, PROMETHEE use 
criteria weights in their aggregation process. Weights 
of criteria play an important role for measuring overall 
preferences of alternatives. It is necessary to aggregate 
the available information in order to make decisions. In 
other words, a central problem in multi-criteria 
problems are an aggregation of the satisfactions to the 
individual criteria to obtain a measure of satisfaction to 
the overall collection of criteria [5].  The overall 
evaluation value is then used to help select alternatives. 
It can be seen that aggregation is the fundamental 
prerequisite of the decision making, in which 
descriptions on how to aggregate individual experts’ 
preference information on alternatives is succinctly 
made [6]. 
Aggregation is easily defined as the process of 
combining several numerical scores with respect to 
72 Informatica 41 (2017) 71–86 W.R.W. Mohd et al.  
 
each criterion by using an aggregation operator in 
order to produce a global score [7].  Detyniecki [8] 
defines an aggregation as ‘mathematical object that has 
the function of reducing a set of numbers into a unique 
representative value’.  Xu [9] defines aggregation is an 
essential process of gathering relevant information 
from multiple source. According to the [10], 
aggregation is made to summarize information in 
decision making. Omar and Fayek [11] defines 
aggregation in the multi-criteria decision making 
environments as a process of combining the values of a 
set of attributes into one representative value for the 
entire set of attributes. Aggregation is important in the 
decision making problem because it is used to derive a 
collective decision made by the decision makers by 
representing in the individual opinions. In addition, 
aggregation of individual judgments or preference is 
used to transform experts’ judgment knowledge and 
expertise in relative weight.  
The interest in the importance of aggregation is 
enhanced by judgments or preferences made by a 
group of decision makers. In group decision problem 
where number of decision makers are multiple, it is 
assumed that there exists a finite number of 
alternatives, as well as a finite set of experts. Each 
expert has their own opinions and may have a variety 
of ideas about the performance of each alternative and 
cannot estimate his/her preferences with crisp 
numerical value. Hence, a more realistic approach to 
be used to represent the situation of the human expert, 
instead of using the crisp numerical values. Thus, each 
variable involved in the problem may be represented in 
linguistic terms. Under those circumstances, 
aggregation methods are the key to tackle the 
mechanism to realize the comprehensive features of 
group decision making [12].  Several related research 
have been conducted to deal with multiplicity features 
in group decision making.  Many researchers have 
studied aggregation operation using different 
aggregation methods.  The way aggregation functions 
are used depends on the nature of the profile that is 
given to each user and the description of items [13].   
In the real world of decision making problems, 
decision makers like to pursue more than one 
aggregation methods to measure the aggregation 
information. In these kinds of problems, many 
aggregation methods have been developed in this area 
for the recent years for judging the alternatives. For 
example, Ogryczak [14] proposed reference point 
method and  implemented to the fair optimization 
method in  analyzing the efficient frontier. The method 
was proposed based on the augmented max-min 
aggregation.   
Aggregation operations on fuzzy sets are 
operations by which several fuzzy sets are combined 
together in some way to produce a single 
representative either fuzzy or crisp set. Therefore, 
aggregation operation needs aggregation operator to 
deal with the situation where the aggregation operator 
is commonly tools that can be used to combine the 
individual preference information into overall 
preference information and deriving collective 
preference values for each alternative. That is to say, 
the information aggregation is to combine individual 
experts’ preference coming from different sources into 
a unique representative value by using an appropriate 
aggregation technique [15]. Aggregation operations are 
used to rank the alternative decisions an expert or 
decision support system, which are established and 
applied in fuzzy logic systems. The information 
aggregation has received much attention from 
practitioners and researchers due to its practical and 
significance in academic [16][17][18][19][20][21]. 
The first overview of the aggregation operators in 
2003 by Xu and Da [22]. The study is reviewed of the 
existing main aggregation operators and proposed 
some new aggregation operators that is induced 
ordered weighted geometric averaging (IOWGA) 
operator, generalized induced ordered weighted 
averaging (GIOWA) operator and hybrid weighted 
averaging (HWA) operator. In 2008, a review of 
aggregation functions which focus on some special 
classes of averaging, conjunctive and disjunctive is 
reviewed by Mesiar et al. [23]. Furthermore, Martinez 
and Acosta in 2015 [24] have made a review an 
aggregation operators taking into account of 
mathematical properties and behavioral measures such 
as disjunctive degree (orness), dispersion, balance 
operator, divergence instead of general mathematical 
properties whose verification might be desirable in 
certain cases: boundary condition, continuity, 
increasing, monotonicity etc. Since then, it is important 
to make a review of the aggregation operator which 
provide the latest method which will be used to solve 
the aggregation in MCDM. Obviously, there is no 
review paper on aggregation operator from year to 
year.  
Our aim in this survey article is to provide an 
accessible overview of some key methods of 
aggregation in MCDM. We focus on development of 
type of aggregation methods that have attracted many 
researchers in this area without neglecting some 
technical details of the aggregation methods.  
Throughout this survey, the terms aggregation 
function, aggregation operator, surveys on aggregation 
in MCDM, an overview of aggregation operation will 
refer to find the journal articles as well. The collected 
journals which is regarding the aggregation is 
retrieving  from the various fields such as engineering, 
medical, operation research,  image processing, 
selection problems, project management selection and 
etc.  
This paper is structured as follows.  Section 2 
begins by laying out the various methods of 
aggregation since 2006 until now.   Section 3 presents 
an analysis out of the survey. Section 4 suggests some 
works that can be extended as future research direction.  
The last chapter concludes.  
Aggregation Methods in Group Decision... Informatica 41 (2017) 71–86 73 
 
2 Review of aggregation method 
This chapter reviews aggregation methods that have 
been developed to aggregate information in order to 
choose a desirable solution, decision makers have to 
aggregate their preference information by means of 
some proper approaches. This review is made by 
analyzing the method used based on the journals and 
conference proceedings that are collected from selected 
popular academic databases such as SpringerLink, 
Scopus, ScienceDirect, IEEE Xplore Digital Library, 
ACM Digital Library, and Wiley Online Library from 
the year 2006 until the year 2016.  
The class of aggregation is huge, making the 
problem of choosing the right method for a given 
application is a difficult one [25]. In this paper, we 
review the methods of aggregation and its various 
applications. Firstly, we segregate the aggregation 
methods by the basic ones which is the most often used 
aggregation operators. For example, the average 
(arithmetic mean), geometric mean and harmonic 
mean. Then, we proceed to the next aggregation 
operator by presenting a generalization of the classical 
one such as Bonferroni Mean (BM), power aggregation 
operator, fuzzy integral, hybrid aggregation operator, 
prioritized average operator and the linguistic 
aggregation operator [26]. 
2.1 Basic operators 
2.1.1 Arithmetic mean operator 
In real life decision situation, the aggregation problems 
in the MCDM are solved using the scoring techniques 
such as the weighted aggregation operator based on 
multi attribute theory. The classical weighted 
aggregation is usually known by the weighted average 
(WA) or simple additive weighting method. A very 
common aggregation operator is the ordered weighted 
averaging (OWA) operator which provides a 
parameterized family aggregation operator between the 
minimum, the maximum, the arithmetic average, and 
the median criteria whose originally introduced by 
Yager [27]. There are two features have been used to 
characterize the OWA operators. The first is the orness 
character and the second is the dispersion. The OWA 
has been widely used because of its ability to model 
linguistically expressed aggregation. Since the OWA 
operator is coming out, the approach has been 
employed by the most authors in a wide range of 
applications such as engineering, neural networks, data 
mining, decision making, and image processing as well 
as expert systems [28]. However, OWA operator was 
assumed that the available information includes crisp 
number or singletons. In the real decision making 
situation, it is found this may not be the crisp number. 
Sometimes, the available information is vague or 
imprecise and it is not possible to analyze it with the 
crisp numbers. Then, it is necessary to use another 
approach that is able to represent the uncertainty such 
as the use of fuzzy numbers (FNs). With the use of 
FNs, it is possible to analyze the real situation into the 
fuzzy value. Here is the definition of OWA.  
 
Definition 1: An OWA operator of dimension n  
is a mapping OWA: RRn   that has associated 
weighting vector W of dimension n  with  1,0jw  and 



n
j
jw
1
1 , such that [27]: 
OWA   


n
j
jjn bwaaa
1
,...,,
21
 
where jb  is the j th largest of the ia . 
 
There are some of the authors that being used 
OWA as aggregation operators such that Xu [29] 
developed the intuitionistic fuzzy ordered weighted 
averaging (IFOWA) operator, and the intuitionistic 
fuzzy hybrid averaging (IFHA) operator. Xu and Chen 
[30] investigated the interval-valued intuitionistic 
fuzzy multi-criteria group decision making based on 
arithmetic aggregation operators such as the interval-
valued intuitionistic fuzzy weighted arithmetic 
aggregation (IIFWA) operator, the interval-valued 
intuitionistic fuzzy ordered weighted aggregation 
(IIFOWA) operator and the interval-valued 
intuitionistic fuzzy hybrid aggregation (IIFHA) 
operator.   
Li and Li [31][32] developed the generalized 
OWA operators using IFSs to solve MADM in which 
weights and ratings of alternatives on attributes are 
expressed in IFS. Chang et al. and Merigo et al.  
[33][34]proposed the fuzzy generalized ordered 
weighted averaging (FGOWA) operator as it is an 
extension of the GOWA operator for the uncertain 
situation where the information given is in the form of 
fuzzy numbers.  Zhao et al. [35] developed some new 
generalized aggregation operators such as generalized 
intuitionistic fuzzy weighted averaging (GIFWA) 
operator, generalized intuitionistic fuzzy ordered 
weighted averaging (GIFOWA) operator, generalized 
intuitionistic fuzzy hybrid averaging (GIFHA) 
operator, generalized interval-valued intuitionistic 
fuzzy weighted averaging (GIIFWA) operator, 
generalized interval-valued intuitionistic fuzzy hybrid 
average (GIIFHA) operator where the proposed 
method is the extension of the GOWA operators taking 
into account of the characterization both of 
intuitionistic fuzzy sets by a membership function and 
non membership function and interval-valued 
intuitionistic fuzzy sets whose fundamental 
characteristic is the values of its membership function 
is represented by the interval numbers rather than exact 
numbers.  
Shen et al. [36] presented a new arithmetic 
aggregation operator which is induced intuitionistic 
trapezoidal fuzzy ordered weighting aggregation 
operator and applied to group decision making. 
Furthermore, in 2011,  Casanovas et al. [37] introduced 
the uncertain induced probabilistic ordered weighted 
74 Informatica 41 (2017) 71–86 W.R.W. Mohd et al.  
 
averaging weighted averaging (UIPOWAWA) operator 
where it provides a parameterized family of 
aggregation operators between minimum and 
maximum in a unified framework between probability, 
the weighted average and the induced ordered 
weighted averaging (IOWA) operator. Merigo [38] 
developed a new aggregation model that unifies the 
weighted average (WA) and the induced ordered 
weighted average (IOWA) operator that is called 
induced ordered weighted averaging-weighted average 
(IOWAWA) operator by considering the degree of 
importance that each concept has in the aggregation. 
Xu and Wang [39] proposed a new aggregation 
operator which calling induced generalized 
intuitionistic fuzzy ordered weighted averaging (I-
GIFOWA) operator by considering the characteristics 
of both the generalized IFOWA and the induced 
IFOWA operator. In order to deal with the 
intuitionistic fuzzy preference information in group 
decision making, the induced generalized intuitionistic 
fuzzy ordered weighted averaging (I-GIFOWA) 
operator based on GIFOWA and the I-IFOWA 
operator. Yu [40] introduced generalized intuitionistic 
trapezoidal fuzzy weighted averaging operator to 
aggregate the intuitionistic trapezoidal fuzzy 
information. Xia et al. [41] proposed several new 
hesitant fuzzy aggregation operators by extending 
quasi-arithmetic means to hesitant fuzzy sets under 
group decision making. Zhou et al. [42] introduced a 
new operator for aggregating the interval-valued 
intuitionistic fuzzy values which called the continuous 
interval-valued intuitionistic fuzzy ordered weighted 
averaging (C-IVIFOWA) operator. Both intuitionistic 
fuzzy ordered weighted averaging (IFOWA) operator 
and the continuous ordered weighted averaging (C-
OWA) operator has combined to control the parameter 
and employed to diminish the fuzziness and improve 
the preciseness of the decision making.  
2.1.2 Geometric mean operator 
The geometric mean operator is the traditional 
aggregation operator that proposed to aggregate 
information given on a ratio scale measurement in 
MCDM models. The main characteristics are its 
guaranties the reciprocity property of the multiplicative 
preference relations used to provide ratio preferences 
[43].  
 
Definition 2: An OWG operator of dimension n  
is a mapping OWG:   RR n  that has associated 
weighting vector  Tnwwww ,...,, 21  with 0jw  and 



n
j
jw
1
1 , such that [43]: 
OWGw   


n
j
w
jn
jbaaa
1
,...,,
21
 
where jb  is the j th largest of the  nja j ,...,2,1 . 
 
Some authors used geometric mean as aggregation 
operator. For example, Wu et al. [44] defined same 
families of geometric aggregation operators to 
aggregate trapezoidal IFNs (TrIFNs). Xu and Yager 
[45], Xu and Chen [46], Wei [47], Das et al. [48] 
developed some geometric aggregation operators based 
on IFS, such as intuitionistic fuzzy weighted geometric 
(IFWG) operator, intuitionistic fuzzy ordered weighted 
geometric (IFOWG) operator and intuitionistic fuzzy 
hybrid geometric (IFHG) operator. Tan [49] developed 
a generalized intuitionistic fuzzy geometric 
aggregation operator for multiple criteria decision 
making by considering the interdependent or 
interactive characteristics and preferences.  
Wei [50], Xu [51] and Xu and Chen [52] proposed 
approach based on interval-valued intuitionistic fuzzy 
weighted geometric (IIFWG) operator, the interval-
valued intuitionistic fuzzy ordered weighted geometric 
(IIFOWG) operator and the interval-valued 
intuitionistic fuzzy hybrid geometric (IIFHG) operator 
in different point of view. Verma and Sharma [53] 
proposed geometric Heronian mean (GHM) under 
hesitant fuzzy environment by developing some new 
GHM such that hesitant fuzzy generalized geometric 
Herinian mean (HFGGHM) operator and weighted 
hesitant fuzzy generalized geometric Herinian mean 
(WHFGGHM) operator.  
2.1.3 Harmonic mean operator 
Harmonic mean is the reciprocal of the arithmetic 
mean of reciprocal which is a conservative average to 
be used to provide for aggregation lying between the 
max and min operators and is widely used as a tool to 
aggregate central tendency data which is usually 
expressed in exact numerical values [54]. 
Definition 3: An ordered weighted harmonic mean 
operator of dimension n  is a mapping OWHM: 
RRn   that has associated weighting vector 
 Tnwwww ,...,, 21  with 0jw  and 


n
j
jw
1
1 , such that 
[54]: 
OWHMw  
 



n
j j
j
n
a
w
aaa
1
1
,...,,
21

 
where       n ,...2,1  is a permutation of  n,...,2,1 , 
such that    jj   1  for all nj ,...,2 . 
 
Some researchers proposed harmonic mean as a 
method to solve aggregation in the decision making 
problem. For example, Xu [55] developed some fuzzy 
harmonic mean operators, such as fuzzy weighted 
harmonic mean (FWHM) operator, fuzzy ordered 
weighted harmonic mean (FOWHM) operator, fuzzy 
hybrid harmonic mean (FHHM) operator. The aim of 
this paper is to extend the induced ordered weighted 
harmonic mean (IOWHM) operator to fuzzy 
environment and propose a new operator called the 
Aggregation Methods in Group Decision... Informatica 41 (2017) 71–86 75 
 
fuzzy induced ordered weighted harmonic mean 
(FIOWHM) operator.  
Wei and Yi [56] proposed an aggregation operator 
including trapezoidal fuzzy ordered weighted harmonic 
averaging (ITFOWHA) operator and applied to the 
decision making.  
Wei [57] proposed a new aggregation operator 
called fuzzy induced ordered weighted harmonic mean 
(FIOWHM) for fuzzy multi criteria group decision 
making. Zhou et al. [58] proposed the generalized 
hesitant fuzzy harmonic mean operators including the 
generalized hesitant fuzzy weighted harmonic mean 
operator (GHFWHM), the generalized hesitant fuzzy 
ordered weighted harmonic mean operator 
(GHFOWHM), the generalized hesitant fuzzy hybrid 
harmonic mean operator (GHFHHM) using the 
technique of obtaining values in the interval to the 
group decision making under hesitant fuzzy 
environment.  
Liu et al. [59] proposed a generalized interval-
valued trapezoidal fuzzy numbers (GIVFTN) is an 
extended of ordered weighted harmonic averaging 
operators to solve the problems in multiple attribute 
group decision making.  
 
2.2 Bonferroni mean (BM) 
The Bonferroni mean (BM) originally introduced by 
Bonferroni [60]. The classical Bonferroni mean is an 
extension of the arithmetic mean and its generalized by 
some researchers based on the idea of the geometric 
mean [61]. The BM is differ from the other classic 
means such as the arithmetic, the geometric and the 
harmonic because this mean reflect the interdependent 
of the individual criterion meanwhile on the classic 
means the individual criterion is independent [62]. The 
BM was originally introduced by Bonferroni [60], 
which was defined as follows: 
 
Definition 4: Let ,0, qp and  niai ,...,2,1 be a 
collection of nonnegative numbers. If  
 
 
qp
n
ji
ji
q
j
p
i
qp aa
nn
aaaB
n


















 
1
1,
,
1
1
,...,,
21
 
Then qpB , is called the Bonferroni mean (BM). 
 
The Bonferroni mean (BM) operator is suitable for 
aggregating crisp data and can capture the expressed 
interrelationships among criteria, which plays a crucial 
role in multi-criteria decision making problems [63]. 
Since the BM introduced, this aggregation operator has 
received much attention from researchers and 
practitioners. Among them are, Yager [64] generalized 
the BM for enhancing its model capability and further 
Xu and Yager [65] developed intuitionistic fuzzy BM 
(IFBM) and applied the weighted IFBM to multi 
criteria decision making. Beliakov et al. [66] gave a 
systematic investigation of a family of composing 
aggregation functions which generalize the Bonferroni 
Mean (BM).  
Zhu et al. [67] explored the geometric Bonferroni 
mean (GBM) by considering both BM and the 
geometric mean (GM) under hesitant fuzzy 
environment. Xia et al. [68] developed the Bonferroni 
geometric mean, which is a generalization of the 
Bonferroni mean and geometric mean and can reflect 
the interrelationships among the aggregated arguments. 
Wei et al. [69] developed two aggregation operators 
called the uncertain linguistic Bonferroni mean 
(ULBM) operator and the uncertain linguistic 
geometric Bonferroni mean (ULGBM) operator for 
aggregating the uncertain linguistic information in the 
multiple attribute decision making (MADM) problems. 
Park and Park [70] extend the works Sun and Sun [61] 
by considering the interactions of any three aggregated 
arguments instead of any two to develop generalized 
fuzzy weighted Bonferroni harmonic mean 
(GFWBHM) operator and generalized fuzzy ordered 
weighted Bonferroni harmonic mean (GFOWBHM) 
operator. Verma [71] proposed a new generalized 
Bonferroni mean operator called generalized fuzzy 
number intuitionistic fuzzy weighted Bonferroni mean 
(GFNIFWBM) operator which is able to aggregate the 
fuzzy number intuitionistic fuzzy correlated 
information.   
 
2.3 Power aggregation operators 
Yager [17] was first introduced a power average (PA) 
operator which uses a non-linear weighted average 
aggregation tool and a power ordered weighted average 
(POWA) operator to provide aggregation tools which 
allow exact arguments to support each other in the 
aggregation process. The weighting vectors of the PA 
operator and the POWA operator depend on the input 
arguments and allow arguments being aggregated to 
support and reinforce each other.   In contrast with 
most aggregation operators, the PA and POWA 
operators incorporate information regarding the 
relationship between the values being combined. 
Recently, these operators have received much attention 
in the literature.  
 
Definition 5:  The power average (PA) operator is 
mapping  PA: RRn   defined by the following 
formula [17]: 
 
  
  






n
i
i
n
i
ii
i
aT
aaT
niaPA
1
1
1
1
,...2,1  
where    



n
ij
j
jii aaSupaT
1
,  
76 Informatica 41 (2017) 71–86 W.R.W. Mohd et al.  
 
and  ji aaSup ,  is the support for ia  from ja . The 
support satisfies the following three properties: 
(1)    ;1,0, ji aaSup  
(2)    ;, , ijji aaSupaaSup   
(3)     tsjitsji aaaaifaaSupaaSup  ,,   
Motivated by the success of the PA and POWA, 
Xu and Yager [72] proposed a power geometric 
average (PG) operator and a power ordered weighted 
average (POWGA) operator. Besides that, power 
aggregation operators have been further extended to 
accommodate multi attribute group decision making 
(MAGDM) under different uncertain environments. 
For instance, Xu and Cai [73] developed the uncertain 
power ordered weighted average (UPOWA) operator 
on the basis of the PA operator and the UOWA 
operator, Xu [74] introduced the uncertain ordered 
weighted geometric average (UOWGA) operator based 
on the PG operator and the UOWA operator.  
Xu [75] under intuitionistic fuzzy and interval-
valued intuitionistic fuzzy decision making 
environments, the linguistic power aggregation 
operators by Zhou et al. [76], generalized argument-
dependent power operators by Zhou and Chen [15] to 
accommodate intuitionistic fuzzy preferences and 
power aggregation operators under interval-valued dual 
hesitant fuzzy linguistic environment and the power 
aggregation operators by Wan [77] under trapezoidal 
intuitionistic fuzzy decision making environments.  
Zhang [78] developed a wide range of hesitant 
fuzzy power aggregation operators for hesitant fuzzy 
information such as the hesitant fuzzy power average 
(HFPA) operators, the hesitant power geometric 
(HFPG) operators, the generalized hesitant fuzzy 
power average (GHFPA) operators, the generalized 
hesitant fuzzy power geometric (GHFPG) operators, 
the weighted generalized hesitant fuzzy power average 
(WGHFPA) operators, the generalized hesitant fuzzy 
power geometric (WGHFPG) operators, the hesitant 
fuzzy power ordered weighted average (HFPOWA) 
operators,  the hesitant fuzzy power ordered weighted 
geometric (HFPOWG) operators, the generalized 
hesitant fuzzy power ordered weighted average 
(GHPOWA) operators and the generalized hesitant 
fuzzy power ordered weighted geometric (GHPOWG) 
operators. 
 However, the arguments of these power 
aggregation operators are exact numbers. In practice, 
we often confront situations in which the input 
arguments cannot be expressed in the form of exact 
numerical values instead, they have to take in the form 
of interval numbers Qi et al. [79], intuitionistic fuzzy 
numbers (IFNs) [80][81][82], interval-valued 
intuitionistic fuzzy numbers (IVIFNs) [83], linguistic 
variables [84][85][86], uncertain linguistic variables 
[67][87], or 2-tuples [88], hesitant fuzzy sets (HFS) 
[81]. Gou et al. [81] developed a family of hesitant 
fuzzy power aggregation operators, for instance the 
hesitant fuzzy power weighted average (HFPWA), 
hesitant fuzzy power weighted geometric (HFPWG) 
generalized hesitant fuzzy power weighted average 
(GHFPWA), generalized hesitant fuzzy power 
weighted geometric (GHFPWG) operators for multi-
criteria group decision making problems.  
Wang et al. [88] proposed a dual hesitant fuzzy 
power aggregation operators based on Archimedean t-
conorm and t-norm for dual hesitant fuzzy information. 
Das and Guha [89] proposed some new aggregation 
operators such as trapezoidal intuitionistic fuzzy 
weighted power harmonic mean (TrIFWPHM) 
operator, trapezoidal intuitionistic fuzzy ordered 
weighted power harmonic mean (TrIFOWPHM) 
operator, trapezoidal intuitionistic fuzzy induced 
ordered weighted power harmonic mean 
(TrIFIOWPHM) operator and trapezoidal intuitionistic 
fuzzy hybrid power harmonic mean (TrIFhPHM) 
operator to aggregate the decision information.  
2.4 Fuzzy integral 
Another types of aggregation operators is fuzzy 
integrals (FI). There are many types of FI, and most of 
the well-known fuzzy integral are Choquet and Sugeno 
integral. 
2.4.1 Choquet integral 
One of the popular aggregation operator fuzzy integrals 
is the Choquet integral which is introduced by Choquet 
[90]. Choquet integral is defined as a subadditive or 
superadditive to integrate functions with respect to the 
fuzzy measures [91].  
Definition 6. Let f  be a real-valued function on 
X , the Choquet integral of f  with respect to a fuzzy 
measure g  on  X  is defined as [90]: 
          

 








n
i
iAgixfixffdgC
1
1   
     (1) 
or equally by  
             
n
i
ixfiAgiAgfdgC 1 1
 
      (2) 
 
where the parentheses used for indices represent a 
permutation on X  such that 
               ,,...,,00,1 nxixiAxfnxfxf 
and   1nA . 
The Choquet integral is a very useful way of 
measuring the expected utility of an uncertain event 
Aggregation Methods in Group Decision... Informatica 41 (2017) 71–86 77 
 
[92]. It is a tool to model the interdependence or 
correlation among different elements where a new 
aggregation operators can be defined. Choquet integral 
has been proposed by many authors as an adequate 
aggregation operator that extends the weighted 
arithmetic mean or OWA operator by taking into 
consideration the interactions among the criteria.  
Yager [93] extended the idea of order induced 
aggregation to the Choquet aggregation and introduced 
the Choquet ordered averaging (I-COA) operator.  
Mayer and Roubens [94] aggregated the fuzzy numbers 
through the Choquet integral. In the other field, 
Hlinena et al. [95] used Choquet integral with respect 
to Lukasiewicz filters to present a partial solution to 
look for an appropriate utility function in a given 
setting. Ming-Lang et al. [96] proposed analytic 
network process (ANP) technique to get the 
relationships of feedback of criteria and Choquet 
integral is used to eliminate the interactivity of the 
expert subjective judgment problem and apply in the 
case study of selection of optimal supplier in supply 
chain management strategy (SCMS).  
Buyukozkan and Ruan [97] proposed a two-
addative Choquet integral to the software development 
experts and managers to enable them to position their 
projects in terms of associated risks. Murofushi and 
Sugeno [91] used the Choquet integral to propose the 
interval-valued intuitionistic fuzzy correlated 
averaging operator and interval-valued intuitionistic 
fuzzy correlated geometric operator to aggregate 
interval-valued intuitionistic fuzzy information and 
applied them to a practical decision making problem. 
Angilella et al. [98] proposed a non-additive robust 
ordinal regression on a set of alternatives by evaluating 
the utility in terms of Choquet integral which represent 
the interaction among thecriteria modelled by the fuzzy 
measure. Huang et al. [99] applied a generalized 
Choquet integral with a signed fuzzy measure based on 
the complexity to evaluate the overall satisfaction of 
the patients.  
Demirel et al. [100] proposed generalization 
Choquet integral by taking consideration of 
information fusion between criteria and linguistic 
terms and fuzzy ANP as a fuzzy measure which can 
handle the dependent criteria and hierarchical problem 
structure and applied to the multi-criteria warehouse 
location.   
Tan and Chen [101] proposed intuitionistic fuzzy 
Choquet integral based on t-norms and t-conorms 
meanwhile Tan [102]  extended the TOPSIS method 
by combining the interval-valued intuitionistic fuzzy 
geometric aggregation operator with Choquet integral-
based Hamming distance to deal with multi-criteria 
interval-valued intuitionistic fuzzy group decision 
making problems.  
Bustince et al. [103] proposed a new MCDM 
method for interval-valued fuzzy preference relation 
which was based on the definition of interval-valued 
Choquet integrals. Yang and Chen [104] introduced 
some new aggregation operator including the 2-tuple 
correlated averaging operator, the 2-tuple correlated 
geometric operator and the generalized 2-tuple 
correlated averaging operator based on the Choquet 
integral. Belles-sampera [10] developed the extensions 
of the degree of balance, the divergence, the variance 
indicator and Renyi entropies to characterize the 
Choquet integral.  
Islam et al. [105] proposed Choquet integral using 
goal programming to multi-criteria based learning 
which combines both experts’ knowledge and data. 
In addition, Choquet integral is applied in the 
hesitant fuzzy environment. Some authors that used the 
Choquet integral in the hesitant fuzzy environment are 
Yu et al. [106], Xia et al. [40]. For example, Yu et al. 
[106] proposed Choquet integral aggregation operator 
for hesitant fuzzy elements (HFEs) and applied it to the 
MCDM problems, Xia et al. [40] applied the Choquet 
integral to get the weights of criteria for group decision 
making, Peng et al. [107] proposed Choquet integral 
methods which is an approach to multi-criteria group 
decision making (MCGDM) problem to rank the 
alternatives where the criteria are interdependent or 
interactive. Wang et al. [108] developed some Choquet 
integral aggregation operators with interval 2-tuple 
linguistic information and applied them to MCGDM 
problems.  
2.4.2 Sugeno integral 
Sugeno integral is one of the fuzzy integral which 
introduced by M. Sugeno in the year of 1974 [109]. 
Sugeno integral is proposed to compute an average 
value of some function with respect to a fuzzy 
measure. In particular, the Sugeno integral uses only 
weighted maximum and minimum functions [16]. The 
definition of Sugeno integral [109] as follows: 
 
Definition 7: The (discrete) Sugeno Integral of a 
function  1,0: Xf with respect to   is defined as 
     ii
ni
Axfdxf  ,minmax)(
1   
where         nxfxfxfxf ,....,,, 321  are the ranges and 
they are defined as        nxfxfxfxf  ....321 . 
 
In recent years, some authors and practitioners that 
used Sugeno integral are Mendoza and Melin [110], 
Liu et al. [111], Tabakov and Podhorska [112], Dubois 
et al. [113]. 
Mendoza and Melin [110] extended the Sugeno 
integral with the interval type-2 fuzzy logic. The 
generalization composed the modifying the original 
equations of the Sugeno measures and Sugeno integral. 
This method is used to combine the simulation vectors 
into only one vector and lastly the system will be 
decided the best choice of recognition in the same 
manner than made with only one monolithic neural 
network, but the problem of complexity resolved.  
Liu et al. [111] extended the componentwise 
decomposition theorem of lattice-valued Sugeno 
integral by introducing the concept of interval fuzzy-
valued, intuitionistic fuzzy-valued and interval 
intuitionistic fuzzy-valued Sugeno integral. As a result, 
78 Informatica 41 (2017) 71–86 W.R.W. Mohd et al.  
 
the intuitionistic fuzzy-valued Sugeno integrals and the 
interval fuzzy-valued Sugeno integrals are 
mathematically equivalent. It shows that the interval 
intuitionistic fuzzy-valued Sugeno integral can be 
decomposed into the interval fuzzy-valued and 
intuitionistic fuzzy-valued Sugeno integrals or the 
original Sugeno integrals.  
Tabakov and Podhorska [112] proposed fuzzy 
Sugeno integral as an aggregation operator of an 
ensemble of fuzzy decision trees in order to classify the 
corresponding HER-2/neu classes. They used three 
different fuzzy decision trees which are built over 
different image characteristics, colour values and 
structural factors and texture information. The fuzzy 
Sugeno integral has been used as an aggregation 
operator to design fuzzy trees and the final medical 
decision support information generated.  
Dubois et al. [113] proposed two new variants of 
weighted minimum and maximum where the criteria 
weights play an important role of tolerance. The 
Sugeno integral is proposed to the residuated 
counterparts, which means, the weight support on 
subsets of criteria. Then, the dual aggregation 
operations called disintegrals are evaluated in terms of 
its defects rather than in terms of its positive features 
proposed. The maximal disintegral is when no defects 
at all are present and maximal integral when all the 
merits are sufficiently present.  
2.5 Hybrid aggregation operators 
The hybrid aggregation operator has been proposed by 
several authors. It is important to propose more than 
one aggregation operator so that a wide range of fuzzy 
aggregation operators can be used in a wide range of 
application in decision making problems. For instance, 
Jianqiang and Zhong [114] developed the intuitionistic 
trapezoidal weighted average arithmetic average 
operator and the intuitionistic trapezoidal weighted 
geometric average operator.  
Zhang and Liu [115] proposed the weighted 
arithmetic averaging operator and the weighted 
geometric average operator to aggregate triangular 
fuzzy intuitionistic fuzzy information and applied it to 
the decision making problem.  
Then, Merigo and Casanovas [116] proposed fuzzy 
generalized hybrid aggregation operators where further 
generalize the fuzzy geometric hybrid averaging 
(FGHA) and the fuzzy induced geometric hybrid 
average (FIGHA) by using quasi-arithmetic means and 
the new result are Quasi-FHA and the Quasi-FIHA 
operator. 
 Xia and Xu [117] first proposed fuzzy weighted 
averaging (HFWA), hesitant fuzzy weighted geometric 
(HFWG) operators, generalized hesitant fuzzy 
weighted averaging (GHFWA), generalized hesitant 
fuzzy weighted geometric (GHFWG) operators in 
solving decision making problems.  
Yu et al. [118] proposed the interval-valued 
intuitionistic fuzzy prioritized weighted average 
(IVIFPWA) operator and interval-valued intuitionistic 
fuzzy prioritized weighted geometric (IVIFPWG) 
operator to aggregate the IVIFNs.  
Verma and Sharma [119] developed some 
prioritized weighted aggregation operators for 
aggregating trapezoid fuzzy linguistic information 
motivated by the idea of prioritized weighted average 
introduced by Yager [122] such that the trapezoid 
linguistic prioritized weighted average (TFLPWA) 
operator, the trapezoid linguistic prioritized weighted 
geometric (TFLWG), and the trapezoid linguistic 
prioritized weighted harmonic (TFLWH) operator.  
Liao and Xu [120] introduced some new 
aggregation operators including the generalized 
hesitant fuzzy hybrid weighted averaging operator, the 
generalized hesitant fuzzy hybrid weighted geometric 
operator, and the generalized quasi hesitant fuzzy 
hybrid weighted geometric operator and their induced 
forms.  
In 2016, Verma [121] proposed a new aggregation 
operator that based on the generalization of mean 
called generalized trapezoid fuzzy linguistic prioritized 
weighted average (GTFLPWA) operator for fusing the 
trapezoid fuzzy linguistic information. The prominent 
characteristics of the proposed operator does not only 
take into account the prioritization among the attributes 
and decision makers but also has a flexible parameter.  
2.6 Prioritized  operator 
Prioritized Average (PA) operator is one of the 
aggregation operators which has a great interest among 
scholars. In practical situations, decision-makers 
usually consider different criteria priorities. To deal 
with this issue, Yager [122] developed prioritized 
average (PA) operators by modeling the criteria 
priority on the weights associated with the criteria, 
which depend on the satisfaction of higher priority 
criteria. The PA operator has many advantages over 
other operators. For example, the PA operator does not 
need to provide weight vectors and, when using this 
operator, it is only necessary to know the priority 
among the criteria. 
Wei [123] extended the prioritized aggregation 
operator to hesitant fuzzy sets and developed some 
prioritized hesitant fuzzy operators in multicriteria 
decision making. As Yager [122] only discussed the 
criteria values and weights in the real number domain, 
thus Wang et al. [124] developed some prioritized 
aggregation operators for aggregating interval-valued 
hesitant fuzzy linguistic information.   
Recently, some researchers have focused on  fuzzy 
prioritized aggregation operator into intuitionistic 
fuzzy sets (IFSs) such as Yu et al.  [117], Chen [125] 
proposed some interval-valued intuitionistic fuzzy 
aggregation operators such as the interval-valued 
intuitionistic fuzzy prioritized weighted average 
(IVIFPWA) operator and  the interval-valued 
intuitionistic fuzzy prioritized weighted geometric 
(IVIFPWG) operator, Verma and Sharma [126] 
proposed two new aggregation operators such as 
intuitionistic fuzzy Einstein  prioritized weighted 
Aggregation Methods in Group Decision... Informatica 41 (2017) 71–86 79 
 
average (IFEPWA) operator and the intuitionistic 
fuzzy Einstein prioritized weighted geometric 
(IFEPWG) operator for aggregating intuitionistic fuzzy 
information  meanwhile Liang et al. [127], Dong et al. 
[128] developed some new aggregation operator called 
generalized intuitionistic trapezoidal fuzzy prioritized 
weighted average operator and generalized 
intuitionistic trapezoidal fuzzy prioritized weighted 
geometric operator and apply to the multi-criteria 
group decision making.  Verma and Sharma [129] also 
proposed two new prioritized aggregation operators for 
aggregate triangular fuzzy information called quasi 
fuzzy prioritized weighted average (QFPWA) operator 
and the quasi fuzzy prioritized weighted ordered 
weighted average (QFPWOWA) operator.  
2.7 Linguistic aggregation operator 
Often, human decision making is too complex or too 
weakly defined to be represented by the numerical 
analysis. It is always considered the available 
information is vague or imprecise and impossible to 
analyze it with numerical values. However, this may 
not represent the real situation found in the decision 
making problem. Therefore, the possible way to solve 
such situation, it is necessary to use a qualitative 
approach which is the linguistic variable to aggregate 
the fused information. The linguistic aggregation 
operators are offered when the situations of the 
information cannot be assessed with numerical values, 
but it is possible to use linguistic assessment [130]. 
There are some authors used the linguistic variables to 
aggregate the information in MCDM. For instance, 
Wang and Hao [131] presented a 2-tuple fuzzy 
linguistic evaluation model for selecting appropriate 
agile manufacturing system in relation to MC 
production. Herrera et al. [132] proposed a fuzzy 
linguistic methodology to deal with unbalanced 
linguistic term sets. Chang et al. [33] proposed a 
linguistic MCDM aggregation model to tackle to solve 
two problems which are the aggregation operators are 
usually independent of aggregation situation and there 
must be a feasible operator for dealing with the actual 
evaluation scores.  
Xu and Chen [52] extended the well-known 
harmonic mean to represent the information in the 
linguistic situation and developed some linguistic 
harmonic mean aggregation operators such as the 
linguistic weighted harmonic mean (LWHM) operator, 
the linguistic ordered weighted harmonic mean 
(LOWHM) operator, and the linguistic hybrid 
harmonic mean (LHHM) operator for aggregating 
linguistic information.  
Wei [133] proposed a method for multiple attribute 
group decision making based on the ET-WG and ET-
OWG operators with 2-tuple linguistic information. 
Shen et al. [36] developed the belief structure-
linguistic ordered weighted averaging (BS-LOWA), 
the BS linguistic hybrid averaging (BS-LHA) and a 
wide range of particular cases. Wei [130] extended the 
TOPSIS method for 2-tuple linguistic multiple attribute 
group decision making with incomplete weight 
information. Then, Merigo et al. [130] developed 
linguistic weighted generalized mean (LWGM) and the 
linguistic generalized OWA (LGOWA) operator and 
applied to the decision making problems.  
Besides that, there are some authors that used 2-
tuple linguistic variables such as Wei [134] proposed 
the GRA-based linear programming methodology for 
multiple attribute group decision making with 2-tuple 
linguistic assessment information. Wei [135] utilized 
the gray relational analysis method for 2-tuple 
linguistic multiple attribute group decision making 
with incomplete weight information. Xu and Wang 
[136] developed some 2-tuple linguistic power 
aggregation operators. Wei [137] proposed some new 
aggregation operators which is the 2-tuple linguistic 
weighted harmonic averaging (TWHA), 2-tuple 
linguistic ordered weighted harmonic averaging 
(TOWHA) and 2-tuple linguistic combined weighted 
harmonic averaging (TCWHA) operators for multiple 
attribute group decision making. Zadeh [83] developed 
some new linguistic aggregation operators such as 2-
tuple linguistic harmonic (2TLH) operator, 2-tuple 
linguistic weighted harmonic (2TLWH) operator, 2-
tuple linguistic ordered weighted harmonic (2TLOWH) 
operator and 2-tuple linguistic hybrid harmonic 
(2TLHH) operator to utilize to aggregate preference 
information considering linguistic variables in the 
decision making problem. Then, Li et al. [138] 
developed a new multiple attribute decision making 
approach for dealing with 2-tuple linguistic variable 
based on induced aggregation operators and distance 
measure by presenting 2-tuple linguistic induced 
generalized ordered weighted averaging distance 
(2LIGOWAD) operator which extension of the 
induced generalized ordered weighted distance 
(IGWOD) with 2-tuple linguistic variables. The 
2LIGOWAD basically uses the IOWA operator 
represented in the form of 2-tuple linguistic variables.  
Furthermore,  Liu and Jin [139] introduced 
operational laws, expected value definitions, score 
functions and accuracy functions of intuitionistic 
uncertain linguistic variables and proposed two 
approaches with intuitionistic uncertain linguistic 
information to the weighted geometric average 
(IULWGA) operator  and ordered weighted geometric 
(IULOWG) operator for multi attribute group decision 
making.  
3 Observation 
Throughout this study, hundred three journal articles 
have been reviewed with different aggregation 
methods within 2006 until 2016 searching via IEEE 
explore, Science Direct, Springer Link and Wiley 
online Library. In this paper, the methods and 
applications of aggregation are discussed in various 
fields. Based on the Table 1, the most popular 
aggregation operator is Choquet integral which is 
17.48%, then it follows by linguistic aggregation 
operator (15.53%), arithmetic average operator 
80 Informatica 41 (2017) 71–86 W.R.W. Mohd et al.  
 
(14.74%), power aggregation operator and geometric 
mean operator (9.71%) respectively, prioritized 
aggregation operator (8.74), Benferroni Mean and 
Hybrid aggregation operator  (7.77%), harmonic mean 
(4.85%) and Sugeno integral (3.88%). The details of 
the percentage are shown in the Figure 1.  
Table 1: Numbers and percentage of the 
aggregation methods. 
Aggregation 
Methods 
No. of 
Authors 
Percentage 
(%) 
Arithmetic 
Mean 
15 14.56 
Geometric 
Mean 
10 9.71 
Harmonic 
Mean 
5 4.85 
Bonferroni 
Mean (BM)  
8 7.77 
Power 10 9.71 
Choquet 
Integral 
18 17.48 
Sugeno 
Integral 
4 3.88 
Hybrid  8 7.77 
Prioritized 9 8.74 
Linguistic  16 15.53 
Total 103 100 
 
Based on the Table 1 and Figure 1, the Choquet 
integral have attracted more attention because it is 
usually known in the literature as a flexible 
aggregation operator and it is a generalization of the 
weighted average (WA) or simple additive weighting 
method, the ordered weighted average, and the max-
min operator [130].  
In addition, the Choquet integral is the appropriate 
tool to solve the interactions among the criteria in 
decision making problem  as the traditional multi-
criteria decision making (MCDM) methods are based 
on the additive concept along with the independence 
assumption where, in fact, each individual criterion is 
not completely independent [95]. 
Furthermore, the aggregation operator based on the 
linguistic variable has received considerable attention 
too. The linguistic information is used when the 
information available is vague or imprecise, but unable 
to analyze it using numerical values [131].  
 
Figure 1: Percentage of aggregation methods. 
Other than that, one of the conventional 
aggregation operators which is arithmetic mean also 
received attention from many scholars. They have been  
widely used because the first aggregation operator 
which introduced by Yager in 1988 is OWA which 
provide parameterized the arithmetic mean. Then, it is 
easy to compute. Since then, many researchers have 
developed aggregation operator that based on the 
arithmetic since because it is practical in the decision 
making problem.  
Besides that, there are many aggregation operators 
used in the decision making problem depending on 
various kinds of factors investigated. For example, 
most of these operators, however, can only be used in 
situations where the input arguments are the exact 
values, and few of them can be used to aggregate the 
linguistic preference information.  
4 Future work  
From the observations, there are many types of 
aggregation operator that have been used by the 
researchers. Recently, the most appealing and great 
attention of researchers is Choquet integral because 
this method can represent the interaction between 
criteria, ranging from negative interaction to positive 
interaction. Even the classical of aggregation operator, 
power operator and linguistic variables have attracted 
numbers of researchers to apply to this field. It is 
suggested that the Choquet integral in order to build a 
more robust method and can be improved or extended 
by taking into account a weighted combination of both 
experts’ knowledge and data. This suggestion is based 
on the only expert opinion can be overly subjective and 
may not result in desired performance.  
Aggregation Methods in Group Decision... Informatica 41 (2017) 71–86 81 
 
The Choquet integral derives from the large 
numbers of coefficients  associated with a 
fuzzy measure, however, this flexibility can drive to be 
a serious drawback, especially when assigning real 
values to the importance of all possible combinations.  
Further research could be further in selecting the 
fuzzy measure to the Choquet integral. It is because 
different fuzzy measure will be impacted to the 
Choquet integral.  
Since the linguistic aggregation operator shows 
more than fifty percent of overall, it is possible to 
further to the next phase. In the future, it may extend 
this approach to other situations that can be assessed 
with other linguistic approaches and introducing the 
new aspects in the formulation by integrating them 
with other types of aggregation operators.  
The arithmetic aggregation operator also shows the 
highest percentage among other aggregation operator. 
It is expected to expand in the future by using a 
generalized aggregation operator, distance measures 
and unified aggregation operators. Moreover, it can be 
represented in the uncertain environment using fuzzy 
numbers and linguistic variables.  
5 Conclusion 
The main purpose of this study is to find the 
appropriate aggregation operator that is able to present 
aggregation by taking account the importance of the 
data that being fused. 
In this paper, we have analyzed the method used 
based on the journals and conference proceedings that 
are collected from selected popular academic 
databases.  
From the collected journal, we have separated 
them according to the method that being used by the 
authors. Each of the aggregation method has been 
presented in the percentage. See Table 1 and Figure 1 
in section 3. 
From the observation, most of the criteria of the 
classical and linguistic aggregation operator in 
decision-making methods mentioned above are 
assumed to be independent of one another, but in 
reality, the criteria of the problems are often 
interdependent or interactive. For real decision making 
problems, it does not need the assumption that criteria 
or preferences are independent of one another and was 
used to show as a powerful tool for modeling 
interaction phenomena in decision making [100]. 
Usually, there is interaction among preference of 
decision makers.  This phenomenon is called correlated 
criteria.  
In the real world of decision making problems, 
most criteria have interdependent, interactive or 
correlative characteristics. The interaction phenomena 
among criteria or the preference of experts is 
considered which is making it more feasible and 
practical than other traditional aggregation operator. In 
addition, it is not suitable for us to aggregate them by 
classical weighted arithmetic mean or geometric mean 
method which based on the additive measure. On the 
contrary, to approximate the human subjective decision 
making process, it would be more suitable to apply 
fuzzy measures, where it is not assuming additivity and 
independence among decision making criteria 
To tackle this problem, Choquet integral is a 
powerful tool to solve the MCDM problems with 
correlated criteria. In the Choquet integral model, 
criteria can be interdependent, a fuzzy measure is used 
to define a weight on each combination of criteria, thus 
making it possible to model the interaction existing 
among the criteria. Besides that, Choquet integral 
which taking into account for correlated inputs may 
give a more accurate prediction of the users’ rating. 
Furthermore, it is a very useful tool to measure the 
expected utility of an uncertain environment. 
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Informatica 41 (2017) 87–97 87
Hidden-layer Ensemble Fusion of MLP Neural Networks for Pedestrian
Detection
Kyaw Kyaw Htike
School of Information Technology, UCSI University, Kuala Lumpur, Malaysia
E-mail: ali.kyaw@gmail.com
Keywords: pedestrian detection, neural networks, fusion, ensembles, multi-layer perceptron
Received: January 6, 2017
Being able to detect pedestrians is a crucial task for intelligent agents especially for autonomous vehicles,
robots navigating in cities, machine vision, automatic traffic control in smart cities, and public safety and
security. Various sophisticated pedestrian detection systems have been presented in literature and most
of the state-of-the-art systems have two main components: feature extraction and classification. Over the
past decade, the majority of the attention has been paid to feature extraction. In this paper, we show that
much can be gained by having a high-performing classification algorithm, and changing only the classifica-
tion component of the detection pipeline while fixing the feature extraction mechanism constant, we show
reduction in pedestrian detection error (in terms of log-average miss rate) by over 40%. To be specific,
we propose a novel algorithm for generating a compact and efficient ensemble of Multi-layer Perceptron
neural networks that is well-suited for pedestrian detection both in terms of detection accuracy and speed.
We demonstrate the efficacy of our proposed method by comparing with several state-of-the-art pedestrian
detection algorithms.
Povzetek: Razvit je nov algoritem z nevronskimi mrežami za zaznavanje pešcev v prometu npr. za
avtonomno vozilo.
1 Introduction
Pedestrian detection is an important problem in Artificial
Intelligence and computer vision. Many pedestrian detec-
tion systems have been proposed with different feature ex-
traction and classification methods. One of the earliest gen-
eral machine learning based pedestrian detection systems
was proposed by Papageorgiou and Poggio [1]. They use
Haar wavelets coefficients measuring (at multiple scales)
differences in intensity levels of pixels as the feature repre-
sentation, and a Support Vector Machine (SVM) with the
quadratic kernel as the classifier.
A year later, this motivated a seminal work on object de-
tection, namely the Viola-Jones face detector [2]. Viola and
Jones use the idea of integral images to speed up the extrac-
tion of rectangular Haar basis functions to use as features
which then serve as input to an adaboost classifier. The
classifier, which is applied to an image in a sliding win-
dow fashion, is specifically designed to be an attentional
cascade so that most of non-face examples can be rejected
with relatively few feature extraction steps. This results
in a fast face detector. Although Haar basis functions are
well-suited for detecting frontal-faces, it was not very clear
whether it is also good for detecting other categories of ob-
jects. Indeed, one of the critical cues human beings use to
classify or detect objects is shape information (for which
the bulding blocks are image gradients or edges); the set
of Haar basis functions does not exploit this. In fact, af-
ter [2] came out, features that make effective use of image
gradient information would soon be proposed in [3].
Leibe et al. [4] propose an Implicit Shape Model which
learns, for each cluster of local image patches, a vote dis-
tribution on the centroid of the object class. At test time,
Generalized Hough Transform [5] is used to detect objects;
to be specific, each local image patch votes with the learnt
distributions for the centroid in the hough voting space and
then local maxima (or peaks) in the voting space corre-
spond to object centroids in the test image. For each of
these local maxima, local image patches that contributed
(i.e. voted for) are identified through a process known as
backprojection. From this, a rough segmentation (and con-
sequently, bounding boxes) of the objects can be obtained.
Their method requires segmentation of the training images
which can be labor-intensive and costly, and identifying the
local maxima and performing backprojection can be highly
sensitive to many types of noise. Moreover, due to the use
of image patches, the system is not robust to illumination
and various image geometric transformations.
In 2005, a groundbreaking work on object detection was
published by Dalal and Triggs [3] who propose Histograms
of Oriented Gradients (HOG) as features for pedestrian de-
tection. In HOG, a histogram of image gradients is con-
structed in each small local region (termed as a cell) in
an image and these histograms are concatenated to form
a high-dimensional feature vector. They carried out a large
number of experiments to explore and evaluate the design
space of the HOG feature extraction process. This includes
highlighting the importance of local contrast normaliza-
88 Informatica 41 (2017) 87–97 K.K. Htike
tion whereby each histogram corresponding to a cell is re-
dundantly normalized with respect to other nearby cells. It
was shown that with HOG features, a linear SVM was suf-
ficient to obtain state-of-the-art results and outperformed
several techniques such as generalized haar wavelets [1],
PCA-SIFT [6] and Shape Contexts [7].
Moreover, HOG is still highly influential to this day; the
majority of current state-of-the-art research is based on ei-
ther variations of HOG or building upon ideas outlined in
HOG [8, 9, 10, 11]. For instance, the well-known part-
based object detection system, Deformable Part Models
(DPM) [12], use HOG features as building blocks. Many
systems have extended DPMs (e.g. [13, 14, 15]). Schwartz
et al. [16] use Partial Least Squares to reduce high dimen-
sional feature vectors that combine edge, texture and color
cues, and Quadratic Discriminant Analysis as the classifier.
Dollar et al. [17] propose Integral Channel Features
(ICF) that can be considered as a generalization of HOG
by including not only gradient when computing local his-
tograms, but also other “channels” of information such as
sums of grayscale and color pixel values, sums of outputs
obtained by convolving the image with linear filters (such
as Difference of Gaussian filters) and sums of outputs of
nonlinear transformation of the image (e.g. gradient mag-
nitude). An attempt was made in [18] to speed up features
such as ICF by approximating some of the scales of the fea-
ture pyramid by interpolating from corresponding nearby
scales during multi-scale sliding window object detection.
Benenson et al. [19] propose an alternative to speed up
object detection by training a separate model for each of
the nearby scales of the image pyramid. Although this in-
creases the object detection speed at test time, it also con-
siderably lengthens the training time.
Benenson et al. [20] extend HOG [3] by automatically
selecting the HOG cell sizes and locations using boosting.
Their approach is very expensive to train. However, their
work shows that the plain HOG is powerful and with the
right sizes and placements of HOG cells, with a single rigid
component, it is possible to outperform various feature ex-
traction methods researchers have proposed over the years
after the invention of HOG.
For these reasons, in this work, we adopt HOG [3] as the
feature extraction method. Furthermore, we focus on the
classification component of the object detection pipeline.
In order to isolate the performance of the classifier, we set
the feature extraction mechanism constant for all the ex-
periments of our proposed method. For the classification
component of the pedestrian detection pipeline, we pro-
pose a powerful and efficient non-linear classifier ensemble
that significantly increases the pedestrian detection perfor-
mance (i.e. accuracy) while at the same time reducing the
computational complexity at test time which is especially
important for a task such as pedestrian detection. Although
the proposed algorithm could also be potentially applied to
other object detection tasks and general machine learning
classification problems, we focus on pedestrian detection
in this paper.
To the best of our knowledge, neural networks, or more
accurately, Multi-layer Perceptrons (MLPs) have not been
used for pedestrian detection. This observation is also true
for ensembles of MLPs (EoMLPs): there have not been
any major work using EoMLPs for object detection, let
alone pedestrian detection. Although there can be many
reasons behind this dearth of application of EoMLPs in
pedestrian detection, one possible reason could be due to
the highly computationally expensive nature of EoMLPs at
test time and due to the popularity of linear Support Vector
Machines (SVMs) and other regularized linear classifiers.
We show in this paper that given the same feature extraction
mechanism, using our proposed non-linear classifier gives
much better pedestrian detection performance than the tra-
ditional classifiers commonly used for pedestrian detection.
Works such as [21], which apply EoMLPs to real-world
problems, exist (although quite rare to find); however, they
are for general pattern tasks rather than pedestrian de-
tection or even object detection. Literature on EoMLPs
have been published in the machine learning and Artifi-
cial Intelligence community; a few well-known ones are
[22, 23, 24, 25]. They demonstrate the effectiveness of
EoMLPs compared to individual MLPs on some standard
statistical benchmarks; none of them have been applied to
any object detection or even computer vision problems.
Furthermore, although there are many different ensem-
ble methods of numerous types of classifiers such as bag-
ging [26], boosting [27], Random subspace [28], Random
Forest [29] and Rulefit [30], the focus of this paper is on
EoMLPs and pedestrian detection. Moreover, our proposed
algorithm differs from [22, 23, 24, 25] in numerous ways
including being suitable to be applied for pedestrian detec-
tion problems and our method outperforms state-of-the-art
EoMLPs as will shown in the experimental results in Sec-
tion 4.
We term our novel algorithm as Hidden-layer Ensemble
Fusion of Multi-layer Perceptrons (HLEF-MLP).
2 Contribution
The contribution that we make in this paper is five-fold:
1. We propose a novel way, for the purpose of pedestrian
detection, of training multiple individual MLPs in an
ensemble and fusing the members of the ensemble at
the hidden-feature level rather than at the output score
level as done in existing EoMLPs [22, 23, 24, 25].
This has the benefit of being able to correct the mis-
takes of the ensemble in a major way since the final
classifier still has access to hidden level features rather
than only output scores or classification labels.
2. We use L1-regularization in a stacking fashion to
jointly and efficiently select individual hidden feature
units of all the members in the ensemble which has the
effect of fusing the members of the ensemble. This re-
sults in only a few active projection units at test time,
Hidden-layer Ensemble Fusion. . . Informatica 41 (2017) 87–97 89
which can be implemented as fast matrix projections
on modern hardware for efficient pedestrian (or ob-
ject) detection.
3. In HLEF-MLP, the decisions of the members are com-
bined or fused in a systematic way using the given su-
pervision labels such that the combination is optimal
for the classification task. This is in contrast to ad-hoc
class-agnostic nature of most fusion schemes (which
turn to use techniques such as averaging).
4. We show that given the same feature extraction mech-
anism, HLEF-MLP gives much better pedestrian de-
tection performance than the state-of-the-art classi-
fiers commonly used for pedestrian detection.
5. HLEF-MLP is stable and robust to initialization con-
ditions and local optima of individual member MLPs
in the ensemble. Therefore, we do not need to be so
careful about training each MLP for two main reasons:
firstly, we are not relying solely on one MLP, and sec-
ondly, we are able to, during fusion, correct mistakes
of the individual MLPs at the hidden features level as
mentioned previously. In fact, each MLP falling in its
own local optima should be seen as achieving diver-
sity in the ensemble which is a desirable goal that can
be obtained for “free”. At the L1-regularized fusion
step, the optimization function is convex, therefore it
is guaranteed to achieve the global optimum.
3 Method
3.1 Overview
Let D = {(x1, y1), (x2, y2), . . . , (xN , yN )} be the la-
belled training dataset, where N is the number of training
data points, xi ∈ Rk is the k-dimensional feature vector
corresponding to the i-th training data point, yi ∈ {1, 0} is
the supervision label associated with xi.
HLEF-MLP consists of two stages. D is split into D =
{D1,D2}, where
D1 = {(x1, y1), . . . , (xN
2
, yN
2
)}
and
D2 = {(xN
2 +1
, yN
2 +1
), . . . , (xN , yN )}
In the first stage which we call “ensemble learning”, we
train each individual MLP on D1 and in the second stage
termed “sparse fusion optimization”, we use D2 to auto-
matically learn how to fuse the decisions of the members
of the ensemble. We now describe each stage.
3.2 Ensemble learning
The inputs to the first stage are D1 (obtained as de-
scribed in Section 3.1) and the number of members
M in the ensemble. The ensemble can be written as
[f1(x), f2(x), . . . , fM (x)] where each member fj(x) of
the ensemble can be formulated as:
fj(x) = logsig(wj tanh(Wjx+ bj) + bj) (1)
where x is the input feature vector, and W and b are the
matrix and vector respectively corresponding to an affine
transformation of x. Each column of W corresponds to the
weights of the incoming connections to a neuron. There-
fore the number of hidden neurons in fj(x) is equal of the
number of columns in Wj and the number of rows of Wj
is k (recall that x ∈ Rk). Therefore, W ∈ Rk×h, where h
is the number of neurons in the hidden layer.
The architecture of this first stage of the ensemble is il-
lustrated in Figure 1. In the figure, each ensemble member
(i.e. a MLP neural network) is shown as a vertical chain of
blocks of mathematical operations for a total of M chains.
The j-th ensemble (chain) corresponds to the function fj
as defined in Equation 1 and it can be seen that the block
chain diagram follows exactly the sequence of operations
defined in Equation 1.
For example, for the first ensemble member f1, the in-
put data vector x is first matrix-multiplied with W1 (first
block), and the resulting vector is then added with the vec-
tor b1 in the second block. The function tanh(·) is then
applied (third block). This is followed by matrix multipli-
cation of the output of the previous block with w1 in the
fourth block. In the last block, a scalar addition with b1 is
performed.
The functions tanh(·) and logsig(·) (visualized in Fig-
ure 2) are non-linear activation functions (applied after re-
spective affine transformations) independently acting on
each dimension of the vector obtained after the affine trans-
formation and are defined as follows:
tanh(a) =
ea − e−a
ea + e−a
(2)
logsig(a) =
ea
1 + ea
(3)
The latent parameters of the members of the first stage
of the ensemble need to be learnt from the training dataD1
(which is a set of pairs of input feature vectors and output
supervision labels) and this training (i.e. learning) process
is depicted in Figure 3.
We set all Wj to be the same size (i.e. each MLP fj(x)
in the ensemble has the same number of hidden neurons).
For each member MLP fj(x) in the ensemble, the follow-
ing loss function can be constructed:
L(D1,Wj ,bj ,wj , bj) =
− 2
N
N
2∑
i=1
yi logsig(wj tanh(Wjxi + bj) + bj)+
(1− yi) logsig(wj tanh(Wjxi + bj) + bj)
(4)
where {xi, yi}
N
2
i=1 are pairs of input feature vectors and out-
put supervision labels from D1.
90 Informatica 41 (2017) 87–97 K.K. Htike
Figure 1: Architecture of the first stage of the ensemble.
a
-10 -8 -6 -4 -2 0 2 4 6 8 10
ta
n
h
(a
)
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
a
-10 -8 -6 -4 -2 0 2 4 6 8 10
lo
g
s
ig
(a
)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Figure 2: Non-linear activation functions; on the left is the
curve for tanh(a) and on the right is logsig(a), where a is
the input signal to the activation function.
The loss function given in Equation 4 is a smooth and
differential function and we optimize it using L-BFGS [31]
since it is a type of efficient semi-newton method that does
not require setting sensitive hyperparameters such as learn-
ing rate, momentum and mini-batch size. After the opti-
mization, all the weights of each network:
{W1,W2, . . . ,WM ,b1,b2, . . . ,bM ,w1, . . . ,
. . . ,wM , b1, b2, . . . , bM}
(5)
are obtained.
3.3 Sparse fusion optimization
In the second stage which is sparse fusion optimization,
function gj(x) is used to project each data point x as given
below:
gj(x) = tanh(Wjx+ bj) (6)
Each data point x ∈ D2 is projected using {gj(x)}Mj=1.
That is, a new training dataset D̂2 is constructed as follows:
g1(x1) g2(x1) . . . gM (x1)
g1(x2) g2(x2) . . . gM (x2)
...
...
. . .
...
g1(xN ) g2(xN ) . . . gM (xN )
 (7)
where each row corresponds to one new data point in D̂2.
The architecture of this second stage of the ensemble is
depicted in Figure 4.
It is important to note that the projection is done using
the function g(·), and not f(·). In other words, this can
be interpreted as projecting to the hidden layers of the in-
dividual MLP neural networks (which are members of the
ensemble) and then learning to fuse in this hidden layer,
hence the name of our proposed algorithm being Hidden-
layer Ensemble Fusion of Multi-layer Perceptrons (HLEF-
MLP).
This improves over the traditional (state-of-the-art) en-
semble techniques in terms of both generating a much more
compact ensemble (making it available to be applied to
very time-critical applications such as pedestrian detection)
and the final pedestrian detection performance (measured
by log-average miss rate).
After constructing the projected dataset from D̂2, a L1-
regularized logistic regression is then trained on D̂2 by op-
timizing:
Hidden-layer Ensemble Fusion. . . Informatica 41 (2017) 87–97 91
Figure 3: Independent ensemble member learning process.
mtrained =
arg min
m
N∑
i=N2 +1
fL(m, [g1(xi), g2(xi), . . . ,
gM (xi)], yi) + βfR(m)
(8)
where mtrained ∈ Rk is the trained classifier and is in fact
a vector of weights. Moreover, fL is the loss function, fR
is the regularization to encourage m to take small values
(hence in a way, favoring simpler models), and β balances
the regularization term and loss term. A higher value of β
would result in a smoother solution (and less fit to the train-
ing data D̂2) whereas lower β would result lower training
error. The loss function is given by:
fL(m, [g1(xi), . . . , gM (xi)], yi) =
log(1 + exp(−yimT [g1(xi), . . . , gM (xi)]))
(9)
In order to encourage sparse solutions, we use L1-
regularization for which the regularization term is given by:
fR = [1, . . . , 1]
Tm (10)
This effectively sets many of the components of the weight
vector m to zero, resulting in a very compact ensemble
(speeding up the pedestrian detection), while at the same
time, retaining or even improving the ensemble perfor-
mance for pedestrian detection. Figure 5 illustrates the
learning process for the sparse fusion of the members in
the ensemble at the hidden layer.
3.4 Prediction at test time
As illustrated in Figure 6, at test time, given a test feature
vector x, the prediction score spred is obtained by:
spred =
1
1 + exp(−(mtrained)T [g1(x), . . . , gM (x)])
(11)
where {g1, g2, . . . , gM} are the hidden-layer-projection
functions (parts of the members of the ensemble) whose
latent parameters have been obtained by the training pro-
cess described in Section 3.2 and mtrained is the set of latent
sparse weights that have been obtained as explained in Sec-
tion 3.3.
Equation 11 can also be equivalently written as:
spred =
1
1 + exp(a)
where a =− (mtrained)T [tanh(W1x+ b1), . . . ,
tanh(WMx+ bM )]
(12)
Since mtrained is a sparse vector (i.e. a vector where
the majority of the elements are zeros), at test time, en-
tire columns of the matrices {W1, . . . ,WM} correspond-
ing to the positions of the zero vector elements in mtrained
can be omitted. This greatly speeds up the pedestrian
detection process, while improving the performance (log-
average miss rate), due to the sparse fusion technique at the
hidden layers as described in Sections 3.2 and 3.3.
92 Informatica 41 (2017) 87–97 K.K. Htike
Figure 4: Architecture of the second stage of the ensemble (fusion).
4 Results and discussion
4.1 Dataset
We use the INRIA Person dataset [3] for evaluating our al-
gorithms. In the dataset, for training, there are 614 images
containing pedestrians for which ground truth is available;
after cropping and flipping each pedestrian, the total num-
ber of pedestrians come up to be 2474. There are also 1218
lage images that do not contain any pedestrians; from these,
data corresponding to non-pedestrian class can be gener-
ated by a combination of initial sampling and hard negative
mining as is common in object detection literature [32, 9].
4.2 Ensemble hyper-parameters
We set the size of the ensemble M (see Section 3.2) to 100
and the number of hidden neurons h in each hidden layer
to 10 for all the experiments involving both our proposed
method and baselines on various ensemble variations.
4.3 Experiment setup
We perform seven different experiments in order to com-
pare our proposed algorithm with state-of-the-art pedes-
trian detection systems. The experiments are described be-
low.
4.3.1 VJ
The Viola-Jones detector of [2] applied to pedestrian detec-
tion.
4.3.2 HOG
Pedestrian detector of the seminal work of Dalal and
Triggs [3], using Histogram of Oriented Gradients (HOG)
for feature extraction and linear SVM as the classifier.
4.3.3 HikSvm
The system proposed by Maji et al. [33] who use a variation
of HOG (concatenation of histograms of oriented gradients
for a few different cell sizes) and SVM with an approxima-
tion of the intersection kernel.
4.3.4 Pls
The pedestrian detection system proposed by Schwartz et
al. [16] using Partial Least Squares (PLS) analysis to re-
duce the dimensions of very high dimensional features ob-
tained by concatenating different sets of features obtained
from edge, texture and color information derived from the
original image. Then Quadratic Discriminant Analysis is
used as the classifier.
4.3.5 OursMLPEnsHidFuse
Our proposed approach in this paper as described in Sec-
tion 3. At test time, due to L1-regularized fusion at the hid-
den layer level, we can expect that the resulting ensemble
will be very small, which will be suitable for time-critical
applications such as pedestrian detection.
Hidden-layer Ensemble Fusion. . . Informatica 41 (2017) 87–97 93
Figure 5: Learning to fuse the members in the ensemble.
Figure 6: Using the fused ensemble at test time.
94 Informatica 41 (2017) 87–97 K.K. Htike
4.3.6 OursMLPEnsScoreFuse
A variation of our proposed method
OursMLPEnsHidFuse; instead of fusing at the
level of hidden features, L1-regularized fusion takes
place at the score level. This is also somewhat similar to
classifier stacking [34] in literature; however most stacking
ensembles in literature is using simple unregularized
classifiers whereas OursMLPEnsScoreFuse is able to
select the most efficient members of the ensemble jointly
using supervised label information.
In terms of efficiency at test time, we can anticipate it
to be worse than OursMLPEnsHidFuse because the su-
pervised L1-regularized selection is being performed at the
score level and also at test time, there is a need to multiple
matrix projections (one for each member of the ensemble)
and then combine them.
4.3.7 OursMLPEns
This is our implementation of the state-of-the-art MLP fu-
sion by bagging. Here unlike OursMLPEnsHidFuse
and OursMLPEns, there are no separation of the train-
ing dataset D into D1 and D2; independent training of the
members of the ensemble takes place on the entire training
data D. Moreover, there is no sparse fusion optimization.
We can expect that compared to OursMLPEns-
HidFuse and OursMLPEnsScoreFuse, this will have
the worst efficiency at test time because the entire ensemble
needs to be evaluated which is highly expensive.
4.4 Evaluation criteria
To evaluate the pedestrian detections from the aformen-
tioned experiments, firstly there needs to be a measure on
what a correct pedestrian detection is. Although this can be
defined in many ways, we use the PASCAL 50% overlap
criterion [35] which is the most widely-used one in state-
of-the-art detection literature. Let a detected bounding box
be rd and the corresponding groundtruth bounding box be
rg . In PASCAL 50% overlap criterion, in order to establish
whether rd is a correct detection, the overlap ratio, αo, de-
fined in Equation 13 is computed and then the detection rd
is deemed to be correct if αo > 0.5.
αo =
area(rg ∩ rd)
area(rg ∪ rd)
(13)
The αo can be understood as the ratio of the intersection
of the detected bounding box with ground truth bounding
box and the union of the detected box with ground truth
box.
Graphs are an important tool to visualize the perfor-
mance of pedestrian detectors since they show the perfor-
mance at various levels of detection thresholds. To this end,
we plot curves where miss rate is on the y-axis and false
positives per image (FPPI) is on the x-axis.
This has been empirically proven in state-of-the-art
pedestrian detection (e.g. by Benenson et al. [9]) to be
Figure 7: ROC curves for all experiments)
Experiment
VJ
H
O
G
H
ik
Sv
m Pl
s
O
ur
sM
LP
En
sS
co
re
Fu
se
O
ur
sM
LP
En
sH
id
Fu
se
O
ur
sM
LP
En
s
L
o
g
-a
v
e
ra
g
e
 m
is
s
 r
a
te
0
10
20
30
40
50
60
70
80
Figure 8: Comparison of log-average miss rate (lower is
better).
a more accurate way of measuring the detection perfor-
mance than the previously used false positives per window
(FPPW).
In a miss rate versus FPPI plot, lower curves denote
higher detection performance. Moreover, to summarize the
performance of a detector with a single value, log-average
miss rate is calculated which is approximately equal to the
area under the miss rate versus FPPI curve.
4.5 Results
The miss rate versus FPPI plots for all the experiments are
shown in Figure 7. In the figure, the curves are ordered in
terms of log-average miss rate (LAMR) from worst to best.
To better focus on the summarized performance, we also
plot the LAMRs of each experiment in terms of a bar graph
as illustrated in Figure 8. Finally, we depict in Figure 9
the comparison of average detection time taken by different
algorithms for a 640×480 image. We also show in Table 1
Hidden-layer Ensemble Fusion. . . Informatica 41 (2017) 87–97 95
Experiment
VJ
H
O
G
H
ik
Sv
m Pl
s
O
ur
sM
LP
En
sS
co
re
Fu
se
O
ur
sM
LP
En
sH
id
Fu
se
O
ur
sM
LP
En
s
M
e
a
n
 t
im
e
 t
a
k
e
n
 (
s
e
c
s
)
0
20
40
60
80
100
Figure 9: Comparison of average detection time for a 640×
480 image.
Experiment Mean time (secs)
VJ 2.23
HOG 4.18
HikSvm 5.41
Pls 55.56
OursMLPEnsScoreFuse 32.72
OursMLPEnsHidFuse 8.49
OursMLPEns 92.45
Table 1: Comparison in terms of detection time.
the raw detection time values for easier comparison.
From Figures 7, 8 and 9, the following observations can
be made:
– VJ has the worst performance among all. This is to be
expected since the Haar-features that it uses does not
capture the object shape information well. Moreover,
the seminal work HOG greatly improves over VJ for
generic object detection.
– Although HikSvm requires complex modification in
the feature extraction and the classifier compared to
HOG, it only results in a modest detection performance
gain (i.e. decrease in LAMR from 46% to 43%). Sim-
ilar observation can be made for Pls although the im-
provement is relatively better.
– Our proposed algorithm OursMLPEnsHidFuse has
the best performance among them, tied in the first
place with OursMLPEns. Given that OursMLP-
EnsHidFuse is using the standard HOG features,
we can see that just by using our proposed algorithm,
the LAMR goes down from 46% and 27%. This is a
significant improvement (corresponding to sover 40%
reduction in LAMR). In comparison, HikSvm, de-
spite proposing both new feature extraction and clas-
sification algorithms, only managed less than 0.1% re-
duction in LAMR.
– OursMLPEnsHidFuse has slightly higher detec-
tion performance than OursMLPEnsScoreFuse
whereas in terms of speed at test time, OursMLP-
EnsHidFuse is almost 4 times faster. This shows
the efficacy of our proposed algorithm OursMLP-
EnsHidFuse and also proves that fusion at the level
of the hidden layers not only results in better detec-
tion performance and also very compact matrix pro-
jections (and hence much faster speed) due to the L1-
regularized sparse fusion learning process having ac-
cess to the hidden layer features.
– Despite OursMLPEnsHidFuse and OursMLPEns
being tied at the first place in terms of detection per-
formance, OursMLPEnsHidFuse is significantly
(over 10 times) faster than OursMLPEns. This shows
that our proposed OursMLPEnsHidFuse has the
same detection accuracy as the very expensive state-
of-the-art MLP bagging which is not practical for de-
tection purposes. In other words, the novel method
that we have presented in this paper OursMLPEns-
HidFuse gives the same (top) performance as a MLP
bagging-ensemble but with 10 times faster speed for
pedestrian detection.
– OursMLPEnsScoreFuse is faster than
OursMLPEns but lower in LAMR than OursMLP-
EnsHidFuse. This again shows that our proposed
OursMLPEnsHidFuse not only results in a much
smaller ensemble model (and consequently, faster
detection speed), but is also more effective in bringing
out the effectiveness of the ensemble compared to
OursMLPEnsScoreFuse.
– OursMLPEnsHidFuse has better detection accu-
racy than PLS while being significantly faster using
only standard HOG features. It shows that we have not
saturated the performance of HOG features yet. There
is still a lot of improvement that can made in the clas-
sification algorithm for pedestrian detection systems.
– Our experiments show for the first time that using
the proposed algorithm OursMLPEnsHidFuse, it is
possible to apply ensemble techniques for efficient ob-
ject detection purposes.
4.5.1 Analysis of trained ensemble model complexity
Although the results and the discussion about detec-
tion performance and speeds in the previous section
should be sufficient, for completeness, we theorize
and analyze the model complexities and sizes for the
three ensemble methods described in the previous sec-
tion: OursMLPEns, OursMLPEnsScoreFuse and
OursMLPEnsHidFuse. It is to be noted that as men-
tioned previously, there are M = 100 MLPs in the ensem-
ble.
96 Informatica 41 (2017) 87–97 K.K. Htike
For OursMLPEns, given a test feature vector x, there
are 100 different matrix projections with each matrix hav-
ing k rows and h columns (which is equal to the number of
dimensions of the feature vector and the number of hidden
neurons in each MLP respectively). After each projection,
tansig(·) function must be applied, followed by a dot prod-
uct between the resulting vector and a linear weight vector,
which will produce a single score (for each member of the
ensemble). Then the logsig(·) function needs to be per-
formed on this score. Then 100 such scores will be aver-
aged to give the final ensemble score. Therefore the ensem-
ble complexity is quite high, especially for a highly compu-
tationally expensive task such as sliding window pedestrian
detection.
For OursMLPEnsScoreFuse, after training the L1-
regularized linear classifier on scores at the end of the
model fusion step, 33 networks are discovered to be non-
zero. This means that theoretically, it should be 10033 ≈ 3
times faster than OursMLPEns. In fact, this theoretical
observation tallies with the experimental results presented
in Table 1.
With regards to OursMLPEnsHidFuse, it was found
that performing L1-regularized classifier optimization on
hidden features resulted in highly sparse weight vector
mtrained with the number of nonzero weight components
equal to a mere 159. Therefore, at test time, there is only
a need to perform a single matrix projection with a ma-
trix having k rows and 159 columns. After that, tansig
function has to be applied followed by a dot product with
a linear weight vector and finally a logsig(·) function. This
means that the effective model complexity is much lower
than either OursMLPEns or OursMLPEnsScoreFuse.
This again matches with the empirical evidence.
5 Conclusion and future work
In this paper, we propose a novel algorithm to train a com-
pact ensemble of Multi-layer Perceptron neural networks
for pedestrian detection. The proposed algorithm integrates
the members of the ensemble at the hidden feature layer
level, resulting in a very small ensemble size (allowing
for fast pedestrian detection) while at the same time out-
performing existing neural network ensembling techniques
and other pedestrian detection systems. We obtain very
encouraging state-of-the-art results, and show for the first
time that, using our proposed algorithm, it is indeed possi-
ble to apply neural network ensemble techniques for the
task for pedestrian detection, something that was previ-
ously thought to be too inefficient due to the very large
model size of ensembles.
There are several interesting directions of research based
upon the work in this paper; firstly, there is an opportunity
to apply the method presented in this paper to other object
detection tasks and applications such as face and vehicle
detection, and other general pattern recognition problems.
Secondly, it will be highly beneficial to explore the effect
of different feature extraction mechanisms on the resulting
ensembles in terms of both detection performance and effi-
ciency.
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98 Informatica 41 (2017) 87–97 K.K. Htike
 Informatica 41 (2017) 99–110 99 
Weighted Majority Voting Based Ensemble of Classifiers Using 
Different Machine Learning Techniques for Classification of EEG 
Signal to Detect Epileptic Seizure 
Sandeep Kumar Satapathy 
Department of Computer Science and Engineering 
Siksha ‘O’ Anusandhan University, Khandagiri, Bhubaneswar-751030, Odisha, India 
E-mail: sandeepkumar04@gmail.com 
 
Alok Kumar Jagadev 
School of Computer Engineering 
KIIT University, Bhubaneswar-751024, Odisha, India 
E-mail: alok.jagadev@gmail.com 
 
Satchidananda Dehuri 
Department of Information and Communication Technology 
Fakir Mohan University, Vyasa Vihar-756019, Balasore, Odisha, India 
E-mail: satchi.lapa@gmail.com 
 
Keywords: EEG signal, epilepsy, classification, machine learning 
Received: January 6, 2017 
 
Electroencephalogram (EEG) signal is a miniature amount of electrical flow in a human brain that 
holds and controls the entire body. It is very difficult to understand these non-linear and non-stationary 
electrical flows through naked eye in the time domain. In specific, epilepsy seizures occur irregularly 
and un-predictively while recording EEG signal. Therefore, it demands a semi-automatic tool in the 
framework of machine learning to understand these signals in general and to predict epilepsy seizure in 
specific. With this motivation, for wide and in-depth understanding of the EEG signal to detect epileptic 
seizure, this paper focus on the study of EEG signal through machine learning approaches. Neural 
networks and support vector machines (SVM) basically two fundamental components of machine 
learning techniques are the primary focus of this paper for classification of EEG signals to label 
epilepsy patients. The neural networks like multi-layer perceptron, probabilistic neural network, radial 
basis function neural networks, and recurrent neural networks are taken into consideration for 
empirical analysis on EEG signal to detect epilepsy seizure. Furthermore, for multi-layer neural 
networks different propagation training algorithms have been studied such as back-propagation, 
resilient-propagation, and quick-propagation. For SVM, several kernel methods were studied such as 
linear, polynomial, and RBF during empirical analysis. Finally, the study confirms with the present 
setting that, in all cases recurrent neural network performs poorly for the prepared epilepsy data. 
However, SVM and probabilistic neural networks are quite effective and competitive. Hence to 
strengthen the poorly performing classifier, this work makes an extension over individual learners by 
ensembling classifier models based on weighted majority voting. 
Povzetek: Sistem s pomočjo strojnega učenja iz EEG signalov zazna epileptični napad. 
1 Introduction 
Epilepsy is a persistent disorder of mental ability that 
has an abnormal EEG signal flow [1], which manifests 
in the disoriented human behaviour. In this world, 
around 40 to 50 million people are mostly affected by 
this disease [2]. Many people also call it as fits that 
causes loss of memory and interruption in 
consciousness, strange sensations, and significant 
alteration in emotions and behaviour. Research related 
to the epilepsy disease is basically used for 
differentiating between Ictal (seizure period) and 
Interictal (period between seizures) EEG signals. 
Hence, the transition from preictal to ictal state for an 
epileptic seizure contains a gradual change from a 
chaotic to ordered wave forms. Moreover, the 
amplitude of the spikes does not necessarily signify the 
harshness of seizures [2].  
The difference between the seizure and the 
common artifact is quite easy to recognize where 
generally the seizures within EEG measurement [3] 
have a prominent spiky, repetitive, transient, or noise-
like pattern. Hence, unlike other general signals, for an 
untrained observer, the EEG signal is quite difficult for 
100 Informatica 41 (2017) 99–110 S. K. Satapathy et al.  
 
understanding and analysis. The recording of these 
signals is mostly done by the help of a set of electrodes 
placed on the scalp using 10 to 20 electrode placement 
systems. The system incorporates the electrodes, which 
are placed with a specific name based on specific parts 
of the brain, e.g., Frontal Lobe (F), Temporal Lobe (T), 
etc. These naming and placement schemes have been 
discussed in more details in [3]. 
For the facilitation and effective diagnosis of the 
epilepsy, several neuro-imaging techniques such as 
functional magnetic resonance imaging (fMRI) and 
position emission tomography (PET) are used. An 
epileptic seizure can be characterized by paroxysmal 
occurrence of synchronous oscillation. This 
impersonation can be separated into two categories 
depending on the extent of involvement in different 
brain regions such as focal or partial and generalized 
seizures [4]. Focal seizure also known as epileptic foci 
are generated at specific sphere in the brain. In contrast 
to this, generalized seizures occur in most parts of the 
brain. 
A careful analysis and diagnosis of EEG signals 
for detecting epileptic seizure in the human brain 
usually contribute to a substantial insight and support 
to the medical science. Thus, EEG is quite a beneficial 
as well as a cost effective way for the study of epilepsy 
disease. For generalized seizure, the duration of seizure 
can be easily detected by naked eyes whereas it is very 
difficult to recognize intervals during focal epilepsy. 
Classification is the most useful and functional 
technique [5] for properly detecting the epileptic 
seizures in EEG signals. Classification being a data 
mining technique is generally used for pattern 
recognition [6]. Other than that, it is used to predict a 
group membership for unknown data instances. Hence, 
by designing a classifier model using different machine 
learning approaches, we can identify epileptic seizures 
in EEG brain signal. Pre-processing is considered as 
one of the necessary task to get it into a proper feature 
set format, even before considering the classification of 
raw EEG signal. Generally, the data sample of EEG is 
not linearly separable. Thus, to obtain non-linear 
discriminating function for classification, we are using 
machine learning techniques. Moreover, the case of 
limiting our focus on machine learning approaches is 
because of their capability and efficiency for smooth 
approximation and pattern recognition. However, there 
are learners who are performing very poorly; hence 
this work makes an extension over individual learners 
by ensembling classifier models based on weighted 
majority voting. 
In this analytical study, a publicly available EEG 
dataset have been considered that is related to epilepsy 
for all experimental evaluations. Based on this there 
are mainly two phases of the epileptic seizure detection 
process that are carried out. The first phase is to 
analyse the EEG signal and convert it into a set of 
samples with set of features. The second phase is to 
classify the already processed data into different 
classes such as epilepsy or normal. 
The rest of the subdivisions of this paper are 
organized as follows. Section 2 describes the recording 
and pre-processing of EEG signals through discrete 
wavelet transform. Some classification methods based 
on machine learning techniques are described in 
Section 3. Section 4 discusses the ensemble of 
classifiers. In Section 5, the detail of empirical work 
and analysis of results obtained by different machine 
learning models and ensemble of classifiers. Section 6 
draws the conclusions and suggests possibilities for 
future work. 
2 Methods for dataset preparation 
In the present work we have collected data from [7] 
which is a publicly available database [8] related to 
diagnosis of epilepsy. This resource provides five sets 
of EEG signals. Each set contains reading of 100 single 
channel EEG segments of 23.6 seconds duration each. 
These five sets are described as follows. Datasets A 
and B are considered from five healthy subjects using a 
standardized electrode placement system. Set A 
contains signals from subjects in a slowed down state 
with eyes open. Set B also contains signal same as A 
but ones with the eyes closed. The data sets C, D and E 
are recorded from epileptic subjects through 
intracranial electrodes for interictal and ictal epileptic 
activities. Set D contains segments recorded from 
within the epileptogenic zone during seizure free 
interval. Set C also contains segments recorded during 
a seizure free interval from the hippocampal formation 
of the opposite hemisphere of the brain. The set E only 
contains segments that are recorded during seizure 
activity. All signals are recorded through the 128 
channel amplifier system. Each set contains 100 single 
channel EEG data. In all there are 500 different single 
channel EEG data. In Subsection 2.1, we illustrate how 
to crack these signals using discrete wavelet transform 
[9] and prepare several statistical features to form a 
proper sample feature dataset. 
2.1 Wavelet transform 
This is a modern signal analysis technique which 
overcomes the limitations of other transformation 
techniques. Other transformation methods may include 
Fast Fourier Transform (FFT), Short Time Fourier 
Transform (STFT), etc. The major restrictions of these 
techniques are the analysis limits to stationary signals. 
These are not effective for analysis of transient signals 
such as EEG signal. Transient in the sense the 
frequency is changing rapidly with respect to time. 
Then, with the help of wavelet coefficients [10] we can 
analyse transient signals easily and also efficiently. 
Wavelet transform can be of two types: Continuous 
Wavelet Transform (CWT) [11] and Discrete Wavelet 
Transform (DWT) [12, 13]. 
2.1.1 Continuous wavelet transform 
It is defined as: 
𝐶𝑊𝑇(𝑎, 𝑏) = ∫ 𝑥(𝑡). 𝜑𝑎,𝑏
∗ (𝑡)𝑑𝑡
∞
−∞
,             (1) 
Weighted Majority Voting Based... Informatica 41 (2017) 99–110 101 
where, x(t) represents the original signal, a and b 
represents the scaling factor and translation along the 
time axis, respectively. The * symbol denotes the 
complex conjugation and 𝜑𝑎,𝑏
∗ is computed by scaling 
the wavelet at time band scale a. 
𝜑𝑎,𝑏
∗ (𝑡) =
1
√|𝑎|
𝜑 (
𝑡−𝑏
𝑎
),                            (2)                                                                                                                                           
where, φa,b
∗ (t) stands for the mother wavelet. In CWT 
it is presumed that the scaling and translation 
parameters a and b changes continuously. But the main 
disadvantage of CWT is the calculation of wavelet 
coefficients for every possible scale can result in a 
large amount of data. It can surmount with the help of 
DWT. 
2.1.2 Discrete wavelet transform 
It is almost same as CWT except that the value of a 
and b does not change continuously. It can be defined 
as: 
𝐷𝑊𝑇 =
1
√|2𝑝|
∫ 𝑥(𝑡)𝜑 (
𝑡−2𝑝𝑞
2𝑝
) 𝑑𝑡,                 
∞
−∞
    (3) 
where a and b of CWT are replaced in DWT by 2p&2q 
respectively.  
Figure 1: Single channel EEG signal decomposition of 
set A using db-2 up to level 4. 
Figure 2: Single channel EEG signal decomposition of 
set D using db-2 up to level 4. 
 
It is a transformation technique that provides a 
new data representation which can spread to multiple 
scales. Therefore, the analysis of transforming signal 
can be performed at a multiple resolution scale. DWT 
is performed by successively passing the signal 
through a series of high pass and low pass filters 
producing a set of detail and approximation 
coefficients. This generates a decomposing tree known 
as Mallat’s decomposition tree. In this analytical work, 
the raw EEG signals that have been picked up from 
web resources is decomposed using DWT [13] 
available as a toolbox in MATLAB. This signal is 
decomposed using the Daubechis Wavelet function of 
order 2 up to 4 levels [12]. Thus, it produces a series of 
wavelet coefficient like four detailed coefficients (D1, 
D2, D3, and D4) and an approximation signal (A4). 
Figures 1, 2, and 3 provides a snapshot of this 
decomposition of a single channel EEG recording from 
set A, D, and E respectively.  
Figure 3: Single channel EEG signal decomposition of 
set E using db-2 up to level 4. 
 
Later, on this decomposition some of the statistical 
features of many have been extracted from the signals 
such as Minimum (MIN), Maximum (MAX), MEAN, 
and Standard Deviation (SD). Figure 4 is a sample 
output of the MATLAB toolbox showing different 
features of a single channel EEG recording from set A 
and set E. The same procedure can be followed for all 
other EEG recordings to make a perfect set. So, after 
this level, we are ready with a sample feature dataset of 
order 500 by 20 matrixes as shown in Table 1. Then it 
can be further used for classification tasks.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 4: Statistical features extraction from signals 
after decomposition. 
 
In addition to DWT, there are other feature extraction 
techniques [5] that can also be used successfully to 
extract features from the raw EEG signal. These 
techniques may include Wavelet Packet 
Decomposition (WPD), Principal Component Analysis 
(PCA), Lyapunov Exponent, ANNOVA test, etc. 
  
102 Informatica 41 (2017) 99–110 S. K. Satapathy et al.  
 
Table 1: Structure and dimension of dataset for EEG 
signal classification. 
Seizure 
Detection 
Sets 
Size of 
Sample 
Class 0 Class 1 
Set1- (A & E) 200x20 100x20 100x20 
Set 2- (D & E) 200x20 100x20 100x20 
Set 3- (A+D & E) 300x20 200x20 100x20 
3 Machine learning classifiers 
Machine learning (ML) is a set of computerized 
techniques, which focus to automatically learn to 
recognize complex patterns and make intelligent 
decisions based on data. ML has proven its ability to 
uncover hidden information present in large complex 
datasets. Using ML, it is possible to cluster similar 
data, classify, or to find association among various 
features [14, 15]. In the context of EEG signal analysis, 
ML is the application of algorithms for extracting 
patterns from EEG signals [16]. However, there are 
other steps also carried out e.g., data cleaning &pre-
processing, data reduction & projection, incorporation 
of prior knowledge, proper validation and 
interpretation of results while analysing EEG signals. 
EEG analysis has number of challenges which make it 
suitable for machine learning techniques [16]. 
 EEG comes in large databases. 
 EEG recordings are very noisy. 
 EEG signals have large temporal variance. 
Some popular machine learning approaches are 
neural networks, evolutionary algorithms, fuzzy 
theory, and probabilistic learning. In this analytical 
work, our focus is restricted with neural networks, its 
variants and support vector machines for classification 
EEG signals.  
 
3.1 Multilayer perceptron neural 
network (MLPNN) 
Artificial neural network simulates the operation of a 
neural network of the human brain and solves a 
problem. Generally, single layer Perceptron neural 
networks are sufficient for solving linear problems, but 
nowadays the most commonly employed technique for 
solving nonlinear problems is Multilayer Perceptron 
Neural Network (MLPNN) [17]. It can hold various 
layers such as one input and one output layer along 
with at least one hidden layer. There are connections 
between different layers for data transmission. The 
connections are generally weighted edges to add some 
extra information’s to the data and it can be propagated 
through different activation functions. 
The heart of designing an MLPNN is the training 
of network for learning the behaviour of input-output 
patterns. In this work, we have designed an MLPNN 
with the help of a Java Encog framework. This 
network is trained with the help of three popular 
training algorithms such as Back- propagation (BP) 
[18], Resilient Propagation (RPROP) [19], and 
Manhattan Update Rule (MUR).  
Back-propagation training algorithm [5, 19, and 
20] is different from other algorithms in terms of the 
weight updating strategies. In back propagation [21, 
22, 23], generally weight is updated by the equation (4) 
[24, 25, 26]. 
𝑤𝑖𝑗(𝑘 + 1) = 𝑤𝑖𝑗(𝑘) + ∆𝑤𝑖𝑗(𝑘),                     (4)                                                                                                                         
where in regular gradient decent 
∆𝑤𝑖𝑗(𝑘) = −𝜂
𝜕𝐸
𝜕𝑤𝑖𝑗
(𝑘)                          (5)     
with a momentum term 
∆𝑤𝑖𝑗(𝑘) = −𝜂
𝜕𝐸
𝜕𝑤𝑖𝑗
(𝑘) + 𝜇∆𝑤𝑖𝑗(𝑘 − 1)                   (6)                                                                                                                                
Resilient propagation [19] is a supervised training 
algorithm for feed forward neural network. Instead of 
magnitude, it takes into account only the sign of the 
partial derivative, or gradient decent and acts 
independently on each weight. The advantage of 
RPROP algorithm is that it needs no setting of 
parameters before applying it. The weight updating is 
done according to the equation (7). Equation 4 is same 
for the RPROP for weight update. 
∆𝑤𝑖𝑗 =
{
 
 
 
 +∆𝑖𝑗 , 𝑖𝑓
𝜕𝐸
𝜕𝑤𝑖𝑗
(𝑘) > 0,
+∆𝑖𝑗 , 𝑖𝑓
𝜕𝐸
𝜕𝑤𝑖𝑗
(𝑘) < 0,
0, 𝑂𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
              (7) 
 
        ∆𝑖𝑗= {
𝜂+ ∗ ∆𝑖𝑗(𝑘 − 1), 𝑆𝑖𝑗 > 0,
𝜂− ∗ ∆𝑖𝑗(𝑘 − 1), 𝑆𝑖𝑗 < 0,
∆𝑖𝑗(𝑘 − 1), 𝑂𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒.
  
                 (8) 
where, 𝑆𝑖𝑗 = 
𝜕𝐸
𝜕𝑤𝑖𝑗
(𝑘 − 1) ∗
𝜕𝐸
𝜕𝑤𝑖𝑗
(𝑘)  and 𝜂+ =
1.2 𝑎𝑛𝑑 𝜂− = 0.5. 
 
Manhattan update rule also works similar to 
RPROP and only uses the sign of the gradient and 
magnitude is discarded. If the magnitude is zero, then 
no change is made to the weight or threshold value. If 
the sign is positive, then the weight or threshold value 
is increased by a specific amount defined by a 
constant. If the sign is negative, then the weight or the 
threshold value decreases by a specific amount defined 
by a constant. This constant must be provided to the 
training algorithm as a parameter.  
3.2 Variants of neural network  
In addition to MLPNN, many different types of neural 
networks have been developed over the year for 
solving problems with varying complexities of pattern 
classification. Some of these includes: Recurrent 
Neural Network (RNN) [41], Probabilistic Neural 
Network (PNN) [42], and Radial Basis Function 
Neural Network (RBFNN) [43].  
Weighted Majority Voting Based... Informatica 41 (2017) 99–110 103 
3.2.1 Recurrent neural network 
RNN [44] is a special type of artificial neural network 
having a fundamental feature is that the network 
contains at least one feedback connection [45], so that 
activation can flow round in a loop. This feature 
enables the network to do temporal processing and 
learn the patterns. The most important common 
features shared by all types of RNN [46, 47] are, they 
incorporate some form of Multilayer Perceptron as 
sub-system. They implement the non-linear capability 
of MLPNN [48, 49] with some form of memory. In 
this research work the ANN architecture, we have 
implemented for modelling and classifying is the 
Elman Recurrent Neural Network (ERNN). It was 
originally developed by Jeffrey Elman in 1990. The 
Back-Propagation through time (BPTT) learning 
algorithm is used for training [50, 51], which is an 
extension of Back-propagation that performs gradient 
decent on a complete unfolded network. 
If a network training sequence starts on time t0 
and ends at time t1, the total cost function can be 
calculated as: 
𝐸𝑡𝑜𝑡𝑎𝑙(𝑡0, 𝑡1) = ∑ 𝐸𝑠𝑠𝑒
𝑐𝑒
𝑡1
𝑡=𝑡0
(𝑡),                            (9) 
and the gradient decent weight update can be 
calculated as: 
∆𝑤𝑖𝑗 = −ƞ∑
𝜕𝐸𝑠𝑠𝑒
𝑐𝑒
(𝑡)
𝜕𝑤𝑖𝑗
𝑡1
𝑡=𝑡0
 .            (10) 
3.2.2 Probabilistic neural network 
PNN was first proposed by Specht in 1990. It is a 
classifier that maps input patterns in a number of class 
levels. It can be forced into a more general function 
approximator. This network is organized into a 
multilayer feed forward network with input layer, 
pattern layer, summation layer, and the output layer. 
PNN [52] is an implementation of a statistical 
algorithm called kernel discriminant analysis. The 
advantages of PNN are like; it has a faster training 
process as compared to Back-Propagation. Also, there 
are no local minima issues. It has a guaranteed 
coverage to an optimal classifier as the size of the 
training set increases. But it has few disadvantages like 
slow execution of the network because of several 
layers and heavy memory requirements, etc. 
In PNN [52] a Probability Distribution Function 
(PDF) is computed for each population. An unknown 
sample s belongs to a class p if, 
𝑃𝐷𝐹𝑝(𝑠) > 𝑃𝐷𝐹𝑞(𝑠)∀𝑝 ≠ 𝑞,             (11) 
where, PDFk(s) is the PDF for class k. 
Other parameters used are Prior Probability - h, 
Misclassification Cost – c, so the classification 
decision becomes, 
ℎ𝑝𝑐𝑝𝑃𝐷𝐹𝑝(𝑠) > ℎ𝑞𝑐𝑞𝑃𝐷𝐹𝑞(𝑠)∀𝑝 ≠ 𝑞              (12) 
 
PDF for a single sample can be calculated by using 
the formula, 
𝑃𝐷𝐹𝑘(𝑠) =
1
𝜎
𝑊(
𝑠−𝑠𝑘
𝜎
),             (13) 
where  𝑠 – Input (unknown), 𝑠𝑘 - k
th sample, 𝑊- 
weighting function, 𝜎 - smoothing parameter. PDF for 
a single population can be calculated by taking the 
average of PDF of n samples. 
𝑃𝐷𝐹𝑘
𝑛(𝑠) =
1
𝑛𝜎
∑ 𝑊 (
𝑠−𝑠𝑘
𝜎
)𝑛1              (14) 
From the result table, it is experimentally proved 
that for epilepsy identification in EEG signal, PNN 
gives the most accurate result by taking minimum 
amount of time. 
3.2.3 Radial basis function neural network 
RBF networks are also a type of feed-forward network, 
trained by using a supervised training algorithm. The 
main advantage of RBF network is, it has only one 
hidden layer. The RBF network, usually trains much 
faster than back-propagation networks. This kind of 
network is less susceptible to problems with non-
stationary inputs because of the behaviour of radial 
basis function hidden units. The general formula for 
the output of RBF network [53] can be represented as 
follows, if we consider the Gaussian function as the 
basis function. 
𝑦(𝑥) = ∑ 𝑤𝑖𝑒
(
−(‖𝑥−𝑐𝑖‖)
2
2𝜎2
)
𝑀
𝑖=1              (15) 
where 𝑥, 𝑦(𝑥), 𝑐i, σ, and 𝑀denotes input, output, 
center, width, and number of basis function centered at 
𝑐i,  similarly 𝑤i  denotes weights. 
For this work, we have constructed a Radial Basis 
Function Network by taking into consideration of the 
Gaussian function as the basis function with a pre-
fixing of randomized centres and widths. 
3.3 Support vector machine (SVM) 
SVM is the most widely used machine learning 
technique based pattern classification technique 
nowadays. It is based on statistical learning theory and 
was developed by Vapnik in the year 1995. The 
primary aim of this technique is to project nonlinear 
separable samples onto another higher dimensional 
space by using different types of kernel functions. In 
late years, kernel methods have received major 
attention, especially due to the increased popularity of 
Support Vector Machines [27]. Kernel functions play a 
significant role in SVM [28, 29] to bridge from 
linearity to nonlinearity. Least square SVM [30] is also 
an important SVM technique that can be applied for 
classification task [31]. Extreme learning Machine and 
Fuzzy SVM [32, 33, 34] and Genetic algorithm tuned 
expert model [32] can also be applied for the purpose 
of classification.  
In this analytical work, we have evaluated three 
different types of kernel functions [35], i.e., Linear, 
Polynomial, and RBF kernel [36]. Linear kernel is the 
simplest kernel function available. Kernel algorithm 
using a linear kernel is often equivalent to their non-
kernel counterparts [37]. From the result table it can be 
clearly understood that for a classification problem 
consisting of only sets A & E or D & E, is providing 
104 Informatica 41 (2017) 99–110 S. K. Satapathy et al.  
 
100% accuracy. But it is not able to classify properly 
by considering sets A+D & E. 
Polynomial kernel is a non-stationary kernel. This 
kernel function can be represented as given in 
equation: 
K(x, y) =  (αxTy + c)d,            (16) 
where 𝛼, 𝑐 and 𝑑 denotes slope, any constant, and 
degree of polynomial, respectively.  
Somehow this kernel function [38, 39] is better as 
compared to linear kernel function. However, the RBF 
kernel function [40] has been proven as the best kernel 
function used for this application, which can classify 
different groups with 100% accuracy with a minimum 
time interval. 
4 Ensemble of machine learning 
classifiers 
From the empirical analysis we conclude that there 
some classifier models e.g., SVM and PNN 
outperforms all other techniques such as MLPNN, 
RNN, and RBFNN. Hence, to boost the poorly 
performer as compared to SVM and PNN, we have 
proposed a model for an ensemble based classifier that 
combines the above three techniques and improves the 
accuracy of classification for epileptic seizure 
detection. This proposed ensemble technique uses a 
weighted majority vote for classification. The main 
goal of an ensemble method is to combine the 
efficiency of several basic classifier models and build a 
learning algorithm to improve the robustness over a 
single classification technique. Here we have combined 
the classification results of MLPNN, RNN, and 
RBFNN and constructed an ensemble classifier. This 
classifier uses a weighted majority based vote for 
classification. Generally in majority based vote the 
class label of a sample is decided by the class label that 
is classified by maximum number of classifiers. Let 
there are two classes (1 and 2) and three classifiers 
(clf1, clf2, and clf3). Let for a sample clf1 and clf2 
classifies to class 2 whereas clf3 classifies to class 1 
then the ensemble of classifiers classifies the sample to 
class 2.  
Figure 5: Proposed framework for ensemble based 
classifier for detection of epileptic seizure. 
Figure 5 describes the architecture of ensemble of 
classifiers to detect epileptic seizure. It combines the 
output of three different classifiers such as classifier 1 
(MLPNN), classifier 2 (RNN), and classifier 3 
(RBFNN) based on a weighted majority voting 
mechanism. Weight parameter has been added to give 
different weightage to different classifiers based on 
their performance. For this we have collected the 
predicted class probabilities for each classifier and 
multiplied it with classifier weight and the average is 
taken. Based on this weighted average probabilities, 
the class label has been assigned. Here we have taken 
simple weighted majority technique where same 
weight is assigned to each class label which is 1/k, 
where k is the number of class labels. 
5 Empirical study 
This section gives an empirical study on different 
classification techniques based on machine learning 
approach for detection of epilepsy in EEG brain signal. 
Various experiments are done to validate this empirical 
study. The Machine Learning based classifiers are 
proved as the most efficient way for pattern 
recognition. It aids to design models that can learn 
from some previous experience (known as training) 
and further it can be able to recognize appropriate 
patterns for unknown samples (known as testing). 
All experiments for this research work are 
performed using a powerful Java Framework known as 
Encog [54] developed by Jeff Heaton and his team. 
Currently we are using Encog 3.2 Java framework for 
all experimental result evaluation. This is the latest 
version and it supports almost all the features of 
machine learning techniques. Along with this 
framework, there are a lot of packages, classes and 
methods that have been defined to support the 
experimental evaluations. Java is the most potent and 
efficient language nowadays. The rightness of the 
experimental works can be verified easily using this 
language. There are almost nine different machine 
learning algorithms that have been implemented for 
EEG signal classification for epileptic seizure 
detection. 
5.1 Environment and parameter setup 
The Encog Java framework provides a vast circle of 
library classes, interfaces, and methods that can be 
utilized for designing different machine learning based 
classifier models. There are lists of parameters (as 
shown in Table 2) required to be set for smooth and 
accurate execution of models. 
5.2 Performance measures and 
validation techniques 
Here, we have hashed out about the performance of all 
machine learning based classifiers for classifying EEG 
signal. The different measures used for performance 
estimation are: Specificity (SPE), Sensitivity (SEN), 
Accuracy (ACC), and Time elapsed for execution of 
models. From the evaluation result given in Table 3, it 
is clear that MLPNN with resilient propagation is the 
most efficient training algorithm both in considerations 
of accuracy as well as the amount of time needed to 
execute the programs in all different setting such as 
A&E, D&E, and A+D & E. This MLPNN technique 
can be compared with other machine learning 
techniques. In this work all the experimental 
evaluations are validated using k- fold cross validation 
Weighted Majority Voting Based... Informatica 41 (2017) 99–110 105 
where value of k is taken as 10. So the total dataset has 
been divided into 10 folds. Each fold is constructed 
with samples having almost same number from each 
class labels. In each iteration one fold is considered for 
testing the classifier and rest of the folds are taken for 
training the classifier. This is a very efficient validation 
technique as it rules out all possibilities of 
misclassification and gives an accurate efficiency 
measure. 
Table 4 shows a comparison of different kernel 
types used for classification using Support Vector 
Machine (SVM). It is the most powerful and efficient 
machine learning tool for designing classifier model. 
This table clearly shows a very good result for SVM 
with RBF kernel. 
Table 5 defines a list of experiments led by 
studying different forms of Neural Network, such as 
Radial Basis Function Neural Network, Probabilistic 
Neural Network, and Recurrent Neural Network. It 
suggests that the effectiveness of using PNN for 
classification of EEG signal for detecting epileptic 
seizures is promising. 
5.3 Comparative analysis 
 
Table 6 gives a detail empirical analysis of the 
performance of different classification techniques 
based on machine learning approaches. As discussed 
above in this experimental evaluation we have used 10-
fold cross validation to validate the results of 
classification.   
Table 7 gives the result of experimental evaluation 
for the proposed ensemble technique and results for 
individual classification techniques. Figure 6 gives a 
graphical representation of comparison of different 
individual machine learning techniques with ensemble 
based classification technique. These experimental 
result shows there is a remarkable increase in the 
accuracy for case 3 (A+D & E) along with other two 
cases. 
Table 3: Experimental evaluation result of MLPNN with different training algorithms.     
                                                                                             
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Cases 
for 
Seizure 
Types 
Multi-Layer Perceptron Neural Network with different Propagation Training Algorithms 
Back-Propagation Resilient-Propagation Manhattan-Update Rule 
SPE SEN ACC TIME SPE SEN ACC TIME SPE SEN ACC TIME 
 
Case1 
(A,E) 
100 90.09 94.5 16.52 99.009 100 99.5 2.846 97.29 77.77 85 7.541 
Case2 
(D,E) 
100 83.33 90 22.22 99.009 100 99.5 2.547 55.68 78.78 60 7.181 
Case3 
(A+D,E) 
100 86.95 92.5 23.12 95.85 85.98 92.33 14.79 93.78 82.24 89.66 14.85 
Table 2: Lists of parameters for models execution. 
Classification 
Techniques 
Required Parameters and 
Values 
MLPNN/BP Activation Function - Sigmoid 
Learning Rate = 0.7 
Momentum Coefficient = 0.8 
Input Bias – Yes 
MLPNN/RPROP Activation Function - Sigmoid 
Learning Rate = NA 
Momentum Coefficient = NA 
Input Bias – Yes 
MLPNN/MUR Activation Function - Sigmoid 
Learning Rate = 0.001 
Momentum Coefficient = NA 
Input Bias – Yes 
SVM/Linear Kernel Type – Linear 
Penalty Factor = 1.0 
SVM/Polynomial Kernel Type – Polynomial 
Penalty Factor = 1.0 
SVM/RBF Kernel Type – Radial Basis Function 
Penalty Factor = 1.0 
PNN Kernel Type – Gaussian 
Sigma low – 0.0001 (Smoothing  
Parameter) 
Sigma high – 10.0 (Smoothing 
Parameter) 
Number of Sigma - 10 
RNN Pattern Type – Elman 
Primary Training Type – Resilient 
Propagation 
Secondary Training Type – Simulated 
Annealing 
Parameters for SA 
Start Temperature – 10.0 
Stop Temperature – 2.0 
Number of Cycles - 100 
RBFNN Basis Function – Inverse Multiquadric 
Center& Spread Selection – Random 
Training Type– SVD (Singular Value 
Decomposition) 
 
106 Informatica 41 (2017) 99–110 S. K. Satapathy et al.  
 
Table 4: Experimental evaluation result of SVM with different kernel types. 
Cases 
for 
Seizure 
Types 
Support Vector Machine with different Kernel Types 
Linear Polynomial RBF 
SPE SEN ACC TIME SPE SEN ACC TIME SPE SEN ACC TIME 
 
Case1 
(A,E) 
100 100 100 2.127 100 100 100 2.101 100 100 100 2.002 
Case2 
(D,E) 
100 100 100 1.904 100 100 100 1.902 100 100 100 2.021 
Case3 
(A+D,E) 
90.67 76.63 85.66 11.61 100 99.009 99.66 7.24 100 100 100 2.511 
Table 5: Experimental evaluation result of RBFNN, RNN, PNN with different training algorithms. 
Cases 
for 
Seizure 
Types 
Other Types of Neural Network 
RBF Neural Network Probabilistic Neural Network Recurrent Neural Network 
SPE SEN ACC TIME SPE SEN ACC TIME SPE SEN ACC TIME 
 
Case1 
(A,E) 
83.076 65.925 71.5 2.051 100 100 100 0.967 77.173 73.148 75 10.31 
Case2 
(D,E) 
100 97.08 98.5 1.828 100 100 100 0.977 64.705 71.604 67.5 13.29 
Case3 
(A+D,E) 
92.30 66.41 81 2.928 100 100 100 1.616 67.346 66.666 67.333 19.58 
Table 6: Comparative analysis of different machine learning classification techniques. 
Machine 
Learning 
Classification 
Technique 
Case-1 (set A & E) Case-2 (set D & E) Case-3 (set A+D & E) 
Overall 
Accuracy in 
%age 
Approximate 
Time taken in 
seconds 
Overall 
Accuracy in 
%age 
Approximate 
Time taken in 
seconds 
Overall 
Accuracy in 
%age 
Approximate 
Time taken in 
seconds 
MLPNN/BP 94.5 16.527 90 22.226 92.5 23.127 
MLPNN/RP 99.5 2.846 99.5 2.547 92.33 14.798 
MLPNN/MUR 85 7.541 60 7.181 89.66 14.85 
SVM/Linear 100 2.127 100 1.904 85.66 11.61 
SVM/Ploy 100 2.101 100 1.902 99.66 7.24 
SVM/RBF 100 2.002 100 2.021 100 2.511 
PNN 100 0.967 100 0.977 100 1.616 
RNN 75 10.31 67.5 13.29 67.33 19.58 
RBFNN 71.5 2.051 98.5 1.828 81 2.928 
Table 7: Comparative analysis of different machine learning classification techniques 
with ensemble based classifier. 
Machine Learning 
Classification 
Technique 
Case-1 (set A & E) Case-2 (set D & E) Case-3 (set A+D & E) 
Overall 
Accuracy in 
%age 
Approximate 
Time taken in 
seconds 
Overall 
Accuracy in 
%age 
Approximate 
Time taken in 
seconds 
Overall 
Accuracy in 
%age 
Approximate 
Time taken in 
seconds 
MLPNN/RP 99.5 2.846 99.5 2.547 92.33 14.798 
RNN 75 10.31 67.5 13.29 67.33 19.58 
RBFNN 71.5 2.051 98.5 1.828 81 2.928 
ENSEBLE 
CLASSIFIER 
99.5 3.745 99.5 3.876 98.3 5.475 
 
Weighted Majority Voting Based... Informatica 41 (2017) 99–110 107 
0
20
40
60
80
100
120
MLPNN RNN RBFNN ENSEMBLE
Performance Comparison
Case 1 Case 2 Case 3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 6: Performance comparison of different machine learning techniques with ensemble based classifier for 
detection of epileptic seizure.
6 Conclusions and future study 
By classifying the EEG signals collected from different 
patients in different situations, detection of the 
epileptic seizure in EEG signal can be performed. Thus 
classification can be accomplished by using different 
machine learning techniques. In this work, the 
efficiency and functioning pattern of different machine 
learning techniques like MLPNN, RBFNN, RNN, 
PNN, and SVM for classification of EEG signal for 
epilepsy identification have been compared. Further, 
the tool MLPNN uses three training algorithms like 
BACKPROP, RPROP, and Manhattan Update Rule. 
Similarly, three kernels such as Linear, Polynomial, 
and RBF kernels are used in SVM. Hence, this 
comparative study clearly shows the differences in the 
efficiency of different machine algorithms with respect 
to the task of classification. Moreover, from the 
experimental study, it can be concluded that SVM is 
the most efficient and powerful machine learning 
technique for the purpose of classification of the EEG 
signal. Also, SVM with RBF kernel provides the 
utmost accuracy in all settings of the classification 
task. Besides this, PNN is a good contender for SVM 
for this specific application. But compared to SVM, 
PNN requires some extra overhead in setting the 
parameters. Also our proposed ensemble of classifiers 
based on weighted majority voting that combines the 
efforts of three different poorly performer classifiers 
such as MLPNN, RNN and RBFNN is enhancing the 
performance in different cases. Our continuous efforts 
in this area of research (both theoretical and 
experimental)is marching with lots of issues and will 
go ahead in future by considering the real cases with 
state-of-the-art meta-heuristic optimization techniques. 
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110 Informatica 41 (2017) 99–110 S. K. Satapathy et al.  
 
 
 Informatica 41 (2017) 111–120 111
  
Software Architectures Evolution Based Merging 
Zine-Eddine Bouras 
LISCO Laboratory, Department of Mathematics and Computer Sciences 
P.O. Box 218, EPST Annaba Algeria 
E-mail: z.bouras@epst-annaba.dz    
 
Mourad Maouche 
Department of Software Engineering, Faculty of Information Technology 
P.O. Box 1 Philadelphia University 19392, Jordan 
E-mail: mmaouch@philadelphia.edu.jo 
 
Keywords: software architecture, software architecture merging, sependency analysis, slicing  
Received: November 12, 2015 
During the last two decades the software evolution community has intensively tackled the software 
merging issue. The main objective is to compare and merge, in a consistent way, different versions of 
software in order to obtain a new version. Well established approaches, mainly based on the 
dependence analysis techniques on the source code, have been used to bring suitable solutions. However 
the fact that we compare and merge a lot of lines of code is very expensive. In this paper we overcome 
this problem by operating at a high level of abstraction. The objective is to investigate the software 
merging at the level of software architecture, which is less expensive than merging source code. The 
purpose is to compare and merge software architectures instead of source code. The proposed 
approach, based on dependence analysis techniques, is illustrated through an appropriate case study. 
Povzetek:  Prispevek se ukvarja z ustvarjanjem nove verzije programskega sistema iz prejšnjih na nivoju 
abstraktne arhitekture. 
1 Introduction 
Software evolution is the response to software systems 
that are constantly changing in response to changes in 
user needs and the operating environment. This arises, 
often, when new requirements are introduced into an 
existing system, specified requirements are not correctly 
implemented, or the system is to be moved into a new 
operating environment [1]. One way to cope with 
evolution is to carry out the software from the scratch, 
but this solution is very expensive. Another way, that is 
less expensive, is to proceed by merging changes. 
Software practitioners are used first to manage 
individually each change in a separate and independent 
way leading to a new version, then to check that all 
resulting individual versions do not exhibit incompatible 
behaviors (non-interference), and finally to merge them 
into a single version that incorporates all changes (if they 
do not interfere) [2]. 
Such techniques, known as program merging, have 
been widely used at the level of source code [2-4]. 
However comparing and merging a huge number of lines 
of codes is very expensive. Our main motivation is to 
overcome this problem by going up at the level of 
software architecture where the number of comparison 
and merge is smaller than in the source code. 
In this way, we must address some problems like (1) 
understanding what an existing architecture does and 
how it works (dependency analysis), (2) how to capture 
the differences between several versions of a given 
architecture, and (3) how to create new architecture. The 
first problem was resolved by Kim et al. [5]. 
The objective of this paper is to suggest an approach 
to deal with the rest of problems, namely finding an 
approach to compare and merge software architectures. 
More precisely, we suggest reusing the well-known and 
efficient program merging algorithm due to Horwitz [6]. 
This paper will show the applicability of this algorithm 
through an appropriate example. 
The rest of the paper is organized as follows. Section 
2 is dedicated to related works. Section 3 presents the 
notion of software architecture description and a running 
example to be used throughout this paper. Section 4 
introduces software merging in general and software 
architecture merging in particular. Section 5 is dedicated 
to the needed concepts in our approach. In section 6 we 
present, detail, and illustrate our approach of software 
architecture description merging. 
2 Related Works 
Besides differencing programs done by Horwitz [6], 
there are other works that investigate differencing 
hierarchical information for a large code such that 
Apiwattanapong et al. in [7] and Raghavan et al. in [8]. 
In the context of design differencing Xing and 
Stroulia in [9] use the assumption that the entities they 
are differencing are uniquely named and many nodes 
match exactly. A basic change due to designers is to 
rename entities in order to become more expressive. In 
this way the proposed approach fails. 
112 Informatica 41 (2017) 111–120 Z. E. Bouras et. al 
 
Abi-Antoun et al. in [10] propose an algorithm based 
on empirical evaluation to cope with architectural 
merging issue. Empirical Evaluation losses information 
in some cases, merging architecture needs the study of 
dependencies, formally, between components. 
Finally there is an approach that copes with software 
architectures evolution based merging. Bouras and 
Maouche [11] use an internal form to represent software 
architecture and proceed by a syntactic differentiation. 
They, also, detect some type of conflicts that can fail the 
process. 
Our approach is more formal and precise in term of 
dependency analysis. It uses the technique of slicing that 
is a formal filter. Slicing permits dependency analysis of 
software architecture by allowing us to find matching’s 
and differences between elements of Software 
Architecture Descriptions (SAD) during merging 
process, and then merge components (if they are 
compatibles) to obtain a new version of SAD. 
3 Software architecture description 
Understanding all aspects of complex system is very 
hard. It therefore makes sense to be able to look at only 
those aspects of a system that are of interest at a given 
time. The concept of architecture views exists for this 
purpose. According to IEEE 2007, a view is a 
representation of a whole system from the perspective of 
a set of concerns. Each view addresses a set of system 
concerns, following the conventions of its viewpoint, 
where a viewpoint is a specification that describes the 
notations and modeling techniques to be used in a view 
to express the architecture in question from a given 
perspective [12]. Examples of viewpoints include: 
Functional viewpoint, Logical viewpoint, Component-
and-connector viewpoint, etc. This paper is based on 
Component-and-connector viewpoint which specify 
structural properties of component and connector models 
in an expressive and intuitive way. They provide means 
to abstract away direct hierarchy, direct connectivity, 
port names and types, and thus can crosscut the 
traditional boundaries of the implementation-oriented 
hierarchical decomposition of systems and sub-systems 
[13, 14]. 
 
3.1 The example: Electronic Commerce 
We introduce the running example, inspired from [5], to 
be used throughout this paper. 
An order entry form is entered, electronically by a 
clerk. This form is taken by the Electronic Order 
Processing System (EOPS) and transformed on several 
actions through its five components: Ordering, 
Order_Entry, Inventory, Shipping, and Accounting. 
Components are distributed over different platforms, 
have a number of connectors between them, are 
independent processes, and communicate with each other 
through parameterized events. EOPS is depicted in figure 
1. 
EOPS stores the order information through CGI, and 
triggers Ordering which is the front-end of the whole 
system. This triggering is done through an I_order event. 
I_order generates a place_order event (internal action 
depicted by a dotted arrow) at the place_order port. The 
payment results from a payment_req event of Ordering 
which takes place when Ordering gets notified from the 
Order_Entry (implicit invocation depicted by a bold 
arrow). 
When the payment gets approval, Ordering gets an 
order_success event and generates I_ship_info event to 
notify the customer of a successful order (internal 
action). Otherwise Ordering gets an order_fail event and 
notifies customer of unsuccessful order through 
I_order_rej event (internal action).  
Order_Entry gets a take_order event from Ordering 
whenever customer places an order (external 
communication depicted by an arrow). An order is 
broken down into several items and each information of 
them is sent to Inventory through a ship_item event along 
with the customer information. ship_item events are 
generated whenever each ordered item is processed by 
Inventory to pass next item information until all the items 
for an order are processed. The done event results from a 
next_item event when there is no more items to be 
processed and this event triggers the payment 
information request payment_req of Ordering. Inventory 
generates a get_next event whenever it gets a find_item 
event to get the other item information for the order. 
Inventory generates two events, a ship event to Shipping 
and add_item to Accounting if an item is in the inventory 
it generates a back_order event to Inventory in order to 
get and ship the out-of-stock item, otherwise 
(concurrency). A restock_items event occurs when a 
customer cancels an order, this event does not cause any 
further event generation and is represented by special 
symbol called internal sink. 
Shipping takes care of gathering the items of an 
order through recv_item events from Inventory. When it 
gets a shipping approval through a recv_receipt event 
from Accounting, it generates a shipping_info event to 
Ordering and it ships ordered items (synchronization). 
When it receives a cancel event (due to canceled order), 
it generates a restock event along with the item 
information it received. Accounting accumulates the total 
amount for an order whenever it receives an items event 
and verifies resources by communicating with outside 
components when it receives a checking request and 
sends the result (e.g., good/bad). Upon receiving 
payment_res (i.e., good or bad), it issues either an 
issue_receipt event as an approval for shipping when 
successful or fail and restock events to inform the failure 
of the order process to the customer. 
4 Software architecture merging 
Merging approaches take a form when concurrent 
modifications of the same system are done by several 
developers. They are able to merge changes in order to 
obtain ultimately one consolidated version of a system 
Software Architectures Evolution Based Merging Informatica 41 (2017) 111–120 113 
again. However, they are faced to two challenges: the 
representation and how to find out differentiation. 
The first one concerns the representation of software 
artifact on which the merge approach operates. It may 
either be text-based or graph-based. The second 
challenge concerns how differences are identified, 
represented, and merged [14, 15]. 
 
 
Figure 1: Software architecture of electronic order 
processing system. 
Text-based merge approaches operate solely on the 
textual representation of a software artifact in terms of 
text files. The unit element of the text file may either be a 
paragraph, a line, a word, or even an arbitrary set of 
characters. Unit element of given version is compared to 
the original unit element in order to create the new one. 
The major advantage of such approaches is their 
independence of the programming languages used in the 
versioned artifacts. However, the major problem when 
merging flat files, syntax and semantics of a 
programming language are losses [14, 15]. 
Graph-based approaches overcome these problems; 
they operate on a graph-based representation of a 
software artifact for achieving more precise merging. 
Such approaches translate the versioned software artifact 
into a specific structure (graph) before merging. The unit 
elements (e.g. components) are represented by nodes and 
their relationships (e.g. connectors) by arcs. Changes 
consist of adding/deleting/updating unit elements [16]. 
However it requires a preliminary and primordial step 
which is known as software architecture understanding 
[16, 17]. It is very important to understand component's 
context and its running environment in order to 
efficiently manage all kinds of dependencies. In general, 
as soon as a new component is installed, removed, or 
updated in a given software architecture, it has an impact 
on a part of the system. The new component may refer to 
certain components, and also be used by other 
components [16-20]. 
5 Software architecture merging 
concepts 
Before starting our software architecture merging 
approach, it is useful to introduce some preliminary 
concepts related to software architectures and their 
understanding. These concepts concern how to represent 
software architecture as a graph (Software Architectural 
Description Graph) and how to find matching’s and 
differences between components (slicing), and finally 
merging them. 
5.1 Software Architectural Description 
Graph 
Understanding a software addresses some problems like 
what it does and how it works. This is due to the implicit 
relationships between lines of codes. An explicit 
representation is needed. 
Kim et al. in [5] propose a suitable dependence graph 
to support SAD named Software Architectural 
Description Graph (SADG). It consists of representing 
explicitly, dependencies between architecture elements 
i.e. component-connector, connector-component, and 
additional dependences. Informally, SADG is an arc-
classified digraph whose vertices represent either the 
components or connectors in the description, and arcs 
represent dependencies between architectural design 
elements. Formal definitions and illustrations of SADG 
are detailed in [5]. 
In this paper we distinguish between two kinds of 
software architectural description: Base and variants. 
Base represents the original software architecture for 
which changes are requested. Variants represent a family 
of related and independent versions resulting from 
changes done on Base by independent developers. Also 
we point out that merge conflicts may occur. They take 
place if one change invalidates another change, or if two 
changes do not commute. Then, it is not decidable where 
to integrate changes [3]. For example if software 
architect of Variant A decides to update boolean 
expression [=n] to [n=10] in component Ordering_Entry 
(between in port next_item and out port done), while 
software architect of Variant B states that the same n will 
be n= 20, we are in the front of a conflict between 
architects, and merging process fails. In this case conflict 
is resolved manually. In this paper, we consider merging 
architectures without conflicts. 
5.2 Architectural slicing 
When a maintenance programmer wants to modify a 
component in order to satisfy new requirements, the 
programmer must first investigate which components 
114 Informatica 41 (2017) 111–120 Z. E. Bouras et. al 
 
will affect the modified component and which 
components will be affected by the modified component. 
By using a slicing method, the programmer can extract 
the parts of a software architecture containing those 
components that might affect, or be affected by, the 
modified component. This can assist the programmer 
greatly by providing such change impact information. 
Using architectural slicing to support change impact 
analysis of software architectures promises benefits for 
architectural evolution. Slicing is a particular application 
of dependence graphs. Together they have come to be 
widely recognized as a centrally important technology in 
software engineering. This due to the fact they operate on 
the deep rather than surface structures, they enable much 
more sophisticated and useful analysis capabilities than 
conventional tools [6]. 
Traditional slicing techniques cannot be directly used 
to slice software architectures. Therefore, to perform 
slicing at the architecture level, appropriate slicing 
notions for software architectures must be defined with 
new types of dependence relationships using components 
and connectors. Some works have investigated the issue 
of adapting the definition PDG to the level of software 
architecture. Between them, we can cite works of 
Rodrigues and Barbosa in [17] which propose the use of 
software slicing techniques to support a component’s 
identification process via a specific dependence graph 
structure, the FDG (Functional Dependency Graph). 
Zhao’s technique, in [21] is based on analyzing the 
architecture of a software system given in Acme ADL. 
He captures various types of dependencies that exist in 
an architectural description. The considered 
dependencies arise as a result of dependence 
relationships existing among ports and/or roles of 
components and/or connectors. Architecture slicing 
technique operates by removing unrelated components 
and connectors, and ensures that the behavior of a sliced 
system remains unaltered. 
Kim introduced an architectural slicing technique 
called dynamic software architecture slicing (DSAS) in 
[5]. A dynamic software architecture slice represents the 
run-time behavior of those parts of the software 
architecture that are selected according to the particular 
slicing criterion of interest to the software architect such 
as a set of resources and events. 
An important distinction between a static and a 
dynamic slice is that static slices is computed without 
making assumptions regarding inputs, whereas the 
computation of dynamic slice relies on a specific test 
case. In other words, the difference between static and 
dynamic slicing is that dynamic slicing assumes fixed 
input, whereas static slicing does not make assumptions 
regarding the input, hence smaller in size than its static 
counterpart. 
In order to illustrate the concept of dynamic slicing 
consider the fact that, we are interested by the run-time 
behavior of those parts of the software architecture of 
EOPS that are selected according to the particular slicing 
criterion when a customer wants to sell only one item 
that is in the inventory. The dynamic slicing concept is 
dedicated to find all implied parts of EOPS. 
This triggering is done through an I_order event. 
I_order generates a place_order event at the place_order 
port. The payment results from a payment_req event of 
Ordering which takes place when Ordering gets notified 
from the Order_Entry. 
Order_Entry gets a take_order event from Ordering 
whenever customer places the order (wiyh n=1). The 
item is sent to Inventory through a ship_item event along 
with the customer information. ship_item event is 
generated whenever the ordered item is processed by 
Inventory. Inventory generates a ship event to Shipping. 
Shipping takes care of gathering the items of an 
order through recv_item events from Inventory. It 
generates a shipping_info event to Ordering and it ships 
ordered items. Finally Ordering gets an order_success 
event and generates CGI_ship_info event to the CGI 
program to notify the customer of a successful order. The 
run-time behavior is depicted in Figure 2. 
Our graph representation is inspired from Kim’s 
researches [5] where the process of architectural slice 
extraction from the software architectural description is 
based on the concept of Software Architectural 
Description Graph (SADG) and is a graph traversal. 
Finally, comparing the behavior of Base with the 
behavior of a given variant consists of comparing static 
architectural slices of Base with static architectural slices 
of the given variant. 
Order_Req_Handler
Order_Entry
Inventory
Accounting
Shipping
place_order  I_order
I_ship_info
order_success
      ship_item
take_order
done
find_item
[found]
restock_items
add_item
items
[=n]
issue_receipt
recv
_rec
eipt
ship
ping
_inf
o
[=n]
Clerk
 
Figure 2: Example of Architecture Dynamic Slice. 
5.3 Graph similarities 
Comparing two graphs needs at first to find, for a given 
node (or edge) in a given graph, its corresponding node 
(or edge) in the other. An efficient way to find out 
similarity is the use of signature and structural matching 
[16]. 
A signature is defined as a pair of corresponding 
elements needs to share a set of properties such as type 
Software Architectures Evolution Based Merging Informatica 41 (2017) 111–120 115 
information, which can be a subset of their syntactical 
information. Type information can be used to select the 
elements of the same type from the candidates to be 
matched because only elements with the same type need 
to be compared. Signature is used as the first criterion to 
match elements as proposed by [16]. If there is more than 
one candidate that has been found, the signature cannot 
identify a node uniquely. It is, therefore, to do further 
analysis by structural matching. 
Structural matching is based on calculation of Graph 
Similarity using Maximum Common Edge Subgraphs 
[16]. The first algorithm to find the candidate node with 
maximal edge similarity for a given host node takes the 
host node and a set of candidate nodes of graph 2 as 
input, computes the edge similarity of every candidate 
node and returns a candidate with maximal edge 
similarity. The second algorithm for computing edge 
similarity between a candidate node and a host node 
takes two maps as, input, stores all the incoming and 
outgoing edges of the host and candidate nodes indexed 
by their edge signature. By examining the mapped edge 
pairs between these two maps, the algorithm computes 
the edge similarity as output. Graph similarities 
algorithm can be summarized as the following: 
Let Base and a Variant SADG’s 
1. For each variant node  
1.1 Use signature matching to find candidate node 
If there is more than one candidate use structural 
matching  
Compare each node and its associated edges of 
Base with its variant peer (similar). 
1.2 Determine and collect sets of changed elements  
If no candidate, host node belongs to Delete set 
            // exists in Base and not in Variant 
Remaining nodes in variant belongs to New set  
          // exists in Variant and not in Base 
Compare names of each pair of nodes mapping 
If values are different, name belongs to Update set 
// all node mapping and differences are found 
2. Edges connecting to delete nodes are Delete edges 
Edges connecting to new nodes are New edges 
Apply signature matching to find out the edge 
mapping 
Remaining edges in Base belongs to Delete edges 
set 
Remaining edges in variant belongs to New 
edges set 
 
All nodes in N1 have been examined by signature 
and structural matching; all possible node mappings 
between N1 and N2 are found.  
6 Software architecture merging 
process 
In this section we show how to reuse and adapt the 
Horwitz algorithm [6] to the context of software 
architecture merging. We show that this approach solves 
the issue of architecture merging because both, program 
and architecture merging may be brought to a graph 
theory problem. 
6.1 Software merging algorithm 
Figure 3 resumes merging process. It starts from (1) a 
Base Software Architecture Description, (2) build a set of 
variants (resulting from Base changes), (3) build 
Software Architectural Description Graph for each 
SADG, (4)  compare each variant with Base to determine 
sets of changed and preserved elements, and (5) combine 
these sets to form a single integrated new version (if 
changes don’t interfere). Steps (1) and (2) are done 
concurrently by developers, in step (3) we construct 
SADG’s according to Kim’s approach. 
 
Figure 3: Merging Process. 
Step 4: Compare each variant with the base to determine 
sets of changed and preserved elements 
For each variant 
4.1. Determine peer nodes and edges with Base by 
signature and structural matching. 
4.2. Extract from each SADG the associated 
slices. 
4.3. Determine sets of changed and preserved 
elements 
4.3.1. Map and compare each slice of the base 
software with its peer in variant. 
4.3.2. Determine and collect changed and 
preserved slices.  
Step 5: Combine changed and preserved slices to form a 
new SADG. 
5.1. Merge preserved of Base and changed slices 
of variants. 
5.2. Check that variants do not interfere  
5.3. Derive the resulting dependency graph. 
5.4. Generate the SADG of the new version of 
software architecture description from the 
resulting SADG.  
Our contribution in this paper is to develop steps (4) 
and (5) in order to merge software architecture. In the 
following we formalize these sub-steps. 
Base
Variant B
Variant A
New
Version
Merging
Detecting Changes
Detecting Preserves
Old Version Copies of Base
with concerned
changes
Merging process
116 Informatica 41 (2017) 111–120 Z. E. Bouras et. al 
 
6.2 Formalization 
Given SADGs SADGBase, SADGA, and SADGB, of Base, 
and variants A and B respectively. The algorithm 
performs three steps.  
The first step identifies three subgraphs that 
represent the changed behavior of A with respect to 
Base( A, Base), the changed behavior of B with respect 
to Base (B, Base) and the preserved behavior that is the 
same in all architectures (PreA,B,Base) by using the set 
of vertices whose slices in SADGBase, SADGA, and 
SADGB are identical (i.e. . PPA,B,Base ).  
The second step unifies these subgraphs to form a 
merged dependence graph SADGM.  
 In the third step, a merged architecture GM is 
generated from graph SADGM. 
 
6.2.1 Construction of a slice 
First, we show how to compute an architecture slice. In 
this section we use the notation Component_name: 
inport/outport_name in order to represent components 
and connectors in an internal form. For example, 
Order_Req_Handler:I_order is the input port I_order of 
component Order_Req_Handler. 
Each SADG is transformed in an internal form. The 
internal form is a set of triplets (a, b, c) which reflects the 
fact that there is an edge of type c from a to b. c can be 
an implicit invocation (ii), an internal action (ia) or an 
external communication (ec) while a and b are 
components and connectors using the previous notation.  
For example (Ordering:I_order,Ordering:place_order,ia) 
means that: there is an internal action (ia) between in port 
I_order of component Ordering (Ordering:I_order) and out port 
place_order of Ordering (Ordering:place_order) 
Table 1 represents a sample of internal form of Base 
SADG. 
Arch
itect
ure 
 
Internal Form 
Base ((External_Source_Clerk, Ordering:I_order,ec), 
(Ordering:I_order,Ordering:place_order,ia), 
(Ordering:payment_req, Ordering:I_payment_req, 
ia), (Ordering:order_success, 
Ordering:I_ship_info,ia), (Ordering:order_fail, 
Ordering:Iorder_rej,ia), (Ordering: I_payment_req, 
Ordering:payment_req,ec), (Ordering: I_ship_info, 
sink1, ec), 
(Ordering:I_payment_req,Accounting:items,ii), 
(Ordering:place_order,Order_Entry:take_order, ii), 
……) 
Table 1: A sample of internal form of Base SADG.  
Because of we are interested by static dependency 
analysis of SADG, we extract all slices starting from 
external source entry (e.g. clerck) to component that is in 
the front-end of the whole system until the end of the 
process (e.g. external sink). A static slice is a graph 
traversal by transitive closure from external source node 
to a final node from where we cannot continue the 
traversal (e.g. external sink). 
In our example of Figure 1 there are more than 
fifteen slices that represent the complete behavior of 
EOPS. Table 2 represents one of them. 
Slice Internal form 
 ((External_Source_Clerk, Ordering:I_order), 
(Ordering:I_order,Ordering:place_order,ia), 
(Ordering:place_order,Order_Entry:take_order,ii), 
(Order_Entry:take_order, 
Order_Entry:ship_item,ia), (Order_Entry:ship_item, 
Inventory:find_item,ii), (Inventory:find_item, 
Inventory:get_next,ia), (Inventory:get_next, 
Order_Entry:next_item,ii), (Order_Entry:next_item, 
Order_Entry:done,ia), (Order_Entry:done, 
Ordering:payment_req,ii), (Ordering:payment_req, 
Ordering:Accounting_payment_req,ia), (Ordering, 
Accounting _payment_req, Accounting:cancel,ii), 
(Accounting:cancel, Accounting:fail,ia), 
(Accounting:fail, Ordering:order_fail,ii), 
(Ordering:order_fail, Ordering:I_order_rej, ia), 
(Ordering:I_order_rej, Sink2,ec)) 
Table 2: Internal form of a static slice.  
Note that this slice reflects the behavior of canceling 
an order. 
At the end of this step, each one (Base and variants) 
SADG’s is transformed into a set of slices and the 
process of comparison can starts.  
6.2.2 Changed slices 
Let X, Base the set of changed slices between variant X 
and Base. Changed slices are computed as the following: 
APA, Base = {v V(SADGA)  (SADGBase/v) ≠ (SADGA/v)} 
APB, Base = {v V (SADGB)  (SADGBase/v) ≠ (SADGB/v)} 
A, Base = b(SADGA, APA, Base) 
B, Base = b(SADGB, APB, Base). 
Where  
V(SADGx) denotes the set of vertices in SADG of 
variant X.  
SADGX/v is a vertex in the SADG of X from where 
we want to inspect its impact in the overall SADG of X.  
b(SADGX, APX, Base) is the set of peer changed slices 
in SADGBase and SADGX. 
In other words, internal forms of peer slices are 
compared. As a result they haven’t the same graph 
traversal (different internal forms).  
An example of changed slices is introduced in the 
section dedicated to application (6.3). 
6.2.3 Preserved slices 
Preserved architectural slices (PreA, Base, B) are computed 
as the following: 
PPA, Base, B = {v V (SADGBase)  (SADGA/v) = 
(SADGBase/v) = (SADGB/v)}. 
PreA, Base, B = (SADGBase, PPA, Base, B). 
The same graph traversal exists in both Base and 
variants. 
Software Architectures Evolution Based Merging Informatica 41 (2017) 111–120 117 
We find an example of preserved slices in the section 
of application. 
6.2.4 Forming the merged SADG 
The merged graph GM characterizes the SADG of the 
new version of the software architecture. GM is computed 
as the following: 
GM = A, Base  B, Base  PreA, Base, B 
Informally, GM is composed of slices that are 
changed in SADG’s of variants A and B with respect to 
Base and those that are unchanged. 
6.3 Application 
In this section we illustrate and validate the suggested 
merging approach through the running example of figure 
1. 
Starting from an initial software architecture 
description (Base) we introduce two independent 
requirement changes that are expected to be compatible. 
For this purpose two independent copies of Base are first 
created and modified concurrently (Variant A and 
Variant B). We will proceed as follows: 
a. Generate the SADG of Base, Variant A, and 
Variant B. 
b. Extract slices from these SADG’s. 
c. Determine the set of changed slices and the set 
of preserved slices. 
d. Show that Variant A and Variant B do not 
interfere. 
e. Merge the set of changed slices and the set of 
preserved slices in order to get the SADG of the 
new version.  
6.4 Building SADG’s of variants A and B 
Two non-interfering variants are considered. In Variant 
A, a credit card payment option is added while in Variant 
B and in case stocks are empty at the order time, the 
request is handled through a back order mechanism. 
6.4.1 Variant A SADG 
In Variant A, Software Architect A inserts a new 
component that will take in charge the credit card 
payment option. This leads to the following changes in 
the architectural description of the software: (1) adding a 
new component (Credit_Checker), (2) creation of new 
connectors (from Ordering to Credit_Checker, and from 
Credit_Checker to Accounting), and (3) removing 
external connection (from Credit_res_out to 
Credit_res_in). Figure 4 represents the SADG of variant 
A. 
6.4.2 Variant B SADG 
In Variant B, Software Architect B inserts a new 
component that will take in charge the back order 
mechanism. This leads to the following changes in the 
architectural description of the software: (1) adding a 
new component (Back_order), (2) creation of new 
connectors (from Inventory to Back_order, from 
Back_order to Accounting, from Back_order to 
Shipping). Figure 5 depicts the SADG of variant B. 
 
Figure 5: SADG of variant B. 
6.5 Slice Extractions 
Slice extraction process outcomes more than fifteen 
slices per SADG. For lack of space, only a pertinent 
sample of computed slices is presented in this paper. 
Selected sample involves an example of changed slices 
 
Figure 4: SADG of variant A. 
 
118 Informatica 41 (2017) 111–120 Z. E. Bouras et. al 
 
.case (A, Base = b(SADGA, APA, Base) and an example of 
preserved slices case (PreA, Base, B = (GBase, PPA, Base, B)). 
These examples focus on the following two behaviors of 
interest: (1) slice traversals that leads to the canceling 
orders (payment unspecified and credit payment), and (2) 
slice traversal that leads to a successful ordering. 
Figures 6 and 7 illustrate changed slice of canceling 
order in Base and Variant A respectively. Differences 
between these peer slices are depicted with double 
arrows in figures 6 and 7. In this case the slice of Variant 
A will belong to A, Base set and is one of slices 
forming the SADG of the new version of Software 
architecture.  
Figure 8 reflects the same behavior in the three 
SADG. The graph traversal of successful ordering slice is 
the same in Base and variants. 
Thus they will be classified in the category of 
preserved slices. They belong to: 
PPA, Base, B  = {v V (SADGBase)  (SADGA/v) 
= (SADGBase/v) = (SADGB/v)} 
Intersection of changed slices between Base and 
Variant A and changed slices between Base and Variant 
B gives an empty set, consequently there is no 
interference between changes. We can continue the 
process. 
6.6 Forming the merged SADG 
This step involves forming a new SADG by using the 
result of previous steps. It consists of merging all 
changed architectural slices between SADG of Base and 
variants, and thus preserved in these SADGs. The union 
of changed and preserved slices forms the SADG of the 
new version of software architectural description. 
 
GM = A, Base  B, Base  PreA, Base, B 
 
Figure 9 depicts the SADG of the new version of 
software architectural description. 
 
7 Conclusion 
First, it is important to situate our works according to 
Horwitz’s works. 
The main contribution of Horwitz’s work was to 
propose a new process of software evolution. This 
process was formalized and implemented at the program 
level. So Horwitz opened a new way for future research 
in evolution through the life cycle of software. This way 
involves mainly the facts (1) make explicit the 
 
Figure 6: Slice of canceling order of Base. 
 
Figure 7: Slice of canceling order of Variant A. 
 
 
Figure 8: Slice of successful ordering in Base, 
Variants A and B. 
Software Architectures Evolution Based Merging Informatica 41 (2017) 111–120 119 
dependencies between elements (e.g. data and control) 
which are usually implicit, (2) extract all behaviors 
(influence of one element over the other) for each version 
(Variants and Base), and (3) compare the behavior of 
each variant according to the Base program and finally 
form the new version that consists of the elements that 
remained preserved in all versions and those that have 
created differences in the variants.  
Since, several studies have been made by exploiting 
this process. We also followed this process, but at the 
level of software architectures. We solved the problem of 
the similarity of graphs, ignored by Horwitz. We 
investigate and found the best way to represent the 
dependencies between elements of architectures that are 
different than those of programs. From there we followed 
the process. So, we showed that software evolution based 
merging at the level of software architecture is possible. 
Consequently this will lessen the cost of evolution. 
Nowadays we continue in the theoretical aspects of 
this approach. Particularly, we are planning to 
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by involving conflicts. Another promising investigation 
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where software architectures are described by well-
known Architecture Description Languages (ADLs). 
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of ADLs. The question is "is it possible to merge 
architectures from ADLS or passing, first, by the graph 
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Ordering
Order_Entry
Inventory
Back_Order
Credit_Checker
Accounting
Shipping
I_payment_info
I_payment_req
paym
ent_
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place_orderI_order
I_ship_info
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order_fail
I_order_rej
[first]       ship_item
take_order
[<n]
[=n]
next_item done
find_item
get_next
[found]
[not found]
take_back_order
restock_items
back_order
add_item
ship
ship
add_item
check_req credit_res
items
cancel
Payment_res
[=n]
[good]
[bad]
issue_receipt
fail
restock
recv_item
recv
_rec
eipt
cancel
restock
ship
ping
_inf
o
[=n]
Clerk
[cancel]
check_res_in
check_res_out
check_res_out check_res_in
Figure 9: SADG of the new version. 
 
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Informatica 41 (2017) 121–128 121
Dynamic Protocol for the Demand Management of Heterogeneous Resources
with Convex Cost Functions
Jernej Zupančič
International Postgraduate School Jozef Stefan and Jozef Stefan Institute
Jamova cesta 39, 1000 Ljubljana
E-mail: jernej.zupancic@ijs.si
Matjaž Gams
Jozef Stefan Institute
Jamova cesta 39, 1000 Ljubljana
E-mail: matjaz.gams@ijs.si
Keywords: pricing mechanism, negotiations, multiagent system, resource demand balancing
Received: March 21, 2017
Increasing demand of resources has driven research towards design of mechanisms that address resource-
demand management, which usually focus on balancing peak demand and optimal device scheduling.
While balancing homogeneous sources is well known, the goal of this paper is to balance the demand of
heterogeneous resources with a convex cost function as well as to encourage consumers to reduce their
demand. We propose a novel dynamic protocol achieving both goals using a serial cost-sharing pricing
mechanism. The pricing-mechanism design is strategy proof and budget balanced, whereas the protocol
does not require the consumers to reveal their utility functions, and supports hierarchical organization of
consumers. We initially applied the proposed approach to a multi-agent system, where the consumers are
smart house devices controlled by a corresponding agent, which in turns negotiates the electricity price with
a smart city. Experimental results show that the proposed protocol successfully reduces the total consump-
tion and lowers the peaks while all the consumers in equilibrium are given a fair price. We further provide
experimental results indicating advantages of serial cost-sharing pricing and tariff pricing mechanisms over
the average cost-pricing mechanism that is commonly used in the resource-demand management.
Povzetek: V prispevku predstavimo mehanizem za uravnoteženje porabe heterogenih virov, katerih cen-
ovne funkcije so konveksne.
1 Introduction
The rising demand of resources such as electricity, water,
natural gas and oil along with a limited amount of natural
resources increases the costs of resource supply [1]. Con-
sumers are typically priced the same per-unit price for a
resource, regardless of whether they increase or decrease
their consumption.
Mechanisms that stimulate the use of electricity when its
generation is cheap and depreciate its consumption when
its generation is expensive, have already been proposed and
implemented in the energy sector [9].
Similar mechanisms can be implemented for any other
resource [13], although this is rarely done, since electricity
demand management is recognised as the main resource-
demand management problem. The main reason for this is
that it is inefficient to build new power generation plants in
order to meet peak demands, while there are parts of the
day, when there is barely any demand at all. While other
reports have focused on shifting the load from one part of
the day to another [9, 12], we propose a mechanism to en-
courage consumers to reduce their consumption in general.
A resource-demand management mechanism that sets
the same price for every consumer in advance does not
fully exploit demand-management capabilities. Dynamic-
pricing mechanisms were proposed in [3], [10] and [4]. In
those papers, the price for the next time unit changes dy-
namically and is the same for every consumer. Addition-
ally, such mechanisms require the consumers either to re-
port their utility function (which raises privacy concerns)
or to leave the decision about consumption to the con-
sumers themselves (which is not as efficient as it could be).
A dynamic-pricing mechanism that negotiates prices with
every individual was proposed in [2]. However, their ap-
proach assumes that the consumers will report their con-
sumption truthfully, which is not always the case. Dis-
tributed real-time electricity pricing mechanism was intro-
duced in [8], however, due to intense communications re-
quirements between the suppliers and consumers it takes
time to calculate the optimum solution. Truth incentive de-
mand management was proposed in [5]. The mechanism
described in [5] has several desirable properties besides be-
122 Informatica 41 (2017) 121–128 J. Zupančič et al.
ing truth incentive: it is proved to converge to a Nash equi-
librium, it is budget balanced (the total cost of resource
provision equals the total cost the consumers pay), and it
reaches the global optimum (minimal peak-to-average ra-
tio). However, it still raises some issues: the resource con-
sumption of every consumer is known to everyone, con-
sumers are only encouraged to shift their load to different
parts of the day and not to reduce consumption, smaller
consumption does not result in smaller per-unit price, and
real-time pricing requires price prediction capabilities.
Further, the only known general technique for designing
truthful and approximately budget-balanced cost-sharing
mechanisms with good efficiency or computational com-
plexity properties is due to Moulin [6].
In some ways similar to our approach is the trading mar-
ket for the smart electricity grid described in [11]. The
authors proposed continuous double auction for the smart
electricity grid. However, their mechanism is not scalable
nor it is dynamic. Further, [14] addresses similar problem
– dynamic resource-demand management and it uses se-
rial cost sharing mechanism. However, no comparison with
other mechanism were made.
To be successfully implemented in practice, a mecha-
nism has to address all the above mentioned shortcom-
ings. It has to be easily understood by consumers, it should
change the prices dynamically and adapt them fairly to
each individual according to the amount of resource con-
sumed. Further, the mechanism should be truth incentive
and should encourage lower resource consumption with-
out asking the consumers to publicly reveal their demand
or utility function. We propose a novel protocol that pos-
sesses those properties. It is based on the following three
key ideas:
1. It ensures consumer satisfaction, by using a fair per-
unit pricing of the resource;
2. It has built-in truth incentive and budget-balancing;
3. To preserve privacy, consumers do not have to reveal
their utility functions.
The rest of the paper is structured as follows. In Section
2, we describe the problem domain to which our mecha-
nism can be applied. In Section 3, we describe the pro-
posed mechanism and its properties. Results from applying
the mechanism to electricity demand management domain
are presented in Section 4, while Section 5 concludes the
paper with summary and discussion.
2 Problem domain
Our mechanism can be applied to a general resource-
demand management problem (when there are many con-
sumers that consume convexly priced resource), but for il-
lustrative purposes we will make use of smart city domain
to provide examples. The proposed protocol applies to
a multi-agent system, where devices and appliances that
consume a resource in one consumer unit (e.g., a smart
house) are controlled by one agent – house representative
agent. House representative agents interact with the nego-
tiator agent forming a multi-agent system (Figure 1).
Agents participating in the proposed protocol are defined
in Definition 1.
Definition 1. Let A be a set of all agents in the system.
Let dij be a particular device j in a house i. Let hri ∈ A
be a house representative agent that is responsible for co-
ordinating all devices dij in the house. Therefore, house
i consists of devices and a representative agent and is de-
noted as Hi.
Let nr ∈ A be a negotiator agent that is responsible for
fair distribution of resource r per-unit prices.
Every house representative agent interacts only with the
resource negotiator agent. House representative agents do
not interact among each other.
nr
hr1 d11
d12
d13
hr2
d21
d22
d23
hr3
d31
d32 d33
d34
H1H2
H3
Figure 1: Multi-agent system example to which the proto-
col applies
We make the following assumptions for our problem do-
main.
House representative agents can control the devices in
the house. House representative agents have the ability to
switch the devices on and off. Further, agents can lower
the power consumption of the device in finite number of
discrete steps if gradual decrease of power consumption is
supported by the device.
House representative agent strives to maximise con-
sumer satisfaction. It is a primary goal of the house repre-
sentative agent to consider users preferences, when nego-
tiating with the resource negotiator. It is unacceptable for
the inhabitant to pay a higher per-unit price for a resource
than he was prepared to.
Cost function of a resource is convex, strictly increas-
ing and non-constant. Examples of functions of this kind
Dynamic Protocol for the Demand Management of. . . Informatica 41 (2017) 121–128 123
are frequently used quadratic cost function and piecewise
linear cost function [5].
3 The Protocol
The protocol defines a resource-demand management
mechanism that enables the matching of resource demand
with resource supply. Supply varies because resource
provision usually depends on some independent variables
(weather, fuel costs, taxes etc.). We assume that for one
protocol run the supply and per-unit prices can be accu-
rately defined in advance. Demand varies due to the supply
and the consumer preferences. We assume that consumer
preferences can be accurately defined in advance before
each protocol run. Demand then depends only on the sup-
ply and using the proposed protocol the equilibrium can be
reached.
The protocol is designed by the following requirements:
– negotiation finishes in a finite number of steps,
– consumers are motivated to reduce resource consump-
tion by the protocol itself,
– the protocol is truth incentive,
– the pricing is fair,
– the total cost of resource provision equals the total
cost the consumers pay, i.e., the protocol is budget-
balanced.
According to those requirements and design the pro-
posed protocol is a variant of a Moulin mechanism [6] -
iterative fair sharing procedure.
3.1 Informal Protocol Description
Informal description of the protocol follows. First the smart
house owners define the maximum per-unit price they are
prepared to pay for every operating state of every device.
For instance, smart house owner can have a dish washer, a
washing machine and a heat pump installed in the house.
For the dishwasher the owner is prepared to pay the per-
unit price of 0.12 EUR/kWh, for the washing machine 0.10
EUR/kWh, for the heat pump at maximum power 0.30 EU-
R/kWh since keeping the home warm is more important
than washing the clothes, and for the heat pump at half
of the maximum power 1.0 EUR/kWh since in the case of
high resource demand the per-unit price will be higher but
the owner is prepared to pay the high per-unit price in order
to maintain the border level of comfort. Other resources,
e.g. water or gas can be added in the same way. By stat-
ing all the per-unit prices the user defines the strategy of
her house representative agent and the agent can partici-
pate in the mechanism defined by our protocol on behalf of
the house owner. Since the house representative agent re-
sides in the house and does not share the information about
prices, the character of the home owner or her representa-
tive agent remains private information.
Our protocol is dynamic, meaning that it is repeated after
some period of time has elapsed from the previous run. For
some resources the per-unit price is expected to be more or
less stable over time and does not change very often. Time
elapsed between protocol runs in these cases can therefore
be longer. However, per-unit prices of some resources can
fluctuate rapidly. For instance, the per-unit price of electric-
ity can change rapidly with the increasing use of renewable
resources for its generation depends on the wind speed, so-
lar radiation, water flow rate etc. Since those variables are
hard to predict a day in advance, an hour or half an hour
time intervals are assumed and the protocol must be able
to support a large number of houses and perform all the
calculations in a short period of time.
When a new time interval in which the decision about
consumption has not been reached yet approaches, the ne-
gotiator agent triggers the protocol. First, it gathers the
information about resource supply from the suppliers in or-
der to construct a resource cost function. Then it asks every
house representative agent it has connection to as to what
is her preferred consumption. Since this is at the start of
the protocol and no price has been set yet, every house rep-
resentative agent responds with the highest required con-
sumption. The negotiator agent then computes the per-unit
prices individually for every house using some kind of a
pricing mechanism and informs the house representative
agents of the per-unit prices. It then again asks every house
representative agent about their required consumption. The
house representative agent can then either agree with the
per-unit price and it responds with the same consumption
as before or it can reduces the required consumption in the
hope of a lower per-unit price. The proposed protocol guar-
antees that the per-unit price for lower consumption is ei-
ther lower or in the worst case equal to the per-unit price
of higher consumption. The cycle ask for consumption -
respond with consumption - compute per-unit prices is re-
peated until every house representative agent is satisfied
with the per-unit price and at that point the protocol run
ends and an agreement is made stating that each house will
consume the specified amount of the resource in the ap-
proaching time interval at the per-unit price that was set by
the negotiator agent and agreed upon by the house repre-
sentative agent.
When new time interval starts the house representative
agents turns the devices and appliances in the home ON
or OFF, depending on the agreements made. Every addi-
tional consumption of the resource, beyond the agreed one,
is paid for at uniform maximal per-unit price that covers
the costs of resource providers for additional amount of re-
source provision.
3.2 The Protocol Definition
During the run of the protocol many rounds take place with
house representative agents reporting their desired con-
124 Informatica 41 (2017) 121–128 J. Zupančič et al.
sumption to the negotiator agent and negotiator agent com-
puting per-unit prices for every house representative agent.
The per-unit prices are computed individually for every
consumer using Serial cost sharing mechanism [7]. The Al-
gorithm 1 determines fair per-unit price for every consumer
– lower consumption results in lower costs. Since the re-
source is convexly priced, it also results in lower per-unit
pricing.
Algorithm 1: Serial cost sharing function
Serial_Cost_Sharing(f, c):
N = length(c)
sort c in increasing order
p = []
for i in range(N):
p[i] = cost(i, f, c)/c[i]
sort p in order to
match original order of c
return p
Algorithm 1 takes two inputs: resource cost function,
denoted as f , and desired consumption vector, denoted as c,
and returns a vector of per-unit prices p. Individual cost is
computed by a function cost(i, f, c) that takes as inputs the
following parameters: consumer i, a sorted consumption
vector c, and resource cost function f . Function cost is
defined recursively in Eq. (1).
cost(0, f, c) = 0
cost(i, f, c) =
f
(∑i−1
j=1 c[j] + (N + 1− i) · c[i]
)
N + 1− i
− (1)
−
∑i−1
j=1 cost(j, f, c)
N + 1− i
whereN is the number of consumers participating in nego-
tiation.
The cost function just needs to be convex, whereas any
source, homogeneous or heterogeneous (water, gas, se-
curity and similar), can be added with the corresponding
weights:
cost =
∑
resource
ωresource · costresource, (2)
where resource is a specific resource type, costresource
is the cost of that resource type and ωresource is the weight
of that resource in the resource portfolio.
If any of the consumers does not agree with the per-unit
price it receives and wants to further reduce its consump-
tion anticipating a lower per-unit price, another round of
negotiation takes place. In the following round, the de-
sired consumption of individual consumer can be the same
or lower. Negotiation stage ends when the demand is the
same for every consumer in two consecutive rounds.
It is a requirement of the protocol that a consumer does
not respond with strictly higher desired consumption than
in the previous round.
3.3 The Protocol Properties
In this subsection we evaluate the proposed protocol ac-
cording to the requirements stated at the beginning of the
Section 3.
Lemma 1. Assume there is a finite number of consumers
and each consumer is in possession of finite number of de-
vices or appliances and every device or appliance has finite
number of resource consumption levels. Then the protocol
finishes in a finite number of steps.
Proof 1. Assume that the protocol does not finish in finite
number of steps. That means that consumption vector (a
vector of consumptions of all the consumers) is different
in each two consecutive rounds. That means that in every
round at least one of the consumers reported strictly lower
consumption than before. Since we assumed infinite num-
ber of steps at least one consumer can strictly lower her
consumption infinite times. This contradicts the premise
from the lemma. Therefore, our assumption is wrong and
the protocol finishes in finite number of steps. 
Definition 2. Let f be a resource cost function, so that
f(x) is the total cost for the provision of x amount of re-
source. Let cost be a pricing mechanism used to deter-
mine the cost for every resource consumer. Let c be a
consumption vector, denoting consumptions for all the N -
consumers participating in the mechanism. The pricing
mechanism is budget-balanced iff
f
(
N∑
i=1
ci
)
=
N∑
i=1
cost(ci).
The following two lemmas are a corollary of [7].
Lemma 2. The pricing mechanism used in the proposed
protocol is budget-balanced.
Proof 2. Let N be a number of consumers, let c be a con-
sumption vector where ci-s are increasingly ordered, let f
be a resource cost function and let the cost be a serial cost
sharing mechanism that is used in the proposed protocol.
The costs of the largest consumer can be computed as
cost(N, f, c) =
f
(∑N−1
j=1 c[j] + (N + 1−N) · c[N ]
)
N + 1−N
−
−
∑N−1
j=1 cost(j, f, c)
N + 1−N
= f
 N∑
j=1
c[j]
− N−1∑
j=1
cost(j, f, c).
Dynamic Protocol for the Demand Management of. . . Informatica 41 (2017) 121–128 125
Therefore,
N∑
j=1
cost(j, f, c) = f
 N∑
j=1
c[j]
 .

Definition 3. Let per-unit price for the consumption x and
using pricing mechanism p be denoted by p(x). Let con-
sumers A and B consume aA and aB amount of resource,
respectively. A pricing mechanism p is fair iff for each aA
and aB so that aA ≤ aB , p(aA) ≤ p(aB) also applies.
Lemma 3. The pricing used by the proposed protocol is
fair.
Proof 3 (sketch). Since resource cost function is convex
and increasing, its derivative is also increasing. Since the
derivative of the cost function represents the marginal price
(per-unit price) of the resource provision, the per-unit price
of the resource is higher when more of it is used. Serial cost
sharing mechanism divides the cost in such a way that for
the same amount of resource the consumers pay the same
per-unit price and any additional consumption is paid at a
different price. Since the per-unit price of additional con-
sumed resource is higher, due to the increasing and convex
cost function, so is the final per-unit price of a larger con-
sumer. Therefore if aA ≤ aB then p(aA) ≤ p(aB). 
Definition 4. Let the pairs {(ci, pi)}i represent the (con-
sumption, price) pairs by which the consumer can state
his preferences: for the consumption of ci amount of re-
source the consumer is prepared to pay pi per-unit price.
Let A = (cA, pA) and B = (cB , pB) be two possible out-
comes of the protocol. The consumer preferres A over B
iff
1. cA > cB and pA ≤ pi where ci = cA, or
2. cA = cB and pA < pB ≤ pi where ci = cA.
In other words, the consumer prefers to consume the
maximum possible amount of resource she can afford. In
this case the following lemma applies.
Lemma 4. Assume the definition of preference as in Defi-
nition 4. Further, assume the consumers are infinitely risk-
averse. Then the proposed protocol is truth incentive and
strategy-proof.
Proof 4 (sketch). During the protocol the consumer can
strategize by responding with the same level of consump-
tion in two consecutive rounds of the protocol although she
can not afford the per-unit price (according to her real pref-
erences). That is because the per-unit price of the resource
can lower when all other consumers lower their consump-
tion. If the per-unit price lowers enough the per-unit price
could become affordable for the strategic consumer. How-
ever, since no consumer has the full knowledge of all the
consumers preferences or the resource prices, there is a
positive probability that the per-unit price will not become
affordable. If the consumer is infinitely risk-averse, she
wants to avoid the worst possible results and therefore, she
will not strategize. In every round the consumer will re-
spond according to her preferences, which will guarantee
a good outcome for her. Therefore, the proposed protocol
is truth incentive and strategy-proof. 
4 Experimental Results
Application of the protocol in electrical energy consump-
tion domain is presented in this section. We carried out
the experiment to examine the amount of electrical energy
consumed and the average per-unit price of the electrical
energy when using the proposed protocol.
4.1 Model description
While there are many variants for house representative
agent implementation, the important properties of the
house representative agent are:
1. It can reason about desired resource consumption in
every round of the protocol, and
2. it can decide whether the offered per-unit price is sat-
isfiable for the house.
In our experiment house representative agent possess the
information about the electrical energy consumption of the
devices and the information about consumers settings –
maximal per-unit price for operating each device. This in-
formation is private to each house representative agent and
sent neither to other house representative agents nor the re-
source negotiator.
The parameters for each house in our experiments were
the following:
– Number of consumption levels: uniformly drawn in-
teger number from the interval [3, 15],
– Consumption level size: uniformly drawn real value
from the interval [0.1kWh, 1.5kWh],
– Acceptable per-unit price for every consump-
tion level: drawn from normal distribution with
mean 0.15EUR/kWh and standard deviation of
0.055EUR/kWh.
A convex, strictly increasing piece-wise linear cost func-
tion was used for electrical energy pricing (suggested in
[5]). It can be represented by a list of tuples (Bi, pupi),
where Bi is the amount of energy available for the per-unit
price of pupi. To obtain a convex function, we assume that
lower priced energy is supplied first.
The parameters for cost function were the following:
– Number of tuples (Bi, pupi): 50 tuples were used;
– Block sizes: on average each block was four time the
size of total minimal level consumption;
126 Informatica 41 (2017) 121–128 J. Zupančič et al.
– Block per-unit prices: drawn from normal distribution
with mean 0.3EUR/kWh and standard deviation of
0.055EUR/kWh.
4.2 Electricity Consumption Experiment
In order to make a comparison of the serial cost sharing
mechanism used in our protocol to standard cost sharing
mechanism, the average cost sharing mechanism was also
implemented [5].
Definition 5. Let f be a resource cost function and let c
be a consumption vector. The average cost sharing mecha-
nism for consumer i is defined as:
averageCost(i, f, c) =
f
(∑N
k=1 c [k]
)
∑N
k=1 c [k]
· c [i] .
The protocol was run 2000 times with serial cost shar-
ing mechanism and average cost sharing mechanism for ev-
ery number of houses we specified – specifically 100, 500,
1000, 1500, 2000. The main variables we observed were
the percentage of total possible consumption that was re-
alized after the protocol run and the average per-unit price
the consumers have to pay.
Since there was a large difference between the two pric-
ing mechanisms a third mechanism was implemented that
combines the average and serial cost sharing mechanisms.
Definition 6. Let T be a number of possible different per-
unit prices of the resource. Let c be a consumption vector,
where consumptions are orderer from smallest to largest,
and let N be a number of consumers. Now group the con-
sumers into T equal sized groups where in t-th group there
are all consumers with index from (t−1)·
⌊
N
T
⌋
+1 to t·
⌊
N
T
⌋
.
Total consumption for every group can be computed and
new consumption vector for groups can be assembled
cT =bNT c∑
i=1
c [i] ,
2·bNT c∑
i=bNT c+1
c [i] , . . . ,
N∑
i=(T−1)·bNT c+1
c [i]
 .
For each group the costs can be calculated using the se-
rial cost sharing mechanism and a cost vector can be ob-
tained costT = (cost1, cost2, . . . , costT ). The per-unit
price for consumer i in group t can be calculated using the
average cost sharing mechanism
pricei =
costt∑t·bNT c
k=(t−1)·bNT c+1
c [k]
· c [i] .
We call this mechanism a tariff pricing mechanism.
We have made the same experiments as before using the
additional pricing mechanism, where we grouped the con-
sumer into two equal sized groups. The final results are
100 500 1000 1500 2000
numberOfHouses
0.00
0.05
0.10
0.15
0.20
a
v
e
ra
g
e
P
ri
c
e
protocolType
average
serial
tarrifs
Figure 2: Bar plot with number of houses and average per-
unit prices on axes
100 500 1000 1500 2000
numberOfHouses
0
5
10
15
20
25
n
o
n
Z
e
ro
C
o
n
s
u
m
p
ti
o
n
protocolType
average
serial
tarrifs
Figure 3: Bar plot with number of houses and percentage
of possible total consumption on axes
presented in Figures 2, 3 and 4. Every point or a bar repre-
sents the average of 2000 runs of the protocol.
From the experiments we concluded that a number of
houses has a negligible effect on the observed variables
when model like ours is used. The largest effect has the
type of the protocol used: protocol with serial or average
cost sharing mechanism. Since the consumers are different
and some are prepared to pay more for the same amount
of resource the consumption increases when using the pro-
tocol with serial cost sharing. It is the advantage of the
serial cost sharing to produce personalized per-unit prices
according to the amount of resource used. For that reason
also, the average per-unit price increases when using se-
rial cost sharing. However, every consumer still receives
the amount of the resource she is prepared to pay for. And
according to definition 4 maximal consumption within the
limits is the preferred result. Therefore, serial cost sharing
Dynamic Protocol for the Demand Management of. . . Informatica 41 (2017) 121–128 127
Figure 4: Scatter plot with average per-unit prices and per-
centage of possible total consumption on axes
mechanism is the preferred mechanism.
By using only two groups the results of the protocol us-
ing tariff pricing are much more similar to the protocol us-
ing serial cost sharing than the average pricing. Consump-
tion and average per-unit price increase due to the flexible
pricing mechanism used.
5 Discussion & Conclusion
We have proposed a truth incentive mechanism that encour-
ages the reduction of consumption of convexly priced re-
sources and can be easily understood by consumers. Fur-
ther, the mechanism scales linearly with the number of con-
sumers, since the per-unit prices can be easily computed.
The novel protocol ensures consumer satisfaction when
the consumer is truthful and deals also with heterogeneous
resources. But there are also some problems associated
with our approach. First, since one can not predict the con-
sumption of others and does not know the exact resource
cost function, one can not predict the possible outcome
of the protocol. Therefore, it is not rational to agree to a
higher per-unit price than one is willing to pay. Second,
the algorithms that deals with homogeneous resources [6]
find optimal solutions and have several desired properties.
Our algorithm meets some of the desired requirements but
needs several rounds to achieve that, however the number
of cycles is still less than in general Moulin mechanisms.
Since serial cost sharing was used the proposed mech-
anism is budget-balanced. The stopping of the protocol
was discussed in Subsection 3.3. However, one of the main
assumptions used to prove the stopping was that after ev-
ery round of the negotiation the consumer does not de-
mand strictly higher desired consumption. Although not
addressed in the proposed version of the protocol, addi-
tional mechanism could be implemented to release this as-
sumption and still maintain the the stopping property.
Further work will include the analysis of the consumer
behaviour when the protocol is applied repeatedly with the
same consumers and the same resource negotiator. Addi-
tionally, other agent organization structures will be consid-
ered in order to enable the protocol implementation in real-
life scenarios.
The proposed protocol demonstrates various desired
properties beside being truth incentive and scalable. Due
to its efficient resource reduction capabilities it is consid-
ered to be applicable to the sustainable smart city domain.
6 Acknowledgements
The research was sponsored by ARTEMIS Joint Undertak-
ing, Grant agreement No. 333020, and Slovenian Ministry
of Economic Development and Technology.
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Informatica 41 (2017) 129
JOŽEF STEFAN INSTITUTE
Jožef Stefan (1835-1893) was one of the most prominent
physicists of the 19th century. Born to Slovene parents,
he obtained his Ph.D. at Vienna University, where he was
later Director of the Physics Institute, Vice-President of the
Vienna Academy of Sciences and a member of several sci-
entific institutions in Europe. Stefan explored many areas
in hydrodynamics, optics, acoustics, electricity, magnetism
and the kinetic theory of gases. Among other things, he
originated the law that the total radiation from a black
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Volume 41 Number 1 March 2017 ISSN 0350-5596
Editors’ Introduction to the Special Issue on
"End-user Privacy, Security, and Copyright issues"
N. Dey, S.Borra,
S.C. Satapathy
1
A Hybrid Wavelet-Shearlet Approach to Robust
Digital Image Watermarking
A.B.A. Hassanat,
V.B.S. Prasath,
K.I. Mseidein, M. Al-awadi,
A.M. Hammouri
3
An Improved Gene Expression Programming Based
on Niche Technology of Outbreeding Fusion
C-x. Wang, J.-j. Zhang,
J.G. Tromp, S.-l. Wu,
F. Zhang
25
Identity-based Signcryption Groupkey Agreement
Protocol using Bilinear Pairing
S. Reddi, S. Borra 31
Performance Evaluation of Lazy, Decision Tree
Classifier and Multilayer Perceptron on Traffic
Accident Analysis
P. Tiwari, H. Dao,
G.N. Nguyen
39
Distributed Fault Tolerant Architecture for Wireless
Sensor Network
S. Mitra, A. Das 47
Combined Zernike Moment and Multiscale Analysis
for Tamper Detection in Digital Images
T. Le-Tien, T. Huynh-Kha,
L. Pham-Cong-Hoan,
A. Tran-Hong
59
End of Special Issue / Start of normal papers
Aggregation Methods in Group Decision Making: A
Decade Survey
W.R.W. Mohd, L. Abdullah 71
Hidden-layer Ensemble Fusion of MLP Neural
Networks for Pedestrian Detection
K.K. Htike 87
Weighted Majority Voting Based Ensemble of
Classifiers Using Different Machine Learning
Techniques for Classification of EEG Signal to
Detect Epileptic Seizure
S.K. Satapathy,
A.K. Jagadev, S. Dehuri
99
Software Architectures Evolution based Merging Z.-E. Bouras, M. Maouche 111
Dynamic Protocol for the Demand Management of
Heterogeneous Resources with Convex Cost
Functions
J. Zupančič, M. Gams 121
Informatica 41 (2017) Number 1, pp. 1–129