©2012-17 International Journal of Information Technology and Electrical Engineering `
ITEE, 6 (2) pp. 40-45, APR 2017
40
ITEE Journal Information Technology & Electrical Engineering
ISSN: – 2306-708X
Volume 6, Issue 2 April 2017
Predicting Critical Cloud Computing Security Issues using Artificial Neural
Network (ANNs) Algorithms in Banking Organizations
Abdelrafe Elzamly1, Burairah Hussin 2, Samy S. Abu Naser3, Tadahiro Shibutani4, and Mohamed Doheir5
1Department of Computer Science, Al-Aqsa University, Gaza, Palestine 2 ,5 Information & Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM), Malaysia
3Department Information Technology, Al-Azhar University, Gaza, Palestine 4Institute of Advanced Sciences, Yokohama National University, Yokohama, Japan
E-mail: 3abunaser@alazhar.edu.ps
ABSTRACT The aim of this study is to predict critical cloud computing security issues by using Artificial Neural Network (ANNs) algorithms.
However, we proposed the Levenberg–Marquardt based Back Propagation (LMBP) Algorithms to predict the performance for
cloud security level. Also LMBP algorithms can be used to estimate the performance of accuracy in predicting cloud security
level. ANNs are more efficiently used for improving performance and learning neural membership functions. Furthermore, we
used the cloud Delphi technique for data gathering and analysis it in this study. In this study, the samples of 40 panelists were
selected from inside and outside Malaysian banking organizations based on their experienced in banking cloud computing.
However, we have indicated that the LMBP is nonlinear optimization models which used to measure accuracy of the prediction
model, the Mean Square Error (MSE) are measured to determine the performance. The performance is goodness, if the MSE is
small as shown in Table 1. This work has been conducted on groups of cloud banking developers and IT managers. As future
work, we intend to combine another optimal technique with ANNs algorithms to predict and mitigate critical security cloud issues.
Though, positive prediction of critical cloud security issues is going to surge the probability of cloud banking success rate.
Keywords: Cloud banking organization, Cloud Computing, Cloud Security Issues, , Artificial Neural Network, Levenberg Marquardt Algorithm,
Back Propagation Algorithm,. 1. NTRODUCTION
Although much research and progress in the area of
cloud computing project, a lot of cloud computing projects
have a very high failure rate particularly when it is related to
the banking area. However, several serious cloud security
issues like data protection and integrity, quality of
services(QoS), Portability and Interoperability, and mobility
need to be controlled and mitigated before cloud computing
able to apply adoptive widely [1]. In addition, cloud
computing has several advantages but cloud computing in
banking organizations is suffering from a lot of cloud
security issues. The aim of cloud risk management is
identification and evaluation of cloud security issues at an
early stage to predict the cloud computing security level [2].
Today, cloud computing risk management became a mutual
practice amongst leading banking organization success. In
the increasing effort to improve development processes and
security; new studies have led to cloud computing risk area.
Risk management aids software project manager and team
to do improved decisions to mitigate cloud-computing risks.
The objective of this study is predicting performance for
cloud computing security issues using Levenberg–
Marquardt based Back Propagation (LMBP) algorithms.
2. LITERATURE REVIEW
Cloud computing risk management consists of
computing processes, methods and techniques that are
useful to mitigate cloud computing risk failure. Security
risk management is increasingly becoming significant
in a diversity of areas linked to information technology
(IT), for example: telecommunications, banking
information systems, cloud computing[3]. Moreover,
the cloud banking model is a resource management
modeling founded on economic philosophies. Its
function like commercial banks in loan and deposit
business [4]. Cloud security is a general subject and any
grouping of policies, controls, and technologies to
safeguard data, services and infrastructure from
conceivable attacks. Additionally, current researches
focused on providing security technologies, instead of
business features such as services stability, availability
and continuity [5]. This study is going to predict the
critical cloud issues in Malaysian banking
organizations. Actually, they presented the conceptual
framework for cloud security banking that involved
components for example security, legal, privacy,
compliance and regulatory issues of banking [6]. As
stated by previous studies we split the framework
modeling cloud computing to five phases as mobility
and banking application, Cloud Deployment Models
(CDM), cloud risk management models (CRMM),
Cloud Service Models (CSM), and cloud security model
(CSM) as follows: Firstly, mobility related to the
possibility of moving and taking place in diverse
locations and through multiple times using any kind of
portable devices like smart phones, Personal Digital
Assistants (PDAs) and wireless laptops. Nonetheless,
mobile banking related to any operation that linked to
banking services like balance check, payments and
receiving banking SMS via a mobile device, and
account transactions [7]. Secondly, CSM depend on
©2012-17 International Journal of Information Technology and Electrical Engineering `
ITEE, 6 (2) pp. 40-45, APR 2017
41
ITEE Journal Information Technology & Electrical Engineering
ISSN: – 2306-708X
Volume 6, Issue 2 April 2017
some state of the art of web technologies like
Application Programming Interface (API), Web
Services, Web 2.0, and etc. [8]. Also, CSM is split into
four categories that are offered from a cloud provider:
Software as a Service (SaaS), Platform as a Service
(PaaS), Banking Process as a Service (BPaaS), and
Infrastructure as a Service (IaaS). Thirdly, CDM can be
split into four dissimilar types: Public cloud is made
obtainable to the general public or a huge industry group
and are possessed by a third party selling cloud services
[9]. Private Cloud is functioned and possessed by a
single organization or company that focuses on
controlling the mechanism of virtualizing resources and
automating services those are used and tailored by many
lines of business and essential groups [4]. Community
cloud falls among public and private clouds with regard
to the target set of consumers [10]. Community cloud,
this model is used by a specific group of community
within an organization that has the same worry,
objectives or security necessities [11]. Hybrid cloud
uses both public and private cloud methods, where it
smears the strategic notions of the services of public
cloud with the basis of the private cloud. Fourthly,
Cloud Risk Management (CRM): in Cloud computing,
risk required to be taken into consideration in all phases
of interactions and investigated at every service stage in
relation to the possessions that should be protected [12].
Besides, there are diverse types of risks that bank
management should be protected against. For numerous
banks, the main risk is credit risk but there are several
other risks that supervising authorities must notify
banks about connected criteria and require them to
follow [13]. There are eight phases for effective cloud
risk management like Cloud Risk Planning Phase
(CRPL), Cloud Risk Analysis (CRA) phase, Cloud Risk
Identification(CRI) phase, Cloud Risk
Prioritization(CRP) phase, Cloud Risk
Evaluation(CRE) phase, Cloud Risk Treatment(CRT)
phase includes four strategies for responding to cloud
risks: cloud risk mitigation, cloud risk avoidance, cloud
risk transfer, cloud risk elimination, cloud risk
acceptance, Cloud Risk Controlling(CRC) phase, and
Cloud Risk Communication & Documentation (CRCD)
phase. Finally, Cloud Security Issues Models (CSIM):
cloud security is a very common topic and any grouping
of policies, technologies, and controls to protect data,
infrastructure and services from possible attacks or
achieving business objectives all the security domains
should work in an effective manner [14].
3. CLOUD SECURITY ISSUES
Though, classification of critical security issues in
cloud banking is needed to be highlighted in this section
[15]: 3rd Party (Providers) and Policies Security Issues:
Lack of standards, Service Level Agreement (SLAs),
Governance, Legally and policy, Dependency, Lack of
transparency, Cloud service provider viability,
Malicious insiders, Regulatory compliance &
requirements, Shared technology issues, Unknown risk
profile, Trusted cloud, Abuse cloud computing;
Application and program (software) security issues:
Authentication, Authorization, Insecure Interfaces
API’s, Availability and Mobility, Portability and
Interoperability; Data and Information Security Issues:
Privacy, Confidentiality, Data Protection, Data
Limitations and Segregation, Data integrity and
scavenging, Data Location, Data Loss/Leakage,
Detection and Recovery, Hijacking of Account or
Service & Traffic; Security Control & Network Issues:
Information flow Controlling, Intrinsic Constrains of
Wireless Network, Network Access Schemes,
Bandwidth, Anonymity and Network Traffic Analysis,
Network Security, Virtual Network Protection, Limited
control, Distributed Denial of Service (DDoS),
Heterogeneity in Mobile cloud Devices, Platform
Reliability and Latency; Security and Service
Management Issues: Session Management,
Identity/Access Management, Quality of Service (QoS),
IT organizational changes; Physical Infrastructure
Security Issues: Flexibility Infrastructure, Single Point
to Attack and Failure, High-value cyber-attack targets,
the multi-tenancy, Scalability, Cost.
4. EMPIRICAL STRATEGY
The Delphi technique use to collect data as
qualified informants, so we focused on two cloud
developers groups and cloud IT managers in banking
organizations. In this regard the Delphi study is
modified to three phases like identifying, analyzing, and
evaluating as described in Figure 1. The data are
collected by secondary data and Delphi study. In current
study, the population samples of forty panelists were
chosen from inside and outside Malaysian banking
organizations according to their experienced in cloud
banking. Actually, we measure the probability of
occurrence according to a 10 scales (1= “very low
probability of occurrence risk” and 10 = “very high
probability of occurrence risk”), and the brutality of the
cloud security issues described on a 10 scales (1= “very
low influence risk” and 10 = “very high impact risk”.
Actually, we used Delphi techniques for data gathering
and analysis it in this study. However, we will begin a
list of cloud security issues based on secondary data,
experienced of cloud managers and cloud developers.
The Delphi method is collected data and aggregated of
cloud security issues. In fact, we divided the phases of
cloud Delphi technique into three phases such as
identifying, analyzing, and evaluating. However, we
illustrate the concept of Delphi technique for identifying
and classifying cloud security issues in Figure 1 as
follows:
Cloud Delphi Technique
Phase 1: Identifying Collected data and aggregated of cloud
security issues.
Select the experts from both inside and outside
the banking organization.
Divide panelist to two groups cloud
©2012-17 International Journal of Information Technology and Electrical Engineering `
ITEE, 6 (2) pp. 40-45, APR 2017
42
ITEE Journal Information Technology & Electrical Engineering
ISSN: – 2306-708X
Volume 6, Issue 2 April 2017
Figure 1: Illustrates the steps of cloud Delphi study of
collecting data [16]
5. METHODOLOGY (MATERIALS &
METHODS)
However, the data gathered for this study to be used
in the modelling is getting from the managers and cloud
developers in banking organizations. We propose
Artificial Neural Network (ANNs) for predicting cloud
security issues in banking organizations. In order to
manage and predict performance of cloud computing
security level, we can use artificial neural networks
methods. In order to establish the intelligent approaches,
first we need to model the relationship between cloud
computing issues. In addition, artificial neural networks
modelling are used as nonlinear statistical data model to
predict cloud-computing issues. Of course, IT managers
and cloud developers must use practical approaches,
methods, and tools to predict cloud security issues in
banking organization. Indeed, the back-propagation
algorithm is used in layered feed- forward ANNs where
the artificial neurons are structured in layers, and lead
their signals “forward”, and then the errors are
transmitted backwards. The neural network gets input
from input layers and yields the output to the output
layer and the processing can be done in hidden layers.
There must be only one input and output layer, however,
there may be an arbitrary number of hidden layers [17-
19]. Additionally, the BP algorithm should minimize
these errors, till the ANN learns the training data.
Typically the training initiates with random weights,
and the learning objective is to modify them so that the
error is reduced [17-19]. The design of procedures for
predicting cloud security issues using Levenberg-
Marquardt (LM) Based Back Propagation (BP)
Algorithm as follows:
1. Collect and prepare the data for cloud security issues based on Cloud Delphi Technique.
2. Assign an estimated probability of occurrence and severity of cloud security issues based on
security models.
3. Build a network analysis 4. Train the network: It generates the neural
network from a Cloud Delphi dataset with
known output data cases.
5. Test the network: A trained neural networks are used to test how well it does at prediction of
known and new output values.
6. Predict cloud security issues based models by using artificial neural networks for evaluating
the performance impact of CSI. A trained neural
network is used to predict unknown output
value.
6. RESULTS AND DISCUSSION
Indeed, we used the Levenberg–Marquardt based
Back Propagation (LMBP) Algorithms, as nonlinear
optimization to predict the performance. So we illustrate
the mean square error and Regression (R) values for the
Training, Validation and Testing as in Table 1.
Table 1 Illustrates the MSE and Regression values for the
three types Types Samples Training
data
(input)%
MSE R
Training 28 70% 4.94160×e-7 9.95213×e-1
Validation 6 15% 8.75807×e-6 9.79262×e-1
Testing 6 15% 1.95378×e-5 9.49600×e-1
Table 1 shows that the overall Mean Square Error
which measure the average squared errors between the
output data and targets data and Regression (R) which
measure correlation between the actual outputs data and
targets data for training, validation and testing samples.
The accuracy of prediction is observed, when the values of
R are closest to 1. Hence, if the dataset was trained by using
(LMBP) Algorithms, the performance obtained was in 3 epochs with 10 hidden neurons yields. The results indicated
that the LMBP algorithms are very efficiently for testing and
training networks. Although, a two-layered feed forward
network hidden neurons and networks are trained using
LMBP Algorithms as shown in Figure 2.
©2012-17 International Journal of Information Technology and Electrical Engineering `
ITEE, 6 (2) pp. 40-45, APR 2017
43
ITEE Journal Information Technology & Electrical Engineering
ISSN: – 2306-708X
Volume 6, Issue 2 April 2017
Figure 2 architecture and algorithms and progress of ANN
system
Figure 3 Performance of LMBP Algorithm (MSE vs.
Epochs)
Figure 4 error histogram with 20 bins based LMBP
Indeed, it is trained to measure the performance of networks
by using LMBP algorithms in Matlab R2013b.
Furthermore, we estimated the best validation performance
0.0000087581 at epoch 3 in Figure 3 and the error histogram
with 20 bins is illustrated in Figure 4. Therefore, regression
R values are measured the correlation between outputs and
targets. Hence, the results in the regression analysis plot are
perfect correlation between the outputs and targets as in
Figure 5. In addition, the one mean a close relation between
outputs and targets, zero a random relationship. LMBP is
nonlinear optimal models which used to measure accuracy
of the prediction model, the Mean Square Error (MSE) are
measured to determine the performance. The performance is
goodness, if the MSE is small.
Figure 5 Regression Analysis Plot – Levenber g-Marquardt
Backpropagation Algorithm
7. CONCLUSIONS
The concern of the study is to predict critical cloud
computing security issues using Artificial Neural
Network (ANNs) algorithms. However, we presented
the Levenberg–Marquardt based Back Propagation
(BP) Algorithms to predict the performance for cloud
security level. Also LMBP algorithm is applied to
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
10-10
10-5
Best Validation Performance is 8.7581e-06 at epoch 3
Me
an
Sq
ua
re
d E
rro
r (m
se
)
5 Epochs
Train
Validation
Test
Best
0
2
4
6
8
10
12
Error Histogram with 20 Bins
Ins
tan
ce
s
Errors = Targets – Outputs
-0.0
049
-0.0
0418
-0.0
0345
-0.0
0272
-0.0
02
-0.0
0127
-0.0
0055
0.0
00181
0.0
00907
0.0
01632
0.0
02358
0.0
03084
0.0
0381
0.0
04536
0.0
05262
0.0
05988
0.0
06714
0.0
0744
0.0
08166
0.0
08892
Training
Validation
Test
Zero Error
0.65 0.66 0.67 0.68 0.690.65
0.655
0.66
0.665
0.67
0.675
0.68
0.685
0.69
Target
Ou
tpu
t ~
= 0
.97
*T
arg
et
+ 0
.02
Training: R=0.99521
Data
Fit
Y = T
0.65 0.66 0.67 0.68 0.690.65
0.655
0.66
0.665
0.67
0.675
0.68
0.685
0.69
Target
Ou
tpu
t ~
= 1
.2*T
arg
et
+ –
0.1
4
Validation: R=0.97926
Data
Fit
Y = T
0.65 0.66 0.67 0.68 0.690.65
0.655
0.66
0.665
0.67
0.675
0.68
0.685
0.69
Target
Ou
tpu
t ~
= 1
.3*T
arg
et
+ –
0.2
3
Test: R=0.9496
Data
Fit
Y = T
0.65 0.66 0.67 0.68 0.690.65
0.655
0.66
0.665
0.67
0.675
0.68
0.685
0.69
Target
Ou
tpu
t ~
= 1
.1*T
arg
et
+ –
0.0
41
All: R=0.9596
Data
Fit
Y = T
©2012-17 International Journal of Information Technology and Electrical Engineering `
ITEE, 6 (2) pp. 40-45, APR 2017
44
ITEE Journal Information Technology & Electrical Engineering
ISSN: – 2306-708X
Volume 6, Issue 2 April 2017
estimate and test the performance of accuracy for
predicting cloud security level. ANNs are more
efficiently used for improving performance and learning
neural membership functions. Indeed, the performance
of cloud security is analyzed by using LMBP to give the
best performance in the predicting models.
Furthermore, we used the cloud Delphi technique for
data gathering and analyzing it in this study. In this
study, the samples of 40 panelists were selected from
inside and outside Malaysian banking organizations based on their experienced in banking cloud computing.
However, we have indicated that the LMBP is nonlinear
optimal models which used to measure accuracy of the
prediction model and to reduce the error between the
actual outputs and targets for training process, the Mean
Square Error(MSE) are measured to determine the
performance. The performance is goodness, if the MSE
is small as shown in Table 1. As future work, we intend to use another optimal technique with Artificial Neural
Network algorithms to predict and mitigate critical
security cloud issues.
8. Acknowledgements
This work is organized by the Welfare Association in
Palestine; financially supported by the Arab Monetary
Fund, and Bank of Palestine under the program name
(Academic Fellowship Program Zamalah). The authors
also would like to thank Al-Aqsa University, Gaza,
Palestine and Faculty of Information & Communication
Technology, Universiti Teknikal Malaysia Melaka
(UTeM), Malaysia.
References
[1] D. Hoang and L. Chen, “Mobile Cloud for Assistive
Healthcare (MoCAsH),” in 2010 IEEE Asia-Pacific
Services Computing Conference Mobile, (2010), pp.
325–332.
[2] J. Miler and J. Górski, “Supporting Team Risk
Management in Software Procurement and
Development Projects,” in 4th National Conference
on Software Engineering, (2002), pp. 1–15.
[3] J. Mounzer, T. Alpcan, and N. Bambos, “Integrated
Security Risk Management for IT-Intensive
Organizations,” in 2010 Sixth International
Conference on Information Assurance and Security,
(2010), pp. 329–334.
[4] H. Li, Y. Pu, and J. Lu, “A Cloud Computing
Resource Pricing Strategy Research-based on
Resource Swarm Algorithm,” in 2012 International
Conference on Computer Science and Service
System, (2012), pp. 2217–2222.
[5] Z. Gao, Y. Li, H. Tang, and Z. Zhu, “Management
Process Based Cloud Service,” in International
Conference on Cyberspace Technology (CCT 2013),
(2013), pp. 278–281.
[6] M. Alemu and A. Omer, “Cloud Computing
Conceptual Security Framework for Banking
Industry,” J. Emerg. Trends Comput. Inf. Sci., vol.
5, no. 12, pp. 921–930, (2014).
[7] A. Alzahrani, N. Alalwan, and M. Sarrab, “Mobile
Cloud Computing: Advantage, Disadvantage and
Open Challenge,” in Proceedings of the 7th Euro
American Conference on Telematics and
Information Systems, (2014), pp. 4–7.
[8] E. Aruna, A. Shri, and A. Lakkshmanan, “Security
Concerns and Risk at Different Levels in Cloud
Computing,” in 2013 International Conference on
Green Computing, Communication and
Conservation of Energy (ICGCE), (2013), pp. 743–
746.
[9] K. Beckers, J.-C. Kuster, H. Schmidt, and S.
Faßbender, “Pattern-Based Support for Context
Establishment and Asset Identification of the ISO
27000 in the Field of Cloud Computing,” in 2011
Sixth International Conference on Availability,
Reliability and Security Pattern-Based, (2011), pp.
327–333.
[10] S. Goyal, “Public vs Private vs Hybrid vs
Community-Cloud Computing: A Critical Review,”
International Journal of Computer Network and
Information Security, vol. 6, no. 3. pp. 20–29,
(2014).
[11] A. Khrisna and Harlili, “Risk Management
Framework with COBIT 5 and Risk Management
Framework for Cloud Computing Integration,” in
2014 International Conference of Advanced
Informatics: Concept, Theory and Application
(ICAICTA) Risk, (2014), pp. 103–108.
[12] M. Kiran, M. Jiang, D. Armstrong, and K. Djemame,
“Towards a Service Lifecycle based Methodology
for Risk Assessment in Cloud Computing,” in 2011
Ninth IEEE International Conference on
Dependable, Autonomic and Secure Computing,
(2011), pp. 450–457.
[13] M. Ahmadalinejad and S. Hashemi, “A National
Model to Supervise on Virtual Banking Systems
through the Bank 2 . 0 Approach,” ACSIJ Adv.
Comput. Sci. an Int. J., vol. 4, no. 1, pp. 83–93,
(2015).
[14] F. Al-anzi, S. Yadav, and J. Soni, “Cloud
Computing: Security Model Comprising
Governance, Risk Management and Compliance,”
in 2014 International Conference on Data Mining
and Intelligent Computing (ICDMIC), (2014), pp.
1–6.
[15] A. Elzamly, B. Hussin, S. A. Naser, K. Khanfar, M.
Doheir, A. Selamat, and A. Rashed, “A New
Conceptual Framework Modelling for Cloud
Computing Risk Management in Banking
Organizations,” Int. J. Grid Distrib. Comput., vol. 9,
no. 9, pp. 137–154, (2016).
[16] A. Elzamly, B. Hussin, and B. ASH, “Classification
of Critical Cloud Computing Security Issues for
Banking Organizations: A Cloud Delphi Study,” Int.
J. Grid Distrib. Comput., vol. 9, no. 8, pp. 137–158,
(2016).
©2012-17 International Journal of Information Technology and Electrical Engineering `
ITEE, 6 (2) pp. 40-45, APR 2017
45
ITEE Journal Information Technology & Electrical Engineering
ISSN: – 2306-708X
Volume 6, Issue 2 April 2017
[17] B. Ibrahim and A. Shanavas, “An Approach to
Predict SOA Security Vulnerabilities using Feed
Forward Artificial Neural Networks,” SIJ Trans.
Comput. Networks Commun. Eng., vol. 3, no. 4, pp.
54–58, (2015)
[18] S. Abu Naser, "Predicting learners performance
using artificial neural networks in linear
programming intelligent tutoring system."
International Journal of Artificial Intelligence &
Applications vol. 3, no. 2, pp.65-73, (2012) .
[19] S. Abu Naser, et al. "Predicting Student Performance
Using Artificial Neural Network: in the Faculty of
Engineering and Information Technology."
International Journal of Hybrid Information
Technology, vol. 8 no. 2, 221-228,(2015)
Authors’ information
Abdelrafe Elzamly, He got a Ph.D.
in Information and Communication
Technology from the Technical
University Malaysia Melaka (UTeM)
in 2016 with a record of about 20
publications. He received his Master
degree in Computer Information
Systems from the University of Banking and Financial
Sciences in 2006. He received his B.Sc. degree in Computer
from Al-Aqsa University, Gaza in 1999. He is currently
working as Assistant Professor in Al-Aqsa University as a
full time. Also, from 1999 to 2007 he worked as a part time
lecturer at the Islamic University in Gaza. Between 2010 and
2012 he worked as a Manager in the Mustafa Center for
Studies and Scientific Research in Gaza. His research
interests are in risk management, software and information
systems engineering, cloud computing security, and data
mining.
Burairah Hussin, He received his
Ph.D. degree in Management
Science-Condition Monitoring
Modelling, from the University of
Salford, UK in 2007. Before that, he
received a M.Sc. degree in
Numerical Analysis and
Programming from the University of Dundee, UK in 1998
and a B.Sc. degree in Computer Science from the University
of Technology Malaysia in 1996. He currently works as a
Professor at the Technical University Malaysia Melaka
(UTeM). He also worked as the Dean at the Faculty of
Information and Communication Technology, Technical
University of Malaysia Melaka (UTeM). His research
interests are in data analysis, data mining, maintenance
modelling, artificial intelligence, risk management,
numerical analysis, and computer network advising and
development.
Samy Abu Naser, He got a Ph.D. in
Computer Science from North
Dakota State University, USA in
1993. He received his M.Sc. Degree
in Computer Science from Western
Kentucky University, USA in 1989.
He received his B.Sc. Degree in
Computer Science from Western
Kentucky University, USA in 1987. He is currently working
as a professor in Al-Azhar University, he worked as the
Dean of the Faculty of Engineering and Information
Technology in AL-Azhar University, he worked as Deputy
Vice President for Planning & Quality Assurance, and he
worked as a deputy dean of the Faculty of Engineering and
Information Technology in Al- Azhar University. His
research interests are in data mining, artificial intelligent,
and risk management.
Tadahiro Shibutani, He received
the Ph.D. degree in mechanical
engineering from Kyoto
University, Kyoto, Japan, in 2000.
He was a Visiting Scholar with the
Center of Advanced Life Cycle
Engineering, University of
Maryland, in 2007. He is currently Associate Professor of
Center for Creation of Symbiosis Society with Risk with
Yokohama National University, Yokohama, Japan. His
research interests include physics of failure, health
monitoring, and risk management for engineering systems.
Mohamed Doheir, He is currently a
PhD candidate in Health Care
Management in University Technical
Malaysia Malaka (UTeM). He
received his M. Sc. degree in Internet
working Technology from University
Technical Malaysia Malaka (UTeM) in
2012. He received his B.Sc. Degree in Educational
Computer Science from Al Aqsa University- Gaza, Palestine
in 2006. His research interests are in Health care, Cloud
Computing and Network Simulation.
