Abstract: In this study a ternary system containing sodium
chloride as solute, water as primary solvent and ethanol as the
antisolvent was considered to investigate the application of artificial
neural network (ANN) in prediction of sodium solubility in the
mixture of water as the solvent and ethanol as the antisolvent. The
system was previously studied using by Extended UNIQUAC model
by the authors of this study. The comparison between the results of
the two models shows an excellent agreement between them
(R2=0.99), and also approves the capability of ANN to predict the
thermodynamic behavior of ternary electrolyte systems which are
difficult to model.
Abstract: Batteries of electric vehicles (BEV) are becoming
more attractive with the advancement of new battery technologies
and promotion of electric vehicles. BEV batteries are recharged on
board vehicles using either the grid (G2V for Grid to Vehicle) or
renewable energies in a stand-alone application (H2V for Home to
Vehicle). This paper deals with the modeling, sizing and control of a
photovoltaic stand-alone application that can charge the BEV at
home. The modeling approach and developed mathematical models
describing the system components are detailed. Simulation and
experimental results are presented and commented.
Abstract: Chemical Reaction Optimization (CRO) is an
optimization metaheuristic inspired by the nature of chemical
reactions as a natural process of transforming the substances from
unstable to stable states. Starting with some unstable molecules with
excessive energy, a sequence of interactions takes the set to a state of
minimum energy. Researchers reported successful application of the
algorithm in solving some engineering problems, like the quadratic
assignment problem, with superior performance when compared with
other optimization algorithms. We adapted this optimization
algorithm to the Printed Circuit Board Drilling Problem (PCBDP)
towards reducing the drilling time and hence improving the PCB
manufacturing throughput. Although the PCBDP can be viewed as
instance of the popular Traveling Salesman Problem (TSP), it has
some characteristics that would require special attention to the
transactions that explore the solution landscape. Experimental test
results using the standard CROToolBox are not promising for
practically sized problems, while it could find optimal solutions for
artificial problems and small benchmarks as a proof of concept.
Abstract: It is widely believed that mobile device is a promising technology for lending the opportunity for the third wave of electronic commerce. Mobile devices have changed the way companies do business. Many applications are under development or being incorporated into business processes. In this day, mobile applications are a vital component of any industry strategy.One of the greatest benefits of selling merchandise and providing services on a mobile application is that it widens a company’s customer base significantly.Mobile applications are accessible to interested customers across regional and international borders in different electronic business (e-business) area. But there is a dark side to this success story. The security risks associated with mobile devices and applications are very significant. This paper introduces a broad risk analysis for the various threats, vulnerabilities, and risks in mobile e-business applications and presents some important risk mitigation approaches. It reviews and compares two different frameworks for security assurance in mobile e-business applications. Based on the comparison, the paper suggests some recommendations for applications developers and business owners in mobile e-business application development process.
Abstract: Content-based image retrieval (CBIR) uses the
contents of images to characterize and contact the images. This paper
focus on retrieving the image by separating images into its three color
mechanism R, G and B and for that Discrete Wavelet Transformation
is applied. Then Wavelet based Generalized Gaussian Density (GGD)
is practical which is used for modeling the coefficients from the
wavelet transforms. After that it is agreed to Histogram of Oriented
Gradient (HOG) for extracting its characteristic vectors with Relevant
Feedback technique is used. The performance of this approach is
calculated by exactness and it confirms that this method is wellorganized
for image retrieval.
Abstract: The idea of the asynchronous transmission in
wavelength division multiplexing (WDM) ring MANs is studied in
this paper. Especially, we present an efficient access technique to
coordinate the collisions-free transmission of the variable sizes of IP
traffic in WDM ring core networks. Each node is equipped with a
tunable transmitter and a tunable receiver. In this way, all the
wavelengths are exploited for both transmission and reception. In
order to evaluate the performance measures of average throughput,
queuing delay and packet dropping probability at the buffers, a
simulation model that assumes symmetric access rights among the
nodes is developed based on Poisson statistics. Extensive numerical
results show that the proposed protocol achieves apart from high
bandwidth exploitation for a wide range of offered load, fairness of
queuing delay and dropping events among the different packets size
categories.
Abstract: Shortfall of electrical energy in Pakistan is a challenge
adversely affecting its industrial output and social growth. As
elsewhere, Pakistan derives its electrical energy from a number of
conventional sources. The exhaustion of petroleum and conventional
resources, the rising costs coupled with extremely adverse climatic
effects are taking its toll especially on the under-developed countries
like Pakistan. As alternate, renewable energy sources like hydropower,
solar, wind, even bio-energy and a mix of some or all of them
could provide a credible alternative to the conventional energy
resources that would not only be cleaner but sustainable as well. As a
model, solar energy-based power grid for the near future has been
attempted to offset the energy shortfalls as a mix with our existing
sustainable natural energy resources. An assessment of solar energy
potential for electricity generation is being presented for fulfilling the
energy demands with higher level of reliability and sustainability.
This model is based on the premise that solar energy potential of
Pakistan is not only reliable but also sustainable. This research
estimates the present & future approaching renewable energy
resource specially the impact of solar energy based power grid for
mitigating energy shortage in Pakistan.
Abstract: Motion Tracking and Stereo Vision are complicated,
albeit well-understood problems in computer vision. Existing
softwares that combine the two approaches to perform stereo motion
tracking typically employ complicated and computationally expensive
procedures. The purpose of this study is to create a simple and
effective solution capable of combining the two approaches. The
study aims to explore a strategy to combine the two techniques
of two-dimensional motion tracking using Kalman Filter; and depth
detection of object using Stereo Vision. In conventional approaches
objects in the scene of interest are observed using a single camera.
However for Stereo Motion Tracking; the scene of interest is
observed using video feeds from two calibrated cameras. Using two
simultaneous measurements from the two cameras a calculation for
the depth of the object from the plane containing the cameras is made.
The approach attempts to capture the entire three-dimensional spatial
information of each object at the scene and represent it through a
software estimator object. In discrete intervals, the estimator tracks
object motion in the plane parallel to plane containing cameras and
updates the perpendicular distance value of the object from the plane
containing the cameras as depth. The ability to efficiently track
the motion of objects in three-dimensional space using a simplified
approach could prove to be an indispensable tool in a variety of
surveillance scenarios. The approach may find application from high
security surveillance scenes such as premises of bank vaults, prisons
or other detention facilities; to low cost applications in supermarkets
and car parking lots.
Abstract: Leukaemia is a blood cancer disease that contributes
to the increment of mortality rate in Malaysia each year. There are
two main categories for leukaemia, which are acute and chronic
leukaemia. The production and development of acute leukaemia cells
occurs rapidly and uncontrollable. Therefore, if the identification of
acute leukaemia cells could be done fast and effectively, proper
treatment and medicine could be delivered. Due to the requirement of
prompt and accurate diagnosis of leukaemia, the current study has
proposed unsupervised pixel segmentation based on clustering
algorithm in order to obtain a fully segmented abnormal white blood
cell (blast) in acute leukaemia image. In order to obtain the
segmented blast, the current study proposed three clustering
algorithms which are k-means, fuzzy c-means and moving k-means
algorithms have been applied on the saturation component image.
Then, median filter and seeded region growing area extraction
algorithms have been applied, to smooth the region of segmented
blast and to remove the large unwanted regions from the image,
respectively. Comparisons among the three clustering algorithms are
made in order to measure the performance of each clustering
algorithm on segmenting the blast area. Based on the good sensitivity
value that has been obtained, the results indicate that moving kmeans
clustering algorithm has successfully produced the fully
segmented blast region in acute leukaemia image. Hence, indicating
that the resultant images could be helpful to haematologists for
further analysis of acute leukaemia.
Abstract: Text mining techniques are generally applied for
classifying the text, finding fuzzy relations and structures in data
sets. This research provides plenty text mining capabilities. One
common application is text classification and event extraction,
which encompass deducing specific knowledge concerning incidents
referred to in texts. The main contribution of this paper is the
clarification of a concept graph generation mechanism, which is based
on a text classification and optimal fuzzy relationship extraction.
Furthermore, the work presented in this paper explains the application
of fuzzy relationship extraction and branch and bound (BB) method
to simplify the texts.
Abstract: This paper presents an evolutionary algorithm for
solving multi-objective optimization problems-based artificial neural
network (ANN). The multi-objective evolutionary algorithm used in
this study is genetic algorithm while ANN used is radial basis
function network (RBFN). The proposed algorithm named memetic
elitist Pareto non-dominated sorting genetic algorithm-based RBFN
(MEPGAN). The proposed algorithm is implemented on medical
diseases problems. The experimental results indicate that the
proposed algorithm is viable, and provides an effective means to
design multi-objective RBFNs with good generalization capability
and compact network structure. This study shows that MEPGAN
generates RBFNs coming with an appropriate balance between
accuracy and simplicity, comparing to the other algorithms found in
literature.
Abstract: In medical imaging, segmentation of different areas of
human body like bones, organs, tissues, etc. is an important issue.
Image segmentation allows isolating the object of interest for further
processing that can lead for example to 3D model reconstruction of
whole organs. Difficulty of this procedure varies from trivial for
bones to quite difficult for organs like liver. The liver is being
considered as one of the most difficult human body organ to segment.
It is mainly for its complexity, shape versatility and proximity of
other organs and tissues. Due to this facts usually substantial user
effort has to be applied to obtain satisfactory results of the image
segmentation. Process of image segmentation then deteriorates from
automatic or semi-automatic to fairly manual one. In this paper,
overview of selected available software applications that can handle
semi-automatic image segmentation with further 3D volume
reconstruction of human liver is presented. The applications are being
evaluated based on the segmentation results of several consecutive
DICOM images covering the abdominal area of the human body.
Abstract: Job Scheduling plays an important role for efficient
utilization of grid resources available across different domains and
geographical zones. Scheduling of jobs is challenging and NPcomplete.
Evolutionary / Swarm Intelligence algorithms have been
extensively used to address the NP problem in grid scheduling.
Artificial Bee Colony (ABC) has been proposed for optimization
problems based on foraging behaviour of bees. This work proposes a
modified ABC algorithm, Cluster Heterogeneous Earliest First Min-
Min Artificial Bee Colony (CHMM-ABC), to optimally schedule
jobs for the available resources. The proposed model utilizes a novel
Heterogeneous Earliest Finish Time (HEFT) Heuristic Algorithm
along with Min-Min algorithm to identify the initial food source.
Simulation results show the performance improvement of the
proposed algorithm over other swarm intelligence techniques.
Abstract: Conventional educational practices, do not offer all
the required skills for teachers to successfully survive in today’s
workplace. Due to poor professional training, a big gap exists across
the curriculum plan and the teacher practices in the classroom. As
such, raising the quality of teaching through ICT-enabled training and
professional development of teachers should be an urgent priority.
‘Mobile Learning’, in that vein, is an increasingly growing field of
educational research and practice across schools and work places. In
this paper, we propose a novel Mobile learning system that allows the
users to learn through an intelligent mobile learning in cooperatively
every-time and every-where. The system will reduce the training cost
and increase consistency, efficiency, and data reliability. To establish
that our system will display neither functional nor performance
failure, the evaluation strategy is based on formal observation of
users interacting with system followed by questionnaires and
structured interviews.
Abstract: In this paper we consider the rule reduct generation
problem. Rule Reduct Generation (RG) and Modified Rule
Generation (MRG) algorithms, that are used to solve this problem,
are well-known. Alternative to these algorithms, we develop Pruning
Rule Generation (PRG) algorithm. We compare the PRG algorithm
with RG and MRG.
Abstract: In this article is reported a construction and some
properties of the 5iD viewer, the system recording simultaneously
5 views of a given experimental object. Properties of the system
are demonstrated on the analysis of fish schooling behaviour. It
is demonstrated the method of instrument calibration which allows
inclusion of image distortion and it is proposed and partly tested
also the method of distance assessment in the case that only two
opposite cameras are available. Finally, we demonstrate how the state
trajectory of the behaviour of the fish school may be constructed from
the entropy of the system.
Abstract: Image or document encryption is needed through egovernment
data base. Really in this paper we introduce two matrices
images, one is the public, and the second is the secret (original). The
analyses of each matrix is achieved using the transformation of
singular values decomposition. So each matrix is transformed or
analyzed to three matrices say row orthogonal basis, column
orthogonal basis, and spectral diagonal basis. Product of the two row
basis is calculated. Similarly the product of the two column basis is
achieved. Finally we transform or save the files of public, row
product and column product. In decryption stage, the original image
is deduced by mutual method of the three public files.
Abstract: Iris codes contain bits with different entropy. This
work investigates different strategies to reduce the size of iris
code templates with the aim of reducing storage requirements and
computational demand in the matching process. Besides simple subsampling
schemes, also a binary multi-resolution representation as
used in the JBIG hierarchical coding mode is assessed. We find that
iris code template size can be reduced significantly while maintaining
recognition accuracy. Besides, we propose a two-stage identification
approach, using small-sized iris code templates in a pre-selection
stage, and full resolution templates for final identification, which
shows promising recognition behaviour.
Abstract: The study of organisations’ information security
cultures has attracted scholars as well as healthcare services industry
to research the topic and find appropriate tools and approaches to
develop a positive culture. The vast majority of studies in Saudi
national health services are on the use of technology to protect and
secure health services information. On the other hand, there is a lack
of research on the role and impact of an organisation’s cultural
dimensions on information security. This research investigated and
analysed the role and impact of cultural dimensions on information
security in Saudi Arabia health service. Hypotheses were tested and
two surveys were carried out in order to collect data and information
from three major hospitals in Saudi Arabia (SA). The first survey
identified the main cultural-dimension problems in SA health
services and developed an initial information security culture
framework model. The second survey evaluated and tested the
developed framework model to test its usefulness, reliability and
applicability. The model is based on human behaviour theory, where
the individual’s attitude is the key element of the individual’s
intention to behave as well as of his or her actual behaviour. The
research identified a set of cultural and sub-cultural dimensions in SA
health information security and services.
Abstract: Image search engines rely on the surrounding textual
keywords for the retrieval of images. It is a tedious work for the
search engines like Google and Bing to interpret the user’s search
intention and to provide the desired results. The recent researches
also state that the Google image search engines do not work well on
all the images. Consequently, this leads to the emergence of efficient
image retrieval technique, which interprets the user’s search intention
and shows the desired results. In order to accomplish this task, an
efficient image re-ranking framework is required. Sequentially, to
provide best image retrieval, the new image re-ranking framework is
experimented in this paper. The implemented new image re-ranking
framework provides best image retrieval from the image dataset by
making use of re-ranking of retrieved images that is based on the
user’s desired images. This is experimented in two sections. One is
offline section and other is online section. In offline section, the reranking
framework studies differently (reference classes or Semantic
Spaces) for diverse user query keywords. The semantic signatures get
generated by combining the textual and visual features of the images.
In the online section, images are re-ranked by comparing the
semantic signatures that are obtained from the reference classes with
the user specified image query keywords. This re-ranking
methodology will increases the retrieval image efficiency and the
result will be effective to the user.