Abstract: Wireless capsule Endoscopy (WCE) has rapidly
shown its wide applications in medical domain last ten years
thanks to its noninvasiveness for patients and support for thorough
inspection through a patient-s entire digestive system including
small intestine. However, one of the main barriers to efficient
clinical inspection procedure is that it requires large amount of
effort for clinicians to inspect huge data collected during the
examination, i.e., over 55,000 frames in video. In this paper, we
propose a method to compute meaningful motion changes of
WCE by analyzing the obtained video frames based on regional
optical flow estimations. The computed motion vectors are used to
remove duplicate video frames caused by WCE-s imaging nature,
such as repetitive forward-backward motions from peristaltic
movements. The motion vectors are derived by calculating
directional component vectors in four local regions. Our
experiments are performed on small intestine area, which is of
main interest to clinical experts when using WCEs, and our
experimental results show significant frame reductions comparing
with a simple frame-to-frame similarity-based image reduction
method.
Abstract: Genetic Algorithms (GAs) are direct searching
methods which require little information from design space. This
characteristic beside robustness of these algorithms makes them to be
very popular in recent decades. On the other hand, while this method
is employed, there is no guarantee to achieve optimum results. This
obliged designer to run such algorithms more than one time to
achieve more reliable results. There are many attempts to modify the
algorithms to make them more efficient. In this paper, by application
of fractal dimension (particularly, Box Counting Method), the
complexity of design space are established for determination of
mutation and crossover probabilities (Pm and Pc). This methodology
is followed by a numerical example for more clarification. It is
concluded that this modification will improve efficiency of GAs and
make them to bring about more reliable results especially for design
space with higher fractal dimensions.
Abstract: Inner class is a specialized class that defined within a
regular outer class. It is used in some programming languages such as
Java to carry out the task which is related to its outer class. The
functional relatedness between inner class and outer class is always
the main concern of defining an inner class. However, excessive use
of inner class could sabotage the class cohesiveness. In addition,
excessive inner class leads to the difficulty of software maintenance
and comprehension. Our research aims at determining the minimum
threshold for the functional relatedness of inner-outer class. Such
minimum threshold is a guideline for removing or relocating the
excessive inner class. Our research provides a feasible way for
software developers to define inner classes which are functionally
related to the outer class.
Abstract: In a recent year usage of VoIP subscription has increased tremendously as compare to Public Switching Telephone System(PSTN). A VoIP subscriber would like to know the exact tariffs of the calls made using VoIP. As the usage increases, the rate of fraud is also increases, causing users complain about excess billing. This in turn hampers the growth of VoIP .This paper describe the common frauds and attack on VoIP based system and make an attempt to solve the billing attack by creating secured channel between caller and callee.
Abstract: This paper gives a novel method for improving
classification performance for cancer classification with very few
microarray Gene expression data. The method employs classification
with individual gene ranking and gene subset ranking. For selection
and classification, the proposed method uses the same classifier. The
method is applied to three publicly available cancer gene expression
datasets from Lymphoma, Liver and Leukaemia datasets. Three
different classifiers namely Support vector machines-one against all
(SVM-OAA), K nearest neighbour (KNN) and Linear Discriminant
analysis (LDA) were tested and the results indicate the improvement
in performance of SVM-OAA classifier with satisfactory results on
all the three datasets when compared with the other two classifiers.
Abstract: Machine-understandable data when strongly
interlinked constitutes the basis for the SemanticWeb. Annotating
web documents is one of the major techniques for creating metadata
on the Web. Annotating websitexs defines the containing data in a
form which is suitable for interpretation by machines. In this paper,
we present a better and improved approach than previous [1] to
annotate the texts of the websites depends on the knowledge base.
Abstract: Emergence of smartphones brings to live the concept
of converged devices with the availability of web amenities. Such
trend also challenges the mobile devices manufactures and service
providers in many aspects, such as security on mobile phones,
complex and long time design flow, as well as higher development
cost. Among these aspects, security on mobile phones is getting more
and more attention. Microkernel based virtualization technology will
play a critical role in addressing these challenges and meeting mobile
market needs and preferences, since virtualization provides essential
isolation for security reasons and it allows multiple operating systems
to run on one processor accelerating development and cutting development
cost. However, virtualization benefits do not come for free.
As an additional software layer, it adds some inevitable virtualization
overhead to the system, which may decrease the system performance.
In this paper we evaluate and analyze the virtualization performance
cost of L4 microkernel based virtualization on a competitive mobile
phone by comparing the L4Linux, a para-virtualized Linux on top of
L4 microkernel, with the native Linux performance using lmbench
and a set of typical mobile phone applications.
Abstract: Although achieving zero-defect software release is
practically impossible, software industries should take maximum
care to detect defects/bugs well ahead in time allowing only bare
minimums to creep into released version. This is a clear indicator of
time playing an important role in the bug detection. In addition to
this, software quality is the major factor in software engineering
process. Moreover, early detection can be achieved only through
static code analysis as opposed to conventional testing.
BugCatcher.Net is a static analysis tool, which detects bugs in .NET®
languages through MSIL (Microsoft Intermediate Language)
inspection. The tool utilizes a Parser based on Finite State Automata
to carry out bug detection. After being detected, bugs need to be
corrected immediately. BugCatcher.Net facilitates correction, by
proposing a corrective solution for reported warnings/bugs to end
users with minimum side effects. Moreover, the tool is also capable
of analyzing the bug trend of a program under inspection.
Abstract: In this paper, a clustering algorithm named KHarmonic
means (KHM) was employed in the training of Radial
Basis Function Networks (RBFNs). KHM organized the data in
clusters and determined the centres of the basis function. The popular
clustering algorithms, namely K-means (KM) and Fuzzy c-means
(FCM), are highly dependent on the initial identification of elements
that represent the cluster well. In KHM, the problem can be avoided.
This leads to improvement in the classification performance when
compared to other clustering algorithms. A comparison of the
classification accuracy was performed between KM, FCM and KHM.
The classification performance is based on the benchmark data sets:
Iris Plant, Diabetes and Breast Cancer. RBFN training with the KHM
algorithm shows better accuracy in classification problem.
Abstract: Longitudinal data typically have the characteristics of
changes over time, nonlinear growth patterns, between-subjects
variability, and the within errors exhibiting heteroscedasticity and
dependence. The data exploration is more complicated than that of
cross-sectional data. The purpose of this paper is to organize/integrate
of various visual-graphical techniques to explore longitudinal data.
From the application of the proposed methods, investigators can
answer the research questions include characterizing or describing the
growth patterns at both group and individual level, identifying the time
points where important changes occur and unusual subjects, selecting
suitable statistical models, and suggesting possible within-error
variance.
Abstract: E-Appointment Scheduling (EAS) has been developed
to handle appointment for UMP students, lecturers in Faculty of
Computer Systems & Software Engineering (FCSSE) and Student
Medical Center. The schedules are based on the timetable and
university activities. Constraints Logic Programming (CLP) has been
implemented to solve the scheduling problems by giving
recommendation to the users in part of determining any available
slots from the lecturers and doctors- timetable. By using this system,
we can avoid wasting time and cost because this application will set
an appointment by auto-generated. In addition, this system can be an
alternative to the lecturers and doctors to make decisions whether to
approve or reject the appointments.