Abstract: E-Learning enables the users to learn at anywhere at
any time. In E-Learning systems, authenticating the E-Learning user
has security issues. The usage of appropriate communication
networks for providing the internet connectivity for E-learning is
another challenge. WiMAX networks provide Broadband Wireless
Access through the Multicast Broadcast Service so these networks
can be most suitable for E-Learning applications. The authentication
of E-Learning user is vulnerable to session hijacking problems. The
repeated authentication of users can be done to overcome these
issues. In this paper, session based Profile Caching Authentication is
proposed. In this scheme, the credentials of E-Learning users can be
cached at authentication server during the initial authentication
through the appropriate subscriber station. The proposed cache based
authentication scheme performs fast authentication by using cached
user profile. Thus, the proposed authentication protocol reduces the
delay in repeated authentication to enhance the security in ELearning.
Abstract: Opportunistic Routing (OR) increases the
transmission reliability and network throughput. Traditional routing
protocols preselects one or more predetermined nodes before
transmission starts and uses a predetermined neighbor to forward a
packet in each hop. The opportunistic routing overcomes the
drawback of unreliable wireless transmission by broadcasting one
transmission can be overheard by manifold neighbors. The first
cooperation-optimal protocol for Multirate OR (COMO) used to
achieve social efficiency and prevent the selfish behavior of the
nodes. The novel link-correlation-aware OR improves the
performance by exploiting the miscellaneous low correlated forward
links. Context aware Adaptive OR (CAOR) uses active suppression
mechanism to reduce packet duplication. The Context-aware OR
(COR) can provide efficient routing in mobile networks. By using
Cooperative Opportunistic Routing in Mobile Ad hoc Networks
(CORMAN), the problem of opportunistic data transfer can be
tackled. While comparing to all the protocols, COMO is the best as it
achieves social efficiency and prevents the selfish behavior of the
nodes.
Abstract: Wireless Sensor Network (WSN) routing is complex
due to its dynamic nature, computational overhead, limited battery
life, non-conventional addressing scheme, self-organization, and
sensor nodes limited transmission range. An energy efficient routing
protocol is a major concern in WSN. LEACH is a hierarchical WSN
routing protocol to increase network life. It performs self-organizing
and re-clustering functions for each round. This study proposes a
better sensor networks cluster head selection for efficient data
aggregation. The algorithm is based on Tabu search.
Abstract: Brain-Computer Interfaces (BCIs) measure brain
signals activity, intentionally and unintentionally induced by users,
and provides a communication channel without depending on the
brain’s normal peripheral nerves and muscles output pathway.
Feature Selection (FS) is a global optimization machine learning
problem that reduces features, removes irrelevant and noisy data
resulting in acceptable recognition accuracy. It is a vital step
affecting pattern recognition system performance. This study presents
a new Binary Particle Swarm Optimization (BPSO) based feature
selection algorithm. Multi-layer Perceptron Neural Network
(MLPNN) classifier with backpropagation training algorithm and
Levenberg-Marquardt training algorithm classify selected features.
Abstract: Digital cameras to reduce cost, use an image sensor to
capture color images. Color Filter Array (CFA) in digital cameras
permits only one of the three primary (red-green-blue) colors to be
sensed in a pixel and interpolates the two missing components
through a method named demosaicking. Captured data is interpolated
into a full color image and compressed in applications. Color
interpolation before compression leads to data redundancy. This
paper proposes a new Vector Quantization (VQ) technique to
construct a VQ codebook with Differential Evolution (DE)
Algorithm. The new technique is compared to conventional Linde-
Buzo-Gray (LBG) method.
Abstract: People, throughout the history, have made estimates
and inferences about the future by using their past experiences.
Developing information technologies and the improvements in the
database management systems make it possible to extract useful
information from knowledge in hand for the strategic decisions.
Therefore, different methods have been developed. Data mining by
association rules learning is one of such methods. Apriori algorithm,
one of the well-known association rules learning algorithms, is not
commonly used in spatio-temporal data sets. However, it is possible
to embed time and space features into the data sets and make Apriori
algorithm a suitable data mining technique for learning spatiotemporal
association rules. Lake Van, the largest lake of Turkey, is a
closed basin. This feature causes the volume of the lake to increase or
decrease as a result of change in water amount it holds. In this study,
evaporation, humidity, lake altitude, amount of rainfall and
temperature parameters recorded in Lake Van region throughout the
years are used by the Apriori algorithm and a spatio-temporal data
mining application is developed to identify overflows and newlyformed
soil regions (underflows) occurring in the coastal parts of
Lake Van. Identifying possible reasons of overflows and underflows
may be used to alert the experts to take precautions and make the
necessary investments.
Abstract: In this paper, we present a comparative study of three
methods of 2D face recognition system such as: Iso-Geodesic Curves
(IGC), Geodesic Distance (GD) and Geodesic-Intensity Histogram
(GIH). These approaches are based on computing of geodesic
distance between points of facial surface and between facial curves.
In this study we represented the image at gray level as a 2D surface in
a 3D space, with the third coordinate proportional to the intensity
values of pixels. In the classifying step, we use: Neural Networks
(NN), K-Nearest Neighbor (KNN) and Support Vector Machines
(SVM). The images used in our experiments are from two wellknown
databases of face images ORL and YaleB. ORL data base was
used to evaluate the performance of methods under conditions where
the pose and sample size are varied, and the database YaleB was used
to examine the performance of the systems when the facial
expressions and lighting are varied.
Abstract: Tamil handwritten document is taken as a key source
of data to identify the writer. Tamil is a classical language which has
247 characters include compound characters, consonants, vowels and
special character. Most characters of Tamil are multifaceted in
nature. Handwriting is a unique feature of an individual. Writer may
change their handwritings according to their frame of mind and this
place a risky challenge in identifying the writer. A new
discriminative model with pooled features of handwriting is proposed
and implemented using support vector machine. It has been reported
on 100% of prediction accuracy by RBF and polynomial kernel based
classification model.
Abstract: Tamil handwritten document is taken as a key source of data to identify the writer. Tamil is a classical language which has 247 characters include compound characters, consonants, vowels and special character. Most characters of Tamil are multifaceted in nature. Handwriting is a unique feature of an individual. Writer may change their handwritings according to their frame of mind and this place a risky challenge in identifying the writer. A new discriminative model with pooled features of handwriting is proposed and implemented using support vector machine. It has been reported on 100% of prediction accuracy by RBF and polynomial kernel based classification model.
Abstract: Laban Movement Analysis (LMA), developed in the
dance community over the past seventy years, is an effective method
for observing, describing, notating, and interpreting human
movement to enhance communication and expression in everyday
and professional life. Many applications that use motion capture data
might be significantly leveraged if the Laban qualities will be
recognized automatically. This paper presents an automated
recognition method of Laban qualities from motion capture skeletal
recordings and it is demonstrated on the output of Microsoft’s Kinect
V2 sensor.
Abstract: The growing number of computer viruses and the
detection of zero day malware have been the concern for security
researchers for a large period of time. Existing antivirus products
(AVs) rely on detecting virus signatures which do not provide a full
solution to the problems associated with these viruses. The use of
logic formulae to model the behaviour of viruses is one of the most
encouraging recent developments in virus research, which provides
alternatives to classic virus detection methods. In this paper, we
proposed a comparative study about different virus detection
techniques. This paper provides the advantages and drawbacks of
different detection techniques. Different techniques will be used in
this paper to provide a discussion about what technique is more
effective to detect computer viruses.
Abstract: This survey paper shows the recent state of model
comparison as it’s applies to Model Driven engineering. In Model
Driven Engineering to calculate the difference between the models is
a very important and challenging task. There are number of tasks
involved in model differencing that firstly starts with identifying and
matching the elements of the model. In this paper, we discuss how
model matching is accomplished, the strategies, techniques and the
types of the model. We also discuss the future direction. We found
out that many of the latest model comparison strategies are geared
near enabling Meta model and similarity based matching. Therefore
model versioning is the most dominant application of the model
comparison. Recently to work on comparison for versioning has
begun to deteriorate, giving way to different applications. Ultimately
there is wide change among the tools in the measure of client exertion
needed to perform model comparisons, as some require more push to
encourage more sweeping statement and expressive force.
Abstract: One of the most critical decision points in the design of a
face recognition system is the choice of an appropriate face representation.
Effective feature descriptors are expected to convey sufficient, invariant
and non-redundant facial information. In this work we propose a set of
Hahn moments as a new approach for feature description. Hahn moments
have been widely used in image analysis due to their invariance, nonredundancy
and the ability to extract features either globally and locally.
To assess the applicability of Hahn moments to Face Recognition we
conduct two experiments on the Olivetti Research Laboratory (ORL)
database and University of Notre-Dame (UND) X1 biometric collection.
Fusion of the global features along with the features from local facial
regions are used as an input for the conventional k-NN classifier. The
method reaches an accuracy of 93% of correctly recognized subjects for
the ORL database and 94% for the UND database.
Abstract: This paper presents the local mesh co-occurrence
patterns (LMCoP) using HSV color space for image retrieval system.
HSV color space is used in this method to utilize color, intensity and
brightness of images. Local mesh patterns are applied to define the
local information of image and gray level co-occurrence is used to
obtain the co-occurrence of LMeP pixels. Local mesh co-occurrence
pattern extracts the local directional information from local mesh
pattern and converts it into a well-mannered feature vector using gray
level co-occurrence matrix. The proposed method is tested on three
different databases called MIT VisTex, Corel, and STex. Also, this
algorithm is compared with existing methods, and results in terms of
precision and recall are shown in this paper.
Abstract: Nowadays, several research studies point up that an
active lifestyle is essential for physical and mental health benefits.
Mobile phones have greatly influenced people’s habits and attitudes
also in the way they exercise. Our research work is mainly focused on
investigating how to exploit mobile technologies to favour people’s
exertion experience. To this end, we developed an exertion framework
users can exploit through a real world mobile application, called
EverywhereSport Run (EWRun), designed to act as a virtual personal
trainer to support runners during their trainings. In this work, inspired
by both previous findings in the field of interaction design for people
with visual impairments, feedback gathered from real users of our
framework, and positive results obtained from two experimentations,
we present some new interaction facilities we designed to enhance
the interaction experience during a training. The positive obtained
results helped us to derive some interaction design recommendations
we believe will be a valid support for designers of future mobile
systems conceived to be used in circumstances where there are limited
possibilities of interaction.
Abstract: Most of the existing video streaming protocols
provide video services without considering security aspects in
decentralized mobile ad-hoc networks. The security policies adapted
to the currently existing non-streaming protocols, do not comply with
the live video streaming protocols resulting in considerable
vulnerability, high bandwidth consumption and unreliability which
cause severe security threats, low bandwidth and error prone
transmission respectively in video streaming applications. Therefore
a synergized methodology is required to reduce vulnerability and
bandwidth consumption, and enhance reliability in the video
streaming applications in MANET. To ensure the security measures
with reduced bandwidth consumption and improve reliability of the
video streaming applications, a Secure Low-bandwidth Video
Streaming through Reliable Multipath Propagation (SLVRMP)
protocol architecture has been proposed by incorporating the two
algorithms namely Secure Low-bandwidth Video Streaming
Algorithm and Reliable Secure Multipath Propagation Algorithm
using Layered Video Coding in non-overlapping zone routing
network topology. The performances of the proposed system are
compared to those of the other existing secure multipath protocols
Sec-MR, SPREAD using NS 2.34 and the simulation results show
that the performances of the proposed system get considerably
improved.
Abstract: The rapid growth of multimedia technology demands
the secure and efficient access to information. This fast growing lose
the confidence of unauthorized duplication. Henceforth the protection
of multimedia content is becoming more important. Watermarking
solves the issue of unlawful copy of advanced data. In this paper,
blind video watermarking technique has been proposed. A luminance
layer of selected frames is interlaced into two even and odd rows of
an image, further it is deinterlaced and equalizes the coefficients of
the two shares. Color watermark is split into different blocks, and the
pieces of block are concealed in one of the share under the wavelet
transform. Stack the two images into a single image by introducing
interlaced even and odd rows in the two shares. Finally, chrominance
bands are concatenated with the watermarked luminance band. The
safeguard level of the secret information is high, and it is
undetectable. Results show that the quality of the video is not
changed also yields the better PSNR values.
Abstract: Ant Colony Optimization (ACO) is a promising
modern approach to the unused combinatorial optimization. Here
ACO is applied to finding the shortest during communication link
failure. In this paper, the performances of the prim’s and ACO
algorithm are made. By comparing the time complexity and program
execution time as set of parameters, we demonstrate the pleasant
performance of ACO in finding excellent solution to finding shortest
path during communication link failure.
Abstract: Cloud computing has emerged as a promising
direction for cost efficient and reliable service delivery across data
communication networks. The dynamic location of service facilities
and the virtualization of hardware and software elements are stressing
the communication networks and protocols, especially when data
centres are interconnected through the internet. Although the
computing aspects of cloud technologies have been largely
investigated, lower attention has been devoted to the networking
services without involving IT operating overhead. Cloud computing
has enabled elastic and transparent access to infrastructure services
without involving IT operating overhead. Virtualization has been a
key enabler for cloud computing. While resource virtualization and
service abstraction have been widely investigated, networking in
cloud remains a difficult puzzle. Even though network has significant
role in facilitating hybrid cloud scenarios, it hasn't received much
attention in research community until recently. We propose Network
as a Service (NaaS), which forms the basis of unifying public and
private clouds. In this paper, we identify various challenges in
adoption of hybrid cloud. We discuss the design and implementation
of a cloud platform.
Abstract: In this paper a new methodology for vendor selection
and supply quotas determination (VSSQD) is proposed. The problem
of VSSQD is solved by the model that combines revised weighting
method for determining the objective function coefficients, and a
multiple objective linear programming (MOLP) method based on the
cooperative game theory for VSSQD. The criteria used for VSSQD
are: (1) purchase costs and (2) product quality supplied by individual
vendors. The proposed methodology has been tested on the example
of flour purchase for a bakery with two decision makers.