Abstract: Nowadays scientific data is inevitably digital and
stored in a wide variety of formats in heterogeneous systems.
Scientists need to access an integrated view of remote or local
heterogeneous data sources with advanced data accessing, analyzing,
and visualization tools. This research suggests the use of Service
Oriented Architecture (SOA) to integrate biological data from
different data sources. This work shows SOA will solve the problems
that facing integration process and if the biologist scientists can
access the biological data in easier way. There are several methods to
implement SOA but web service is the most popular method. The
Microsoft .Net Framework used to implement proposed architecture.
Abstract: In this paper, a new learning approach for network
intrusion detection using naïve Bayesian classifier and ID3 algorithm
is presented, which identifies effective attributes from the training
dataset, calculates the conditional probabilities for the best attribute
values, and then correctly classifies all the examples of training and
testing dataset. Most of the current intrusion detection datasets are
dynamic, complex and contain large number of attributes. Some of
the attributes may be redundant or contribute little for detection
making. It has been successfully tested that significant attribute
selection is important to design a real world intrusion detection
systems (IDS). The purpose of this study is to identify effective
attributes from the training dataset to build a classifier for network
intrusion detection using data mining algorithms. The experimental
results on KDD99 benchmark intrusion detection dataset demonstrate
that this new approach achieves high classification rates and reduce
false positives using limited computational resources.
Abstract: This paper presents the results of the authors in designing, experimenting, assessing and transferring an innovative approach to energy education in secondary schools, aimed to enhance the quality of learning in terms of didactic curricula and pedagogic methods. The training is online delivered to youngsters via e-Books and portals specially designed for this purpose or by learning by doing via interactive games. An online educational methodology is available teachers.
Abstract: Supply Chain Management (SCM) is the integration
between manufacturer, transporter and customer in order to form one
seamless chain that allows smooth flow of raw materials, information
and products throughout the entire network that help in minimizing
all related efforts and costs. The main objective of this paper is to
develop a model that can accept a specified number of spare-parts
within the supply chain, simulating its inventory operations
throughout all stages in order to minimize the inventory holding
costs, base-stock, safety-stock, and to find the optimum quantity of
inventory levels, thereby suggesting a way forward to adapt some
factors of Just-In-Time to minimizing the inventory costs throughout
the entire supply chain. The model has been developed using Micro-
Soft Excel & Visual Basic in order to study inventory allocations in
any network of the supply chain. The application and reproducibility
of this model were tested by comparing the actual system that was
implemented in the case study with the results of the developed
model. The findings showed that the total inventory costs of the
developed model are about 50% less than the actual costs of the
inventory items within the case study.
Abstract: The identification and elimination of bad
measurements is one of the basic functions of a robust state estimator
as bad data have the effect of corrupting the results of state
estimation according to the popular weighted least squares method.
However this is a difficult problem to handle especially when dealing
with multiple errors from the interactive conforming type. In this
paper, a self adaptive genetic based algorithm is proposed. The
algorithm utilizes the results of the classical linearized normal
residuals approach to tune the genetic operators thus instead of
making a randomized search throughout the whole search space it is
more likely to be a directed search thus the optimum solution is
obtained at very early stages(maximum of 5 generations). The
algorithm utilizes the accumulating databases of already computed
cases to reduce the computational burden to minimum. Tests are
conducted with reference to the standard IEEE test systems. Test
results are very promising.
Abstract: As mobile ad hoc networks (MANET) have different
characteristics from wired networks and even from standard wireless
networks, there are new challenges related to security issues that
need to be addressed. Due to its unique features such as open nature,
lack of infrastructure and central management, node mobility and
change of dynamic topology, prevention methods from attacks on
them are not enough. Therefore intrusion detection is one of the
possible ways in recognizing a possible attack before the system
could be penetrated. All in all, techniques for intrusion detection in
old wireless networks are not suitable for MANET. In this paper, we
classify the architecture for Intrusion detection systems that have so
far been introduced for MANETs, and then existing intrusion
detection techniques in MANET presented and compared. We then
indicate important future research directions.
Abstract: The wireless sensor networks have been extensively
deployed and researched. One of the major issues in wireless sensor
networks is a developing energy-efficient clustering protocol.
Clustering algorithm provides an effective way to prolong the lifetime
of a wireless sensor networks. In the paper, we compare several
clustering protocols which significantly affect a balancing of energy
consumption. And we propose an Energy-Efficient Distributed
Unequal Clustering (EEDUC) algorithm which provides a new way of
creating distributed clusters. In EEDUC, each sensor node sets the
waiting time. This waiting time is considered as a function of residual
energy, number of neighborhood nodes. EEDUC uses waiting time to
distribute cluster heads. We also propose an unequal clustering
mechanism to solve the hot-spot problem. Simulation results show that
EEDUC distributes the cluster heads, balances the energy
consumption well among the cluster heads and increases the network
lifetime.
Abstract: In this paper we consider a one-dimensional random
geometric graph process with the inter-nodal gaps evolving according
to an exponential AR(1) process. The transition probability matrix
and stationary distribution are derived for the Markov chains concerning
connectivity and the number of components. We analyze the
algorithm for hitting time regarding disconnectivity. In addition to
dynamical properties, we also study topological properties for static
snapshots. We obtain the degree distributions as well as asymptotic
precise bounds and strong law of large numbers for connectivity
threshold distance and the largest nearest neighbor distance amongst
others. Both exact results and limit theorems are provided in this
paper.
Abstract: The shortest path routing problem is a multiobjective
nonlinear optimization problem with constraints. This problem has
been addressed by considering Quality of service parameters, delay
and cost objectives separately or as a weighted sum of both
objectives. Multiobjective evolutionary algorithms can find multiple
pareto-optimal solutions in one single run and this ability makes them
attractive for solving problems with multiple and conflicting
objectives. This paper uses an elitist multiobjective evolutionary
algorithm based on the Non-dominated Sorting Genetic Algorithm
(NSGA), for solving the dynamic shortest path routing problem in
computer networks. A priority-based encoding scheme is proposed
for population initialization. Elitism ensures that the best solution
does not deteriorate in the next generations. Results for a sample test
network have been presented to demonstrate the capabilities of the
proposed approach to generate well-distributed pareto-optimal
solutions of dynamic routing problem in one single run. The results
obtained by NSGA are compared with single objective weighting
factor method for which Genetic Algorithm (GA) was applied.
Abstract: This paper proposes a Particle Swarm Optimization
(PSO) based technique for the optimal allocation of Distributed
Generation (DG) units in the power systems. In this paper our aim is
to decide optimal number, type, size and location of DG units for
voltage profile improvement and power loss reduction in distribution
network. Two types of DGs are considered and the distribution load
flow is used to calculate exact loss. Load flow algorithm is combined
appropriately with PSO till access to acceptable results of this
operation. The suggested method is programmed under MATLAB
software. Test results indicate that PSO method can obtain better
results than the simple heuristic search method on the 30-bus and 33-
bus radial distribution systems. It can obtain maximum loss reduction
for each of two types of optimally placed multi-DGs. Moreover,
voltage profile improvement is achieved.
Abstract: Most of the real queuing systems include special properties and constraints, which can not be analyzed directly by using the results of solved classical queuing models. Lack of Markov chains features, unexponential patterns and service constraints, are the mentioned conditions. This paper represents an applied general algorithm for analysis and optimizing the queuing systems. The algorithm stages are described through a real case study. It is consisted of an almost completed non-Markov system with limited number of customers and capacities as well as lots of common exception of real queuing networks. Simulation is used for optimizing this system. So introduced stages over the following article include primary modeling, determining queuing system kinds, index defining, statistical analysis and goodness of fit test, validation of model and optimizing methods of system with simulation.
Abstract: Following the research in the Department of environmental engineering in Faculty of mechanical engineering on Technical University of Kosice and experiences with electrocoagulation style of disposal waste water, there were designed and partly examining the equipment of two stage revitalization on the standing and little fusible water of tenet electrolysis on the little tarns. With the cooperation with vet experts was that manners prove and it is innocuous for animals, during which time cyanobacteria are totally paralyzed. For the implementation of science and research results have been obtained by means EU funds for structural development.
Abstract: Cosmic showers, during the transit through space, produce
sub - products as a result of interactions with the intergalactic
or interstellar medium which after entering earth generate secondary
particles called Extensive Air Shower (EAS). Detection and analysis
of High Energy Particle Showers involve a plethora of theoretical and
experimental works with a host of constraints resulting in inaccuracies
in measurements. Therefore, there exist a necessity to develop a
readily available system based on soft-computational approaches
which can be used for EAS analysis. This is due to the fact that soft
computational tools such as Artificial Neural Network (ANN)s can be
trained as classifiers to adapt and learn the surrounding variations. But
single classifiers fail to reach optimality of decision making in many
situations for which Multiple Classifier System (MCS) are preferred
to enhance the ability of the system to make decisions adjusting
to finer variations. This work describes the formation of an MCS
using Multi Layer Perceptron (MLP), Recurrent Neural Network
(RNN) and Probabilistic Neural Network (PNN) with data inputs
from correlation mapping Self Organizing Map (SOM) blocks and
the output optimized by another SOM. The results show that the setup
can be adopted for real time practical applications for prediction
of primary energy and location of EAS from density values captured
using detectors in a circular grid.
Abstract: In this paper we suggest a method for setting
electronic credits for the customers. In this method banks and
market-sites help each other to make doing large shopping through
internet so easy. By developing this system, the people who have less
money to buy most of the things they want, become able to buy all of
them just through a credit. This credit is given by market-sites
through a banking control on it. The method suggested can stop
being imprisoned because of banking debts.
Abstract: Since communications between tag and reader in RFID
system are by radio, anyone can access the tag and obtain its any
information. And a tag always replies with the same ID so that it is
hard to distinguish between a real and a fake tag. Thus, there are many
security problems in today-s RFID System. Firstly, unauthorized
reader can easily read the ID information of any Tag. Secondly,
Adversary can easily cheat the legitimate reader using the collected
Tag ID information, such as the any legitimate Tag. These security
problems can be typically solved by encryption of messages
transmitted between Tag and Reader and by authentication for Tag.
In this paper, to solve these security problems on RFID system, we
propose the Tag Authentication Scheme based on self shrinking
generator (SSG). SSG Algorithm using in our scheme is proposed by
W.Meier and O.Staffelbach in EUROCRYPT-94. This Algorithm is
organized that only one LFSR and selection logic in order to generate
random stream. Thus it is optimized to implement the hardware logic
on devices with extremely limited resource, and the output generating
from SSG at each time do role as random stream so that it is allow our
to design the light-weight authentication scheme with security against
some network attacks. Therefore, we propose the novel tag
authentication scheme which use SSG to encrypt the Tag-ID
transmitted from tag to reader and achieve authentication of tag.
Abstract: The design of technological procedures for
manufacturing certain products demands the definition and
optimization of technological process parameters. Their
determination depends on the model of the process itself and its
complexity. Certain processes do not have an adequate mathematical
model, thus they are modeled using heuristic methods. First part of
this paper presents a state of the art of using soft computing
techniques in manufacturing processes from the perspective of
applicability in modern CAx systems. Methods of artificial
intelligence which can be used for this purpose are analyzed. The
second part of this paper shows some of the developed models of
certain processes, as well as their applicability in the actual
calculation of parameters of some technological processes within the
design system from the viewpoint of productivity.
Abstract: Abstract–The objectives of the current study are to determine the
prevalence, etiological agents, drug susceptibility pattern and plasmid
profile of Acinetobacter baumannii isolates from Hospital-Acquired
Infections (HAI) at Community Hospital, Al Jouf Province, Saudi
Arabia. A total of 1890 patients had developed infection during
hospital admission and were included in the study. Among those who
developed nosocomial infections, 15(9.4), 10(2.7) and 118 (12.7) had
respiratory tract infection (RTI), blood stream infections (BSI) and
urinary tract (UTI) respectively. A total of 268 bacterial isolates were
isolated from nosocomial infection. S. aureus was reported in 23.5%
for of the total isolates followed by Klebsiella pneumoniae (17.5%), E.
coli (17.2%), P. aeruginosa (11.9%), coagulase negative
staphylococcus (9%), A. baumannii (7.1%), Enterobacter spp.
(3.4%), Citrobacter freundii (3%), Proteus mirabilis (2.6%), and
Proteus vulgaris and Enterococcous faecalis (0.7%). Isolated
organisms are multi-drug resistant, predominantly Gram-positive
pathogens with a high incidence of methicillin-resistant S. aureus,
extended spectrum beta lactamase and vancomycin resistant
enterococci organisms. The RFLP (Fragment Length Polymorphisms)
patterns of plasmid preparations from isolated A. baumannii isolates
had altered RFLP patterns, possibly due to the presence of plasmid(s).
Five A. baumannii isolates harbored plasmids all of which were not
less than 2.71kbp in molecular weight. Hence, it showed that the gene
coding for the isolates were located on the plasmid DNA while the
remaining isolates which have no plasmid might showed gene coding
for antibiotic resistance being located on chromosomal DNA.
Nosocomial infections represent a current problem in Community
Hospital, Al Jouf Province, Saudi Arabia. Problems associated with
SSI include infection with multidrug resistant pathogens which are
difficult to treat and are associated with increased mortality.
Abstract: The pipe inspection operation is the difficult detective
performance. Almost applications are mainly relies on a manual
recognition of defective areas that have carried out detection by an
engineer. Therefore, an automation process task becomes a necessary
in order to avoid the cost incurred in such a manual process. An
automated monitoring method to obtain a complete picture of the
sewer condition is proposed in this work. The focus of the research is
the automated identification and classification of discontinuities in
the internal surface of the pipe. The methodology consists of several
processing stages including image segmentation into the potential
defect regions and geometrical characteristic features. Automatic
recognition and classification of pipe defects are carried out by means
of using an artificial neural network technique (ANN) based on
Radial Basic Function (RBF). Experiments in a realistic environment
have been conducted and results are presented.
Abstract: We consider a network of two M/M/1 parallel queues having the same poisonnian arrival stream with rate λ. Upon his arrival to the system a customer heads to the shortest queue and stays until being served. If the two queues have the same length, an arriving customer chooses one of the two queues with the same probability. Each duration of service in the two queues is an exponential random variable with rate μ and no jockeying is permitted between the two queues. A new numerical method, based on linear programming and convex optimization, is performed for the computation of the steady state solution of the system.
Abstract: The design of a pattern classifier includes an attempt
to select, among a set of possible features, a minimum subset of
weakly correlated features that better discriminate the pattern classes.
This is usually a difficult task in practice, normally requiring the
application of heuristic knowledge about the specific problem
domain. The selection and quality of the features representing each
pattern have a considerable bearing on the success of subsequent
pattern classification. Feature extraction is the process of deriving
new features from the original features in order to reduce the cost of
feature measurement, increase classifier efficiency, and allow higher
classification accuracy. Many current feature extraction techniques
involve linear transformations of the original pattern vectors to new
vectors of lower dimensionality. While this is useful for data
visualization and increasing classification efficiency, it does not
necessarily reduce the number of features that must be measured
since each new feature may be a linear combination of all of the
features in the original pattern vector. In this paper a new approach is
presented to feature extraction in which feature selection, feature
extraction, and classifier training are performed simultaneously using
a genetic algorithm. In this approach each feature value is first
normalized by a linear equation, then scaled by the associated weight
prior to training, testing, and classification. A knn classifier is used to
evaluate each set of feature weights. The genetic algorithm optimizes
a vector of feature weights, which are used to scale the individual
features in the original pattern vectors in either a linear or a nonlinear
fashion. By this approach, the number of features used in classifying
can be finely reduced.