Abstract: DNA microarrays allow the measurement of expression levels for a large number of genes, perhaps all genes of an organism, within a number of different experimental samples. It is very much important to extract biologically meaningful information from this huge amount of expression data to know the current state of the cell because most cellular processes are regulated by changes in gene expression. Association rule mining techniques are helpful to find association relationship between genes. Numerous association rule mining algorithms have been developed to analyze and associate this huge amount of gene expression data. This paper focuses on some of the popular association rule mining algorithms developed to analyze gene expression data.
Abstract: Genetic Folding (GF) a new class of EA named as is
introduced for the first time. It is based on chromosomes composed
of floating genes structurally organized in a parent form and
separated by dots. Although, the genotype/phenotype system of GF
generates a kernel expression, which is the objective function of
superior classifier. In this work the question of the satisfying
mapping-s rules in evolving populations is addressed by analyzing
populations undergoing either Mercer-s or none Mercer-s rule. The
results presented here show that populations undergoing Mercer-s
rules improve practically models selection of Support Vector
Machine (SVM). The experiment is trained multi-classification
problem and tested on nonlinear Ionosphere dataset. The target of this
paper is to answer the question of evolving Mercer-s rule in SVM
addressed using either genetic folding satisfied kernel-s rules or not
applied to complicated domains and problems.
Abstract: The contribution deals with analysis of identity style
at adolescents (N=463) at the age from 16 to 19 (the average age is
17,7 years). We used the Identity Style Inventory by Berzonsky,
distinguishing three basic, measured identity styles: informational,
normative, diffuse-avoidant identity style and also commitment. The
informational identity style influencing on personal adaptability,
coping strategies, quality of life and the normative identity style, it
means the style in which an individual takes on models of authorities
at self-defining were found to have the highest representation in the
studied group of adolescents by higher scores at girls in comparison
with boys. The normative identity style positively correlates with the
informational identity style. The diffuse-avoidant identity style was
found to be positively associated with maladaptive decisional
strategies, neuroticism and depressive reactions. There is the style,
in which the individual shifts aside defining his personality. In our
research sample the lowest score represents it and negatively
correlates with commitment, it means with coping strategies, thrust in
oneself and the surrounding world. The age of adolescents did not
significantly differentiate representation of identity style. We were
finding the model, in which informational and normative identity
style had positive relationship and the informational and diffuseavoidant
style had negative relationship, which were determinated
with commitment. In the same time the commitment is influenced
with other outside factors.
Abstract: Facial features are frequently used to represent local
properties of a human face image in computer vision applications. In
this paper, we present a fast algorithm that can extract the facial
features online such that they can give a satisfying representation of a
face image. It includes one step for a coarse detection of each facial
feature by AdaBoost and another one to increase the accuracy of the
found points by Active Shape Models (ASM) in the regions of interest.
The resulted facial features are evaluated by matching with artificial
face models in the applications of physiognomy. The distance measure
between the features and those in the fate models from the database is
carried out by means of the Hausdorff distance. In the experiment, the
proposed method shows the efficient performance in facial feature
extractions and online system of physiognomy.
Abstract: CIM is the standard formalism for modeling management
information developed by the Distributed Management Task
Force (DMTF) in the context of its WBEM proposal, designed to
provide a conceptual view of the managed environment. In this
paper, we propose the inclusion of formal knowledge representation
techniques, based on Description Logics (DLs) and the Web Ontology
Language (OWL), in CIM-based conceptual modeling, and then we
examine the benefits of such a decision. The proposal is specified as a
CIM metamodel level mapping to a highly expressive subset of DLs
capable of capturing all the semantics of the models. The paper shows
how the proposed mapping can be used for automatic reasoning
about the management information models, as a design aid, by means
of new-generation CASE tools, thanks to the use of state-of-the-art
automatic reasoning systems that support the proposed logic and use
algorithms that are sound and complete with respect to the semantics.
Such a CASE tool framework has been developed by the authors and
its architecture is also introduced. The proposed formalization is not
only useful at design time, but also at run time through the use of
rational autonomous agents, in response to a need recently recognized
by the DMTF.
Abstract: Protein and Esterase electrophoresis were used to
genetically identify two Saudi tick species. Engorged females of the
camel tick Hyalomma dromedarii (Koch) (Acari: Ixodidae) and the
cattle tick Boophilus annulatus (Say) (Acari: Ixodidae) ticks
collected from infested camels and cattle in the animals resting
house at Hail region in KSA were used. The results showed that
there are a variation in both of protein and esterase activity levels and
a high polymorphism within and between the genera and species of
Hyalomma and Boophilus . In conclusion, the protein and esterase
electrophoretic analysis used in the present study could successfully
distinguish among tick species, commonly found in Saudi Arabia.
Abstract: According to the governmental data, the cases of oral
cancers doubled in the past 10 years. This had brought heavy burden to
the patients- family, the society, and the country. The literature
generally evidenced the betel nut contained particular chemicals that
can cause oral cancers. Research in Taiwan had also proofed that 90
percent of oral cancer patients had experience of betel nut chewing. It
is thus important to educate the betel-nut hobbyists to cease such a
hazardous behavior. A program was then organized to establish
several training classes across different areas specific to help ceasing
this particular habit. Purpose of this research was to explore the
attitude and intention toward ceasing betel-nut chewing before and
after attending the training classes. 50 samples were taken from a
ceasing class with average age at 45 years old with high school
education (54%). 74% of the respondents were male in service or
agricultural industries. Experiences in betel-nut chewing were 5-20
years with a dose of 1-20 pieces per day. The data had shown that 60%
of the respondents had cigarette smoking habit, and 30% of the
respondents were concurrently alcoholic dependent. Research results
indicated that the attitude, intentions, and the knowledge on oral
cancers were found significant different between before and after
attendance. This provided evidence for the effectiveness of the training
class. However, we do not perform follow-up after the class.
Noteworthy is the test result also shown that participants who were
drivers as occupation, or habitual smokers or alcoholic dependents
would be less willing to quit the betel-nut chewing. The test results
indicated as well that the educational levels and the type of occupation
may have significant impacts on an individual-s decisions in taking
betel-nut or substance abuse.
Abstract: Recently, genetic algorithms (GA) and particle swarm optimization (PSO) technique have attracted considerable attention among various modern heuristic optimization techniques. The GA has been popular in academia and the industry mainly because of its intuitiveness, ease of implementation, and the ability to effectively solve highly non-linear, mixed integer optimization problems that are typical of complex engineering systems. PSO technique is a relatively recent heuristic search method whose mechanics are inspired by the swarming or collaborative behavior of biological populations. In this paper both PSO and GA optimization are employed for finding stable reduced order models of single-input- single-output large-scale linear systems. Both the techniques guarantee stability of reduced order model if the original high order model is stable. PSO method is based on the minimization of the Integral Squared Error (ISE) between the transient responses of original higher order model and the reduced order model pertaining to a unit step input. Both the methods are illustrated through numerical example from literature and the results are compared with recently published conventional model reduction technique.
Abstract: Parallel programming models exist as an abstraction
of hardware and memory architectures. There are several parallel
programming models in commonly use; they are shared memory
model, thread model, message passing model, data parallel model,
hybrid model, Flynn-s models, embarrassingly parallel computations
model, pipelined computations model. These models are not specific
to a particular type of machine or memory architecture. This paper
expresses the model program for concurrent approach to data parallel
model through java programming.
Abstract: This paper presents a compact thermoelectric power generator system based on temperature difference across the element. The system can transfer the burning heat energy to electric energy directly. The proposed system has a thermoelectric generator and a power control box. In the generator, there are 4 thermoelectric modules (TEMs), each of which uses 2 thermoelectric chips (TEs) and 2 cold sinks, 1 thermal absorber, and 1 thermal conduction flat board. In the power control box, there are 1 storing energy device, 1 converter, and 1 inverter. The total net generating power is about 11W. This system uses commercial portable gas stoves or burns timber or the coal as the heat source, which is easily obtained. It adopts solid-state thermoelectric chips as heat inverter parts. The system has the advantages of being light-weight, quite, and mobile, requiring no maintenance, and havng easily-supplied heat source. The system can be used a as long as burning is allowed. This system works well for highly-mobilized outdoors situations by providing a power for illumination, entertainment equipment or the wireless equipment at refuge. Under heavy storms such as typhoon, when the solar panels become ineffective and the wind-powered machines malfunction, the thermoelectric power generator can continue providing the vital power.
Abstract: Histogram equalization is often used in image enhancement, but it can be also used in auto exposure. However, conventional histogram equalization does not work well when many pixels are concentrated in a narrow luminance range.This paper proposes an auto exposure method based on 2-way histogram equalization. Two cumulative distribution functions are used, where one is from dark to bright and the other is from bright to dark. In this paper, the proposed auto exposure method is also designed and implemented for image signal processors with full-HD images.
Abstract: Text categorization is the problem of classifying text
documents into a set of predefined classes. In this paper, we
investigated three approaches to build a meta-classifier in order to
increase the classification accuracy. The basic idea is to learn a metaclassifier
to optimally select the best component classifier for each
data point. The experimental results show that combining classifiers
can significantly improve the accuracy of classification and that our
meta-classification strategy gives better results than each individual
classifier. For 7083 Reuters text documents we obtained a
classification accuracies up to 92.04%.
Abstract: Statistical learning theory was developed by Vapnik. It
is a learning theory based on Vapnik-Chervonenkis dimension. It also
has been used in learning models as good analytical tools. In general, a
learning theory has had several problems. Some of them are local
optima and over-fitting problems. As well, statistical learning theory
has same problems because the kernel type, kernel parameters, and
regularization constant C are determined subjectively by the art of
researchers. So, we propose an evolutionary statistical learning theory
to settle the problems of original statistical learning theory.
Combining evolutionary computing into statistical learning theory,
our theory is constructed. We verify improved performances of an
evolutionary statistical learning theory using data sets from KDD cup.
Abstract: This paper introduces a temporal epistemic logic
CBCTL that updates agent-s belief states through communications
in them, based on computational tree logic (CTL). In practical
environments, communication channels between agents may not be
secure, and in bad cases agents might suffer blackouts. In this study,
we provide inform* protocol based on ACL of FIPA, and declare the
presence of secure channels between two agents, dependent on time.
Thus, the belief state of each agent is updated along with the progress
of time. We show a prover, that is a reasoning system for a given
formula in a given a situation of an agent ; if it is directly provable
or if it could be validated through the chains of communications, the
system returns the proof.
Abstract: The authors present an algorithm for order reduction of linear dynamic systems using the combined advantages of stability equation method and the error minimization by Genetic algorithm. The denominator of the reduced order model is obtained by the stability equation method and the numerator terms of the lower order transfer function are determined by minimizing the integral square error between the transient responses of original and reduced order models using Genetic algorithm. The reduction procedure is simple and computer oriented. It is shown that the algorithm has several advantages, e.g. the reduced order models retain the steady-state value and stability of the original system. The proposed algorithm has also been extended for the order reduction of linear multivariable systems. Two numerical examples are solved to illustrate the superiority of the algorithm over some existing ones including one example of multivariable system.
Abstract: Conventionally the selection of parameters depends
intensely on the operator-s experience or conservative technological
data provided by the EDM equipment manufacturers that assign
inconsistent machining performance. The parameter settings given by
the manufacturers are only relevant with common steel grades. A
single parameter change influences the process in a complex way.
Hence, the present research proposes artificial neural network (ANN)
models for the prediction of surface roughness on first commenced
Ti-15-3 alloy in electrical discharge machining (EDM) process. The
proposed models use peak current, pulse on time, pulse off time and
servo voltage as input parameters. Multilayer perceptron (MLP) with
three hidden layer feedforward networks are applied. An assessment
is carried out with the models of distinct hidden layer. Training of the
models is performed with data from an extensive series of
experiments utilizing copper electrode as positive polarity. The
predictions based on the above developed models have been verified
with another set of experiments and are found to be in good
agreement with the experimental results. Beside this they can be
exercised as precious tools for the process planning for EDM.
Abstract: Reconfigurable optical add/drop multiplexers
(ROADMs) can be classified into three categories based on their
underlying switching technologies. Category I consists of a single
large optical switch; category II is composed of a number of small
optical switches aligned in parallel; and category III has a single
optical switch and only one wavelength being added/dropped. In this
paper, to evaluate the wavelength-routing capability of ROADMs of
category-II in dynamic optical networks,the dynamic traffic models
are designed based on Bernoulli, Poisson distributions for smooth
and regular types of traffic. Through Analytical and Simulation
results, the routing power of cat-II of ROADM networks for two
traffic models are determined.
Abstract: Through the time, the higher education has changed
the learning system since mother tongue to bilingual, and in this new
century has been coming develop a multilingual education. All as
part of globalization process of the countries and the education.
Nevertheless, this change only has been effectively in countries of the
first world, the rest have been lagging. Therefore, these countries
require strengthen their higher education systems through models that
give way to multilingual and bilingual education. In this way, shows
a new model adapted from a systemic form to allow a higher
bilingual and multilingual education in Latin America. This
systematization aims to increase the skills and competencies
student’s, decrease the time learning of a second tongue, add to
multilingualism in the American Latin Universities, also, contribute
to position the region´s countries in a better global status, and
stimulate the development of new research in this area.
Abstract: Different numerical methods are employed and developed for simulating interfacial flows. A large range of applications belong to this group, e.g. two-phase flows of air bubbles in water or water drops in air. In such problems surface tension effects often play a dominant role. In this paper, various models of surface tension force for interfacial flows, the CSF, CSS, PCIL and SGIP models have been applied to simulate the motion of small air bubbles in water and the results were compared and reviewed. It has been pointed out that by using SGIP or PCIL models, we are able to simulate bubble rise and obtain results in close agreement with the experimental data.
Abstract: In this paper we designed and implemented a new
ensemble of classifiers based on a sequence of classifiers which were
specialized in regions of the training dataset where errors of its
trained homologous are concentrated. In order to separate this
regions, and to determine the aptitude of each classifier to properly
respond to a new case, it was used another set of classifiers built
hierarchically. We explored a selection based variant to combine the
base classifiers. We validated this model with different base
classifiers using 37 training datasets. It was carried out a statistical
comparison of these models with the well known Bagging and
Boosting, obtaining significantly superior results with the
hierarchical ensemble using Multilayer Perceptron as base classifier.
Therefore, we demonstrated the efficacy of the proposed ensemble,
as well as its applicability to general problems.