Abstract: Understanding the cell's large-scale organization is an
interesting task in computational biology. Thus, protein-protein
interactions can reveal important organization and function of the
cell. Here, we investigated the correspondence between protein
interactions and function for the yeast. We obtained the correlations
among the set of proteins. Then these correlations are clustered using
both the hierarchical and biclustering methods. The detailed analyses
of proteins in each cluster were carried out by making use of their
functional annotations. As a result, we found that some functional
classes appear together in almost all biclusters. On the other hand, in
hierarchical clustering, the dominancy of one functional class is
observed. In brief, from interaction data to function, some correlated
results are noticed about the relationship between interaction and
function which might give clues about the organization of the
proteins.
Abstract: Whereas cellular wireless communication systems are
subject to short-and long-term fading. The effect of wireless channel
has largely been ignored in most of the teletraffic assessment
researches. In this paper, a mathematical teletraffic model is proposed
to estimate blocking and forced termination probabilities of cellular
wireless networks as a result of teletraffic behavior as well as the
outage of the propagation channel. To evaluate the proposed
teletraffic model, gamma inter-arrival and general service time
distributions have been considered based on wireless channel fading
effect. The performance is evaluated and compared with the classical
model. The proposed model is dedicated and investigated in different
operational conditions. These conditions will consider not only the
arrival rate process, but also, the different faded channels models.
Abstract: As days go by, we hear more and more about HIV,
Ebola, Bird Flu and other dreadful viruses which were unknown a
few decades ago. In both detecting and fighting viral diseases
ordinary methods have come across some basic and important
difficulties. Vaccination is by a sense introduction of the virus to the
immune system before the occurrence of the real case infection. It is
very successful against some viruses (e.g. Poliomyelitis), while
totally ineffective against some others (e.g. HIV or Hepatitis-C). On
the other hand, Anti-virus drugs are mostly some tools to control and
not to cure a viral disease. This could be a good motivation to try
alternative treatments. In this study, some key features of possible
physical-based alternative treatments for viral diseases are presented.
Electrification of body parts or fluids (especially blood) with micro
electric signals with adjusted current or frequency is also studied. The
main approach of this study is to find a suitable energy field, with
appropriate parameters that are able to kill or deactivate viruses. This
would be a lengthy, multi-disciplinary research which needs the
contribution of virology, physics, and signal processing experts. It
should be mentioned that all the claims made by alternative cures
researchers must be tested carefully and are not advisable at the time
being.
Abstract: A new conceptual architecture for low-level neural
pattern recognition is presented. The key ideas are that the brain
implements support vector machines and that support vectors are
represented as memory patterns in competitive queuing memories. A
binary classifier is built from two competitive queuing memories
holding positive and negative valence training examples respectively.
The support vector machine classification function is calculated in
synchronized evaluation cycles. The kernel is computed by bisymmetric
feed-forward networks feed by sensory input and by
competitive queuing memories traversing the complete sequence of
support vectors. Temporary summation generates the output
classification. It is speculated that perception apparatus in the brain
reuses structures that have evolved for enabling fluent execution of
prepared action sequences so that pattern recognition is built on
internalized motor programmes.
Abstract: The transformation of vocal characteristics aims at
modifying voice such that the intelligibility of aphonic voice is
increased or the voice characteristics of a speaker (source speaker) to
be perceived as if another speaker (target speaker) had uttered it. In
this paper, the current state-of-the-art voice characteristics
transformation methodology is reviewed. Special emphasis is placed
on voice transformation methodology and issues for improving the
transformed speech quality in intelligibility and naturalness are
discussed. In particular, it is suggested to use the modulation theory
of speech as a base for research on high quality voice transformation.
This approach allows one to separate linguistic, expressive, organic
and perspective information of speech, based on an analysis of how
they are fused when speech is produced. Therefore, this theory
provides the fundamentals not only for manipulating non-linguistic,
extra-/paralinguistic and intra-linguistic variables for voice
transformation, but also for paving the way for easily transposing the
existing voice transformation methods to emotion-related voice
quality transformation and speaking style transformation. From the
perspectives of human speech production and perception, the popular
voice transformation techniques are described and classified them
based on the underlying principles either from the speech production
or perception mechanisms or from both. In addition, the advantages
and limitations of voice transformation techniques and the
experimental manipulation of vocal cues are discussed through
examples from past and present research. Finally, a conclusion and
road map are pointed out for more natural voice transformation
algorithms in the future.
Abstract: The purpose of this paper is to present teacher candidates- beliefs about technology integration in their field of study, which is classroom teaching in this case. The study was conducted among the first year students in college of education in Turkey. This study is based on both quantitative and qualitative data. For the quantitative data- Likert scale was used and for the qualitative data pattern matching was employed. The primary findings showed that students defined educational technology as technologies that improve learning with their visual, easily accessible, and productive features. They also believe these technologies could affect their future students- learning positively.
Abstract: Delivering course material via a virtual environment
is beneficial to today-s students because it offers the interactivity,
real-time interaction and social presence that students of all ages
have come to accept in our gaming rich community. It is essential
that the Net Generation also known as Generation Why, have
exposure to learning communities that encompass interactivity to
form social and educational connections. As student and professor
become interconnected through collaboration and interaction in a
virtual learning space, relationships develop and students begin to
take on an individual identity. With this in mind the research project
was developed to investigate the use of virtual environments on
student satisfaction and the effectiveness of course delivery.
Furthermore, the project was designed to integrate both interactive
(real-time) classes conducted in the Virtual Reality (VR)
environment while also creating archived VR sessions for student use
in retaining and reviewing course content.
Abstract: Cameron Highlands is known for upland tourism area
with vast natural wealth, mountainous landscape endowed with rich
diverse species as well as people traditions and cultures. With these
various resources, CH possesses an interesting visual and panorama
that can be offered to the tourist. However this benefit may not be
utilized without obtaining the understanding of existing landscape
structure and visual. Given a limited data, this paper attempts to
classify landscape visual of Cameron Highlands using land use and
contour data. Visual points of view were determined from the given
tourist attraction points in the CH Local Plan 2003-2015. The result
shows landscape visual and structure categories offered in the study
area. The result can be used for further analysis to determine the best
alternative tourist trails for tourism planning and decision making
using readily available data.
Abstract: Games can be classified as games of skill, games of chance or otherwise be classified as mixed. This paper deals with the topic of scientifically classifying mixed games as more reliant on elements of chance or elements of skill and ways to scientifically measure the amount of skill involved. This is predominantly useful for classification of games as legal or illegal in deferent jurisdictions based on the local gaming laws. We propose a novel measure of skill to chance ratio called the Game Skill Measure (GSM) and utilize it to calculate the skill component of a popular variant of Poker.
Abstract: Prime Factorization based on Quantum approach in
two phases has been performed. The first phase has been achieved at
Quantum computer and the second phase has been achieved at the
classic computer (Post Processing). At the second phase the goal is to
estimate the period r of equation xrN ≡ 1 and to find the prime factors
of the composite integer N in classic computer. In this paper we
present a method based on Randomized Approach for estimation the
period r with a satisfactory probability and the composite integer N
will be factorized therefore with the Randomized Approach even the
gesture of the period is not exactly the real period at least we can find
one of the prime factors of composite N. Finally we present some
important points for designing an Emulator for Quantum Computer
Simulation.
Abstract: In this paper, we present a new and effective image indexing technique that extracts features directly from DCT domain. Our proposed approach is an object-based image indexing. For each block of size 8*8 in DCT domain a feature vector is extracted. Then, feature vectors of all blocks of image using a k-means algorithm is clustered into groups. Each cluster represents a special object of the image. Then we select some clusters that have largest members after clustering. The centroids of the selected clusters are taken as image feature vectors and indexed into the database. Also, we propose an approach for using of proposed image indexing method in automatic image classification. Experimental results on a database of 800 images from 8 semantic groups in automatic image classification are reported.
Abstract: Our study proposes an alternative method in building
Fuzzy Rule-Based System (FRB) from Support Vector Machine
(SVM). The first set of fuzzy IF-THEN rules is obtained through
an equivalence of the SVM decision network and the zero-ordered
Sugeno FRB type of the Adaptive Network Fuzzy Inference System
(ANFIS). The second set of rules is generated by combining the
first set based on strength of firing signals of support vectors using
Gaussian kernel. The final set of rules is then obtained from the
second set through input scatter partitioning. A distinctive advantage
of our method is the guarantee that the number of final fuzzy IFTHEN
rules is not more than the number of support vectors in the
trained SVM. The final FRB system obtained is capable of performing
classification with results comparable to its SVM counterpart, but it
has an advantage over the black-boxed SVM in that it may reveal
human comprehensible patterns.
Abstract: Ontology Matching is a task needed in various applica-tions, for example for comparison or merging purposes. In literature,many algorithms solving the matching problem can be found, butmost of them do not consider instances at all. Mappings are deter-mined by calculating the string-similarity of labels, by recognizinglinguistic word relations (synonyms, subsumptions etc.) or by ana-lyzing the (graph) structure. Due to the facts that instances are oftenmodeled within the ontology and that the set of instances describesthe meaning of the concepts better than their meta information,instances should definitely be incorporated into the matching process.In this paper several novel instance-based matching algorithms arepresented which enhance the quality of matching results obtainedwith common concept-based methods. Different kinds of formalismsare use to classify concepts on account of their instances and finallyto compare the concepts directly.KeywordsInstances, Ontology Matching, Semantic Web
Abstract: This paper presented a modified efficient inductive
powering link based on ASK modulator and proposed efficient class-
E power amplifier. The design presents the external part which is
located outside the body to transfer power and data to the implanted
devices such as implanted Microsystems to stimulate and monitoring
the nerves and muscles. The system operated with low band
frequency 10MHZ according to industrial- scientific – medical (ISM)
band to avoid the tissue heating. For external part, the modulation
index is 11.1% and the modulation rate 7.2% with data rate 1 Mbit/s
assuming Tbit = 1us. The system has been designed using 0.35-μm
fabricated CMOS technology. The mathematical model is given and
the design is simulated using OrCAD P Spice 16.2 software tool and
for real-time simulation, the electronic workbench MULISIM 11 has
been used.
Abstract: Bagging and boosting are among the most popular resampling ensemble methods that generate and combine a diversity of classifiers using the same learning algorithm for the base-classifiers. Boosting algorithms are considered stronger than bagging on noisefree data. However, there are strong empirical indications that bagging is much more robust than boosting in noisy settings. For this reason, in this work we built an ensemble using a voting methodology of bagging and boosting ensembles with 10 subclassifiers in each one. We performed a comparison with simple bagging and boosting ensembles with 25 sub-classifiers, as well as other well known combining methods, on standard benchmark datasets and the proposed technique was the most accurate.
Abstract: Support Vector Domain Description (SVDD) is one of the best-known one-class support vector learning methods, in which one tries the strategy of using balls defined on the feature space in order to distinguish a set of normal data from all other possible abnormal objects. As all kernel-based learning algorithms its performance depends heavily on the proper choice of the kernel parameter. This paper proposes a new approach to select kernel's parameter based on maximizing the distance between both gravity centers of normal and abnormal classes, and at the same time minimizing the variance within each class. The performance of the proposed algorithm is evaluated on several benchmarks. The experimental results demonstrate the feasibility and the effectiveness of the presented method.
Abstract: Soil stabilization has been widely used to improve
soil strength and durability or to prevent erosion and dust generation.
Generally to reduce problems of clayey soils in engineering work and
to stabilize these soils additional materials are used. The most
common materials are lime, fly ash and cement. Using this materials,
although improve soil property , but in some cases due to financial
problems and the need to use special equipment are limited .One of
the best methods for stabilization clayey soils is neutralization the
clay particles. For this purpose we can use ion exchange materials.
Ion exchange solution like CBR plus can be used for soil
stabilization. One of the most important things in using CBR plus is
determination the amount of this solution for various soils with
different properties. In this study a laboratory experiment is conduct
to evaluate the ion exchange capacity of three soils with various
plasticity index (PI) to determine amount or required CBR plus
solution for soil stabilization.
Abstract: The knowledge base of welding defect recognition is
essentially incomplete. This characteristic determines that the recognition results do not reflect the actual situation. It also has a further influence on the classification of welding quality. This paper is
concerned with the study of a rough set based method to reduce the influence and improve the classification accuracy. At first, a rough set
model of welding quality intelligent classification has been built. Both condition and decision attributes have been specified. Later on, groups
of the representative multiple compound defects have been chosen
from the defect library and then classified correctly to form the
decision table. Finally, the redundant information of the decision table has been reducted and the optimal decision rules have been reached. By this method, we are able to reclassify the misclassified defects to
the right quality level. Compared with the ordinary ones, this method
has higher accuracy and better robustness.
Abstract: Tourism and coastal lines are the business sectors
since centuries especially in the European Nations and Albania is one
such spots. However, in recent decades tourism is experienced as
vulnerability of the surrounding ecological conditions of air, soil,
water, land and the communities that are dependant and sharing the
ecosystem among flora and fauna. Experts opine that apart from the
maintenance of near-originality of ecological biodiversity the tourism
rather known as ecotourism an indigenous socio-cultural
maintenance of indigenous/traditional knowledge of the local people
must be well cared in order to sustain on sustainable grounds. As a
general tendency, growth of tourism has been affected by the deterioration in the economic conditions on one aspect and unsustainable ecological areas affected since human interventions
earlier to this has negative impact on futuristic tourist spots. However, tourism in Albania as of now is 11% of GDP and coastal regions accounting to 2-4%. An amicable Mediterranean
climate with 300 sunny days similar parameters of Greece and Spain
throws up sustainable ecotourism in future decades provided public services namely, transportation, road safety, lodging, food
availability, recreational regiments, banking accessibility are as per
the World Tourism Organizations- protocols. Thus as of Albanian
situation, classification of ecotourism activities to safe-guard the localities with its maintenance of ecological land, water and climate
has become a paramount importance with a wanting and satisfactory options through harnessing human energy for profit and fitness of
ecological flora and fauna. A check on anthropogenic wastes and
their safer utilizations inclusive of agricultural and industrial
operations in line with Lalzi Bay Coastal Line are of utmost importance for the reason that the Adriatic Sea Coast is the one long
stretch of Albanian Lifeline. The present work is based on the methodology of the sustainable management of the same issue.
Abstract: In this paper, the problem of stability analysis for a class of impulsive stochastic fuzzy neural networks with timevarying delays and reaction-diffusion is considered. By utilizing suitable Lyapunov-Krasovskii funcational, the inequality technique and stochastic analysis technique, some sufficient conditions ensuring global exponential stability of equilibrium point for impulsive stochastic fuzzy cellular neural networks with time-varying delays and diffusion are obtained. In particular, the estimate of the exponential convergence rate is also provided, which depends on system parameters, diffusion effect and impulsive disturbed intention. It is believed that these results are significant and useful for the design and applications of fuzzy neural networks. An example is given to show the effectiveness of the obtained results.