Abstract: The aim of this paper is to propose a general
framework for storing, analyzing, and extracting knowledge from
two-dimensional echocardiographic images, color Doppler images,
non-medical images, and general data sets. A number of high
performance data mining algorithms have been used to carry out this
task. Our framework encompasses four layers namely physical
storage, object identification, knowledge discovery, user level.
Techniques such as active contour model to identify the cardiac
chambers, pixel classification to segment the color Doppler echo
image, universal model for image retrieval, Bayesian method for
classification, parallel algorithms for image segmentation, etc., were
employed. Using the feature vector database that have been
efficiently constructed, one can perform various data mining tasks
like clustering, classification, etc. with efficient algorithms along
with image mining given a query image. All these facilities are
included in the framework that is supported by state-of-the-art user
interface (UI). The algorithms were tested with actual patient data
and Coral image database and the results show that their performance
is better than the results reported already.
Abstract: Sentiment analysis means to classify a given review
document into positive or negative polar document. Sentiment
analysis research has been increased tremendously in recent times
due to its large number of applications in the industry and academia.
Sentiment analysis models can be used to determine the opinion of
the user towards any entity or product. E-commerce companies can
use sentiment analysis model to improve their products on the basis
of users’ opinion. In this paper, we propose a new One-class Support
Vector Machine (One-class SVM) based sentiment analysis model
for movie review documents. In the proposed approach, we initially
extract features from one class of documents, and further test the
given documents with the one-class SVM model if a given new test
document lies in the model or it is an outlier. Experimental results
show the effectiveness of the proposed sentiment analysis model.
Abstract: A seizure prediction method is proposed by extracting
global features using phase correlation between adjacent epochs for
detecting relative changes and local features using fluctuation/
deviation within an epoch for determining fine changes of different
EEG signals. A classifier and a regularization technique are applied
for the reduction of false alarms and improvement of the overall
prediction accuracy. The experiments show that the proposed method
outperforms the state-of-the-art methods and provides high prediction
accuracy (i.e., 97.70%) with low false alarm using EEG signals in
different brain locations from a benchmark data set.
Abstract: In this paper, a robust fault detection and isolation
(FDI) scheme is developed to monitor a multivariable nonlinear
chemical process called the Chylla-Haase polymerization reactor,
when it is under the cascade PI control. The scheme employs a radial
basis function neural network (RBFNN) in an independent mode to
model the process dynamics, and using the weighted sum-squared
prediction error as the residual. The Recursive Orthogonal Least
Squares algorithm (ROLS) is employed to train the model to
overcome the training difficulty of the independent mode of the
network. Then, another RBFNN is used as a fault classifier to isolate
faults from different features involved in the residual vector. Several
actuator and sensor faults are simulated in a nonlinear simulation of
the reactor in Simulink. The scheme is used to detect and isolate the
faults on-line. The simulation results show the effectiveness of the
scheme even the process is subjected to disturbances and
uncertainties including significant changes in the monomer feed rate,
fouling factor, impurity factor, ambient temperature, and
measurement noise. The simulation results are presented to illustrate
the effectiveness and robustness of the proposed method.
Abstract: Although it is fully impossible to ensure that a software system is quite secure, developing an acceptable secure software system in a convenient platform is not unreachable. In this paper, we attempt to analyze software development life cycle (SDLC) models from the hardware systems and circuits point of view. To date, the SDLC models pay merely attention to the software security from the software perspectives. In this paper, we present new features for SDLC stages to emphasize the role of systems and circuits in developing secure software system through the software development stages, the point that has not been considered previously in the SDLC models.
Abstract: This paper concentrates on the sustainable traditional
architecture and urban planning in hot-humid regions of Iran. In a
vast country such as Iran with different climatic zones traditional
builders have presented series of logical solutions for human comfort.
The aim of this paper is to demonstrate traditional architecture in hothumid
climate of Iran as a sample of sustainable architecture. Iranian
traditional architecture has been able to response to environmental
problems for a long period of time. Its features are based on climatic
factors, local construction materials of hot-humid regions and culture.
This paper concludes that Iranian traditional architecture can be
addressed as a sustainable architecture.
Abstract: The increase of technogenic and natural accidents,
accompanied by air pollution, for example, by combustion products,
leads to the necessity of respiratory protection. This work is devoted to the development of a calorimetric method
and a device which allows investigating quickly the kinetics of
carbon dioxide sorption by chemisorbents on the base of potassium
superoxide in order to assess the protective properties of respiratory
protective closed circuit apparatus. The features of the traditional approach for determining the
sorption properties in a thin layer of chemisorbent are described, as
well as methods and devices, which can be used for the sorption
kinetics study. The authors developed an approach (as opposed to the traditional
approach) based on the power measurement of internal heat sources
in the chemisorbent layer. The emergence of the heat sources is a
result of exothermic reaction of carbon dioxide sorption. This
approach eliminates the necessity of chemical analysis of samples
and can significantly reduce the time and material expenses during
chemisorbents testing. Error of determining the volume fraction of adsorbed carbon
dioxide by the developed method does not exceed 12%. Taking into
account the efficiency of the method, we consider that it is a good
alternative to traditional methods of chemical analysis under the
assessment of the protection sorbents quality.
Abstract: The paper will focus on the strategic development
deriving from the evolution of the traditional courtyard spatial
organization towards a new, contemporary sustainable way of living.
New sustainable approaches that engulf the social issues, the notion
of place, the understanding of weather architecture blended together
with the bioclimatic behavior will be seen through a series of
experimental case studies in the island of Cyprus, inspired and
originated from its traditional wisdom, ranging from small scale of
living to urban interventions. Weather and nature will be seen as co-architectural authors with
architects. Furthermore, the building will be seen not as an object but
rather as a vessel of human activities. This will further enhance the
notion of merging the material and immaterial, the built and unbuilt,
subject-human, and the object-building. This eventually will enable
to generate the discussion of the understanding of the building in
relation to the place and its inhabitants, where the human topography
is more important than the material topography. The specificities of
the divided island and the dealing with sites that are in vicinity with
the diving Green Line will further trigger explorations dealing with
the regeneration issues and the social sustainability offering
unprecedented opportunities for innovative sustainable ways of
living. Opening up a discourse with premises of weather-nature, materialimmaterial,
human-material topographies in relation to the contested
sites of the borders will lead us to develop innovative strategies for a
profound, both technical and social sustainability, which fruitfully
yields to innovative living built environments, responding to the ever
changing environmental and social needs. As a starting point, a case study in Kaimakli in Nicosia, a
refurbishment with an extension of a traditional house, already
engulfs all the traditional/ vernacular wisdom of the bioclimatic
architecture. The project focusses on the direct and quite obvious
bioclimatic features such as south orientation and cross ventilation.
Furthermore, it tries to reinvent the adaptation of these parameters in
order to turn the whole house to a contemporary living environment.
In order to succeed this, evolutions of traditional architectural
elements and spatial conditions are integrated in a way that does not
only respond to some certain weather conditions, but they integrate
and blend the weather within the built environment. A series of
innovations aiming at maximum flexibility is proposed. The house
can finally be transformed into a winter enclosure, while for the most
part of the year it turns into a ‘camping’ living environment. Parallel to experimental interventions in existing traditional units,
we will proceed examining the implementation of the same
developed methodology in designing living units and complexes.
Malleable courtyard organizations that attempt to blend the
traditional wisdom with the contemporary needs for living, the
weather and nature with the built environment will be seen tested in
both horizontal and vertical developments. Social activities are seen as directly affected and forged by the
weather conditions thus generating a new social identity of people where people are directly involved and interacting with the weather.
The human actions and interaction with the built, material
environment in order to respond to weather will be seen as the result
of balancing the social with the technological sustainability, the
immaterial, and the material aspects of the living environment.
Abstract: Large-scale machine tools for the manufacturing of
large work pieces, e.g. blades, casings or gears for wind turbines,
feature pose-dependent dynamic behavior. Small structural damping
coefficients lead to long decay times for structural vibrations that
have negative impacts on the production process. Typically, these
vibrations are handled by increasing the stiffness of the structure by
adding mass. This is counterproductive to the needs of sustainable
manufacturing as it leads to higher resource consumption both in
material and in energy. Recent research activities have led to higher
resource efficiency by radical mass reduction that is based on controlintegrated
active vibration avoidance and damping methods. These
control methods depend on information describing the dynamic
behavior of the controlled machine tools in order to tune the
avoidance or reduction method parameters according to the current
state of the machine. This paper presents the appearance, consequences and challenges
of the pose-dependent dynamic behavior of lightweight large-scale
machine tool structures in production. It starts with the theoretical
introduction of the challenges of lightweight machine tool structures
resulting from reduced stiffness. The statement of the pose-dependent
dynamic behavior is corroborated by the results of the experimental
modal analysis of a lightweight test structure. Afterwards, the
consequences of the pose-dependent dynamic behavior of lightweight
machine tool structures for the use of active control and vibration
reduction methods are explained. Based on the state of the art of
pose-dependent dynamic machine tool models and the modal
investigation of an FE-model of the lightweight test structure, the
criteria for a pose-dependent model for use in vibration reduction are
derived. The description of the approach for a general posedependent
model of the dynamic behavior of large lightweight
machine tools that provides the necessary input to the aforementioned
vibration avoidance and reduction methods to properly tackle
machine vibrations is the outlook of the paper.
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: Obturator Foramen is a specific structure in Pelvic
bone images and recognition of it is a new concept in medical image
processing. Moreover, segmentation of bone structures such as
Obturator Foramen plays an essential role for clinical research in
orthopedics. In this paper, we present a novel method to analyze the
similarity between the substructures of the imaged region and a hand
drawn template as a preprocessing step for computation of Pelvic
bone rotation on hip radiographs. This method consists of integrated
usage of Marker-controlled Watershed segmentation and Zernike
moment feature descriptor and it is used to detect Obturator Foramen
accurately. Marker-controlled Watershed segmentation is applied to
separate Obturator Foramen from the background effectively. Then,
Zernike moment feature descriptor is used to provide matching
between binary template image and the segmented binary image for
final extraction of Obturator Foramens. Finally, Pelvic bone rotation
rate calculation for each hip radiograph is performed automatically to
select and eliminate hip radiographs for further studies which depend
on Pelvic bone angle measurements. The proposed method is tested
on randomly selected 100 hip radiographs. The experimental results
demonstrated that the proposed method is able to segment Obturator
Foramen with 96% accuracy.
Abstract: Countryside has been generally recognized and
regarded as a characteristic symbol which presents in human memory
for a long time. As a result of the change of times, because of it is
failure to meet the growing needs of the growing life and mental
decline, the vast rural area began to decline. But their history feature
image which accumulated by the ancient tradition provides people
with the origins of existence on the spiritual level, such as "identity"
and "belonging", makes people closer to the others in the spiritual and
psychological aspects of a common experience about the past, thus the
sense of a lack of culture caused by the losing of memory symbols is
weakened. So, in the modernization process, how to repair its vitality
and transform and planning it in a sustainable way has become a hot
topics in architectural and urban planning. This paper aims to break
the constraints of disciplines, from the perspective of interdiscipline,
using the research methods of systems science to analyze and discuss
the theories and methods of rural form factors, which based on the
viewpoint of memory in psychology. So we can find a right way to
transform the Rural to give full play to the role of the countryside in
the actual use and the shape of history spirits.
Abstract: In this paper, a comparative performance analysis of
mostly used four nonlinearity cancellation techniques used to realize
the passive resistor by MOS transistors, is presented. The comparison
is done by using an integrator circuit which is employing sequentially
Op-amp, OTRA and ICCII as active element. All of the circuits are
implemented by MOS-C realization and simulated by PSPICE
program using 0.35μm process TSMC MOSIS model parameters.
With MOS-C realization, the circuits became electronically tunable
and fully integrable which is very important in IC design. The output
waveforms, frequency responses, THD analysis results and features
of the nonlinearity cancellation techniques are also given.
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: Imperialist Competitive Algorithm (ICA) is a recent
meta-heuristic method that is inspired by the social evolutions for
solving NP-Hard problems. The ICA is a population-based algorithm
which has achieved a great performance in comparison to other metaheuristics.
This study is about developing enhanced ICA approach to
solve the Cell Formation Problem (CFP) using sequence data. In
addition to the conventional ICA, an enhanced version of ICA,
namely EICA, applies local search techniques to add more
intensification aptitude and embed the features of exploration and
intensification more successfully. Suitable performance measures are
used to compare the proposed algorithms with some other powerful
solution approaches in the literature. In the same way, for checking
the proficiency of algorithms, forty test problems are presented. Five
benchmark problems have sequence data, and other ones are based on
0-1 matrices modified to sequence based problems. Computational
results elucidate the efficiency of the EICA in solving CFP problems.
Abstract: In the context of the handwriting recognition, we
propose an off line system for the recognition of the Arabic
handwritten words of the Algerian departments. The study is based
mainly on the evaluation of neural network performances, trained
with the gradient back propagation algorithm. The used parameters to
form the input vector of the neural network are extracted on the
binary images of the handwritten word by several methods. The
Distribution parameters, the centered moments of the different
projections of the different segments, the centered moments of the
word image coding according to the directions of Freeman, and the
Barr features applied binary image of the word and on its different
segments. The classification is achieved by a multi layers perceptron.
A detailed experiment is carried and satisfactory recognition results
are reported.
Abstract: Analytical techniques for measuring and planning
railway capacity expansion activities have been considered in this
article. A preliminary mathematical framework involving track
duplication and section sub divisions is proposed for this task. In
railways, these features have a great effect on network performance
and for this reason they have been considered. Additional motivations
have also arisen from the limitations of prior models that have not
included them.
Abstract: Myocardial infarction is one of the leading causes of
death in the world. Some of these deaths occur even before the
patient reaches the hospital. Myocardial infarction occurs as a result
of impaired blood supply. Because the most of these deaths are due to
coronary artery disease, hence the awareness of the warning signs of
a heart attack is essential. Some heart attacks are sudden and intense,
but most of them start slowly, with mild pain or discomfort, then
early detection and successful treatment of these symptoms is vital to
save them. Therefore, importance and usefulness of a system
designing to assist physicians in early diagnosis of the acute heart
attacks is obvious. The main purpose of this study would be to enable patients to
become better informed about their condition and to encourage them
to seek professional care at an earlier stage in the appropriate
situations. For this purpose, the data were collected on 711 heart
patients in Iran hospitals. 28 attributes of clinical factors can be
reported by patients; were studied. Three logistic regression models
were made on the basis of the 28 features to predict the risk of heart
attacks. The best logistic regression model in terms of performance
had a C-index of 0.955 and with an accuracy of 94.9%. The variables,
severe chest pain, back pain, cold sweats, shortness of breath, nausea
and vomiting, were selected as the main features.
Abstract: The aim of the performed work is to establish the 2D
and 3D model of direct unsteady task of sample heat treatment by
moving source employing computer model on the basis of finite
element method. Complex boundary condition on heat loaded sample
surface is the essential feature of the task. Computer model describes
heat treatment of the sample during heat source movement over the
sample surface. It is started from 2D task of sample cross section as a
basic model. Possibilities of extension from 2D to 3D task are
discussed. The effect of the addition of third model dimension on
temperature distribution in the sample is showed. Comparison of
various model parameters on the sample temperatures is observed.
Influence of heat source motion on the depth of material heat
treatment is shown for several velocities of the movement. Presented
computer model is prepared for the utilization in laser treatment of
machine parts.
Abstract: In this talk, we introduce a newly developed quantile
function model that can be used for estimating conditional
distributions of financial returns and for obtaining multi-step ahead
out-of-sample predictive distributions of financial returns. Since we
forecast the whole conditional distributions, any predictive quantity
of interest about the future financial returns can be obtained simply
as a by-product of the method. We also show an application of the
model to the daily closing prices of Dow Jones Industrial Average
(DJIA) series over the period from 2 January 2004 - 8 October 2010.
We obtained the predictive distributions up to 15 days ahead for
the DJIA returns, which were further compared with the actually
observed returns and those predicted from an AR-GARCH model.
The results show that the new model can capture the main features
of financial returns and provide a better fitted model together with
improved mean forecasts compared with conventional methods. We
hope this talk will help audience to see that this new model has the
potential to be very useful in practice.