Abstract: In this paper, an approach for the liver tumor detection
in computed tomography (CT) images is represented. The detection
process is based on classifying the features of target liver cell to
either tumor or non-tumor. Fractional differential (FD) is applied for
enhancement of Liver CT images, with the aim of enhancing texture
and edge features. Later on, a fusion method is applied to merge
between the various enhanced images and produce a variety of
feature improvement, which will increase the accuracy of
classification. Each image is divided into NxN non-overlapping
blocks, to extract the desired features. Support vector machines
(SVM) classifier is trained later on a supplied dataset different from
the tested one. Finally, the block cells are identified whether they are
classified as tumor or not. Our approach is validated on a group of
patients’ CT liver tumor datasets. The experiment results
demonstrated the efficiency of detection in the proposed technique.
Abstract: The acceptance of sustainable products by the final
consumer is still one of the challenges of the industry, which
constantly seeks alternative approaches to successfully be accepted in
the global market. A large set of methods and approaches have been
discussed and analysed throughout the literature. Considering the current need for sustainable development and the
current pace of consumption, the need for a combined solution
towards the development of new products became clear, forcing
researchers in product development to propose alternatives to the
previous standard product development models. This paper presents, through a systemic analysis of the literature
on product development, eco-design and consumer involvement, a set
of alternatives regarding consumer involvement towards the
development of sustainable products and how these approaches could
help improve the sustainable industry’s establishment in the general
market. Still being developed in the course of the author’s PhD, the initial
findings of the research show that the understanding of the benefits of
sustainable behaviour lead to a more conscious acquisition and
eventually to the implementation of sustainable change in the
consumer. Thus this paper is the initial approach towards the
development of new sustainable products using the fashion industry
as an example of practical implementation and acceptance by the
consumers. By comparing the existing literature and critically analysing it, this
paper concluded that the consumer involvement is strategic to
improve the general understanding of sustainability and its features.
The use of consumers and communities has been studied since the
early 90s in order to exemplify uses and to guarantee a fast
comprehension. The analysis done also includes the importance of
this approach for the increase of innovation and ground breaking
developments, thus requiring further research and practical
implementation in order to better understand the implications and
limitations of this methodology.
Abstract: Game-based learning can enhance the learning
motivation of students and provide a means for them to learn through
playing games. This study used augmented reality technology to
develop an interactive card game as a game-based teaching aid for
delivering elementary school science course content with the aim of
enhancing student learning processes and outcomes. Through playing
the proposed card game, students can familiarize themselves with
appearance, features, and foraging behaviors of insects. The system
records the actions of students, enabling teachers to determine their
students’ learning progress. In this study, 37 students participated in an
assessment experiment and provided feedback through questionnaires.
Their responses indicated that they were significantly more motivated
to learn after playing the game, and their feedback was mostly
positive.
Abstract: Background modeling and subtraction in video
analysis has been widely used as an effective method for moving
objects detection in many computer vision applications. Recently, a
large number of approaches have been developed to tackle different
types of challenges in this field. However, the dynamic background
and illumination variations are the most frequently occurred problems
in the practical situation. This paper presents a favorable two-layer
model based on codebook algorithm incorporated with local binary
pattern (LBP) texture measure, targeted for handling dynamic
background and illumination variation problems. More specifically,
the first layer is designed by block-based codebook combining with
LBP histogram and mean value of each RGB color channel. Because
of the invariance of the LBP features with respect to monotonic
gray-scale changes, this layer can produce block wise detection results
with considerable tolerance of illumination variations. The pixel-based
codebook is employed to reinforce the precision from the output of the
first layer which is to eliminate false positives further. As a result, the
proposed approach can greatly promote the accuracy under the
circumstances of dynamic background and illumination changes.
Experimental results on several popular background subtraction
datasets demonstrate very competitive performance compared to
previous models.
Abstract: Social networking sites such as Twitter and Facebook
attracts over 500 million users across the world, for those users, their
social life, even their practical life, has become interrelated. Their
interaction with social networking has affected their life forever.
Accordingly, social networking sites have become among the main
channels that are responsible for vast dissemination of different kinds
of information during real time events. This popularity in Social
networking has led to different problems including the possibility of
exposing incorrect information to their users through fake accounts
which results to the spread of malicious content during life events.
This situation can result to a huge damage in the real world to the
society in general including citizens, business entities, and others. In this paper, we present a classification method for detecting the
fake accounts on Twitter. The study determines the minimized set of
the main factors that influence the detection of the fake accounts on
Twitter, and then the determined factors are applied using different
classification techniques. A comparison of the results of these
techniques has been performed and the most accurate algorithm is
selected according to the accuracy of the results. The study has been
compared with different recent researches in the same area; this
comparison has proved the accuracy of the proposed study. We claim
that this study can be continuously applied on Twitter social network
to automatically detect the fake accounts; moreover, the study can be
applied on different social network sites such as Facebook with minor
changes according to the nature of the social network which are
discussed in this paper.
Abstract: In this paper, de Laval rotor system has been
characterized by a hinge model and its transient response numerically
treated for a dynamic solution. The effect of the ensuing non-linear
disturbances namely rub and breathing crack is numerically
simulated. Subsequently, three analysis methods: Orbit Analysis, Fast
Fourier Transform (FFT), and Wavelet Transform (WT) are
employed to extract features of the vibration signal of the faulty
system. An analysis of the system response orbits clearly indicates
the perturbations due to the rotor-to-stator contact. The sensitivities
of WT to the variation in system speed have been investigated by
Continuous Wavelet Transform (CWT). The analysis reveals that
features of crack, rubs and unbalance in vibration response can be
useful for condition monitoring. WT reveals its ability to detect nonlinear
signal, and obtained results provide a useful tool method for
detecting machinery faults.
Abstract: This paper presents the development of a mobile
application for students at the Faculty of Information Technology,
Rangsit University (RSU), Thailand. RSU upgrades an enrollment
process by improving its information systems. Students can
download the RSU APP easily in order to access the RSU substantial
information. The reason of having a mobile application is to help
students to access the system regardless of time and place. The objectives of this paper include: 1. To develop an application
on iOS platform for those students at the Faculty of Information
Technology, Rangsit University, Thailand. 2. To obtain the students’
perception towards the new mobile app. The target group is those
from the freshman year till the senior year of the faculty of
Information Technology, Rangsit University. The new mobile application, called as RSU APP, is developed by
the department of Information Technology, Rangsit University. It
contains useful features and various functionalities particularly on
those that can give support to students. The core contents of the app
consist of RSU’s announcement, calendar, events, activities, and ebook.
The mobile app is developed on the iOS platform. The user
satisfaction is analyzed from the interview data from 81 interviewees
as well as a Google application like a Google form which 122
interviewees are involved. The result shows that users are satisfied
with the application as they score it the most satisfaction level at 4.67
SD 0.52. The score for the question if users can learn and use the
application quickly is high which is 4.82 SD 0.71. On the other hand,
the lowest satisfaction rating is in the app’s form, apps lists, with the
satisfaction level as 4.01 SD 0.45.
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: 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: 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.