Abstract: Geopolymer concretes are new class of construction
materials that have emerged as an alternative to Ordinary Portland
cement concrete. Considerable researches have been carried out on
material development of geopolymer concrete; however, a few studies
have been reported on the structural use of them. This paper presents
the bond behaviors of reinforcement embedded in fly ash based
geopolymer concrete. The development lengths of reinforcement for
various compressive strengths of concrete, 20, 30 and 40 MPa, and
reinforcement diameters, 10, 16 and 25 mm, are investigated. Total 27
specimens were manufactured and pull-out test according to EN 10080
was applied to measure bond strength and slips between concrete and
reinforcements. The average bond strengths decreased from 23.06MPa
to 17.26 MPa, as the diameters of reinforcements increased from
10mm to 25mm. The compressive strength levels of geopolymer
concrete showed no significant influence on bond strengths in this
study. Also, the bond-slip relations between geopolymer concrete and
reinforcement are derived using non-linear regression analysis for
various experimental conditions.
Abstract: In this paper, we present a robust algorithm to recognize extracted text from grocery product images captured by mobile phone cameras. Recognition of such text is challenging since text in grocery product images varies in its size, orientation,
style, illumination, and can suffer from perspective distortion.
Pre-processing is performed to make the characters scale and
rotation invariant. Since text degradations can not be appropriately
defined using well-known geometric transformations such
as translation, rotation, affine transformation and shearing, we
use the whole character black pixels as our feature vector.
Classification is performed with minimum distance classifier
using the maximum likelihood criterion, which delivers very
promising Character Recognition Rate (CRR) of 89%. We
achieve considerably higher Word Recognition Rate (WRR) of
99% when using lower level linguistic knowledge about product
words during the recognition process.
Abstract: This work was one of the tasks of the
Manufacturing2Client project, whose objective was to develop a
frontal deflector to be commercialized in the automotive industry,
using new project and manufacturing methods. In this task, in
particular, it was proposed to develop the ability to predict
computationally the aerodynamic influence of flow in vehicles, in an
effort to reduce fuel consumption in vehicles from class 3 to 8. With
this aim, two deflector models were developed and their aerodynamic
performance analyzed. The aerodynamic study was done using the
Computational Fluid Dynamics (CFD) software Ansys CFX and
allowed the calculation of the drag coefficient caused by the vehicle
motion for the different configurations considered. Moreover, the
reduction of diesel consumption and carbon dioxide (CO2) emissions
associated with the optimized deflector geometry could be assessed.
Abstract: Red blood cells (RBC) are the most common types of
blood cells and are the most intensively studied in cell biology. The
lack of RBCs is a condition in which the amount of hemoglobin level
is lower than normal and is referred to as “anemia”. Abnormalities in
RBCs will affect the exchange of oxygen. This paper presents a
comparative study for various techniques for classifying the RBCs as
normal or abnormal (anemic) using WEKA. WEKA is an open
source consists of different machine learning algorithms for data
mining applications. The algorithms tested are Radial Basis Function
neural network, Support vector machine, and K-Nearest Neighbors
algorithm. Two sets of combined features were utilized for
classification of blood cells images. The first set, exclusively consist
of geometrical features, was used to identify whether the tested blood
cell has a spherical shape or non-spherical cells. While the second
set, consist mainly of textural features was used to recognize the
types of the spherical cells. We have provided an evaluation based on
applying these classification methods to our RBCs image dataset
which were obtained from Serdang Hospital - Malaysia, and
measuring the accuracy of test results. The best achieved
classification rates are 97%, 98%, and 79% for Support vector
machines, Radial Basis Function neural network, and K-Nearest
Neighbors algorithm respectively.
Abstract: The classroom of the 21st century is an ever changing
forum for new and innovative thoughts and ideas. With increasing
technology and opportunity, students have rapid access to
information that only decades ago would have taken weeks to obtain.
Unfortunately, new techniques and technology are not the cure for
the fundamental problems that have plagued the classroom ever since
education was established. Class size has been an issue long debated
in academia. While it is difficult to pin point an exact number, it is
clear that in this case more does not mean better. By looking into the
success and pitfalls of classroom size the true advantages of smaller
classes will become clear. Previously, one class was comprised of 50
students. Being seventeen and eighteen- year- old students,
sometimes it was quite difficult for them to stay focused. To help
them understand and gain much knowledge, a researcher introduced
“The Theory of Multiple Intelligence” and this, in fact, enabled
students to learn according to their own learning preferences no
matter how they were being taught. In this lesson, the researcher
designed a cycle of learning activities involving all intelligences so
that everyone had equal opportunities to learn.
Abstract: Image spam is a kind of email spam where the spam
text is embedded with an image. It is a new spamming technique
being used by spammers to send their messages to bulk of internet
users. Spam email has become a big problem in the lives of internet
users, causing time consumption and economic losses. The main
objective of this paper is to detect the image spam by using histogram
properties of an image. Though there are many techniques to
automatically detect and avoid this problem, spammers employing
new tricks to bypass those techniques, as a result those techniques are
inefficient to detect the spam mails. In this paper we have proposed a
new method to detect the image spam. Here the image features are
extracted by using RGB histogram, HSV histogram and combination
of both RGB and HSV histogram. Based on the optimized image
feature set classification is done by using k- Nearest Neighbor(k-NN)
algorithm. Experimental result shows that our method has achieved
better accuracy. From the result it is known that combination of RGB
and HSV histogram with k-NN algorithm gives the best accuracy in
spam detection.
Abstract: The growth in the volume of text data such as books
and articles in libraries for centuries has imposed to establish
effective mechanisms to locate them. Early techniques such as
abstraction, indexing and the use of classification categories have
marked the birth of a new field of research called "Information
Retrieval". Information Retrieval (IR) can be defined as the task of
defining models and systems whose purpose is to facilitate access to
a set of documents in electronic form (corpus) to allow a user to find
the relevant ones for him, that is to say, the contents which matches
with the information needs of the user.
Most of the models of information retrieval use a specific data
structure to index a corpus which is called "inverted file" or "reverse
index".
This inverted file collects information on all terms over the corpus
documents specifying the identifiers of documents that contain the
term in question, the frequency of each term in the documents of the
corpus, the positions of the occurrences of the word...
In this paper we use an oriented object database (db4o) instead of
the inverted file, that is to say, instead to search a term in the inverted
file, we will search it in the db4o database.
The purpose of this work is to make a comparative study to see if
the oriented object databases may be competing for the inverse index
in terms of access speed and resource consumption using a large
volume of data.
Abstract: Many of the ever-growing elderly population require
exercise, such as running, for health management. One important
element of a runner’s training is the choice of shoes for exercise; shoes
are important because they provide the interface between the feet and
road. When we purchase shoes, we may instinctively choose a pair
after trying on many different pairs of shoes. Selecting the shoes
instinctively may work, but it does not guarantee a suitable fit for
running activities. Therefore, if we could select suitable shoes for each
runner from the viewpoint of brain activities, it would be helpful for
validating shoe selection. In this paper, we describe how brain
activities show different characteristics during particular task,
corresponding to different properties of shoes. Using five subjects, we
performed a verification experiment, applying weight, softness, and
flexibility as shoe properties. In order to affect the shoe property’s
differences to the brain, subjects run for 10 min. Before and after
running, subjects conducted a paced auditory serial addition task
(PASAT) as the particular task; and the subjects’ brain activities
during the PASAT are evaluated based on oxyhemoglobin and
deoxyhemoglobin relative concentration changes, measured by
near-infrared spectroscopy (NIRS). When the brain works actively,
oxihemoglobin and deoxyhemoglobin concentration drastically
changes; therefore, we calculate the maximum values of concentration
changes. In order to normalize relative concentration changes after
running, the maximum value are divided by before running maximum
value as evaluation parameters. The classification of the groups of
shoes is expressed on a self-organizing map (SOM). As a result,
deoxyhemoglobin can make clusters for two of the three types of
shoes.
Abstract: This paper reports a structured literature review of the
application of Health Information Technology in developing
countries, defined as the World Bank categories Low-income
countries, Lower-middle-income, and Upper-middle-income
countries. The aim was to identify and classify the various
applications of health information technology to assess its current
state in developing countries and explore potential areas of research.
We offer specific analysis and application of HIT in Libya as one of
the developing countries. A structured literature review was
conducted using the following online databases: IEEE, Science
Direct, PubMed, and Google Scholar. Publication dates were set for
2000-2013. For the PubMed search, publications in English, French,
and Arabic were specified. Using a content analysis approach, 159
papers were analyzed and a total number of 26 factors were identified
that affect the adoption of health information technology. Of the 2681
retrieved articles, 159 met the inclusion criteria which were carefully
analyzed and classified. The implementation of health information
technology across developing countries is varied. Whilst it was
initially expected financial constraints would have severely limited
health information technology implementation, some developing
countries like India have nevertheless dominated the literature and
taken the lead in conducting scientific research. Comparing the
number of studies to the number of countries in each category, we
found that Low-income countries and Lower-middle-income had
more studies carried out than Upper-middle-income countries.
However, whilst IT has been used in various sectors of the economy,
the healthcare sector in developing countries is still failing to benefit
fully from the potential advantages that IT can offer.
Abstract: The financial crises caused a collapse in prices of
most asset classes, raising the attention on alternative investments
such as sukuk, a smaller, fast growing but often misunderstood
market. We study diversification benefits of sukuk, their correlation
with other asset classes and the effects of their inclusion in
investment portfolios of institutional and retail investors, through a
comprehensive comparison of their risk/return profiles during and
after the financial crisis.
We find a beneficial performance adjusted for the specific
volatility together with a lower correlation especially during the
financial crisis. The distribution of sukuk returns is positively skewed
and leptokurtic, with a risk/return profile similarly to high yield
bonds. Overall, our results suggest that sukuk present diversification
opportunities, a significant volatility-adjusted performance and lower
correlations especially during the financial crisis.
Our findings are relevant for a number of institutional investors.
Long term investors, such as life insurers would benefit from sukuk’s
protective features during financial crisis yet keeping return and
growth opportunities, whereas banks would gain due to their role of
placers, advisors, market makers or underwriters.
Abstract: Tumor is an uncontrolled growth of tissues in any part
of the body. Tumors are of different types and they have different
characteristics and treatments. Brain tumor is inherently serious and
life-threatening because of its character in the limited space of the
intracranial cavity (space formed inside the skull). Locating the tumor
within MR (magnetic resonance) image of brain is integral part of the
treatment of brain tumor. This segmentation task requires
classification of each voxel as either tumor or non-tumor, based on
the description of the voxel under consideration. Many studies are
going on in the medical field using Markov Random Fields (MRF) in
segmentation of MR images. Even though the segmentation process
is better, computing the probability and estimation of parameters is
difficult. In order to overcome the aforementioned issues, Conditional
Random Field (CRF) is used in this paper for segmentation, along
with the modified artificial bee colony optimization and modified
fuzzy possibility c-means (MFPCM) algorithm. This work is mainly
focused to reduce the computational complexities, which are found in
existing methods and aimed at getting higher accuracy. The
efficiency of this work is evaluated using the parameters such as
region non-uniformity, correlation and computation time. The
experimental results are compared with the existing methods such as
MRF with improved Genetic Algorithm (GA) and MRF-Artificial
Bee Colony (MRF-ABC) algorithm.
Abstract: In oases, the surface water resources are becoming
increasingly scarce and groundwater resources, which generally have
a poor quality due to the high levels of salinity, are often
overexploited. Water saving have therefore become imperative for
better oases sustainability. If drip irrigation is currently recommended
in Morocco for saving water and valuing, its use in the sub-desert
areas does not keep water safe from high evaporation rates. An
alternative to this system would be the use of subsurface drip
irrigation. This technique is defined as an application of water under
the soil surface through drippers, which deliver water at rates
generally similar to surface drip irrigation. As subsurface drip
irrigation is a recently introduced in Morocco, a better understanding
of the infiltration process around a buried source, in local conditions,
and its impact on plant growth is necessarily required. This study
aims to contribute to improving the water use efficiency by testing
the performance of subsurface irrigation system, especially in areas
where water is a limited source. The objectives of this research are
performance evaluation in arid conditions of the subsurface drip
irrigation system for young date palms compared to the surface drip.
In this context, an experimental test is installed at a farmer’s field in
the area of Erfoud (Errachidia Province, southeastern Morocco),
using the subsurface drip irrigation system in comparison with the
classic drip system for young date palms. Flow measurement to
calculate the uniformity of the application of water was done through
two methods: a flow measurement of drippers above the surface and
another one underground. The latter method has also helped us to
estimate losses through evaporation for both irrigation techniques. In
order to compare the effect of two irrigation modes, plants were
identified for each type of irrigation to monitor certain agronomic
parameters (cumulative numbers of palms and roots development).
Experimentation referred to a distribution uniformity of about 88%;
considered acceptable for subsurface drip irrigation while it is around
80% for the surface drip irrigation. The results also show an increase
in root development and in the number of palm, as well as a
substantial water savings due to lower evaporation losses compared
to the classic drip irrigation.
The results of this study showed that subsurface drip irrigation is
an efficient technique, which allows sustainable irrigation in arid
areas.
Abstract: The author introduced the integral operator , by using this
operator a new subclasses of analytic functions are introduced. For
these classes, several Fekete-Szeg¨ type coefficient inequalities are
obtained.
Abstract: The purpose of this study is to compare Self
Compacting Concrete (SCC) and Conventional Concrete (CC) in
terms of their capillary water absorption. During the comparison of
SCC and CC, the effects of two different factors were also
investigated: concrete strength class and curing condition. In the
study, both SCC and CC were produced in three different concrete
classes (C25, C50 and C70) and the other parameter (i.e. curing
condition) was determined as two levels: moisture and air curing. It
was observed that, for both curing environments and all strength
classes of concrete, SCCs had lower capillary water absorption values
than that of CCs. It was also detected that, for both SCC and CC,
capillary water absorption values of samples kept in moisture curing
were significantly lower than that of samples stored in air curing.
Additionally, it was determined that capillary water absorption values
for both SCC and CC decrease with increasing strength class of
concrete for both curing environments.
Abstract: Mammography has been one of the most reliable
methods for early detection of breast cancer. There are different
lesions which are breast cancer characteristic such as
microcalcifications, masses, architectural distortions and bilateral
asymmetry. One of the major challenges of analysing digital
mammogram is how to extract efficient features from it for accurate
cancer classification. In this paper we proposed a hybrid feature
extraction method to detect and classify all four signs of breast
cancer. The proposed method is based on multiscale surrounding
region dependence method, Gabor filters, multi fractal analysis,
directional and morphological analysis. The extracted features are
input to self adaptive resource allocation network (SRAN) classifier
for classification. The validity of our approach is extensively
demonstrated using the two benchmark data sets Mammographic
Image Analysis Society (MIAS) and Digital Database for Screening
Mammograph (DDSM) and the results have been proved to be
progressive.
Abstract: Skin detection is an important task for computer
vision systems. A good method of skin detection means a good and
successful result of the system.
The colour is a good descriptor for image segmentation and
classification; it allows detecting skin colour in the images. The
lighting changes and the objects that have a colour similar than skin
colour make the operation of skin detection difficult.
In this paper, we proposed a method using the YCbCr colour space
for skin detection and lighting effects elimination, then we use the
information of texture to eliminate the false regions detected by the
YCbCr skin model.
Abstract: The importance of using mother tongue and
translation in foreign language classrooms cannot be ignored and
translation can be utilized as a method in English Language Teaching
courses. There exist researches advocating or objecting to the use of
translation in foreign language learning but they all have a point in
common: Translation should be used as an aid to teaching, not an end
in itself. In this research, prospective English language teachers’
opinions about translation use and use of mother tongue in foreign
language teaching are investigated and according to the findings,
some explanations and recommendations are made.
Abstract: This paper proposes the designing direct adaptive
neural controller to apply for a class of a nonlinear pendulum
dynamic system. The radial basis function (RBF) neural adaptive
controller is robust in presence of external and internal uncertainties.
Both the effectiveness of the controller and robustness against
disturbances are importance of this paper. The simulation results
show the promising performance of the proposed controller.
Abstract: Applied industrial engineering is concerned with
imparting employable skills to improve the productivity for current
situation of products and services. The purpose of this case study is to
present the results of an initial research study conducted to identify
the desired professional characteristics of an industrial engineer with
an undergraduate degree and the emerging topic areas that should be
incorporated into the curriculum to prepare industrial engineering
(IE) graduates for the future workforce. Conclusions and
recommendations for applied industrial engineering syllabus have
been gathered and reported below. A two-pronged approach was
taken which included a method of benchmarking by comparing the
applied industrial engineering curricula of various universities and an
industry survey to identify job market requirements. This
methodology produced an analysis of the changing nature of
industrial engineering from learning to practical education. A
curriculum study for engineering is a relatively unexplored area of
research in the Middle East, much less for applied industrial
engineering. This work is an effort to bridge the gap between
theoretical study in the classroom and the real world work
applications in the industrial and service sectors.
Abstract: Artificial Immune Systems (AIS), inspired by the
human immune system, are algorithms and mechanisms which are
self-adaptive and self-learning classifiers capable of recognizing and
classifying by learning, long-term memory and association. Unlike
other human system inspired techniques like genetic algorithms and
neural networks, AIS includes a range of algorithms modeling on
different immune mechanism of the body. In this paper, a mechanism
of a human immune system based on apoptosis is adopted to build an
Intrusion Detection System (IDS) to protect computer networks.
Features are selected from network traffic using Fisher Score. Based
on the selected features, the record/connection is classified as either
an attack or normal traffic by the proposed methodology. Simulation
results demonstrates that the proposed AIS based on apoptosis
performs better than existing AIS for intrusion detection.