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: We regard forecasting of energy consumption by
private production areas of a large industrial facility as well as by the
facility itself. As for production areas, the forecast is made based on
empirical dependencies of the specific energy consumption and the
production output. As for the facility itself, implementation of the
task to minimize the energy consumption forecasting error is based
on adjustment of the facility’s actual energy consumption values
evaluated with the metering device and the total design energy
consumption of separate production areas of the facility. The
suggested procedure of optimal energy consumption was tested based
on the actual data of core product output and energy consumption by
a group of workshops and power plants of the large iron and steel
facility. Test results show that implementation of this procedure gives
the mean accuracy of energy consumption forecasting for winter
2014 of 0.11% for the group of workshops and 0.137% for the power
plants.
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 study, the time-dependent behavior of damaged
reinforced concrete shear wall structures strengthened with composite
plates having variable fibers spacing was investigated to analyze their
seismic response. In the analytical formulation, the adherent and the
adhesive layers are all modeled as shear walls, using the mixed Finite
Element Method (FEM). The anisotropic damage model is adopted to
describe the damage extent of the Reinforced Concrete shear walls.
The phenomenon of creep and shrinkage of concrete has been
determined by Eurocode 2. Large earthquakes recorded in Algeria
(El-Asnam and Boumerdes) have been tested to demonstrate the
accuracy of the proposed method. Numerical results are obtained for non-uniform distributions of
carbon fibers in epoxy matrices. The effects of damage extent and the
delay mechanism creep and shrinkage of concrete are highlighted.
Prospects are being studied.
Abstract: In this paper, we have reported birefringence
manipulation in regenerated high birefringent fiber Bragg grating
(RPMG) by using CO2 laser annealing method. The results indicate
that the birefringence of RPMG remains unchanged after CO2 laser
annealing followed by slow cooling process, but reduced after fast
cooling process (~5.6×10-5). After a series of annealing procedures
with different cooling rates, the obtained results show that slower the
cooling rate, higher the birefringence of RPMG. The volume, thermal
expansion coefficient (TEC) and glass transition temperature (Tg)
change of stress applying part in RPMG during cooling process are
responsible for the birefringence change. Therefore, these findings
are important to the RPMG sensor in high and dynamic temperature
environment. The measuring accuracy, range and sensitivity of
RPMG sensor is greatly affected by its birefringence value. This
work also opens up a new application of CO2 laser for fiber annealing
and birefringence modification.
Abstract: Liver segmentation from medical images poses more
challenges than analogous segmentations of other organs. This
contribution introduces a liver segmentation method from a series of
computer tomography images. Overall, we present a novel method for
segmenting liver by coupling density matching with shape priors.
Density matching signifies a tracking method which operates via
maximizing the Bhattacharyya similarity measure between the
photometric distribution from an estimated image region and a model
photometric distribution. Density matching controls the direction of
the evolution process and slows down the evolving contour in regions
with weak edges. The shape prior improves the robustness of density
matching and discourages the evolving contour from exceeding liver’s
boundaries at regions with weak boundaries. The model is
implemented using a modified distance regularized level set (DRLS)
model. The experimental results show that the method achieves a
satisfactory result. By comparing with the original DRLS model, it is
evident that the proposed model herein is more effective in addressing
the over segmentation problem. Finally, we gauge our performance of
our model against matrices comprising of accuracy, sensitivity, and
specificity.
Abstract: The practical efficient approach is suggested for
estimation of the seismoacoustic sources energy in C-OTDR
monitoring systems. This approach is represents the sequential plan
for confidence estimation both the seismoacoustic sources energy, as
well the absorption coefficient of the soil. The sequential plan
delivers the non-asymptotic guaranteed accuracy of obtained
estimates in the form of non-asymptotic confidence regions with
prescribed sizes. These confidence regions are valid for a finite
sample size when the distributions of the observations are unknown.
Thus, suggested estimates are non-asymptotic and nonparametric,
and also these estimates guarantee the prescribed estimation accuracy
in form of prior prescribed size of confidence regions, and prescribed
confidence coefficient value.
Abstract: High Performance Liquid Chromatography (HPLC)
method was developed and validated for simultaneous estimation of
6-Gingerol(6G) and 6-Shogaol(6S) in joint pain relief gel containing
ginger extract. The chromatographic separation was achieved by
using C18 column, 150 x 4.6mm i.d., 5μ Luna, mobile phase
containing acetonitrile and water (gradient elution). The flow rate
was 1.0 ml/min and the absorbance was monitored at 282 nm. The
proposed method was validated in terms of the analytical parameters
such as specificity, accuracy, precision, linearity, range, limit of
detection (LOD), limit of quantification (LOQ), and determined
based on the International Conference on Harmonization (ICH)
guidelines. The linearity ranges of 6G and 6S were obtained over 20-
60 and 6-18 μg/ml respectively. Good linearity was observed over the
above-mentioned range with linear regression equation Y= 11016x-
23778 for 6G and Y = 19276x-19604 for 6S (x is concentration of
analytes in μg/ml and Y is peak area). The value of correlation
coefficient was found to be 0.9994 for both markers. The limit of
detection (LOD) and limit of quantification (LOQ) for 6G were
0.8567 and 2.8555 μg/ml and for 6S were 0.3672 and 1.2238 μg/ml
respectively. The recovery range for 6G and 6S were found to be
91.57 to 102.36 % and 84.73 to 92.85 % for all three spiked levels.
The RSD values from repeated extractions for 6G and 6S were 3.43
and 3.09% respectively. The validation of developed method on
precision, accuracy, specificity, linearity, and range were also
performed with well-accepted results.
Abstract: A method of effective planning and control of
industrial facility energy consumption is offered. The method allows
optimally arranging the management and full control of complex
production facilities in accordance with the criteria of minimal
technical and economic losses at the forecasting control. The method
is based on the optimal construction of the power efficiency
characteristics with the prescribed accuracy. The problem of optimal
designing of the forecasting model is solved on the basis of three
criteria: maximizing the weighted sum of the points of forecasting
with the prescribed accuracy; the solving of the problem by the
standard principles at the incomplete statistic data on the basis of
minimization of the regularized function; minimizing the technical
and economic losses due to the forecasting errors.
Abstract: A retrospective study conducted at Christian Medical
College (CMC) Teaching Hospital, Vellore, India on 14th August
2014 to assess the accuracy of clinically estimated foetal weight upon
labour admission. Estimating foetal weight is a crucial factor in
assessing maternal and foetal complications during and after labour.
Medical notes of ninety-eight postnatal women who fulfilled the
inclusion criteria were studied to evaluate the correlation between
their recorded Estimated Foetal Weight (EFW) on admission and
actual birth weight (ABW) of the newborn after delivery. Data
concerning maternal and foetal demographics was also noted.
Accuracy was determined by absolute percentage error and
proportion of estimates within 10% of ABW. Actual birth weights
ranged from 950-4080g. A strong positive correlation between EFW
and ABW (r=0.904) was noted. Term deliveries (≥40 weeks) in the
normal weight range (2500-4000g) had a 59.5% estimation accuracy
(n=74) compared to pre-term (4000g) were underestimated by 25% (n=3) and low birthweight
(LBW) babies were overestimated by 12.7% (n=9). Registrars who
estimated foetal weight were accurate in babies within normal weight
ranges. However, there needs to be an improvement in predicting
weight of macrosomic and LBW foetuses. We have suggested the use
of an amended version of the Johnson’s formula for the Indian
population for improvement and a need to re-audit once
implemented.
Abstract: Paper deals with the modeling and simulation of energy consumption and GHG production of two different modes of regional passenger transport – road and railway. These two transport modes use the same type of fuel – diesel. Modeling and simulation of the energy consumption in transport is often used due to calculation satisfactory accuracy and cost efficiency. Paper deals with the calculation based on EN standards and information collected from technical information from vehicle producers and characteristics of tracks. Calculation included maximal theoretical capacity of bus and train and real passenger’s measurement from operation. Final energy consumption and GHG production is calculated by using software simulation. In evaluation of the simulation is used system “well to wheel”.
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: Wireless Sensor Networks (WSNs), which sense
environmental data with battery-powered nodes, require multi-hop
communication. This power-demanding task adds an extra workload
that is unfairly distributed across the network. As a result, nodes run
out of battery at different times: this requires an impractical
individual node maintenance scheme. Therefore we investigate a new
Cooperative Sensing approach that extends the WSN operational life
and allows a more practical network maintenance scheme (where all
nodes deplete their batteries almost at the same time). We propose a
novel cooperative algorithm that derives a piecewise representation
of the sensed signal while controlling approximation accuracy.
Simulations show that our algorithm increases WSN operational life
and spreads communication workload evenly. Results convey a
counterintuitive conclusion: distributing workload fairly amongst
nodes may not decrease the network power consumption and yet
extend the WSN operational life. This is achieved as our cooperative
approach decreases the workload of the most burdened cluster in the
network.
Abstract: The ultimate purpose of this investigation was to
determine the teachers’ opinions as well as students’ opinions
towards the Adapted English Lessons. The subjects of the study were
5 Thai teachers, who teach English, and 85 Grade 10 mixed-ability
students at Triamudom Suksa Pattanakarn Ratchada School,
Bangkok, Thailand. The research instruments included questionnaires
and the informal interview. The data from the research instruments
was collected and analyzed concerning linguistic principles of
minimal pair and articulatory phonetics as well as teaching
techniques of mimicry-memorization; vocabulary substitution drills,
language pattern drills, reading comprehension exercise, practicing
listening, speaking and writing skill and communicative activities;
informal talk and free writing. The data was statistically compiled
according to an arithmetic percentage. The results showed that the
teachers and students have very highly positive opinions towards
adapting linguistic principles for teaching and learning phonological
accuracy. Teaching techniques provided in the Adapted English
Lessons can be used efficiently in the classroom. The teachers and
students have positive opinions towards them too.
Abstract: Lateral torsional buckling is a global buckling mode
which should be considered in design of slender structural members
under flexure about their strong axis. It is possible to compute the
load which causes lateral torsional buckling of a beam by finite
element analysis, however, closed form equations are needed in
engineering practice for calculation ease which can be obtained by
using energy method. In lateral torsional buckling applications of
energy method, a proper function for the critical lateral torsional
buckling mode should be chosen which can be thought as the
variation of twisting angle along the buckled beam. Accuracy of the
results depends on how close is the chosen function to the exact
mode. Since critical lateral torsional buckling mode of the cantilever
I-beams varies due to material properties, section properties and
loading case, the hardest step is to determine a proper mode function
in application of energy method. This paper presents an approximate function for critical lateral
torsional buckling mode of doubly symmetric cantilever I-beams.
Coefficient matrices are calculated for concentrated load at free end,
uniformly distributed load and constant moment along the beam
cases. Critical lateral torsional buckling modes obtained by presented
function and exact solutions are compared. It is found that the modes
obtained by presented function coincide with differential equation
solutions for considered loading cases.
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: In this article, we used the residual correction method
to deal with transient thermoelastic problems with a hollow spherical
region when the continuum medium possesses spherically isotropic
thermoelastic properties. Based on linear thermoelastic theory, the
equations of hyperbolic heat conduction and thermoelastic motion
were combined to establish the thermoelastic dynamic model with
consideration of the deformation acceleration effect and non-Fourier
effect under the condition of transient thermal shock. The approximate
solutions of temperature and displacement distributions are obtained
using the residual correction method based on the maximum principle
in combination with the finite difference method, making it easier and
faster to obtain upper and lower approximations of exact solutions.
The proposed method is found to be an effective numerical method
with satisfactory accuracy. Moreover, the result shows that the effect
of transient thermal shock induced by deformation acceleration is
enhanced by non-Fourier heat conduction with increased peak stress.
The influence on the stress increases with the thermal relaxation time.
Abstract: The relationship dependence between RSS and distance
in an enclosed environment is an important consideration because it is
a factor that can influence the reliability of any localization algorithm
founded on RSS. Several algorithms effectively reduce the variance of
RSS to improve localization or accuracy performance. Our proposed
algorithm essentially avoids this pitfall and consequently, its high
adaptability in the face of erratic radio signal. Using 3 anchors in
close proximity of each other, we are able to establish that RSS can be
used as reliable indicator for localization with an acceptable degree of
accuracy. Inherent in this concept, is the ability for each prospective
anchor to validate (guarantee) the position or the proximity of the
other 2 anchors involved in the localization and vice versa. This
procedure ensures that the uncertainties of radio signals due to
multipath effects in enclosed environments are minimized. A major
driver of this idea is the implicit topological relationship among
sensors due to raw radio signal strength. The algorithm is an area
based algorithm; however, it does not trade accuracy for precision
(i.e the size of the returned area).
Abstract: In this paper, we present an application of Riemannian
geometry for processing non-Euclidean image data. We consider the
image as residing in a Riemannian manifold, for developing a new
method to brain edge detection and brain extraction. Automating this
process is a challenge due to the high diversity in appearance brain
tissue, among different patients and sequences. The main contribution, in this paper, is the use of an edge-based
anisotropic diffusion tensor for the segmentation task by integrating
both image edge geometry and Riemannian manifold (geodesic,
metric tensor) to regularize the convergence contour and extract
complex anatomical structures. We check the accuracy of the
segmentation results on simulated brain MRI scans of single
T1-weighted, T2-weighted and Proton Density sequences. We
validate our approach using two different databases: BrainWeb
database, and MRI Multiple sclerosis Database (MRI MS DB). We
have compared, qualitatively and quantitatively, our approach with
the well-known brain extraction algorithms. We show that using
a Riemannian manifolds to medical image analysis improves the
efficient results to brain extraction, in real time, outperforming the
results of the standard techniques.
Abstract: In this article, the radial displacement error correction
capability of a high precision spindle grinding caused by unbalance
force was investigated. The spindle shaft is considered as a flexible
rotor mounted on two sets of angular contact ball bearing. Finite
element methods (FEM) have been adopted for obtaining the
equation of motion of the spindle. In this paper, firstly, natural
frequencies, critical frequencies, and amplitude of the unbalance
response caused by residual unbalance are determined in order to
investigate the spindle behaviors. Furthermore, an optimization
design algorithm is employed to minimize radial displacement of the
spindle which considers dimension of the spindle shaft, the dynamic
characteristics of the bearings, critical frequencies and amplitude of
the unbalance response, and computes optimum spindle diameters
and stiffness and damping of the bearings. Numerical simulation
results show that by optimizing the spindle diameters, and stiffness
and damping in the bearings, radial displacement of the spindle can
be reduced. A spindle about 4 μm radial displacement error can be
compensated with 2 μm accuracy. This certainly can improve the
accuracy of the product of machining.