Abstract: Recommender systems are usually regarded as an
important marketing tool in the e-commerce. They use important
information about users to facilitate accurate recommendation. The
information includes user context such as location, time and interest
for personalization of mobile users. We can easily collect information
about location and time because mobile devices communicate with the
base station of the service provider. However, information about user
interest can-t be easily collected because user interest can not be
captured automatically without user-s approval process. User interest
usually represented as a need. In this study, we classify needs into two
types according to prior research. This study investigates the
usefulness of data mining techniques for classifying user need type for
recommendation systems. We employ several data mining techniques
including artificial neural networks, decision trees, case-based
reasoning, and multivariate discriminant analysis. Experimental
results show that CHAID algorithm outperforms other models for
classifying user need type. This study performs McNemar test to
examine the statistical significance of the differences of classification
results. The results of McNemar test also show that CHAID performs
better than the other models with statistical significance.
Abstract: In this paper a novel algorithm is proposed that integrates the process of fuzzy hierarchy generation and rule discovery for automated discovery of Production Rules with Fuzzy Hierarchy (PRFH) in large databases.A concept of frequency matrix (Freq) introduced to summarize large database that helps in minimizing the number of database accesses, identification and removal of irrelevant attribute values and weak classes during the fuzzy hierarchy generation.Experimental results have established the effectiveness of the proposed algorithm.
Abstract: Existing work in temporal logic on representing the
execution of infinitely many transactions, uses linear-time temporal
logic (LTL) and only models two-step transactions. In this paper,
we use the comparatively efficient branching-time computational tree
logic CTL and extend the transaction model to a class of multistep
transactions, by introducing distinguished propositional variables
to represent the read and write steps of n multi-step transactions
accessing m data items infinitely many times. We prove that the
well known correspondence between acyclicity of conflict graphs
and serializability for finite schedules, extends to infinite schedules.
Furthermore, in the case of transactions accessing the same set of
data items in (possibly) different orders, serializability corresponds
to the absence of cycles of length two. This result is used to give an
efficient encoding of the serializability condition into CTL.
Abstract: E-learning refers to the specific kind of learning
experienced within the domain of educational technology, which can
be used in or out of the classroom. In this paper, we give an
overview of an e-learning platform 'An Innovative Interactive and
Online English Platform for Upper Primary Students' is an
interactive web-based application which will serve as an aid to the
primary school students in Mauritius. The objectives of this platform
are to offer quality learning resources for the English subject at our
primary level of education, encourage self-learning and hence
promote e-learning. The platform developed consists of several
interesting features, for example, the English Verb Conjugation tool,
Negative Form tool, Interrogative Form tool and Close Test
Generator. Thus, this learning platform will be useful at a time
where our country is looking for an alternative to private tuition and
also, looking forward to increase the pass rate.
Abstract: In recent years asymmetric cross section aluminum
alloy stock has been finding increasing use in various industrial manufacturing areas such as general structures and automotive
components. In these areas, components are generally required to have
complex curved configuration and, as such, a bending process is required during manufacture. Undesirable deformation in bending
processes such as flattening or wrinkling can easily occur when thin-walled sections are bent. Hence, a thorough understanding of the
bending behavior of such sections is needed to prevent these undesirable deformations. In this study, the bending behavior of
asymmetric channel section was examined using finite element analysis (FEA). Typical methods of preventing undesirable
deformation, such as asymmetric laminated elastic mandrels were included in FEA model of draw bending. Additionally, axial tension
was applied to prevent wrinkling. By utilizing the FE simulations effect of restriction dies and axial tension on undesirable deformation during the process was clarified.
Abstract: A number of studies highlighted problems related to
ERP systems, yet, most of these studies focus on the problems during
the project and implementation stages but not during the postimplementation
use process. Problems encountered in the process of
using ERP would hinder the effective exploitation and the extended
and continued use of ERP systems and their value to organisations.
This paper investigates the different types of problems users
(operational, supervisory and managerial) faced in using ERP and
how 'feral system' is used as the coping mechanism. The paper
adopts a qualitative method and uses data collected from two cases
and 26 interviews, to inductively develop a casual network model of
ERP usage problem and its coping mechanism. This model classified
post ERP usage problems as data quality, system quality, interface
and infrastructure. The model is also categorised the different coping
mechanism through use of 'feral system' inclusive of feral
information system, feral data and feral use of technology.
Abstract: A systematic and exhaustive method based on the group
structure of a unitary Lie algebra is proposed to generate an enormous
number of quantum codes. With respect to the algebraic structure,
the orthogonality condition, which is the central rule of generating
quantum codes, is proved to be fully equivalent to the distinguishability
of the elements in this structure. In addition, four types of
quantum codes are classified according to the relation of the codeword
operators and some initial quantum state. By linking the unitary Lie
algebra with the additive group, the classical correspondences of some
of these quantum codes can be rendered.
Abstract: This paper proposes a new approach to perform the
problem of real-time face detection. The proposed method combines
primitive Haar-Like feature and variance value to construct a new
feature, so-called Variance based Haar-Like feature. Face in image
can be represented with a small quantity of features using this
new feature. We used SVM instead of AdaBoost for training and
classification. We made a database containing 5,000 face samples
and 10,000 non-face samples extracted from real images for learning
purposed. The 5,000 face samples contain many images which have
many differences of light conditions. And experiments showed that
face detection system using Variance based Haar-Like feature and
SVM can be much more efficient than face detection system using
primitive Haar-Like feature and AdaBoost. We tested our method on
two Face databases and one Non-Face database. We have obtained
96.17% of correct detection rate on YaleB face database, which is
higher 4.21% than that of using primitive Haar-Like feature and
AdaBoost.
Abstract: This paper focuses on the quadratic stabilization problem for a class of uncertain impulsive switched systems. The uncertainty is assumed to be norm-bounded and enters both the state and the input matrices. Based on the Lyapunov methods, some results on robust stabilization and quadratic stabilization for the impulsive switched system are obtained. A stabilizing state feedback control law realizing the robust stabilization of the closed-loop system is constructed.
Abstract: We estimate snow velocity and snow drift density on hilly terrain under the assumption that the drifting snow mass can be represented using a micro-continuum approach (i.e. using a nonclassical mechanics approach assuming a class of fluids for which basic equations of mass, momentum and energy have been derived). In our model, the theory of coupled stress fluids proposed by Stokes [1] has been employed for the computation of flow parameters. Analyses of bulk drift velocity, drift density, drift transport and mass transport of snow particles have been carried out and computations made, considering various parametric effects. Results are compared with those of classical mechanics (logarithmic wind profile). The results indicate that particle size affects the flow characteristics significantly.
Abstract: Field mapping activity for an active volcano mainly in
the Torrid Zone is usually hampered by several problems such as steep
terrain and bad atmosphere conditions. In this paper we present a
simple solution for such problem by a combination Synthetic Aperture
Radar (SAR) and geostatistical methods. By this combination, we
could reduce the speckle effect from the SAR data and then estimate
roughness distribution of the pyroclastic flow deposits. The main
purpose of this study is to detect spatial distribution of new pyroclastic
flow deposits termed as P-zone accurately using the β°data from two
RADARSAT-1 SAR level-0 data. Single scene of Hyperion data and
field observation were used for cross-validation of the SAR results.
Mt. Merapi in central Java, Indonesia, was chosen as a study site and
the eruptions in May-June 2006 were examined. The P-zones were
found in the western and southern flanks. The area size and the longest
flow distance were calculated as 2.3 km2 and 6.8 km, respectively. The
grain size variation of the P-zone was mapped in detail from fine to
coarse deposits regarding the C-band wavelength of 5.6 cm.
Abstract: The restoration of extinct ponds is considered as one
of ways to gain new retention capacities for water which is getting
much more important issue with respect to expected impacts of a
climate change. However, there are also other pressures on the
landscape which must be all taken into consideration when making a
decision on the possible restoration of extinct ponds. The research
presented here focuses besides others on the restoration of former
ponds which could be important for both the flood protection and
drought impacts prevention. The first step of the methodology
development for the assessment of such areas is the assessment of
their present state. In this paper, the results of land use types
assessment for 22 localities are presented. These results confirm the
assumption that the most present land use type in such areas is the
permanent grassland. However, the spectra of land use types present
in extinct pond areas is very diverse and include besides others also
airport areas and industry.
Abstract: The mechanical properties including flexural and
tensile of neat vinyl ester and polymer based on layered silicate
nanocomposite materials are discussed. The addition of layered
silicate into the polymer matrix increased the tensile and flexural
modulus up to 1 wt.% clay loading. The incorporation of more clay
resulted in decreasing the mechanical properties which was traced to
the existence of aggregation layers. Likewise, up to 1 wt.% clay
loading, the thermal behaviour showed significant improvements and
at higher clay loading the thermal pattern was reduced. The
aggregation layers imparted a negative impact on the overall
mechanical and thermal properties. Wide Angle X-ray Diffraction,
Scanning Electron Microscopy and Transmission Electron
Microscopy were utilised in order to characterise the interlamellar
structure of nanocomposites.
Abstract: A comparison between the performance of Latin and
Arabic handwritten digits recognition problems is presented. The
performance of ten different classifiers is tested on two similar
Arabic and Latin handwritten digits databases. The analysis shows
that Arabic handwritten digits recognition problem is easier than that
of Latin digits. This is because the interclass difference in case of
Latin digits is smaller than in Arabic digits and variances in writing
Latin digits are larger. Consequently, weaker yet fast classifiers are
expected to play more prominent role in Arabic handwritten digits
recognition.
Abstract: In many industries, control charts is one of the most
frequently used tools for quality management. Hotelling-s T2 is used
widely in multivariate control chart. However, it has little defect when
detecting small or medium process shifts. The use of supplementary
sensitizing rules can improve the performance of detection. This study
applied sensitizing rules for Hotelling-s T2 control chart to improve the
performance of detection. Support vector machines (SVM) classifier
to identify the characteristic or group of characteristics that are
responsible for the signal and to classify the magnitude of the mean
shifts. The experimental results demonstrate that the support vector
machines (SVM) classifier can effectively identify the characteristic
or group of characteristics that caused the process mean shifts and the
magnitude of the shifts.
Abstract: High Speed PM Generators driven by micro-turbines
are widely used in Smart Grid System. So, this paper proposes
comparative study among six classical, optimized and genetic
analytical design cases for 400 kW output power at tip speed 200
m/s. These six design trials of High Speed Permanent Magnet
Synchronous Generators (HSPMSGs) are: Classical Sizing;
Unconstrained optimization for total losses and its minimization;
Constrained optimized total mass with bounded constraints are
introduced in the problem formulation. Then a genetic algorithm is
formulated for obtaining maximum efficiency and minimizing
machine size. In the second genetic problem formulation, we attempt
to obtain minimum mass, the machine sizing that is constrained by
the non-linear constraint function of machine losses. Finally, an
optimum torque per ampere genetic sizing is predicted. All results are
simulated with MATLAB, Optimization Toolbox and its Genetic
Algorithm. Finally, six analytical design examples comparisons are
introduced with study of machines waveforms, THD and rotor losses.
Abstract: One of the main environmental problems which affect extensive areas in the world is soil salinity. Traditional data collection methods are neither enough for considering this important environmental problem nor accurate for soil studies. Remote sensing data could overcome most of these problems. Although satellite images are commonly used for these studies, however there are still needs to find the best calibration between the data and real situations in each specified area. Neyshaboor area, North East of Iran was selected as a field study of this research. Landsat satellite images for this area were used in order to prepare suitable learning samples for processing and classifying the images. 300 locations were selected randomly in the area to collect soil samples and finally 273 locations were reselected for further laboratory works and image processing analysis. Electrical conductivity of all samples was measured. Six reflective bands of ETM+ satellite images taken from the study area in 2002 were used for soil salinity classification. The classification was carried out using common algorithms based on the best composition bands. The results showed that the reflective bands 7, 3, 4 and 1 are the best band composition for preparing the color composite images. We also found out, that hybrid classification is a suitable method for identifying and delineation of different salinity classes in the area.
Abstract: Support Vector Machine (SVM) is a recent class of statistical classification and regression techniques playing an increasing role in applications to detection problems in various engineering problems, notably in statistical signal processing, pattern recognition, image analysis, and communication systems. In this paper, SVM is applied to an infrared (IR) binary communication system with different types of channel models including Ricean multipath fading and partially developed scattering channel with additive white Gaussian noise (AWGN) at the receiver. The structure and performance of SVM in terms of the bit error rate (BER) metric is derived and simulated for these channel stochastic models and the computational complexity of the implementation, in terms of average computational time per bit, is also presented. The performance of SVM is then compared to classical binary signal maximum likelihood detection using a matched filter driven by On-Off keying (OOK) modulation. We found that the performance of SVM is superior to that of the traditional optimal detection schemes used in statistical communication, especially for very low signal-to-noise ratio (SNR) ranges. For large SNR, the performance of the SVM is similar to that of the classical detectors. The implication of these results is that SVM can prove very beneficial to IR communication systems that notoriously suffer from low SNR at the cost of increased computational complexity.
Abstract: SoftBoost is a recently presented boosting algorithm,
which trades off the size of achieved classification margin and
generalization performance. This paper presents a performance
evaluation of SoftBoost algorithm on the generic object recognition
problem. An appearance-based generic object recognition
model is used. The evaluation experiments are performed using
a difficult object recognition benchmark. An assessment with respect
to different degrees of label noise as well as a comparison to
the well known AdaBoost algorithm is performed. The obtained
results reveal that SoftBoost is encouraged to be used in cases
when the training data is known to have a high degree of noise.
Otherwise, using Adaboost can achieve better performance.
Abstract: Speed estimation is one of the important and practical tasks in machine vision, Robotic and Mechatronic. the availability of high quality and inexpensive video cameras, and the increasing need for automated video analysis has generated a great deal of interest in machine vision algorithms. Numerous approaches for speed estimation have been proposed. So classification and survey of the proposed methods can be very useful. The goal of this paper is first to review and verify these methods. Then we will propose a novel algorithm to estimate the speed of moving object by using fuzzy concept. There is a direct relation between motion blur parameters and object speed. In our new approach we will use Radon transform to find direction of blurred image, and Fuzzy sets to estimate motion blur length. The most benefit of this algorithm is its robustness and precision in noisy images. Our method was tested on many images with different range of SNR and is satisfiable.