Abstract: This paper presents the development of a Bayesian
belief network classifier for prediction of graft status and survival
period in renal transplantation using the patient profile information
prior to the transplantation. The objective was to explore feasibility
of developing a decision making tool for identifying the most suitable
recipient among the candidate pool members. The dataset was
compiled from the University of Toledo Medical Center Hospital
patients as reported to the United Network Organ Sharing, and had
1228 patient records for the period covering 1987 through 2009. The
Bayes net classifiers were developed using the Weka machine
learning software workbench. Two separate classifiers were induced
from the data set, one to predict the status of the graft as either failed
or living, and a second classifier to predict the graft survival period.
The classifier for graft status prediction performed very well with a
prediction accuracy of 97.8% and true positive values of 0.967 and
0.988 for the living and failed classes, respectively. The second
classifier to predict the graft survival period yielded a prediction
accuracy of 68.2% and a true positive rate of 0.85 for the class
representing those instances with kidneys failing during the first year
following transplantation. Simulation results indicated that it is
feasible to develop a successful Bayesian belief network classifier for
prediction of graft status, but not the graft survival period, using the
information in UNOS database.
Abstract: In this study, a new criterion for determining the number of classes an image should be segmented is proposed. This criterion is based on discriminant analysis for measuring the separability among the segmented classes of pixels. Based on the new discriminant criterion, two algorithms for recursively segmenting the image into determined number of classes are proposed. The proposed methods can automatically and correctly segment objects with various illuminations into separated images for further processing. Experiments on the extraction of text strings from complex document images demonstrate the effectiveness of the proposed methods.1
Abstract: Tracing and locating the geographical location of users (Geolocation) is used extensively in todays Internet. Whenever we, e.g., request a page from google we are - unless there was a specific configuration made - automatically forwarded to the page with the relevant language and amongst others, dependent on our location identified, specific commercials are presented. Especially within the area of Network Security, Geolocation has a significant impact. Because of the way the Internet works, attacks can be executed from almost everywhere. Therefore, for an attribution, knowledge of the origination of an attack - and thus Geolocation - is mandatory in order to be able to trace back an attacker. In addition, Geolocation can also be used very successfully to increase the security of a network during operation (i.e. before an intrusion actually has taken place). Similar to greylisting in emails, Geolocation allows to (i) correlate attacks detected with new connections and (ii) as a consequence to classify traffic a priori as more suspicious (thus particularly allowing to inspect this traffic in more detail). Although numerous techniques for Geolocation are existing, each strategy is subject to certain restrictions. Following the ideas of Endo et al., this publication tries to overcome these shortcomings with a combined solution of different methods to allow improved and optimized Geolocation. Thus, we present our architecture for improved Geolocation, by designing a new algorithm, which combines several Geolocation techniques to increase the accuracy.
Abstract: Due to a high unemployment rate among local people
and a high reliance on expatriate workers, the governments in the
Gulf Co-operation Council (GCC) countries have been implementing
programmes of localisation (replacing foreign workers with GCC
nationals). These programmes have been successful in the public
sector but much less so in the private sector. However, there are now
insufficient jobs for locals in the public sector and the onus to provide
employment has fallen on the private sector. This paper is concerned
with a study, which is a work in progress (certain elements are
complete but not the whole study), investigating the effective
implementation of localisation policies in four- and five-star hotels in
the Kingdom of Saudi Arabia (KSA) and the United Arab Emirates
(UAE). The purpose of the paper is to identify the research gap, and
to present the need for the research. Further, it will explain how this
research was conducted.
Studies of localisation in the GCC countries are under-represented
in scholarly literature. Currently, the hotel sectors in KSA and UAE
play an important part in the countries’ economies. However, the
total proportion of Saudis working in the hotel sector in KSA is
slightly under 8%, and in the UAE, the hotel sector remains highly
reliant on expatriates. There is therefore a need for research on
strategies to enhance the implementation of the localisation policies
in general and in the hotel sector in particular.
Further, despite the importance of the hotel sector to their
economies, there remains a dearth of research into the
implementation of localisation policies in this sector. Indeed, as far as
the researchers are aware, there is no study examining localisation in
the hotel sector in KSA, and few in the UAE. This represents a
considerable research gap.
Regarding how the research was carried out, a multiple case study
strategy was used. The four- and five-star hotel sector in KSA is one
of the cases, while the four- and five-star hotel sector in the UAE is
the other case. Four- and five-star hotels in KSA and the UAE were
chosen as these countries have the longest established localisation
policies of all the GCC states and there are more hotels of these
classifications in these countries than in any of the other Gulf
countries. A literature review was carried out to underpin the
research. The empirical data were gathered in three phases. In order
to gain a pre-understanding of the issues pertaining to the research
context, Phase I involved eight unstructured interviews with officials
from the Saudi Commission for Tourism and Antiquities (three
interviewees); the Saudi Human Resources Development Fund (one);
the Abu Dhabi Tourism and Culture Authority (three); and the Abu
Dhabi Development Fund (one).
In Phase II, a questionnaire was administered to 24 managers and
24 employees in four- and five-star hotels in each country to obtain
their beliefs, attitudes, opinions, preferences and practices concerning
localisation.
Unstructured interviews were carried out in Phase III with six
managers in each country in order to allow them to express opinions
that may not have been explored in sufficient depth in the
questionnaire. The interviews in Phases I and III were analysed using
thematic analysis and SPSS will be used to analyse the questionnaire
data.
It is recommended that future research be undertaken on a larger
scale, with a larger sample taken from all over KSA and the UAE
rather than from only four cities (i.e., Riyadh and Jeddah in KSA and
Abu Dhabi and Sharjah in the UAE), as was the case in this research.
Abstract: In the present article, nonlinear vibration analysis of
single layer graphene sheets is presented and the effect of small
length scale is investigated. Using the Hamilton's principle, the three
coupled nonlinear equations of motion are obtained based on the von
Karman geometrical model and Eringen theory of nonlocal
continuum. The solutions of Free nonlinear vibration, based on a one
term mode shape, are found for both simply supported and clamped
graphene sheets. A complete analysis of graphene sheets with
movable as well as immovable in-plane conditions is also carried out.
The results obtained herein are compared with those available in the
literature for classical isotropic rectangular plates and excellent
agreement is seen. Also, the nonlinear effects are presented as
functions of geometric properties and small scale parameter.
Abstract: Automatic currency note recognition invariably
depends on the currency note characteristics of a particular country
and the extraction of features directly affects the recognition ability.
Sri Lanka has not been involved in any kind of research or
implementation of this kind. The proposed system “SLCRec" comes
up with a solution focusing on minimizing false rejection of notes.
Sri Lankan currency notes undergo severe changes in image quality
in usage. Hence a special linear transformation function is adapted to
wipe out noise patterns from backgrounds without affecting the
notes- characteristic images and re-appear images of interest. The
transformation maps the original gray scale range into a smaller
range of 0 to 125. Applying Edge detection after the transformation
provided better robustness for noise and fair representation of edges
for new and old damaged notes. A three layer back propagation
neural network is presented with the number of edges detected in row
order of the notes and classification is accepted in four classes of
interest which are 100, 500, 1000 and 2000 rupee notes. The
experiments showed good classification results and proved that the
proposed methodology has the capability of separating classes
properly in varying image conditions.
Abstract: This paper presents an approach for early breast
cancer diagnostic by employing combination of artificial neural
networks (ANN) and multiwaveletpacket based subband image
decomposition. The microcalcifications correspond to high-frequency
components of the image spectrum, detection of microcalcifications
is achieved by decomposing the mammograms into different
frequency subbands,, reconstructing the mammograms from the
subbands containing only high frequencies. For this approach we
employed different types of multiwaveletpacket. We used the result
as an input of neural network for classification. The proposed
methodology is tested using the Nijmegen and the Mammographic
Image Analysis Society (MIAS) mammographic databases and
images collected from local hospitals. Results are presented as the
receiver operating characteristic (ROC) performance and are
quantified by the area under the ROC curve.
Abstract: The existing image coding standards generally degrades at low bit-rates because of the underlying block based Discrete Cosine Transform scheme. Over the past decade, the success of wavelets in solving many different problems has contributed to its unprecedented popularity. Due to implementation constraints scalar wavelets do not posses all the properties such as orthogonality, short support, linear phase symmetry, and a high order of approximation through vanishing moments simultaneously, which are very much essential for signal processing. New class of wavelets called 'Multiwavelets' which posses more than one scaling function overcomes this problem. This paper presents a new image coding scheme based on non linear approximation of multiwavelet coefficients along with multistage vector quantization. The performance of the proposed scheme is compared with the results obtained from scalar wavelets.
Abstract: The functional equation f(3x) = 4f(3x-3)+f(3x- 6) will be solved and its Hyers-Ulam stability will be also investigated
in the class of functions f : R → X, where X is a real Banach space.
Abstract: Today-s Wi Fi generation utilize the latest technology in their daily lives. Instructors at National University, the second largest non profit private institution of higher learning in California, are incorporating these new tools to modify their Online class formats to better accommodate these new skills in their distance education delivery modes. The University provides accelerated learning in a one-course per month format both Onsite and Online. Since there has been such a significant increase in Online classes over the past three years, and it is expected to grow even more over the over the next five years, Instructors cannot afford to maintain the status quo and not take advantage of these new options. It is at the discretion of the instructors which accessory they use and how comfortable and familiar they are with the technology. This paper explores the effects and summarizes students- comments of some of these new technological options which have been recently provided in order to make students- online learning experience more exciting and meaningful.
Abstract: Symbolic dynamics studies dynamical systems on the basis of the symbol sequences obtained for a suitable partition of the state space. This approach exploits the property that system dynamics reduce to a shift operation in symbol space. This shift operator is a chaotic mapping. In this article we show that in the symbol space exist other chaotic mappings.
Abstract: It has often been said that the strength of any country
resides in the strength of its industrial sector, and Progress in
industrial society has been accomplished by the creation of new
technologies. Developments have been facilitated by the increasing
availability of advanced manufacturing technology (AMT), in
addition the implementation of advanced manufacturing technology
(AMT) requires careful planning at all levels of the organization to
ensure that the implementation will achieve the intended goals.
Justification and implementation of advanced manufacturing
technology (AMT) involves decisions that are crucial for the
practitioners regarding the survival of business in the present days of
uncertain manufacturing world. This paper assists the industrial
managers to consider all the important criteria for success AMT
implementation, when purchasing new technology. Concurrently,
this paper classifies the tangible benefits of a technology that are
evaluated by addressing both cost and time dimensions, and the
intangible benefits are evaluated by addressing technological,
strategic, social and human issues to identify and create awareness of
the essential elements in the AMT implementation process and
identify the necessary actions before implementing AMT.
Abstract: Data mining can be called as a technique to extract
information from data. It is the process of obtaining hidden
information and then turning it into qualified knowledge by statistical
and artificial intelligence technique. One of its application areas is
medical area to form decision support systems for diagnosis just by
inventing meaningful information from given medical data. In this
study a decision support system for diagnosis of illness that make use
of data mining and three different artificial intelligence classifier
algorithms namely Multilayer Perceptron, Naive Bayes Classifier and
J.48. Pima Indian dataset of UCI Machine Learning Repository was
used. This dataset includes urinary and blood test results of 768
patients. These test results consist of 8 different feature vectors.
Obtained classifying results were compared with the previous studies.
The suggestions for future studies were presented.
Abstract: Globalization and therefore increasing tight competition among companies, have resulted to increase the importance of making well-timed decision. Devising and employing effective strategies, that are flexible and adaptive to changing market, stand a greater chance of being effective in the long-term. In other side, a clear focus on managing the entire product lifecycle has emerged as critical areas for investment. Therefore, applying wellorganized tools to employ past experience in new case, helps to make proper and managerial decisions. Case based reasoning (CBR) is based on a means of solving a new problem by using or adapting solutions to old problems. In this paper, an adapted CBR model with k-nearest neighbor (K-NN) is employed to provide suggestions for better decision making which are adopted for a given product in the middle of life phase. The set of solutions are weighted by CBR in the principle of group decision making. Wrapper approach of genetic algorithm is employed to generate optimal feature subsets. The dataset of the department store, including various products which are collected among two years, have been used. K-fold approach is used to evaluate the classification accuracy rate. Empirical results are compared with classical case based reasoning algorithm which has no special process for feature selection, CBR-PCA algorithm based on filter approach feature selection, and Artificial Neural Network. The results indicate that the predictive performance of the model, compare with two CBR algorithms, in specific case is more effective.
Abstract: management of medical devices in hospitals includes
the planning of medical equipment acquisition and maintenance. The
presence of critical and non-critical areas together with technological
proliferation render the management of medical devices very
complex. This study creates an easy and objective methodology for
the analysis of medical equipment maintenance, that makes the
management of medical devices more feasible. The study has been
carried out at Florence Hospital Careggi and it aims to help the
clinical engineering department to manage medical equipment by
clarifying the hospital situation through a characterization of the
different areas, technologies and fault typologies.
Abstract: In this paper, stabilization of an Active Magnetic Bearing (AMB) system with varying rotor speed using Sliding Mode Control (SMC) technique is considered. The gyroscopic effect inherited in the system is proportional to rotor speed in which this nonlinearity effect causes high system instability as the rotor speed increases. Also, transformation of the AMB dynamic model into a new class of uncertain system shows that this gyroscopic effect lies in the mismatched part of the system matrix. Moreover, the current gain parameter is allowed to be varied in a known bound as an uncertainty in the input matrix. SMC design method is proposed in which the sufficient condition that guarantees the global exponential stability of the reduced-order system is represented in Linear Matrix Inequality (LMI). Then, a new chattering-free control law is established such that the system states are driven to reach the switching surface and stay on it thereafter. The performance of the controller applied to the AMB model is demonstrated through simulation works under various system conditions.
Abstract: Flow through micro and mini channels requires relatively
high driving pressure due to the large fluid pressure drop
through these channels. Consequently the forces acting on the walls of
the channel due to the fluid pressure are also large. Due to these forces
there are displacement fields set up in the solid substrate containing
the channels. If the movement of the substrate is constrained at some
points, then stress fields are established in the substrate. On the other
hand, if the deformation of the channel shape is sufficiently large
then its effect on the fluid flow is important to be calculated. Such
coupled fluid-solid systems form a class of problems known as fluidstructure
interactions. In the present work a co-located finite volume
discretization procedure on unstructured meshes is described for
solving fluid-structure interaction type of problems. A linear elastic
solid is assumed for which the effect of the channel deformation
on the flow is neglected. Thus the governing equations for the
fluid and the solid are decoupled and are solved separately. The
procedure is validated by solving two benchmark problems, one from
fluid mechanics and another from solid mechanics. A fluid-structure
interaction problem of flow through a U-shaped channel embedded
in a plate is solved.
Abstract: A property is persistent if for any many-sorted term rewriting system , has the property if and only if term rewriting system , which results from by omitting its sort information, has the property. Zantema showed that termination is persistent for term rewriting systems without collapsing or duplicating rules. In this paper, we show that the Zantema's result can be extended to term rewriting systems on ordered sorts, i.e., termination is persistent for term rewriting systems on ordered sorts without collapsing, decreasing or duplicating rules. Furthermore we give the example as application of this result. Also we obtain that completeness is persistent for this class of term rewriting systems.
Abstract: This paper reports a new pattern recognition approach for face recognition. The biological model of light receptors - cones and rods in human eyes and the way they are associated with pattern vision in human vision forms the basis of this approach. The functional model is simulated using CWD and WPD. The paper also discusses the experiments performed for face recognition using the features extracted from images in the AT & T face database. Artificial Neural Network and k- Nearest Neighbour classifier algorithms are employed for the recognition purpose. A feature vector is formed for each of the face images in the database and recognition accuracies are computed and compared using the classifiers. Simulation results show that the proposed method outperforms traditional way of feature extraction methods prevailing for pattern recognition in terms of recognition accuracy for face images with pose and illumination variations.
Abstract: A predictive clustering hybrid regression (pCHR)
approach was developed and evaluated using dataset from H2-
producing sucrose-based bioreactor operated for 15 months. The aim
was to model and predict the H2-production rate using information
available about envirome and metabolome of the bioprocess. Selforganizing
maps (SOM) and Sammon map were used to visualize the
dataset and to identify main metabolic patterns and clusters in
bioprocess data. Three metabolic clusters: acetate coupled with other
metabolites, butyrate only, and transition phases were detected. The
developed pCHR model combines principles of k-means clustering,
kNN classification and regression techniques. The model performed
well in modeling and predicting the H2-production rate with mean
square error values of 0.0014 and 0.0032, respectively.