Abstract: Methods for organizing web data into groups in order
to analyze web-based hypertext data and facilitate data availability
are very important in terms of the number of documents available
online. Thereby, the task of clustering web-based document structures
has many applications, e.g., improving information retrieval on the
web, better understanding of user navigation behavior, improving web
users requests servicing, and increasing web information accessibility.
In this paper we investigate a new approach for clustering web-based
hypertexts on the basis of their graph structures. The hypertexts will
be represented as so called generalized trees which are more general
than usual directed rooted trees, e.g., DOM-Trees. As a important
preprocessing step we measure the structural similarity between the
generalized trees on the basis of a similarity measure d. Then,
we apply agglomerative clustering to the obtained similarity matrix
in order to create clusters of hypertext graph patterns representing
navigation structures. In the present paper we will run our approach
on a data set of hypertext structures and obtain good results in
Web Structure Mining. Furthermore we outline the application of
our approach in Web Usage Mining as future work.
Abstract: Most standard software development methodologies
are often not applied to software projects in many developing
countries of the world. The approach generally practice is close to
what eXtreme Programming (XP) is likely promoting, just keep
coding and testing as the requirement evolves. XP is an agile
software process development methodology that has inherent
capability for improving efficiency of Business Software
Development (BSD). XP can facilitate Business-to-Development
(B2D) relationship due to its customer-oriented advocate. From
practitioner point of view, we applied XP to BSD and result shows
that customer involvement has positive impact on productivity, but
can as well frustrate the success of the project. In an effort to
promote software engineering practice in developing countries of
Africa, we present the experiment performed, lessons learned,
problems encountered and solution adopted in applying XP
methodology to BSD.
Abstract: Trust and Energy consumption is the most challenging
issue in routing protocol design for Mobile ad hoc networks
(MANETs), since mobile nodes are battery powered and nodes
behaviour are unpredictable. Furthermore replacing and recharging
batteries and making nodes co-operative is often impossible in
critical environments like military applications. In this paper, we
propose a trust based energy aware routing model in MANET.
During route discovery, node with more trust and maximum energy
capacity is selected as a router based on a parameter called
'Reliability'. Route request from the source is accepted by a node
only if its reliability is high. Otherwise, the route request is
discarded. This approach forms a reliable route from source to
destination thus increasing network life time, improving energy
utilization and decreasing number of packet loss during transmission.
Abstract: Pattern recognition is the research area of Artificial
Intelligence that studies the operation and design of systems that
recognize patterns in the data. Important application areas are image
analysis, character recognition, fingerprint classification, speech
analysis, DNA sequence identification, man and machine
diagnostics, person identification and industrial inspection. The
interest in improving the classification systems of data analysis is
independent from the context of applications. In fact, in many
studies it is often the case to have to recognize and to distinguish
groups of various objects, which requires the need for valid
instruments capable to perform this task. The objective of this article
is to show several methodologies of Artificial Intelligence for data
classification applied to biomedical patterns. In particular, this work
deals with the realization of a Computer-Aided Detection system
(CADe) that is able to assist the radiologist in identifying types of
mammary tumor lesions. As an additional biomedical application of
the classification systems, we present a study conducted on blood
samples which shows how these methods may help to distinguish
between carriers of Thalassemia (or Mediterranean Anaemia) and
healthy subjects.
Abstract: This paper presents the development and application of an adaptive neuro fuzzy inference system (ANFIS) based intelligent hybrid neuro fuzzy controller for automatic generation control (AGC) of two-area interconnected thermal power system with reheat non linearity. The dynamic response of the system has been studied for 1% step load perturbation in area-1. The performance of the proposed neuro fuzzy controller is compared against conventional proportional-integral (PI) controller, state feedback linear quadratic regulator (LQR) controller and fuzzy gain scheduled proportionalintegral (FGSPI) controller. Comparative analysis demonstrates that the proposed intelligent neuro fuzzy controller is the most effective of all in improving the transients of frequency and tie-line power deviations against small step load disturbances. Simulations have been performed using Matlab®.
Abstract: Increasing growth of information volume in the
internet causes an increasing need to develop new (semi)automatic
methods for retrieval of documents and ranking them according to
their relevance to the user query. In this paper, after a brief review
on ranking models, a new ontology based approach for ranking
HTML documents is proposed and evaluated in various
circumstances. Our approach is a combination of conceptual,
statistical and linguistic methods. This combination reserves the
precision of ranking without loosing the speed. Our approach
exploits natural language processing techniques for extracting
phrases and stemming words. Then an ontology based conceptual
method will be used to annotate documents and expand the query.
To expand a query the spread activation algorithm is improved so
that the expansion can be done in various aspects. The annotated
documents and the expanded query will be processed to compute
the relevance degree exploiting statistical methods. The outstanding
features of our approach are (1) combining conceptual, statistical
and linguistic features of documents, (2) expanding the query with
its related concepts before comparing to documents, (3) extracting
and using both words and phrases to compute relevance degree, (4)
improving the spread activation algorithm to do the expansion based
on weighted combination of different conceptual relationships and
(5) allowing variable document vector dimensions. A ranking
system called ORank is developed to implement and test the
proposed model. The test results will be included at the end of the
paper.
Abstract: Not many studies have been undertaken on shareholder activism in emerging economies, including Malaysia. Shareholder activism in emerging economies is on the rise. This paper seeks to comprehend the elements of this activism that are unique to Malaysia, specifically with respect to how the agency problem is controlled through shareholder activism in improving corporate governance practices within target companies. Through shareholder activism, shareholders make contact with a target company to voice their dissatisfaction, suggestions, or recommendations. This paper utilises agency theory to explain institutional shareholder activism. This theory has been extensively used within literature on corporate governance with regards to shareholder activism. The effectiveness of shareholder activism in improving corporate governance will be examined as well. This research provides a further understanding of shareholder activism in emerging economies, such as Malaysia; this research also has the potential to enhance shareholder activism and corporate governance practices in general.
Abstract: Today, the preferences and participation of the TD groups such as the elderly and disabled is still lacking in decision-making of transportation planning, and their reactions to certain type of policies are not well known. Thus, a clear methodology is needed. This study aimed to develop a method to extract the preferences of the disabled to be used in the policy-making stage that can also guide to future estimations. The method utilizes the combination of cluster analysis and data filtering using the data of the Arao city (Japan). The method is a process that follows: defining the TD group by the cluster analysis tool, their travel preferences in tabular form from the household surveys by policy variableimpact pairs, zones, and by trip purposes, and the final outcome is the preference probabilities of the disabled. The preferences vary by trip purpose; for the work trips, accessibility and transit system quality policies with the accompanying impacts of modal shifts towards public mode use as well as the decreasing travel costs, and the trip rate increase; for the social trips, the same accessibility and transit system policies leading to the same mode shift impact, together with the travel quality policy area leading to trip rate increase. These results explain the policies to focus and can be used in scenario generation in models, or any other planning purpose as decision support tool.
Abstract: In this paper, we propose a selective mutation method
for improving the performances of genetic algorithms. In selective
mutation, individuals are first ranked and then additionally mutated
one bit in a part of their strings which is selected corresponding to
their ranks. This selective mutation helps genetic algorithms to fast
approach the global optimum and to quickly escape local optima.
This results in increasing the performances of genetic algorithms.
We measured the effects of selective mutation with four function
optimization problems. It was found from extensive experiments that
the selective mutation can significantly enhance the performances of
genetic algorithms.
Abstract: A conventional image posterization method
occasionally fails to preserve the shape and color of objects due to the
uneffective color reduction. This paper proposes a new image
posterizartion method by using modified color quantization for
preserving the shape and color of objects and color contrast
enhancement for improving lightness contrast and saturation.
Experiment results show that our proposed method can provide
visually more satisfactory posterization result than that of the
conventional method.
Abstract: Government of Indonesia held a certification program to enhance the professionalism of teachers by using portfolio assessment. This research discusses about the effectiveness of certification programs to enhance the professionalism of teacher in Indonesia. Portfolio assessment method has drawbacks. The certified teachers do not show significant performance improvement. Therefore, the government changes the portfolio assessment method to the education and training for teachers.
Abstract: The mitigation of crop loss due to damaging freezes
requires accurate air temperature prediction models. Previous work
established that the Ward-style artificial neural network (ANN) is a
suitable tool for developing such models. The current research
focused on developing ANN models with reduced average prediction
error by increasing the number of distinct observations used in
training, adding additional input terms that describe the date of an
observation, increasing the duration of prior weather data included in
each observation, and reexamining the number of hidden nodes used
in the network. Models were created to predict air temperature at
hourly intervals from one to 12 hours ahead. Each ANN model,
consisting of a network architecture and set of associated parameters,
was evaluated by instantiating and training 30 networks and
calculating the mean absolute error (MAE) of the resulting networks
for some set of input patterns. The inclusion of seasonal input terms,
up to 24 hours of prior weather information, and a larger number of
processing nodes were some of the improvements that reduced
average prediction error compared to previous research across all
horizons. For example, the four-hour MAE of 1.40°C was 0.20°C, or
12.5%, less than the previous model. Prediction MAEs eight and 12
hours ahead improved by 0.17°C and 0.16°C, respectively,
improvements of 7.4% and 5.9% over the existing model at these
horizons. Networks instantiating the same model but with different
initial random weights often led to different prediction errors. These
results strongly suggest that ANN model developers should consider
instantiating and training multiple networks with different initial
weights to establish preferred model parameters.
Abstract: Architecture education was based on apprenticeship
models and its nature has not changed much during long period but
the Source of changes was its evaluation process and system. It is
undeniable that art and architecture education is completely based on
transmitting knowledge from instructor to students. In contrast to
other majors this transmitting is by iteration and practice and studio
masters try to control the design process and improving skills in the
form of supervision and criticizing. Also the evaluation will end by
giving marks to students- achievements. Therefore the importance of
the evaluation and assessment role is obvious and it is not irrelevant
to say that if we want to know about the architecture education
system, we must first study its assessment procedures. The evolution
of these changes in western countries has literate and documented
well. However it seems that this procedure has unregarded in
Malaysia and there is a severe lack of research and documentation in
this area. Malaysia as an under developing and multicultural country
which is involved different races and cultures is a proper origin for
scrutinizing and understanding the evaluation systems and
acceptability amount of current implemented models to keep the
evaluation and assessment procedure abreast with needs of different
generations, cultures and even genders. This paper attempts to
answer the questions of how evaluation and assessments are
performed and how students perceive this evaluation system in the
context Malaysia. The main advantage of this work is that it
contributes in international debate on evaluation model.
Abstract: The neurogenic potential of many herbal extracts used
in Indian medicine is hitherto unknown. Extracts derived from
Clitoria ternatea Linn have been used in Indian Ayurvedic system of
medicine as an ingredient of “Medhya rasayana", consumed for
improving memory and longevity in humans and also in treatment of
various neurological disorders. Our earlier experimental studies with
oral intubation of Clitoria ternatea aqueous root extract (CTR) had
shown significant enhancement of learning and memory in postnatal
and young adult Wistar rats. The present study was designed to
elucidate the in vitro effects of 200ng/ml of CTR on proliferation,
differentiation and growth of anterior subventricular zone neural
stem cells (aSVZ NSC-s) derived from prenatal and postnatal rat
pups. Results show significant increase in proliferation and growth of
neurospheres and increase in the yield of differentiated neurons of
aSVZ neural precursor cells (aSVZNPC-s) at 7 days in vitro when
treated with 200ng/ml of CTR as compared to age matched control.
Results indicate that CTR has growth promoting neurogenic effect on
aSVZ neural stem cells and their survival similar to neurotrophic
factors like Survivin, Neuregulin 1, FGF-2, BDNF possibly the basis
for enhanced learning and memory.
Abstract: In this paper we compare the accuracy of data mining
methods to classifying students in order to predicting student-s class
grade. These predictions are more useful for identifying weak
students and assisting management to take remedial measures at early
stages to produce excellent graduate that will graduate at least with
second class upper. Firstly we examine single classifiers accuracy on
our data set and choose the best one and then ensembles it with a
weak classifier to produce simple voting method. We present results
show that combining different classifiers outperformed other single
classifiers for predicting student performance.
Abstract: Transport and logistics are the lifeblood of societies.
There is a strong correlation between overall growth in economic
activity and growth of transport. The movement of people and goods
has the potential for creating wealth and prosperity, therefore the
state of transportation infrastructure and especially the condition of
road networks is often a governmental priority. The design, building
and maintenance of national roads constitute a substantial share of
government budgets. Taking into account the magnitude and
importance of these investments, the expedience, efficiency and
sustainability of these projects are of great public interest. This paper
provides an overview of supply chain management principles applied
to road construction. In addition, road construction performance
measurement systems and ICT solutions are discussed. Road
construction in Estonia is analyzed. The authors propose the
development of a national performance measurement system for road
construction.
Abstract: In literature, there are metrics for identifying the
quality of reusable components but the framework that makes use of
these metrics to precisely predict reusability of software components
is still need to be worked out. These reusability metrics if identified
in the design phase or even in the coding phase can help us to reduce
the rework by improving quality of reuse of the software component
and hence improve the productivity due to probabilistic increase in
the reuse level. As CK metric suit is most widely used metrics for
extraction of structural features of an object oriented (OO) software;
So, in this study, tuned CK metric suit i.e. WMC, DIT, NOC, CBO
and LCOM, is used to obtain the structural analysis of OO-based
software components. An algorithm has been proposed in which the
inputs can be given to K-Means Clustering system in form of
tuned values of the OO software component and decision tree is
formed for the 10-fold cross validation of data to evaluate the in
terms of linguistic reusability value of the component. The developed
reusability model has produced high precision results as desired.
Abstract: The primary objective of this paper was to construct a
“kinematic parameter-independent modeling of three-axis machine
tools for geometric error measurement" technique. Improving the
accuracy of the geometric error for three-axis machine tools is one of
the machine tools- core techniques. This paper first applied the
traditional method of HTM to deduce the geometric error model for
three-axis machine tools. This geometric error model was related to the
three-axis kinematic parameters where the overall errors was relative
to the machine reference coordinate system. Given that the
measurement of the linear axis in this model should be on the ideal
motion axis, there were practical difficulties. Through a measurement
method consolidating translational errors and rotational errors in the
geometric error model, we simplified the three-axis geometric error
model to a kinematic parameter-independent model. Finally, based on
the new measurement method corresponding to this error model, we
established a truly practical and more accurate error measuring
technique for three-axis machine tools.
Abstract: The objective of this research was to identify the
vegetation-soil relationships in Nodushan arid rangelands of Yazd. 5
sites were selected for measuring the cover of plant species and soil
attributes. Soil samples were taken in 0-10 and 10-80 cm layers. The
species studied were Salsola tomentosa, Salsola arbuscula, Peganum
harmala, Zygophylum eurypterum and Eurotia ceratoides. Canonical
correspondence analysis (CCA) was used to analyze the data. Based
on the CCA results, 74.9 % of vegetation-soil variation was explained
by axis 1-3. Axis 1, 2 and 3 accounted for 27.2%, 24.9 % and 22.8%
of variance respectively. Correlation between axis 1, 2, 3 and speciesedaphic
variables were 0.995, 0.989, 0.981 respectively. Soil texture,
lime, salinity and organic matter significantly influenced the
distribution of these plant species. Determination of soil-vegetation
relationships will be useful for managing and improving rangelands
in arid and semi arid environments.
Abstract: Previous studies have shown that there are arguments
regarding the reliability and validity of the Ashworth and Modified
Ashworth Scale towards evaluating patients diagnosed with upper
limb disorders. These evaluations depended on the raters’ experiences.
This initiated us to develop an upper limb disorder part-task trainer
that is able to simulate consistent upper limb disorders, such as
spasticity and rigidity signs, based on the Modified Ashworth Scale to
improve the variability occurring between raters and intra-raters
themselves. By providing consistent signs, novice therapists would be
able to increase training frequency and exposure towards various
levels of signs. A total of 22 physiotherapists and occupational
therapists participated in the study. The majority of the therapists
agreed that with current therapy education, they still face problems
with inter-raters and intra-raters variability (strongly agree 54%; n =
12/22, agree 27%; n = 6/22) in evaluating patients’ conditions. The
therapists strongly agreed (72%; n = 16/22) that therapy trainees
needed to increase their frequency of training; therefore believe that
our initiative to develop an upper limb disorder training tool will help
in improving the clinical education field (strongly agree and agree
63%; n = 14/22).