Abstract: This paper introduces new algorithms (Fuzzy relative
of the CLARANS algorithm FCLARANS and Fuzzy c Medoids
based on randomized search FCMRANS) for fuzzy clustering of
relational data. Unlike existing fuzzy c-medoids algorithm (FCMdd)
in which the within cluster dissimilarity of each cluster is minimized
in each iteration by recomputing new medoids given current
memberships, FCLARANS minimizes the same objective function
minimized by FCMdd by changing current medoids in such away
that that the sum of the within cluster dissimilarities is minimized.
Computing new medoids may be effected by noise because outliers
may join the computation of medoids while the choice of medoids in
FCLARANS is dictated by the location of a predominant fraction of
points inside a cluster and, therefore, it is less sensitive to the
presence of outliers. In FCMRANS the step of computing new
medoids in FCMdd is modified to be based on randomized search.
Furthermore, a new initialization procedure is developed that add
randomness to the initialization procedure used with FCMdd. Both
FCLARANS and FCMRANS are compared with the robust and
linearized version of fuzzy c-medoids (RFCMdd). Experimental
results with different samples of the Reuter-21578, Newsgroups
(20NG) and generated datasets with noise show that FCLARANS is
more robust than both RFCMdd and FCMRANS. Finally, both
FCMRANS and FCLARANS are more efficient and their outputs
are almost the same as that of RFCMdd in terms of classification
rate.
Abstract: One of the most important aspects expected from ERP systems is to integrate various operations existing in administrative, financial, commercial, human resources, and production departments of the consumer organization. Also, it is often needed to integrate the new ERP system with the organization legacy systems when implementing the ERP package in the organization. Without relying on an appropriate software architecture to realize the required integration, ERP implementation processes become error prone and time consuming; in some cases, the ERP implementation may even encounters serious risks. In this paper, we propose a new architecture that is based on the agent oriented vision and supplies the integration expected from ERP systems using several independent but cooperator agents. Besides integration which is the main issue of this paper, the presented architecture will address some aspects of intelligence and learning capabilities existing in ERP systems
Abstract: Face Recognition has always been a fascinating research area. It has drawn the attention of many researchers because of its various potential applications such as security systems, entertainment, criminal identification etc. Many supervised and unsupervised learning techniques have been reported so far. Principal Component Analysis (PCA), Self Organizing Maps (SOM) and Independent Component Analysis (ICA) are the three techniques among many others as proposed by different researchers for Face Recognition, known as the unsupervised techniques. This paper proposes integration of the two techniques, SOM and PCA, for dimensionality reduction and feature selection. Simulation results show that, though, the individual techniques SOM and PCA itself give excellent performance but the combination of these two can also be utilized for face recognition. Experimental results also indicate that for the given face database and the classifier used, SOM performs better as compared to other unsupervised learning techniques. A comparison of two proposed methodologies of SOM, Local and Global processing, shows the superiority of the later but at the cost of more computational time.
Abstract: The use of artificial neural network (ANN) modeling
for prediction and forecasting variables in water resources
engineering are being increasing rapidly. Infrastructural applications
of ANN in terms of selection of inputs, architecture of networks,
training algorithms, and selection of training parameters in different
types of neural networks used in water resources engineering have
been reported. ANN modeling conducted for water resources
engineering variables (river sediment and discharge) published in
high impact journals since 2002 to 2011 have been examined and
presented in this review. ANN is a vigorous technique to develop
immense relationship between the input and output variables, and
able to extract complex behavior between the water resources
variables such as river sediment and discharge. It can produce robust
prediction results for many of the water resources engineering
problems by appropriate learning from a set of examples. It is
important to have a good understanding of the input and output
variables from a statistical analysis of the data before network
modeling, which can facilitate to design an efficient network. An
appropriate training based ANN model is able to adopt the physical
understanding between the variables and may generate more effective
results than conventional prediction techniques.
Abstract: Nowadays, organizing a repository of documents and
resources for learning on a special field as Information Technology
(IT), together with search techniques based on domain knowledge or
document-s content is an urgent need in practice of teaching, learning
and researching. There have been several works related to methods of
organization and search by content. However, the results are still
limited and insufficient to meet user-s demand for semantic
document retrieval. This paper presents a solution for the
organization of a repository that supports semantic representation and
processing in search. The proposed solution is a model which
integrates components such as an ontology describing domain
knowledge, a database of document repository, semantic
representation for documents and a file system; with problems,
semantic processing techniques and advanced search techniques
based on measuring semantic similarity. The solution is applied to
build a IT learning materials management system of a university with
semantic search function serving students, teachers, and manager as
well. The application has been implemented, tested at the University
of Information Technology, Ho Chi Minh City, Vietnam and has
achieved good results.
Abstract: Managing knowledge of research is one way to ensure
just in time information and knowledge to support research strategist
and activities. Unfortunately researcher found the vital research
knowledge in IHL (Institutions of Higher Learning) are scattered,
unstructured and unorganized. Aiming on lay aside conceptual
foundations for understanding and developing OMS (Organizational
Memory System) to facilitate research in IHL, this research revealed
ten factors contributed to the needs of research in the IHL and seven
internal challenges of IHL in promoting research to their academic
members. This study then suggested a comprehensive support of
managing research knowledge using Organizational Memory System
(OMS). Eight OMS characteristics to support research were
identified. Finally the initial work in designing OMS was projected
using knowledge taxonomy. All analysis is derived from pertinent
research paper related to research in IHL and OMS. Further study can
be conducted to validate and verify results presented.
Abstract: An attractor neural network on the small-world topology
is studied. A learning pattern is presented to the network, then
a stimulus carrying local information is applied to the neurons and
the retrieval of block-like structure is investigated. A synaptic noise
decreases the memory capability. The change of stability from local
to global attractors is shown to depend on the long-range character
of the network connectivity.
Abstract: Knowledge Discovery in Databases (KDD) has
evolved into an important and active area of research because of
theoretical challenges and practical applications associated with the
problem of discovering (or extracting) interesting and previously
unknown knowledge from very large real-world databases. Rough
Set Theory (RST) is a mathematical formalism for representing
uncertainty that can be considered an extension of the classical set
theory. It has been used in many different research areas, including
those related to inductive machine learning and reduction of
knowledge in knowledge-based systems. One important concept
related to RST is that of a rough relation. In this paper we presented
the current status of research on applying rough set theory to KDD,
which will be helpful for handle the characteristics of real-world
databases. The main aim is to show how rough set and rough set
analysis can be effectively used to extract knowledge from large
databases.
Abstract: Availability of high dimensional biological datasets such as from gene expression, proteomic, and metabolic experiments can be leveraged for the diagnosis and prognosis of diseases. Many classification methods in this area have been studied to predict disease states and separate between predefined classes such as patients with a special disease versus healthy controls. However, most of the existing research only focuses on a specific dataset. There is a lack of generic comparison between classifiers, which might provide a guideline for biologists or bioinformaticians to select the proper algorithm for new datasets. In this study, we compare the performance of popular classifiers, which are Support Vector Machine (SVM), Logistic Regression, k-Nearest Neighbor (k-NN), Naive Bayes, Decision Tree, and Random Forest based on mock datasets. We mimic common biological scenarios simulating various proportions of real discriminating biomarkers and different effect sizes thereof. The result shows that SVM performs quite stable and reaches a higher AUC compared to other methods. This may be explained due to the ability of SVM to minimize the probability of error. Moreover, Decision Tree with its good applicability for diagnosis and prognosis shows good performance in our experimental setup. Logistic Regression and Random Forest, however, strongly depend on the ratio of discriminators and perform better when having a higher number of discriminators.
Abstract: Weblog is an Internet tool that is believed to possess
great potential to facilitate learning in education. This study wants to
know if weblog can be used to promote students- critical thinking. It
used a group of secondary two students from a Singapore school to
write weblogs as a means of substitution for their traditional
handwritten assignments. The topics for the weblogging are taken
from History syllabus but modified to suit the purpose of this study.
Weblogs from the students were collected and analysed using a
known coding system for measuring critical thinking. Results show
that the topic for blogging is crucial in determining the types of
critical thinking employed by the students. Students are seen to
display critical thinking traits in the areas of information sourcing,
linking information to arguments and viewpoints justification.
Students- criticalness is more profound when the information for
writing a topic is readily available. Otherwise, they tend to be less
critical and subjective. The study also found that students lack the
ability to source for external information suggesting that students
may need to be taught information literacy in order to widen their use
of critical thinking skills.
Abstract: With the exponential progress of technological
development comes a strong sense that events are moving too quickly
for our schools and that teachers may be losing control of them in the
process. This paper examines the impact of e-learning and e-teaching
in universities, from both the student and teacher perspective. In
particular, it is shown that e-teachers should focus not only on the
technical capacities and functions of IT materials and activities, but
must attempt to more fully understand how their e-learners perceive
the learning environment. From the e-learner perspective, this paper
indicates that simply having IT tools available does not automatically
translate into all students becoming effective learners. More
evidence-based evaluative research is needed to allow e-learning and
e-teaching to reach full potential.
Abstract: Repetitive systems stand for a kind of systems that
perform a simple task on a fixed pattern repetitively, which are
widely spread in industrial fields. Hence, many researchers have been
interested in those systems, especially in the field of iterative learning
control (ILC). In this paper, we propose a finite-horizon tracking
control scheme for linear time-varying repetitive systems with uncertain
initial conditions. The scheme is derived both analytically
and numerically for state-feedback systems and only numerically for
output-feedback systems. Then, it is extended to stable systems with
input constraints. All numerical schemes are developed in the forms
of linear matrix inequalities (LMIs). A distinguished feature of the
proposed scheme from the existing iterative learning control is that
the scheme guarantees the tracking performance exactly even under
uncertain initial conditions. The simulation results demonstrate the
good performance of the proposed scheme.
Abstract: This paper proposed a novel model for short term load
forecast (STLF) in the electricity market. The prior electricity
demand data are treated as time series. The model is composed of
several neural networks whose data are processed using a wavelet
technique. The model is created in the form of a simulation program
written with MATLAB. The load data are treated as time series data.
They are decomposed into several wavelet coefficient series using
the wavelet transform technique known as Non-decimated Wavelet
Transform (NWT). The reason for using this technique is the belief
in the possibility of extracting hidden patterns from the time series
data. The wavelet coefficient series are used to train the neural
networks (NNs) and used as the inputs to the NNs for electricity load
prediction. The Scale Conjugate Gradient (SCG) algorithm is used as
the learning algorithm for the NNs. To get the final forecast data, the
outputs from the NNs are recombined using the same wavelet
technique. The model was evaluated with the electricity load data of
Electronic Engineering Department in Mandalay Technological
University in Myanmar. The simulation results showed that the
model was capable of producing a reasonable forecasting accuracy in
STLF.
Abstract: The purpose of this study was to explore the learning
effects on dance domain in Arts Curriculum at junior and senior high
levels. A total of 1,366 students from 9th to 11th grade of different
areas from Taiwan were administered a self-designed dance
achievement test. Data were analyzed through descriptive analysis,
independent sample t test, one-way ANOVA and Post hoc comparison
analysis using Scheffé Test. The results showed (1) female students
Abstract: Bone remodeling occurs by the balanced action of
bone resorbing osteoclasts (OC) and bone-building osteoblasts.
Increased bone resorption by excessive OC activity contributes
to malignant and non-malignant diseases including osteoporosis.
To study OC differentiation and function, OC formed in
in vitro cultures are currently counted manually, a tedious
procedure which is prone to inter-observer differences. Aiming
for an automated OC-quantification system, classification of
OC and precursor cells was done on fluorescence microscope
images based on the distinct appearance of fluorescent nuclei.
Following ellipse fitting to nuclei, a combination of eight
features enabled clustering of OC and precursor cell nuclei.
After evaluating different machine-learning techniques, LOGREG
achieved 74% correctly classified OC and precursor cell
nuclei, outperforming human experts (best expert: 55%). In
combination with the automated detection of total cell areas,
this system allows to measure various cell parameters and most
importantly to quantify proteins involved in osteoclastogenesis.
Abstract: The development of competences and practical
capacities of students is getting an important incidence into the
guidelines of the European Higher Education Area (EHEA). The
methodology applied in this work is based on the education through
directed resolution of practical cases. All cases are related to
professional tasks that the students will have to develop in their
future career. The method is intended to form the necessary
competences of students of the Marine Engineering and Maritime
Transport Degree in the matter of “Physics".
The experience was applied in the course of 2011/2012. Students
were grouped, and a practical task was assigned to them, that should
be developed and solved within the team. The aim was to realize
students learning by three ways: their own knowledge, the
contribution of their teammates and the teacher's direction. The
results of the evaluation were compared with those obtained
previously by the traditional teaching method.