Abstract: A Novel fuzzy neural network combining with support vector learning mechanism called support-vector-based fuzzy neural networks (SVBFNN) is proposed. The SVBFNN combine the capability of minimizing the empirical risk (training error) and expected risk (testing error) of support vector learning in high dimensional data spaces and the efficient human-like reasoning of FNN.
Abstract: In the last years numerous applications of Human-
Computer Interaction have exploited the capabilities of Time-of-
Flight cameras for achieving more and more comfortable and precise
interactions. In particular, gesture recognition is one of the most active
fields. This work presents a new method for interacting with a virtual
object in a 3D space. Our approach is based on the fusion of depth
data, supplied by a ToF camera, with color information, supplied
by a HD webcam. The hand detection procedure does not require
any learning phase and is able to concurrently manage gestures of
two hands. The system is robust to the presence in the scene of
other objects or people, thanks to the use of the Kalman filter for
maintaining the tracking of the hands.
Abstract: Resource discovery is one of the chief services of a grid. A new approach to discover the provenances in grid through learning automata has been propounded in this article. The objective of the aforementioned resource-discovery service is to select the resource based upon the user-s applications and the mercantile yardsticks that is to say opting for an originator which can accomplish the user-s tasks in the most economic manner. This novel service is submitted in two phases. We proffered an applicationbased categorization by means of an intelligent nerve-prone plexus. The user in question sets his or her application as the input vector of the nerve-prone nexus. The output vector of the aforesaid network limns the appropriateness of any one of the resource for the presented executive procedure. The most scrimping option out of those put forward in the previous stage which can be coped with to fulfill the task in question is picked out. Te resource choice is carried out by means of the presented algorithm based upon the learning automata.
Abstract: Application of Information Technology (IT) has
revolutionized the functioning of business all over the world. Its
impact has been felt mostly among the information of dependent
industries. Tourism is one of such industry. The conceptual
framework in this study represents an innovation of travel
information searching system on mobile devices which is used as
tools to deliver travel information (such as hotels, restaurants, tourist
attractions and souvenir shops) for each user by travelers
segmentation based on data mining technique to segment the tourists-
behavior patterns then match them with tourism products and
services. This system innovation is designed to be a knowledge
incremental learning. It is a marketing strategy to support business to
respond traveler-s demand effectively.
Abstract: The learning society has currently transformed from 'wired society' to become 'mobile society' which is facilitated by wireless network. To suit to this new paradigm, m-learning was given birth and rapidly building its prospect to be included in the future curriculum. Research and studies on m-learning spruced up in numerous aspects but there is still scarcity in studies on curriculum design of m-learning. This study is a part of an ongoing bigger study probing into the m-learning curriculum for secondary schools. The paper reports on the first phase of the study which aims to probe into the needs of curriculum design for m-learning at the secondary school level and the researcher adopted the needs analysis method. Data accrued from responses on survey questionnaires based on Lickert-point scale were analyzed statistically. The findings from this preliminary study serve as a basis for m-learning curriculum development for secondary schools.
Abstract: The current paper presents the findings of a research
study on learners- barriers and motivators engaged into blended
programs in a workplace context. In this study, the participants were
randomly assigned to one of four parallel e-learning courses, each of
which was delivered using a different learning strategy. Data were
collected through web-based and telephone surveys developed by the
researchers. The results showed that vague instruction, time
management, and insufficient feedback were the top-most barriers to
blended learning. The major motivators for blended learning included
content relevance, flexibility in time, and the ability to work at own
pace.
Abstract: This paper discusses ways to foster cooperative learning through the integration of online communication technology. While the education experts believe constructivism produces a more positive learning experience, the educators are still facing problems in getting students to participate due to numerous reasons such as shy personality, language and cultural barriers. This paper will look into the factors that lead to lack of participations among students and how technology can be implemented to overcome these issues.
Abstract: The proposed Multimedia Pronunciation Learning
Management System (MPLMS) in this study is a technology with
profound potential for inducing improvement in pronunciation
learning. The MPLMS optimizes the digitised phonetic symbols with
the integration of text, sound and mouth movement video. The
components are designed and developed in an online management
system which turns the web to a dynamic user-centric collection of
consistent and timely information for quality sustainable learning.
The aim of this study is to design and develop the MPLMS which
serves as an innovative tool to improve English pronunciation. This
paper discusses the iterative methodology and the three-phase Alessi
and Trollip model in the development of MPLMS. To align with the
flexibility of the development of educational software, the iterative
approach comprises plan, design, develop, evaluate and implement is
followed. To ensure the instructional appropriateness of MPLMS, the
instructional system design (ISD) model of Alessi and Trollip serves
as a platform to guide the important instructional factors and process.
It is expected that the results of future empirical research will support
the efficacy of MPLMS and its place as the premier pronunciation
learning system.
Abstract: Fault-proneness of a software module is the
probability that the module contains faults. To predict faultproneness
of modules different techniques have been proposed which
includes statistical methods, machine learning techniques, neural
network techniques and clustering techniques. The aim of proposed
study is to explore whether metrics available in the early lifecycle
(i.e. requirement metrics), metrics available in the late lifecycle (i.e.
code metrics) and metrics available in the early lifecycle (i.e.
requirement metrics) combined with metrics available in the late
lifecycle (i.e. code metrics) can be used to identify fault prone
modules using Genetic Algorithm technique. This approach has been
tested with real time defect C Programming language datasets of
NASA software projects. The results show that the fusion of
requirement and code metric is the best prediction model for
detecting the faults as compared with commonly used code based
model.
Abstract: This study examined the effects of two dynamic
visualizations on 60 Malaysian primary school student-s performance
(time on task), retention and transference. The independent variables
in this study were the two dynamic visualizations, the video and the
animated instructions. The dependent variables were the gain score of
performance, retention and transference. The results showed that the
students in the animation group significantly outperformed the
students in the video group in retention. There were no significant
differences in terms of gain scores in the performance and
transference among the animation and the video groups, although the
scores were slightly higher in the animation group compared to the
video group. The conclusion of this study is that the animation
visualization is superior compared to the video in the retention for a
procedural task.
Abstract: The notion of communicative competence has been deemed fuzzy in communication studies. This fuzziness has led to tensions among engineers across tenures in interpreting what constitutes communicative competence. The study seeks to investigate novice and professional engineers- understanding of the said notion in terms of two main elements of communicative competence: linguistic and rhetorical competence. Novice engineers are final year engineering students, whilst professional engineers represent engineers who have at least 5 years working experience. Novice and professional engineers were interviewed to gauge their perceptions on linguistic and rhetorical features deemed necessary to enhance communicative competence for the profession. Both groups indicated awareness and differences on the importance of the sub-sets of communicative competence, namely, rhetorical explanatory competence, linguistic oral immediacy competence, technical competence and meta-cognitive competence. Such differences, a possible attribute of the learning theory, inadvertently indicate sublime differences in the way novice and professional engineers perceive communicative competence.
Abstract: Serial Analysis of Gene Expression is a powerful
quantification technique for generating cell or tissue gene expression
data. The profile of the gene expression of cell or tissue in several
different states is difficult for biologists to analyze because of the large
number of genes typically involved. However, feature selection in
machine learning can successfully reduce this problem. The method
allows reducing the features (genes) in specific SAGE data, and
determines only relevant genes. In this study, we used a genetic
algorithm to implement feature selection, and evaluate the
classification accuracy of the selected features with the K-nearest
neighbor method. In order to validate the proposed method, we used
two SAGE data sets for testing. The results of this study conclusively
prove that the number of features of the original SAGE data set can be
significantly reduced and higher classification accuracy can be
achieved.
Abstract: In this paper, the relationship between learning
motivation and learning performance is explored by using exchange
theory. The relationship is concluded that external performance can
raise learning motivation and then increase learning performance. The
internal performance should be not completely neglected and the
external performance should be not attached important excessively.
The parents need self-study and must be also reeducated. The existing
education must be improved in raise of internal performance. The
incorrect learning thinking will mislead the students, parents, and
educators of next generation, when the students obtain good learning
performance in the learning environment with excess stimulants. Over
operation of external performance will result abnormal learning
thinking and violating learning goal. Learning is not only to obtain
performance. Learning quality and learning performance will be
limited as without learning motivation. The best learning motivation
is, the best learning performance is. The learning for reward is not
good for learning performance. Strategies of promoting life-long
learning are including the encouraging for learner, establishment of
good interaction learning environment, and the advertisement of the
merit and the importance of life-long learning, which can let the
learner with the correct learning motivation.
Abstract: Thanks to VR technology advanced, there are many
researches had used VR technology to develop a training system.
Using VR characteristics can simulate many kinds of situations to
reach our training-s goal. However, a good training system not only
considers real simulation but also considers learner-s learning
motivation. So, there are many researches started to conduct game-s
features into VR training system. We typically called this is a serious
game. It is using game-s features to engage learner-s learning
motivation. However, VR or Serious game has another important
advantage. That is simulating feature. Using this feature can create
any kinds of pressured environments. Because in the real
environment may happen any emergent situations. So, increasing the
trainees- pressure is more important when they are training. Most
pervious researches are investigated serious game-s applications and
learning performance. Seldom researches investigated how to
increase the learner-s mental workload when they are training. So, in
our study, we will introduce a real case study and create two types
training environments. Comparing the learner-s mental workload
between VR training and serious game.
Abstract: In this paper, a Web-based e-Training platform that is dedicated to multimodal breast imaging is presented. The assets of this platform are summarised in (i) the efficient representation of the curriculum flow that will permit efficient training; (ii) efficient tagging of multimodal content appropriate for the completion of realistic cases and (iii) ubiquitous accessibility and platform independence via a web-based approach.
Abstract: Previous researches found that conventional WBL is effective for meaningful learner, because rote learner learn by repeating without thinking or trying to understand. It is impossible to have full benefit from conventional WBL. Understanding of rote learner-s intention and what influences it becomes important. Poorly designed user interface will discourage rote learner-s cultivation and intention to use WBL. Thus, user interface design is an important factor especially when WBL is used as comprehensive replacement of conventional teaching. This research proposes the influencing factors that can enhance learner-s intention to use the system. The enhanced TAM is used for evaluating the proposed factors. The research result points out that factors influencing rote learner-s intention are Perceived Usefulness of Homepage Content Structure, Perceived User Friendly Interface, Perceived Hedonic Component, and Perceived (homepage) Visual Attractiveness.
Abstract: More and more natural disasters are happening every
year: floods, earthquakes, volcanic eruptions, etc. In order to reduce
the risk of possible damages, governments all around the world are
investing into development of Early Warning Systems (EWS) for
environmental applications. The most important task of the EWS is
identification of the onset of critical situations affecting environment
and population, early enough to inform the authorities and general
public. This paper describes an approach for monitoring of flood
protections systems based on machine learning methods. An
Artificial Intelligence (AI) component has been developed for
detection of abnormal dike behaviour. The AI module has been
integrated into an EWS platform of the UrbanFlood project (EU
Seventh Framework Programme) and validated on real-time
measurements from the sensors installed in a dike.
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: An algorithm for learning an overcomplete dictionary
using a Cauchy mixture model for sparse decomposition of an underdetermined
mixing system is introduced. The mixture density
function is derived from a ratio sample of the observed mixture
signals where 1) there are at least two but not necessarily more
mixture signals observed, 2) the source signals are statistically
independent and 3) the sources are sparse. The basis vectors of the
dictionary are learned via the optimization of the location parameters
of the Cauchy mixture components, which is shown to be more
accurate and robust than the conventional data mining methods
usually employed for this task. Using a well known sparse
decomposition algorithm, we extract three speech signals from two
mixtures based on the estimated dictionary. Further tests with
additive Gaussian noise are used to demonstrate the proposed
algorithm-s robustness to outliers.
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.