Abstract: Despite of many scholars and practitioners recognize
the knowledge management implementation in an organizations but
insufficient attention has been paid by researchers to select suitable
knowledge portal system (KPS) selection. This study develops a
Multi Criteria Decision making model based on the fuzzy VIKOR
approach to help organizations in selecting KPS. The suitable portal
is the critical influential factors on the success of knowledge
management (KM) implementation in an organization.
Abstract: In the recent past Learning Classifier Systems have
been successfully used for data mining. Learning Classifier System
(LCS) is basically a machine learning technique which combines
evolutionary computing, reinforcement learning, supervised or
unsupervised learning and heuristics to produce adaptive systems. A
LCS learns by interacting with an environment from which it
receives feedback in the form of numerical reward. Learning is
achieved by trying to maximize the amount of reward received. All
LCSs models more or less, comprise four main components; a finite
population of condition–action rules, called classifiers; the
performance component, which governs the interaction with the
environment; the credit assignment component, which distributes the
reward received from the environment to the classifiers accountable
for the rewards obtained; the discovery component, which is
responsible for discovering better rules and improving existing ones
through a genetic algorithm. The concatenate of the production rules
in the LCS form the genotype, and therefore the GA should operate
on a population of classifier systems. This approach is known as the
'Pittsburgh' Classifier Systems. Other LCS that perform their GA at
the rule level within a population are known as 'Mitchigan' Classifier
Systems. The most predominant representation of the discovered
knowledge is the standard production rules (PRs) in the form of IF P
THEN D. The PRs, however, are unable to handle exceptions and do
not exhibit variable precision. The Censored Production Rules
(CPRs), an extension of PRs, were proposed by Michalski and
Winston that exhibit variable precision and supports an efficient
mechanism for handling exceptions. A CPR is an augmented
production rule of the form: IF P THEN D UNLESS C, where
Censor C is an exception to the rule. Such rules are employed in
situations, in which conditional statement IF P THEN D holds
frequently and the assertion C holds rarely. By using a rule of this
type we are free to ignore the exception conditions, when the
resources needed to establish its presence are tight or there is simply
no information available as to whether it holds or not. Thus, the IF P
THEN D part of CPR expresses important information, while the
UNLESS C part acts only as a switch and changes the polarity of D
to ~D. In this paper Pittsburgh style LCSs approach is used for
automated discovery of CPRs. An appropriate encoding scheme is
suggested to represent a chromosome consisting of fixed size set of
CPRs. Suitable genetic operators are designed for the set of CPRs
and individual CPRs and also appropriate fitness function is proposed
that incorporates basic constraints on CPR. Experimental results are
presented to demonstrate the performance of the proposed learning
classifier system.
Abstract: Link reliability and transmitted power are two important design constraints in wireless network design. Error control coding (ECC) is a classic approach used to increase link reliability and to lower the required transmitted power. It provides coding gain, resulting in transmitter energy savings at the cost of added decoder power consumption. But the choice of ECC is very critical in the case of wireless sensor network (WSN). Since the WSNs are energy constraint in nature, both the BER and power consumption has to be taken into count. This paper develops a step by step approach in finding suitable error control codes for WSNs. Several simulations are taken considering different error control codes and the result shows that the RS(31,21) fits both in BER and power consumption criteria.
Abstract: In this paper, a new time-delay estimation
technique based on the cross IB-energy operator [5] is
introduced. This quadratic energy detector measures how
much a signal is present in another one. The location of the
peak of the energy operator, corresponding to the maximum of
interaction between the two signals, is the estimate of the
delay. The method is a fully data-driven approach. The
discrete version of the continuous-time form of the cross IBenergy
operator, for its implementation, is presented. The
effectiveness of the proposed method is demonstrated on real
underwater acoustic signals arriving from targets and the
results compared to the cross-correlation method.
Abstract: A Comparison and evaluation of the different
condition monitoring (CM) techniques was applied experimentally
on RC e.g. Dynamic cylinder pressure and crankshaft Instantaneous
Angular Speed (IAS), for the detection and diagnosis of valve faults
in a two - stage reciprocating compressor for a programme of
condition monitoring which can successfully detect and diagnose a
fault in machine. Leakage in the valve plate was introduced
experimentally into a two-stage reciprocating compressor. The effect
of the faults on compressor performance was monitored and the
differences with the normal, healthy performance noted as a fault
signature been used for the detection and diagnosis of faults.
The paper concludes with what is considered to be a unique
approach to condition monitoring. First, each of the two most useful
techniques is used to produce a Truth Table which details the
circumstances in which each method can be used to detect and
diagnose a fault. The two Truth Tables are then combined into a
single Decision Table to provide a unique and reliable method of
detection and diagnosis of each of the individual faults introduced
into the compressor. This gives accurate diagnosis of compressor
faults.
Abstract: The temporal nature of negative selection is an under exploited area. In a negative selection system, newly generated antibodies go through a maturing phase, and the survivors of the phase then wait to be activated by the incoming antigens after certain number of matches. These without having enough matches will age and die, while these with enough matches (i.e., being activated) will become active detectors. A currently active detector may also age and die if it cannot find any match in a pre-defined (lengthy) period of time. Therefore, what matters in a negative selection system is the dynamics of the involved parties in the current time window, not the whole time duration, which may be up to eternity. This property has the potential to define the uniqueness of negative selection in comparison with the other approaches. On the other hand, a negative selection system is only trained with “normal" data samples. It has to learn and discover unknown “abnormal" data patterns on the fly by itself. Consequently, it is more appreciate to utilize negation selection as a system for pattern discovery and recognition rather than just pattern recognition. In this paper, we study the potential of using negative selection in discovering unknown temporal patterns.
Abstract: The technology usages of high speed Internet leads to
establish and start new era of online education. With the
advancement of the information technology and communication
systems new opportunities have been created. This leads universities
to have various online education channels to meet the demand of
different learners- needs. One of these channels is M-learning, which
can be used to improve the online education environment. With using
such mobile technology in learning both students and instructors can
easily access educational courses anytime from anywhere. The paper
first presents literature about mobile learning and to what extent this
approach can be utilized to enhance the overall learning system. It
provides a comparison between mobile learning and traditional elearning
showing the wide array of benefits of the new generation of
technology. The possible challenges and potential advantages of Mlearning
in the online education system are also discussed.
Abstract: Magnetic Nanoparticles (MNPs) have great potential
to overcome many of the shortcomings of the present diagnostic and
therapeutic approaches used in cancer diagnosis and treatment. This
Literature review discusses the use of Magnetic Nanoparticles
focusing mainly on Iron oxide based MNPs in cancer imaging using
MRI.
Abstract: Trust management and Reputation models are
becoming integral part of Internet based applications such as CSCW,
E-commerce and Grid Computing. Also the trust dimension is a
significant social structure and key to social relations within a
collaborative community. Collaborative Decision Making (CDM) is
a difficult task in the context of distributed environment (information
across different geographical locations) and multidisciplinary
decisions are involved such as Virtual Organization (VO). To aid
team decision making in VO, Decision Support System and social
network analysis approaches are integrated. In such situations social
learning helps an organization in terms of relationship, team
formation, partner selection etc. In this paper we focus on trust
learning. Trust learning is an important activity in terms of
information exchange, negotiation, collaboration and trust
assessment for cooperation among virtual team members. In this
paper we have proposed a reinforcement learning which enhances the
trust decision making capability of interacting agents during
collaboration in problem solving activity. Trust computational model
with learning that we present is adapted for best alternate selection of
new project in the organization. We verify our model in a multi-agent
simulation where the agents in the community learn to identify
trustworthy members, inconsistent behavior and conflicting behavior
of agents.
Abstract: We have previously introduced an ultrasonic imaging
approach that combines harmonic-sensitive pulse sequences with a
post-beamforming quadratic kernel derived from a second-order
Volterra filter (SOVF). This approach is designed to produce images
with high sensitivity to nonlinear oscillations from microbubble
ultrasound contrast agents (UCA) while maintaining high levels of
noise rejection. In this paper, a two-step algorithm for computing the
coefficients of the quadratic kernel leading to reduction of tissue
component introduced by motion, maximizing the noise rejection and
increases the specificity while optimizing the sensitivity to the UCA
is presented. In the first step, quadratic kernels from individual
singular modes of the PI data matrix are compared in terms of their
ability of maximize the contrast to tissue ratio (CTR). In the second
step, quadratic kernels resulting in the highest CTR values are
convolved. The imaging results indicate that a signal processing
approach to this clinical challenge is feasible.
Abstract: This paper discusses the designing of knowledge
integration of clinical information extracted from distributed medical
ontologies in order to ameliorate a machine learning-based multilabel
coding assignment system. The proposed approach is
implemented using a decision tree technique of the machine learning
on the university hospital data for patients with Coronary Heart
Disease (CHD). The preliminary results obtained show a satisfactory
finding that the use of medical ontologies improves the overall
system performance.
Abstract: A traffic light gives security from traffic congestion,reducing the traffic jam, and organizing the traffic flow. Furthermore,increasing congestion level in public road networks is a growingproblem in many countries. Using Intelligent Transportation Systemsto provide emergency vehicles a green light at intersections canreduce driver confusion, reduce conflicts, and improve emergencyresponse times. Nowadays, the technology of wireless sensornetworks can solve many problems and can offer a good managementof the crossroad. In this paper, we develop a new approach based onthe technique of clustering and the graphical possibilistic fusionmodeling. So, the proposed model is elaborated in three phases. Thefirst one consists to decompose the environment into clusters,following by the fusion intra and inter clusters processes. Finally, wewill show some experimental results by simulation that proves theefficiency of our proposed approach.KeywordsTraffic light, Wireless sensor network, Controller,Possibilistic network/Bayesain network.
Abstract: There are several approaches for handling multiclass classification. Aside from one-against-one (OAO) and one-against-all (OAA), hierarchical classification technique is also commonly used. A binary classification tree is a hierarchical classification structure that breaks down a k-class problem into binary sub-problems, each solved by a binary classifier. In each node, a set of classes is divided into two subsets. A good class partition should be able to group similar classes together. Many algorithms measure similarity in term of distance between class centroids. Classes are grouped together by a clustering algorithm when distances between their centroids are small. In this paper, we present a binary classification tree with tuned observation-based clustering (BCT-TOB) that finds a class partition by performing clustering on observations instead of class centroids. A merging step is introduced to merge any insignificant class split. The experiment shows that performance of BCT-TOB is comparable to other algorithms.
Abstract: In this paper we present a method of abrupt cut detection with a novel logic of frames- comparison. Actual frame is compared with its motion estimated prediction instead of comparison with successive frame. Four different similarity metrics were employed to estimate the resemblance of compared frames. Obtained results were evaluated by standard used measures of test accuracy and compared with existing approach. Based on the results, we claim the proposed method is more effective and Pearson correlation coefficient obtained the best results among chosen similarity metrics.
Abstract: This study presents a new approach based on Tanaka's
fuzzy linear regression (FLP) algorithm to solve well-known power
system economic load dispatch problem (ELD). Tanaka's fuzzy linear
regression (FLP) formulation will be employed to compute the
optimal solution of optimization problem after linearization. The
unknowns are expressed as fuzzy numbers with a triangular
membership function that has middle and spread value reflected on
the unknowns. The proposed fuzzy model is formulated as a linear
optimization problem, where the objective is to minimize the sum of
the spread of the unknowns, subject to double inequality constraints.
Linear programming technique is employed to obtain the middle and
the symmetric spread for every unknown (power generation level).
Simulation results of the proposed approach will be compared with
those reported in literature.
Abstract: In this paper, the modified optimal sliding mode control with a proposed method to design a sliding surface is presented. Because of the inability of the previous approach of the sliding mode method to design a bounded and suitable input, the new variation is proposed in the sliding manifold to obviate problems in a structural system. Although the sliding mode control is a powerful method to reject disturbances and noises, the chattering problem is not good for actuators. To decrease the chattering phenomena, the optimal control is added to the sliding mode control. Not only the proposed method can decline the intense variations in the inputs of the system but also it can produce the efficient responses respect to the sliding mode control and optimal control that are shown by performing some numerical simulations.
Abstract: Recent theorizations on the cognitive process of moral
judgment have focused on the role of intuitions and emotions, marking
a departure from previous emphasis on conscious, step-by-step
reasoning. My study investigated how being in a disgusted mood state
affects moral judgment.
Participants were induced to enter a disgusted mood state through
listening to disgusting sounds and reading disgusting descriptions.
Results shows that they, when compared to control who have not been
induced to feel disgust, are more likely to endorse actions that are
emotionally aversive but maximizes utilitarian return
The result is analyzed using the 'emotion-as-information' approach
to decision making. The result is consistent with the view that
emotions play an important role in determining moral judgment.
Abstract: High quality requirements analysis is one of the most
crucial activities to ensure the success of a software project, so that
requirements verification for software system becomes more and more
important in Requirements Engineering (RE) and it is one of the most
helpful strategies for improving the quality of software system.
Related works show that requirement elicitation and analysis can be
facilitated by ontological approaches and semantic web technologies.
In this paper, we proposed a hybrid method which aims to verify
requirements with structural and formal semantics to detect
interactions. The proposed method is twofold: one is for modeling
requirements with the semantic web language OWL, to construct a
semantic context; the other is a set of interaction detection rules which
are derived from scenario-based analysis and represented with
semantic web rule language (SWRL). SWRL based rules are working
with rule engines like Jess to reason in semantic context for
requirements thus to detect interactions. The benefits of the proposed
method lie in three aspects: the method (i) provides systematic steps
for modeling requirements with an ontological approach, (ii) offers
synergy of requirements elicitation and domain engineering for
knowledge sharing, and (3)the proposed rules can systematically assist
in requirements interaction detection.
Abstract: Recently studies in area of supply chain network
(SCN) have focused on the disruption issues in distribution systems.
Also this paper extends the previous literature by providing a new biobjective
model for cost minimization of designing a three echelon
SCN across normal and failure scenarios with considering multi
capacity option for manufacturers and distribution centers. Moreover,
in order to solve the problem by means of LINGO software, novel
model will be reformulated through a branch of LP-Metric method
called Min-Max approach.
Abstract: There have been different approaches to compute the
analytic instantaneous frequency with a variety of background reasoning
and applicability in practice, as well as restrictions. This paper presents an adaptive Fourier decomposition and (α-counting) based
instantaneous frequency computation approach. The adaptive Fourier
decomposition is a recently proposed new signal decomposition
approach. The instantaneous frequency can be computed through the so called mono-components decomposed by it. Due to the fast energy
convergency, the highest frequency of the signal will be discarded by the adaptive Fourier decomposition, which represents the noise of
the signal in most of the situation. A new instantaneous frequency
definition for a large class of so-called simple waves is also proposed
in this paper. Simple wave contains a wide range of signals for which
the concept instantaneous frequency has a perfect physical sense.
The α-counting instantaneous frequency can be used to compute the highest frequency for a signal. Combination of these two approaches one can obtain the IFs of the whole signal. An experiment is demonstrated the computation procedure with promising results.