Abstract: A new conceptual architecture for low-level neural
pattern recognition is presented. The key ideas are that the brain
implements support vector machines and that support vectors are
represented as memory patterns in competitive queuing memories. A
binary classifier is built from two competitive queuing memories
holding positive and negative valence training examples respectively.
The support vector machine classification function is calculated in
synchronized evaluation cycles. The kernel is computed by bisymmetric
feed-forward networks feed by sensory input and by
competitive queuing memories traversing the complete sequence of
support vectors. Temporary summation generates the output
classification. It is speculated that perception apparatus in the brain
reuses structures that have evolved for enabling fluent execution of
prepared action sequences so that pattern recognition is built on
internalized motor programmes.
Abstract: Delivering course material via a virtual environment
is beneficial to today-s students because it offers the interactivity,
real-time interaction and social presence that students of all ages
have come to accept in our gaming rich community. It is essential
that the Net Generation also known as Generation Why, have
exposure to learning communities that encompass interactivity to
form social and educational connections. As student and professor
become interconnected through collaboration and interaction in a
virtual learning space, relationships develop and students begin to
take on an individual identity. With this in mind the research project
was developed to investigate the use of virtual environments on
student satisfaction and the effectiveness of course delivery.
Furthermore, the project was designed to integrate both interactive
(real-time) classes conducted in the Virtual Reality (VR)
environment while also creating archived VR sessions for student use
in retaining and reviewing course content.
Abstract: The Prediction of aerodynamic characteristics and
shape optimization of airfoil under the ground effect have been carried
out by integration of computational fluid dynamics and the multiobjective
Pareto-based genetic algorithm. The main flow
characteristics around an airfoil of WIG craft are lift force, lift-to-drag
ratio and static height stability (H.S). However, they show a strong
trade-off phenomenon so that it is not easy to satisfy the design
requirements simultaneously. This difficulty can be resolved by the
optimal design. The above mentioned three characteristics are chosen
as the objective functions and NACA0015 airfoil is considered as a
baseline model in the present study. The profile of airfoil is
constructed by Bezier curves with fourteen control points and these
control points are adopted as the design variables. For multi-objective
optimization problems, the optimal solutions are not unique but a set
of non-dominated optima and they are called Pareto frontiers or Pareto
sets. As the results of optimization, forty numbers of non- dominated
Pareto optima can be obtained at thirty evolutions.
Abstract: The major purpose of this study is to use network and multimedia technologies to build a game-based learning system for junior high school students to apply in learning “World Geography" through the “role-playing" game approaches. This study first investigated the motivation and habits of junior high school students to use the Internet and online games, and then designed a game-based learning system according to situated and game-based learning theories. A teaching experiment was conducted to analyze the learning effectiveness of students on the game-based learning system and the major factors affecting their learning. A questionnaire survey was used to understand the students- attitudes towards game-based learning. The results showed that the game-based learning system can enhance students- learning, but the gender of students and their habits in using the Internet have no significant impact on learning. Game experience has a significant impact on students- learning, and the higher the experience value the better the effectiveness of their learning. The results of questionnaire survey also revealed that the system can increase students- motivation and interest in learning "World Geography".
Abstract: Our study proposes an alternative method in building
Fuzzy Rule-Based System (FRB) from Support Vector Machine
(SVM). The first set of fuzzy IF-THEN rules is obtained through
an equivalence of the SVM decision network and the zero-ordered
Sugeno FRB type of the Adaptive Network Fuzzy Inference System
(ANFIS). The second set of rules is generated by combining the
first set based on strength of firing signals of support vectors using
Gaussian kernel. The final set of rules is then obtained from the
second set through input scatter partitioning. A distinctive advantage
of our method is the guarantee that the number of final fuzzy IFTHEN
rules is not more than the number of support vectors in the
trained SVM. The final FRB system obtained is capable of performing
classification with results comparable to its SVM counterpart, but it
has an advantage over the black-boxed SVM in that it may reveal
human comprehensible patterns.
Abstract: This work presents a new phonetic transcription system based on a tree of hierarchical pronunciation rules expressed as context-specific grapheme-phoneme correspondences. The tree is automatically inferred from a phonetic dictionary by incrementally analyzing deeper context levels, eventually representing a minimum set of exhaustive rules that pronounce without errors all the words in the training dictionary and that can be applied to out-of-vocabulary words. The proposed approach improves upon existing rule-tree-based techniques in that it makes use of graphemes, rather than letters, as elementary orthographic units. A new linear algorithm for the segmentation of a word in graphemes is introduced to enable outof- vocabulary grapheme-based phonetic transcription. Exhaustive rule trees provide a canonical representation of the pronunciation rules of a language that can be used not only to pronounce out-of-vocabulary words, but also to analyze and compare the pronunciation rules inferred from different dictionaries. The proposed approach has been implemented in C and tested on Oxford British English and Basic English. Experimental results show that grapheme-based rule trees represent phonetically sound rules and provide better performance than letter-based rule trees.
Abstract: A transient finite element model has been developed
to study the heat transfer and fluid flow during spot Gas Tungsten
Arc Welding (GTAW) on stainless steel. Temperature field, fluid
velocity and electromagnetic fields are computed inside the cathode,
arc-plasma and anode using a unified MHD formulation. The
developed model is then used to study the influence of different
helium-argon gas mixtures on both the energy transferred to the
workpiece and the time evolution of the weld pool dimensions. It is
found that the addition of helium to argon increases the heat flux
density on the weld axis by a factor that can reach 6.5. This induces
an increase in the weld pool depth by a factor of 3. It is also found
that the addition of only 10% of argon to helium decreases
considerably the weld pool depth, which is due to the electrical
conductivity of the mixture that increases significantly when argon is
added to helium.
Abstract: This paper explores university course timetabling
problem. There are several characteristics that make scheduling and
timetabling problems particularly difficult to solve: they have huge
search spaces, they are often highly constrained, they require
sophisticated solution representation schemes, and they usually
require very time-consuming fitness evaluation routines. Thus
standard evolutionary algorithms lack of efficiency to deal with
them. In this paper we have proposed a memetic algorithm that
incorporates the problem specific knowledge such that most of
chromosomes generated are decoded into feasible solutions.
Generating vast amount of feasible chromosomes makes the progress
of search process possible in a time efficient manner. Experimental
results exhibit the advantages of the developed Hybrid Genetic
Algorithm than the standard Genetic Algorithm.
Abstract: This paper suggests a rethinking of the existing
research about Genetically Modified (GM) food. Since the first batch
of GM food was commercialised in the UK market, GM food rapidly
received and lost media attention in the UK. Disagreement on GM
food policy between the US and the EU has also drawn scholarly
attention to this issue. Much research has been carried out intending to
understand people-s views about GM food and the shaping of these
views. This paper was based on the data collected in twenty-nine
semi-structured interviews, which were examined through Erving
Goffman-s idea of self-presentation in interactions to suggest that the
existing studies investigating “consumer attitudes" towards GM food
have only considered the “front stage" in the dramaturgic metaphor.
This paper suggests that the ways in which people choose to present
themselves when participating these studies should be taken into
account during the data analysis.
Abstract: In the present work, we propose a new technique to
enhance the learning capabilities and reduce the computation
intensity of a competitive learning multi-layered neural network
using the K-means clustering algorithm. The proposed model use
multi-layered network architecture with a back propagation learning
mechanism. The K-means algorithm is first applied to the training
dataset to reduce the amount of samples to be presented to the neural
network, by automatically selecting an optimal set of samples. The
obtained results demonstrate that the proposed technique performs
exceptionally in terms of both accuracy and computation time when
applied to the KDD99 dataset compared to a standard learning
schema that use the full dataset.
Abstract: Decrease in hardware costs and advances in computer
networking technologies have led to increased interest in the use of
large-scale parallel and distributed computing systems. One of the
biggest issues in such systems is the development of effective
techniques/algorithms for the distribution of the processes/load of a
parallel program on multiple hosts to achieve goal(s) such as
minimizing execution time, minimizing communication delays,
maximizing resource utilization and maximizing throughput.
Substantive research using queuing analysis and assuming job
arrivals following a Poisson pattern, have shown that in a multi-host
system the probability of one of the hosts being idle while other host
has multiple jobs queued up can be very high. Such imbalances in
system load suggest that performance can be improved by either
transferring jobs from the currently heavily loaded hosts to the lightly
loaded ones or distributing load evenly/fairly among the hosts .The
algorithms known as load balancing algorithms, helps to achieve the
above said goal(s). These algorithms come into two basic categories -
static and dynamic. Whereas static load balancing algorithms (SLB)
take decisions regarding assignment of tasks to processors based on
the average estimated values of process execution times and
communication delays at compile time, Dynamic load balancing
algorithms (DLB) are adaptive to changing situations and take
decisions at run time.
The objective of this paper work is to identify qualitative
parameters for the comparison of above said algorithms. In future this
work can be extended to develop an experimental environment to
study these Load balancing algorithms based on comparative
parameters quantitatively.
Abstract: This paper simulates the ad-hoc mesh network in rural areas, where such networks receive great attention due to their cost, since installing the infrastructure for regular networks in these areas is not possible due to the high cost. The distance between the communicating nodes is the most obstacles that the ad-hoc mesh network will face. For example, in Terranet technology, two nodes can communicate if they are only one kilometer far from each other. However, if the distance between them is more than one kilometer, then each node in the ad-hoc mesh networks has to act as a router that forwards the data it receives to other nodes. In this paper, we try to find the critical number of nodes which makes the network fully connected in a particular area, and then propose a method to enhance the intermediate node to accept to be a router to forward the data from the sender to the receiver. Much work was done on technological changes on peer to peer networks, but the focus of this paper will be on another feature which is to find the minimum number of nodes needed for a particular area to be fully connected and then to enhance the users to switch on their phones and accept to work as a router for other nodes. Our method raises the successful calls to 81.5% out of 100% attempt calls.
Abstract: The element of justice or al-‘adl in the context of
Islamic critical thinking deals with the notion of justice in a thinking
process which critically rationalizes the truth in a fair and objective
manner with no irrelevant interference that can jeopardize a sound
judgment. This Islamic axiological element is vital in technological
decision making as it addresses the issues of religious values and
ethics that are primarily set to fulfill the purpose of human life on
earth. The main objective of this study was to examine and analyze
the perception of Muslim engineering students in Malaysian higher
education institutions towards the concept of al-‘adl as an essential
element of Islamic critical thinking. The study employed mixed
methods approach that comprises data collection from the
questionnaire survey and the interview responses. A total of 557
Muslim engineering undergraduates from six Malaysian universities
participated in the study. The study generally indicated that Muslim
engineering undergraduates in the higher institutions have rather
good comprehension and consciousness for al-‘adl with a slight
awareness on the importance of objective thinking. Nonetheless there
were a few items on the concept that have implied a comparatively
low perception on the rational justice in Islam as the means to grasp
the ultimate truth.
Abstract: Finding the minimal logical functions has important applications in the design of logical circuits. This task is solved by many different methods but, frequently, they are not suitable for a computer implementation. We briefly summarise the well-known Quine-McCluskey method, which gives a unique procedure of computing and thus can be simply implemented, but, even for simple examples, does not guarantee an optimal solution. Since the Petrick extension of the Quine-McCluskey method does not give a generally usable method for finding an optimum for logical functions with a high number of values, we focus on interpretation of the result of the Quine-McCluskey method and show that it represents a set covering problem that, unfortunately, is an NP-hard combinatorial problem. Therefore it must be solved by heuristic or approximation methods. We propose an approach based on genetic algorithms and show suitable parameter settings.
Abstract: Wind farms (WFs) with high level of penetration are
being established in power systems worldwide more rapidly than
other renewable resources. The Independent System Operator (ISO),
as a policy maker, should propose appropriate places for WF
installation in order to maximize the benefits for the investors. There
is also a possibility of congestion relief using the new installation of
WFs which should be taken into account by the ISO when proposing
the locations for WF installation. In this context, efficient wind farm
(WF) placement method is proposed in order to reduce burdens on
congested lines. Since the wind speed is a random variable and load
forecasts also contain uncertainties, probabilistic approaches are used
for this type of study. AC probabilistic optimal power flow (P-OPF)
is formulated and solved using Monte Carlo Simulations (MCS). In
order to reduce computation time, point estimate methods (PEM) are
introduced as efficient alternative for time-demanding MCS.
Subsequently, WF optimal placement is determined using generation
shift distribution factors (GSDF) considering a new parameter
entitled, wind availability factor (WAF). In order to obtain more
realistic results, N-1 contingency analysis is employed to find the
optimal size of WF, by means of line outage distribution factors
(LODF). The IEEE 30-bus test system is used to show and compare
the accuracy of proposed methodology.
Abstract: In our modern world, more physical transactions are being substituted by electronic transactions (i.e. banking, shopping, and payments), many businesses and companies are performing most of their operations through the internet. Instead of having a physical commerce, internet visitors are now adapting to electronic commerce (e-Commerce). The ability of web users to reach products worldwide can be greatly benefited by creating friendly and personalized online business portals. Internet visitors will return to a particular website when they can find the information they need or want easily. Dealing with this human conceptualization brings the incorporation of Artificial/Computational Intelligence techniques in the creation of customized portals. From these techniques, Fuzzy-Set technologies can make many useful contributions to the development of such a human-centered endeavor as e-Commerce. The main objective of this paper is the implementation of a Paradigm for the Intelligent Design and Operation of Human-Computer interfaces. In particular, the paradigm is quite appropriate for the intelligent design and operation of software modules that display information (such Web Pages, graphic user interfaces GUIs, Multimedia modules) on a computer screen. The human conceptualization of the user personal information is analyzed throughout a Cascaded Fuzzy Inference (decision-making) System to generate the User Ascribe Qualities, which identify the user and that can be used to customize portals with proper Web links.
Abstract: Diagnosis can be achieved by building a model of a
certain organ under surveillance and comparing it with the real time
physiological measurements taken from the patient. This paper deals
with the presentation of the benefits of using Data Mining techniques
in the computer-aided diagnosis (CAD), focusing on the cancer
detection, in order to help doctors to make optimal decisions quickly
and accurately. In the field of the noninvasive diagnosis techniques,
the endoscopic ultrasound elastography (EUSE) is a recent elasticity
imaging technique, allowing characterizing the difference between
malignant and benign tumors. Digitalizing and summarizing the main
EUSE sample movies features in a vector form concern with the use
of the exploratory data analysis (EDA). Neural networks are then
trained on the corresponding EUSE sample movies vector input in
such a way that these intelligent systems are able to offer a very
precise and objective diagnosis, discriminating between benign and
malignant tumors. A concrete application of these Data Mining
techniques illustrates the suitability and the reliability of this
methodology in CAD.
Abstract: In this paper, the problem of stability analysis for a class of impulsive stochastic fuzzy neural networks with timevarying delays and reaction-diffusion is considered. By utilizing suitable Lyapunov-Krasovskii funcational, the inequality technique and stochastic analysis technique, some sufficient conditions ensuring global exponential stability of equilibrium point for impulsive stochastic fuzzy cellular neural networks with time-varying delays and diffusion are obtained. In particular, the estimate of the exponential convergence rate is also provided, which depends on system parameters, diffusion effect and impulsive disturbed intention. It is believed that these results are significant and useful for the design and applications of fuzzy neural networks. An example is given to show the effectiveness of the obtained results.
Abstract: Over the past decade, mobile has experienced a
revolution that will ultimately change the way we communicate.All
these technologies have a common denominator exploitation of
computer information systems, but their operation can be tedious
because of problems with heterogeneous data sources.To overcome
the problems of heterogeneous data sources, we propose to use a
technique of adding an extra layer interfacing applications of
management or supervision at the different data sources.This layer
will be materialized by the implementation of a mediator between
different host applications and information systems frequently used
hierarchical and relational manner such that the heterogeneity is
completely transparent to the VoIP platform.
Abstract: In this paper, a new efficient method for load balancing in low voltage distribution systems is presented. The proposed method introduces an improved Leap-frog method for optimization. The proposed objective function includes the difference between three phase currents, as well as two other terms to provide the integer property of the variables; where the latter are the status of the connection of loads to different phases. Afterwards, a new algorithm is supplemented to undertake the integer values for the load connection status. Finally, the method is applied to different parts of Tabriz low voltage network, where the results have shown the good performance of the proposed method.