Abstract: In this paper, a novel associative memory model will be proposed and applied to memory retrievals based on the conventional continuous time model. The conventional model presents memory capacity is very low and retrieval process easily converges to an equilibrium state which is very different from the stored patterns. Genetic Algorithms is well-known with the capability of global optimal search escaping local optimum on progress to reach a global optimum. Based on the well-known idea of Genetic Algorithms, this work proposes a heuristic rule to make a mutation when the state of the network is trapped in a spurious memory. The proposal heuristic associative memory show the stored capacity does not depend on the number of stored patterns and the retrieval ability is up to ~ 1.
Abstract: In this paper, we present a new method for
incorporating global shift invariance in support vector machines.
Unlike other approaches which incorporate a feature extraction stage,
we first scale the image and then classify it by using the modified
support vector machines classifier. Shift invariance is achieved by
replacing dot products between patterns used by the SVM classifier
with the maximum cross-correlation value between them. Unlike the
normal approach, in which the patterns are treated as vectors, in our
approach the patterns are treated as matrices (or images). Crosscorrelation
is computed by using computationally efficient
techniques such as the fast Fourier transform. The method has been
tested on the ORL face database. The tests indicate that this method
can improve the recognition rate of an SVM classifier.
Abstract: In this research, the authors analyze network stability
using agent-based simulation. Firstly, the authors focus on analyzing
large networks (eight agents) by connecting different two stable small
social networks (A small stable network is consisted on four agents.).
Secondly, the authors analyze the network (eight agents) shape which
is added one agent to a stable network (seven agents). Thirdly, the
authors analyze interpersonal comparison of utility. The “star-network
"was not found on the result of interaction among stable two small
networks. On the other hand, “decentralized network" was formed
from several combination. In case of added one agent to a stable
network (seven agents), if the value of “c"(maintenance cost of per
a link) was larger, the number of patterns of stable network was
also larger. In this case, the authors identified the characteristics of a
large stable network. The authors discovered the cases of decreasing
personal utility under condition increasing total utility.
Abstract: Text Mining is around applying knowledge discovery techniques to unstructured text is termed knowledge discovery in text (KDT), or Text data mining or Text Mining. In Neural Network that address classification problems, training set, testing set, learning rate are considered as key tasks. That is collection of input/output patterns that are used to train the network and used to assess the network performance, set the rate of adjustments. This paper describes a proposed back propagation neural net classifier that performs cross validation for original Neural Network. In order to reduce the optimization of classification accuracy, training time. The feasibility the benefits of the proposed approach are demonstrated by means of five data sets like contact-lenses, cpu, weather symbolic, Weather, labor-nega-data. It is shown that , compared to exiting neural network, the training time is reduced by more than 10 times faster when the dataset is larger than CPU or the network has many hidden units while accuracy ('percent correct') was the same for all datasets but contact-lences, which is the only one with missing attributes. For contact-lences the accuracy with Proposed Neural Network was in average around 0.3 % less than with the original Neural Network. This algorithm is independent of specify data sets so that many ideas and solutions can be transferred to other classifier paradigms.
Abstract: Knowledge of factors, which influence stress and its
distribution, is of key importance to the successful production of
durable restorations. One of this is the marginal geometry. The
objective of this study was to evaluate, by finite element analysis
(FEA), the influence of different marginal designs on the stress
distribution in teeth prepared for cast metal crowns. Five margin
designs were taken into consideration: shoulderless, chamfer,
shoulder, sloped shoulder and shoulder with bevel. For each kind of
preparation three dimensional finite element analyses were initiated.
Maximal equivalent stresses were calculated and stress patterns were
represented in order to compare the marginal designs. Within the
limitation of this study, the shoulder and beveled shoulder margin
preparations of the teeth are preferred for cast metal crowns from
biomechanical point of view.
Abstract: Allowing diagonalizability of sign pattern is still an open problem. In this paper, we make a carefully discussion about allowing unitary diagonalizability of two sign pattern. Some sufficient and necessary conditions of allowing unitary diagonalizability are also obtained.
Abstract: Energy Efficiency Management is the heart of a
worldwide problem. The capability of a multi-agent system as a
technology to manage the micro-grid operation has already been
proved. This paper deals with the implementation of a decisional
pattern applied to a multi-agent system which provides intelligence to
a distributed local energy network considered at local consumer level.
Development of multi-agent application involves agent
specifications, analysis, design, and realization. Furthermore, it can
be implemented by following several decisional patterns. The
purpose of present article is to suggest a new approach for a
decisional pattern involving a multi-agent system to control a
distributed local energy network in a decentralized competitive
system. The proposed solution is the result of a dichotomous
approach based on environment observation. It uses an iterative
process to solve automatic learning problems and converges
monotonically very fast to system attracting operation point.
Abstract: The stability of a software system is one of the most
important quality attributes affecting the maintenance effort. Many
techniques have been proposed to support the analysis of software
stability at the architecture, file, and class level of software systems,
but little effort has been made for that at the feature (i.e., method and
attribute) level. And the assumptions the existing techniques based
on always do not meet the practice to a certain degree. Considering
that, in this paper, we present a novel metric, Stability of Software
(SoS), to measure the stability of object-oriented software systems
by software change propagation analysis using a simulation way
in software dependency networks at feature level. The approach is
evaluated by case studies on eight open source Java programs using
different software structures (one employs design patterns versus one
does not) for the same object-oriented program. The results of the
case studies validate the effectiveness of the proposed metric. The
approach has been fully automated by a tool written in Java.
Abstract: This paper proposes an investment cost recovery
based efficient and fast sequential optimization approach to optimal
allocation of thyristor controlled series compensator (TCSC) in
competitive power market. The optimization technique has been used
with an objective to maximizing the social welfare and minimizing
the device installation cost by suitable location and rating of TCSC in
the system. The effectiveness of proposed approach for location of
TCSC has been compared with some existing methods of TCSC
placement, in terms of its impact on social welfare, TCSC investment
recovery and optimal generation as well as load patterns. The results
have been obtained on modified IEEE 14-bus system.
Abstract: Data Mining aims at discovering knowledge out of
data and presenting it in a form that is easily comprehensible to
humans. One of the useful applications in Egypt is the Cancer
management, especially the management of Acute Lymphoblastic
Leukemia or ALL, which is the most common type of cancer in
children.
This paper discusses the process of designing a prototype that can
help in the management of childhood ALL, which has a great
significance in the health care field. Besides, it has a social impact
on decreasing the rate of infection in children in Egypt. It also
provides valubale information about the distribution and
segmentation of ALL in Egypt, which may be linked to the possible
risk factors.
Undirected Knowledge Discovery is used since, in the case of this
research project, there is no target field as the data provided is
mainly subjective. This is done in order to quantify the subjective
variables. Therefore, the computer will be asked to identify
significant patterns in the provided medical data about ALL. This
may be achieved through collecting the data necessary for the
system, determimng the data mining technique to be used for the
system, and choosing the most suitable implementation tool for the
domain.
The research makes use of a data mining tool, Clementine, so as to
apply Decision Trees technique. We feed it with data extracted from
real-life cases taken from specialized Cancer Institutes. Relevant
medical cases details such as patient medical history and diagnosis
are analyzed, classified, and clustered in order to improve the disease
management.
Abstract: The increasing usage of antibiotics in the animal
farming industry is an emerging worldwide problem contributing to
the development of antibiotic resistance. The purpose of this work was
to investigate the prevalence and antibiotic resistance profile of
bacterial isolates collected from aquatic environments and meats in a
peri-urban community in Daejeon, Korea. In an antibacterial
susceptibility test, the bacterial isolates showed a high incidence of
resistance (~ 26.04 %) to cefazolin, tetracycline, gentamycin,
norfloxacin, erythromycin and vancomycin. The results from a test for
multiple antibiotic resistance indicated that the isolates were
displaying an approximately 5-fold increase in the incidence of
multiple antibiotic resistance to combinations of two different
antibiotics compared to combinations of three or more antibiotics.
Most of the isolates showed multi-antibiotic resistance, and the
resistance patterns were similar among the sampling groups.
Sequencing data analysis of 16S rRNA showed that most of the
resistant isolates appeared to be dominated by the classes
Betaproteobacteria and Gammaproteobacteria in the phylum
Proteobacteria.
Abstract: Hemodialysis patients might suffer from unhealthy
care behaviors or long-term dialysis treatments. Ultimately they need
to be hospitalized. If the hospitalization rate of a hemodialysis center
is high, its quality of service would be low. Therefore, how to decrease
hospitalization rate is a crucial problem for health care. In this study
we combined temporal abstraction with data mining techniques for
analyzing the dialysis patients' biochemical data to develop a decision
support system. The mined temporal patterns are helpful for clinicians
to predict hospitalization of hemodialysis patients and to suggest them
some treatments immediately to avoid hospitalization.
Abstract: In spite of all advancement in software testing,
debugging remains a labor-intensive, manual, time consuming, and
error prone process. A candidate solution to enhance debugging
process is to fuse it with testing process. To achieve this integration,
a possible solution may be categorizing common software tests and
errors followed by the effort on fixing the errors through general
solutions for each test/error pair. Our approach to address this issue is
based on Christopher Alexander-s pattern and pattern language
concepts. The patterns in this language are grouped into three major
sections and connect the three concepts of test, error, and debug.
These patterns and their hierarchical relationship shape a pattern
language that introduces a solution to solve software errors in a
known testing context.
Finally, we will introduce our developed framework ADE as a
sample implementation to support a pattern of proposed language,
which aims to automate the whole process of evolving software
design via evolutionary methods.
Abstract: The dominant judgment for earthquake damaged reinforced concrete (RC) structures is to rebuild them with the new ones. Consequently, this paper estimates if there is chance to repair earthquake RC beams and obtain economical contribution to modern day society. Therefore, the totally damaged (damaged in shear under cyclic load) reinforced concrete (RC) beams repaired and strengthened by externally bonded carbon fibre reinforced polymer (CFRP) strips in this study. Four specimens, apart from the reference beam, were separated into two distinct groups. Two experimental beams in the first group primarily tested up to failure then appropriately repaired and strengthened with CFRP strips. Two undamaged specimens from the second group were not repaired but strengthened by the identical strengthening scheme as the first group for comparison. This study studies whether earthquake damaged RC beams that have been repaired and strengthened will validate similar strength and behavior to equally strengthened, undamaged RC beams. Accordingly, a strength correspondence according to strengthened specimens was acquired for the repaired and strengthened specimens. Test results confirmed that repair and strengthening, which were estimated in the experimental program, were effective for the specimens with the cracking patterns considered in the experimental program.
Abstract: The concentrations of As, Hg, Co, Cr and Cd were
tested for each soil sample, and their spatial patterns were analyzed
by the semivariogram approach of geostatistics and geographical
information system technology. Multivariate statistic approaches
(principal component analysis and cluster analysis) were used to
identify heavy metal sources and their spatial pattern. Principal
component analysis coupled with correlation between heavy metals
showed that primary inputs of As, Hg and Cd were due to
anthropogenic while, Co, and Cr were associated with pedogenic
factors. Ordinary kriging was carried out to map the spatial patters of
heavy metals. The high pollution sources evaluated was related with
usage of urban and industrial wastewater. The results of this study
helpful for risk assessment of environmental pollution for decision
making for industrial adjustment and remedy soil pollution.
Abstract: Data mining (DM) is the process of finding and extracting frequent patterns that can describe the data, or predict unknown or future values. These goals are achieved by using various learning algorithms. Each algorithm may produce a mining result completely different from the others. Some algorithms may find millions of patterns. It is thus the difficult job for data analysts to select appropriate models and interpret the discovered knowledge. In this paper, we describe a framework of an intelligent and complete data mining system called SUT-Miner. Our system is comprised of a full complement of major DM algorithms, pre-DM and post-DM functionalities. It is the post-DM packages that ease the DM deployment for business intelligence applications.
Abstract: The changes in quality properties and nutritional
components in two fermented mugworts (Artemisia capillaries
Thumberg, Artemisiaeasiaticae Nakai) were characterized followed
by the rapid pattern analysis of volatile flavor compounds by Electric
Nose based on SAW(Surface Acoustic Wave) sensor in GC system.
There were remarkable decreases in the pH and small changes in the
total soluble solids after fermentation. The L (lightness) and b
(yellowness) values in Hunter's color system were shown to be
decreased, whilst the a (redness) value was increased by fermentation.
The HPLC analysis demonstrated that total amino acids were
increased in quantity and the essential amino acids were contained
higher in A. asiaticaeNakai than in A. capillaries Thumberg. While
the total polyphenol contents were not affected by fermentation, the
total sugar contents were dramatically decreased. Scopoletinwere
highly abundant in A. capillarisThumberg, however, it was not
detected in A. asiaticaeNakai. Volatile flavor compounds by Electric
Nose showed that the intensity of several peaks were increased much
and seven additional flavor peaks were newly produced after
fermentation. The flavor differences of two mugworts were clearly
distinguished from the image patterns of VaporPrintTM which indicate
that the fermentation enables the two mugworts to have subtle flavor
differences.
Abstract: In this note, some properties of potentially powerpositive sign patterns are established, and all the potentially powerpositive sign patterns of order ≤ 3 are classified completely.
Abstract: In the recent years multimedia traffic and in particular
VoIP services are growing dramatically. We present a new algorithm
to control the resource utilization and to optimize the voice codec
selection during SIP call setup on behalf of the traffic condition
estimated on the network path.
The most suitable methodologies and the tools that perform realtime
evaluation of the available bandwidth on a network path have
been integrated with our proposed algorithm: this selects the best
codec for a VoIP call in function of the instantaneous available
bandwidth on the path. The algorithm does not require any explicit
feedback from the network, and this makes it easily deployable over
the Internet. We have also performed intensive tests on real network
scenarios with a software prototype, verifying the algorithm
efficiency with different network topologies and traffic patterns
between two SIP PBXs.
The promising results obtained during the experimental validation
of the algorithm are now the basis for the extension towards a larger
set of multimedia services and the integration of our methodology
with existing PBX appliances.
Abstract: Bio-chips are used for experiments on genes and
contain various information such as genes, samples and so on. The
two-dimensional bio-chips, in which one axis represent genes and the
other represent samples, are widely being used these days. Instead of
experimenting with real genes which cost lots of money and much
time to get the results, bio-chips are being used for biological
experiments. And extracting data from the bio-chips with high
accuracy and finding out the patterns or useful information from such
data is very important. Bio-chip analysis systems extract data from
various kinds of bio-chips and mine the data in order to get useful
information. One of the commonly used methods to mine the data is
classification. The algorithm that is used to classify the data can be
various depending on the data types or number characteristics and so
on. Considering that bio-chip data is extremely large, an algorithm that
imitates the ecosystem such as the ant algorithm is suitable to use as an
algorithm for classification. This paper focuses on finding the
classification rules from the bio-chip data using the Ant Colony
algorithm which imitates the ecosystem. The developed system takes
in consideration the accuracy of the discovered rules when it applies it
to the bio-chip data in order to predict the classes.