|
عنوان مقاله |
نویسنده (ها) |
مربوط به کنفرانس |
چکیده |
|
|
A Novel Functional Sized Population Quantum Evolutionary Algorithm for Fractal Image Compression |
Ali Nodehi
Mohamad Tayarani
Fariborz Mahmoudi
|
چهاردهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران |
Quantum Evolutionary Algorithm (QEA) is a
novel optimization algorithm which uses a probabilistic
representation for solution and is highly suitable for
combinatorial problems like Knapsack problem. Fractal image
compression is a well-known problem which ... مشاهده کامل
Quantum Evolutionary Algorithm (QEA) is a
novel optimization algorithm which uses a probabilistic
representation for solution and is highly suitable for
combinatorial problems like Knapsack problem. Fractal image
compression is a well-known problem which is in the class of
NP-Hard problems. Genetic algorithms are widely used for
fractal image compression problems, but QEA is not used for
this kind of problems yet. This paper uses a novel Functional
Sized population Quantum Evolutionary Algorithm for fractal
image compression. Experimental results show that the
proposed algorithm has a better performance than GA and
conventional fractal image compression algorithms. عدم مشاهده کامل
Quantum Evolutionary Algorithm (QEA) is a
novel optimization algorithm which uses a probabilistic
representation for solution and is highly suitable for
combinatorial problems like Knapsack problem. Fractal image
compression is a well-known problem which ... مشاهده کامل |
خرید مقاله
|
|
A Heuristic Approach for Value at Risk Based Portfolio Optimization |
Mohammad Zeiaee
Mohammad Reza Jahed-Motlagh
|
چهاردهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران |
Portfolio optimization under classic mean-variance
framework of Markowitz must be revised as variance
fails to be a good risk measure. This is especially true
when the asset returns are not normal. In this ... مشاهده کامل
Portfolio optimization under classic mean-variance
framework of Markowitz must be revised as variance
fails to be a good risk measure. This is especially true
when the asset returns are not normal. In this paper,
we utilize Value at Risk (VaR) as the risk measure and
Historical Simulation (HS) is used to obtain an
acceptable estimate of the VaR. Also, a well known
multi-objective evolutionary approach is used to
address the inherent bi-objective problem; In fact,
NSGA-II is incorporated here. This method is tested on
a set of past return data of 12 assets on Tehran Stock
Exchange (TSE). A comparison of the obtained results,
shows that the proposed method offers high quality
solutions and a wide range of risk return trade-offs. عدم مشاهده کامل
Portfolio optimization under classic mean-variance
framework of Markowitz must be revised as variance
fails to be a good risk measure. This is especially true
when the asset returns are not normal. In this ... مشاهده کامل |
خرید مقاله
|
|
XML Document Clustering Based on Common Tag Names Anywhere in the Structure |
Mohamad Alishahi
Mehdi Ravakhah
Baharak Shakeriaski
Mahmud Naghibzade
|
چهاردهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران |
One of the most effective ways to extract knowledge
from large information resources is applying data
mining methods. Since the amount of information on
the Internet is exploding, using XML documents is
common as ... مشاهده کامل
One of the most effective ways to extract knowledge
from large information resources is applying data
mining methods. Since the amount of information on
the Internet is exploding, using XML documents is
common as they have many advantages. Knowledge
extraction from XML documents is a way to provide
more utilizable results. XCLS is one of the most
efficient algorithms for XML documents clustering. In
this paper we represent a new algorithm for clustering
XML documents. This algorithm is an improvement
over XCLS algorithm which tries to obviate its
problems. We implemented both algorithms and
evaluated their clustering quality and running time on
the same data sets. In both cases, it is shown that the
performance of the new algorithm is better. عدم مشاهده کامل
One of the most effective ways to extract knowledge
from large information resources is applying data
mining methods. Since the amount of information on
the Internet is exploding, using XML documents is
common as ... مشاهده کامل |
خرید مقاله
|
|
A High Capacity Image hiding Method based on Fuzzy Image Coding/Decoding |
Zahra Toony
Hedieh Sajedi
Mansour Jamzad
|
چهاردهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران |
Recently, a technique has been proposed for image
hiding, that is based on block texture similarity where,
blocks of secret image are compared with blocks of a
set of cover images and the ... مشاهده کامل
Recently, a technique has been proposed for image
hiding, that is based on block texture similarity where,
blocks of secret image are compared with blocks of a
set of cover images and the cover image with the most
similar blocks to those of the secret image is selected
as the best candidate cover image to conceal the secret
image. In this paper, we propose a new image hiding
method in which, the secret image is initially coded
using a fuzzy coding/decoding method. By applying the
fuzzy coder, each block of the secret image is
compressed to a smaller block. In this way, after
compressing the secret image to a smaller one, we hide
it in a cover image. Obviously hiding a smaller secret
image causes less distortion in the stego-image (the
image that has secret image or data) and therefore
higher quality stego-image is obtained. Consequently,
the proposed method provides higher embedding rate
and enhanced security. عدم مشاهده کامل
Recently, a technique has been proposed for image
hiding, that is based on block texture similarity where,
blocks of secret image are compared with blocks of a
set of cover images and the ... مشاهده کامل |
خرید مقاله
|
|
Improvement of Language Identification Performance Using Generalized Phone Recognizer |
S.A. Hosseini Amereii
M.M. Homayounpour
|
چهاردهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران |
Two popular and better performing approaches to
language Identification (LID) are Phone Recognition
followed by Language Modeling (PRLM) and Parallel
PRLM. In this paper, we report several improvements
in Phone Recognition which reduces error ... مشاهده کامل
Two popular and better performing approaches to
language Identification (LID) are Phone Recognition
followed by Language Modeling (PRLM) and Parallel
PRLM. In this paper, we report several improvements
in Phone Recognition which reduces error rate in
PRLM and PPRLM based LID systems. In our previous
paper, we introduced APRLM approach that reduces
error rate for about 1.3% in LID tasks. In this paper,
we suggest other solution that overcomes APRLM. This
new LID approach is named Generalized PRLM or
GPRLM. Several language identification experiments
were conducted and the proposed improvements were
evaluated using OGI-MLTS corpus. Our results show
that GPRLM overcomes PPRLM and APRLM about
2.5% and 1.2% respectively in two language
classification tasks. عدم مشاهده کامل
Two popular and better performing approaches to
language Identification (LID) are Phone Recognition
followed by Language Modeling (PRLM) and Parallel
PRLM. In this paper, we report several improvements
in Phone Recognition which reduces error ... مشاهده کامل |
خرید مقاله
|
|
Fast and Parsimonious Self-Organizing Fuzzy Neural Network |
Omid Khayat
Javad Razjouyan
Hadi ChahkandiNejad
Mahdi Mohammad Abadi
Mohammad Mehdi
|
چهاردهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران |
This paper introduces a revisited hybrid algorithm for
function approximation. In this paper, a simple and fast
learning algorithm is proposed, which automates
structure and parameter identification simultaneously
based on input-target samples. First, without ... مشاهده کامل
This paper introduces a revisited hybrid algorithm for
function approximation. In this paper, a simple and fast
learning algorithm is proposed, which automates
structure and parameter identification simultaneously
based on input-target samples. First, without need of
clustering, the initial structure of the network with the
specified number of rules is established, and then a
training process based on the error of other training
samples is applied to obtain a more precision model.
After the network structure is identified, an optimization
learning, based on the criteria error, is performed to
optimize the obtained parameter set of the premise parts
and the consequent parts. At the end, comprehensive
comparisons are made with other approaches to
demonstrate that the proposed algorithm is superior in
term of compact structure, convergence speed, memory
usage and learning efficiency. عدم مشاهده کامل
This paper introduces a revisited hybrid algorithm for
function approximation. In this paper, a simple and fast
learning algorithm is proposed, which automates
structure and parameter identification simultaneously
based on input-target samples. First, without ... مشاهده کامل |
خرید مقاله
|
|
Cellular Learning Automata-Based Color Image Segmentation using Adaptive Chains |
Ahmad Ali Abin
Mehran Fotouhi
Shohreh Kasaei
|
چهاردهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران |
This paper presents a new segmentation method for
color images. It relies on soft and hard segmentation
processes. In the soft segmentation process, a cellular
learning automata analyzes the input image and closes
together ... مشاهده کامل
This paper presents a new segmentation method for
color images. It relies on soft and hard segmentation
processes. In the soft segmentation process, a cellular
learning automata analyzes the input image and closes
together the pixels that are enclosed in each region to
generate a soft segmented image. Adjacency and
texture information are encountered in the soft
segmentation stage. Soft segmented image is then fed
to the hard segmentation process to generate the final
segmentation result. As the proposed method is based
on CLA it can adapt to its environment after some
iterations. This adaptive behavior leads to a semi
content-based segmentation process that performs well
even in presence of noise. Experimental results show
the effectiveness of the proposed segmentation method. عدم مشاهده کامل
This paper presents a new segmentation method for
color images. It relies on soft and hard segmentation
processes. In the soft segmentation process, a cellular
learning automata analyzes the input image and closes
together ... مشاهده کامل |
خرید مقاله
|
|
Coronary Heart Disease Risk Assessment Using Dempster-Shafer Theory |
Vahid Khatibi
Gholam Ali Montazer
|
چهاردهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران |
In this paper, a novel inference engine named fuzzyevidential
hybrid engine has been proposed using
Dempster-Shafer theory of evidence and fuzzy sets
theory. This hybrid engine operates in two phases. In
the first phase, ... مشاهده کامل
In this paper, a novel inference engine named fuzzyevidential
hybrid engine has been proposed using
Dempster-Shafer theory of evidence and fuzzy sets
theory. This hybrid engine operates in two phases. In
the first phase, it models the input information’s
vagueness through fuzzy sets. In following, extracting
the fuzzy rule set for the problem, it applies the fuzzy
inference rules on the acquired fuzzy sets to produce
the first phase results. At second phase, the acquired
results of previous stage are assumed as basic beliefs
for the problem propositions and in this way, the belief
and plausibility functions (or the belief interval) are
set. Gathering information from different sources, they
provide us with diverse basic beliefs which should be
fused to produce an integrative result. For this
purpose, evidential combination rules are used to
perform the information fusion. Having applied the
proposed engine on the coronary heart disease (CHD)
risk assessment, it has yielded 86 percent accuracy
rate in the CHD risk prediction. عدم مشاهده کامل
In this paper, a novel inference engine named fuzzyevidential
hybrid engine has been proposed using
Dempster-Shafer theory of evidence and fuzzy sets
theory. This hybrid engine operates in two phases. In
the first phase, ... مشاهده کامل |
خرید مقاله
|
|
Intuitionistic Fuzzy Set Application in Bacteria Recognition |
Vahid Khatibi
Gholam Ali Montazer
|
چهاردهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران |
One of the toughest challenges in medical diagnosis
is uncertainty handling. The recognition of intestinal
bacteria such as Salmonella and Shigella which cause
typhoid fever and dysentery, respectively, is one such
challenging problem for ... مشاهده کامل
One of the toughest challenges in medical diagnosis
is uncertainty handling. The recognition of intestinal
bacteria such as Salmonella and Shigella which cause
typhoid fever and dysentery, respectively, is one such
challenging problem for microbiologists. In this paper,
we take an intelligent approach towards the bacteria
classification problem by using five similarity
measures of fuzzy sets (FSs) and intuitionistic fuzzy
sets (IFSs) to examine their capabilities in
encountering uncertainty in the medical pattern
recognition. Finally, the recognition rates of the
measures are calculated among which IFS Mitchel and
Hausdorf similarity measures score the best results
with 95.27% and 94.48% recognition rates,
respectively. On the other hand, FS Euclidean distance
yieldes only 85% recognition rate. عدم مشاهده کامل
One of the toughest challenges in medical diagnosis
is uncertainty handling. The recognition of intestinal
bacteria such as Salmonella and Shigella which cause
typhoid fever and dysentery, respectively, is one such
challenging problem for ... مشاهده کامل |
خرید مقاله
|
|
Adaptive Parameter Selection Scheme for PSO: A Learning Automata Approach |
Ali B. Hashemi
M.R Meybodi
|
چهاردهمین کنفرانس بین‌المللی سالانه انجمن کامپیوتر ایران |
PSO, like many stochastic search methods, is very
sensitive to efficient parameter setting. As modifying a
single parameter may result in a large effect. In this
paper, we propose a new a new ... مشاهده کامل
PSO, like many stochastic search methods, is very
sensitive to efficient parameter setting. As modifying a
single parameter may result in a large effect. In this
paper, we propose a new a new learning automatabased
approach for adaptive PSO parameter selection.
In this approach three learning automata are utilized
to determine values of each parameter for updating
particles velocity namely inertia weight, cognitive and
social components. Experimental results show that the
proposed algorithms compared to other schemes such
as SPSO, PSO-IW, PSO TVAC, PSO-LP, DAPSO,
GPSO, and DCPSO have the same or even higher
ability to find better local minima. In addition,
proposed algorithms converge to stopping criteria
significantly faster than most of the PSO algorithms. عدم مشاهده کامل
PSO, like many stochastic search methods, is very
sensitive to efficient parameter setting. As modifying a
single parameter may result in a large effect. In this
paper, we propose a new a new ... مشاهده کامل |
خرید مقاله
|
.gif){kind=icon}
