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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
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