Sharif Digital Repository

To reduce the semantic gap in the content based image retrieval (CBIR) systems we propose a fuzzy rule base approach. By submitting a query to the proposed system, it first extracts its low-level features and then checks its rule base for determining the proper weight vector for its distance measure. It then uses this weight vector to determine what images are more similar to the query image. For the training purpose, an algorithm is provided by which the system adjusts its fuzzy rules' parameters by gathering the trainers' opinions on which and how much the image pairs are relevant. For further improving the performance of the system, a feature space dimensionality reduction method is also... 

A hybrid algorithm of a particle swarm optimization and a fuzzy rule-based classification system is proposed in this study to diagnose breast cancer. Two orthogonal and triangular types of fuzzy sets are applied to represent the input variables. In additional, different input membership functions are considered to increase the classification accuracy. The performance of the proposed hybrid algorithm is studied using a classification accuracy measure on the Wisconsin breast cancer dataset. The results of the comparison using different training data sets show the higher performance of the proposed methodology  

This paper shows how a subset of features can be selected for designing interpretable fuzzy rule-based system. This method consists of two phases: feature subset selection based on Michigan Learning approach and Training fuzzy rule-based system using the selected subset from the first phase. First, a number of independent fuzzy rule-based systems are trained using genetic operations, and then the dominated rules of each trained system with the highest fitness values are selected. From the selected rules, a pre-specified number of features are chosen with the highest frequency. In the second phase, a fuzzy rule-based system is trained based on the selected features from the previous phase.... 

This paper presents a novel approach to isolated word speech recognition using fuzzy modeling which is specifically designed to ignore noise. The task is based on conversion of speech spectrogram into a linguistic fuzzy description and comparison of this representation with fuzzy linguistic descriptions of words. The method is tested on single speaker and multiple speaker tests and the results are compared with a widely used speech recognition approach, showing much higher noise resistance. Copyright - World Automation Congress (WAC) 2006  

The process of scanning the events occurring in a computer system or network and analyzing them for warning of intrusions is known as intrusion detection system (IDS). This paper presents a new intrusion detection system based on tabu search based fuzzy system. Here, we use tabu search algorithm to effectively explore and exploit the large state space associated with intrusion detection as a complicated classification problem. Experiments were performed on KDD-Cup99 data set which has information about intrusive and normal behaviors on computer networks. Results show that the proposed method obtains notable accuracy and lower cost in comparison with several renowned algorithms  

In traditional game theory, the players play with policy of maximizing their payoffs. In real world, there are many situations where payoffs have uncertainty and are fuzzy in nature. In this paper, a new method for finding pure strategy Nash equilibriums, to realistically analyze the games with fuzzy payoffs is investigated. Using ranking fuzzy numbers, a fuzzy preference relation is constructed over payoffs. The priorities of payoffs are considered as the degree of being Nash equilibriums  

Order statistic (OS) CFAR processor is a powerful detector, but similar to many other CFAR detectors, suffers from clutter edge as well as interfering targets. To overcome these problems, two derivations of this detector have been developed. These are order statistic greatest of (OSGO) and order statistic smallest of (OSSO) CFAR processors. The cost, paid for this improvement, is a considerable decrease in detection probability for homogenous Weibull clutter. Furthermore, a substantial performance degradation of OSGO in interfering target (high target masking effect) and an excessive false alarm of OSSO in clutter edge, also, occur. This behavior is a result of non-soft rules used in these... 

In this paper, the use of simulated annealing (SA) metaheuristic for constructing a fuzzy classification system is presented. In several previous investigations, the capability of fuzzy systems to solve different kinds of problems has been demonstrated. Simulated annealing based fuzzy classification system (SAFCS), hybridizes the learning capability of SA metaheuristic with the approximate reasoning method of fuzzy systems. The objective of this paper is to illustrate the ability of SA to develop an accurate fuzzy classifier. The use of SA in classification is an attempt to effectively explore and exploit the large search space usually associated with classification problems, and find the... 

This paper presents a novel hybrid approach for intrusion detection in computer networks. The proposed approach combines an evolutionary based fuzzy system with an artificial immune system to generate high quality fuzzy classification rules. The performance of final fuzzy classification system has been investigated using the KDD-Cup99 benchmark dataset. The results indicate that in comparison to several traditional techniques, such as C4.5, Naïve Bayes, k-NN and SVM, the proposed hybrid approach achieves better classification accuracies for most of the classes of the intrusion detection classification problem. Therefore, the resulted fuzzy classification rules can be used to produce a... 

Mesh-based motion estimation is an important tool for video coding especially with low bit rate. In this paper, a new method for interpolating pixel motion from adjacent mesh nodes with the ability of omitting independent nodes is proposed. By exploiting fuzzy rules to determine the association of pixel and neighboring nodes, the proposed interpolation can detach pixels from some nodes. Consequently, it can deal with those critical patches on objects boundary which their nodes do not belong to one object. Updating the membership functions of each rule with specified strategy makes the interpolation adaptive with non-stationary conditions of image sequences and decreases sensitivity to... 

This paper presents a fuzzy approach for contrast enhancement, based on two multi-scale transforms, namely wavelet and contourlet transforms. Separability and nondirectionality of conventional 2D wavelet transform, makes it unsuitable for sparsely representation of curve or line shaped image objects. On the other hand, the contourlet transform is a good alternative for this purpose. In this paper, coefficient enhancement, both in wavelet and contourlet spaces, is carried out by making use of simple fuzzy rules. These rules make the enhancement procedure more understandable and flexible. With this method, the knowledge and experience of the expert from the distribution of the coefficients can... 

Data mining is a very popular technique which is successfully used in many areas. The aim of this paper is to present a data mining system for extracting knowledge from input datasets. We use the hybrid ant colony and simulated annealing algorithms to optimize extracted fuzzy rule set. The proposed method has the main feature of data mining techniques which is high accuracy. The proposed method is then implemented on UCI datasets. The results are compared with those of well-known methods, and show the competitive systems efficiency  

Data mining is a very popular technique which is successfully used in many areas. The aim of this paper is to present a Hybrid model for data classification from input datasets. The proposed model extracts knowledge using fuzzy rule based systems and performs classification task by fuzzy if-then rules. The proposed method performs the classification task and extracts required knowledge using fuzzy rule based systems which consists of fuzzy if-then rules. In order to do so the hybrid ant colony and simulated annealing algorithms have been used to optimize extracted fuzzy rule set. "ACSA", a self development data mining software system based on swarm intelligence, is applied to experiment on... 

The capability of fuzzy systems to solve different kinds of problems has been demonstrated in several previous investigations. Genetic fuzzy systems (GFSs) hybridize the approximate reasoning method of fuzzy systems with the learning capability of evolutionary algorithms. The objective of this paper is to design and analysis of various kinds of genetic fuzzy systems to deal with intrusion detection problem as a new real-world application area which is not previously tackled with GFSs. The resulted intrusion detection system would be capable of detecting normal and abnormal behaviors in computer networks. We have presented three kinds of genetic fuzzy systems based on Michigan, Pittsburgh and... 

The process of monitoring the events occurring in a computer system or network and analyzing them for sign of intrusions is known as intrusion detection system (IDS). The objective of this paper is to extract fuzzy classification rules for intrusion detection in computer networks. The proposed method is based on the iterative rule learning approach (IRL) to fuzzy rule base system design. The fuzzy rule base is generated in an incremental fashion, in that the evolutionary algorithm optimizes one fuzzy classifier rule at a time. The performance of final fuzzy classification system has been investigated using intrusion detection problem as a high-dimensional classification problem. Results show... 

Uncertainty is a critical factor in medical datasets needed to be overcome for increasing diagnosis efficiency. This paper proposes an intelligent classification algorithm comprising a fuzzy rule-based approach, a harmony search (HS) algorithm, and a heuristic algorithm to classify medical datasets intelligently. Two fuzzy approaches, as well as orthogonal and triangular fuzzy sets, are first utilized to define the attributes of data. Then, an HS algorithm is integrated with a heuristic to generate fuzzy rules to select the best rules in the fuzzy rule-based systems. Moreover, to improve the performance of the proposed classification approach, a three-phase parameter tuning approach is... 

We are concerned with the design of a model and an algorithm for computing the shortest path in a network having various types of fuzzy arc lengths. First, a new technique is devised for the addition of various fuzzy numbers in a path using α-cuts by proposing a least squares model to obtain membership functions for the considered additions. Due to the complexity of the addition of various fuzzy numbers for larger problems, a genetic algorithm is presented for finding the shortest path in the network. For this, we apply a recently proposed distance function for comparison of fuzzy numbers. Examples are worked out to illustrate the applicability of the proposed approach  

Due to the important role of commitment and trust in the relationship marketing, the factors which can directly result in a committed relationship along with the factors which can influence the commitment through influencing trust, according to the model of commitment and trust by (Morgan & Hunt, 1994) have been introduced and their level of importance has been investigated here. The article uses fuzzy cognitive maps (FCMs) in the proposed model to find the most important paths leading to relationship commitment. The FCM analyzes the responses of a group of 30 people including general practitioners in dentistry, managers of dental departments in some of the public clinics and hospitals who... 

The security of networked computers plays a strategic role in modern computer systems. This task is so complicated because the determination of normal and abnormal behaviors in computer networks is hard, as the boundaries cannot be well defined. One of the difficulties in such a prediction process is the generation of false alarms in many anomaly based intrusion detection systems. However, fuzzy logic is an important solution to reduce the false alarm rate in determining intrusive activities. This paper proposes a parallel genetic local search algorithm (PAGELS) to generate fuzzy rules capable of detecting intrusive behaviors in computer networks. The system uses the Michigan's approach,...