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.... 

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... 

In this paper, by a simple example it is shown that existing market-based criteria alone cannot completely and correctly evaluate the transmission network expansion from market view. However criteria congestion cost (CC) and social welfare (SW) together are able to correctly evaluate transmission network from market view and so they are adopted for the market-based transmission expansion planning. To simply indicate the limits of CC and SW social welfare percentage (SWP) and congestion cost percentage (CCP) are defined. To consider uncertainty in bids of market producers and consumers, and also indeterminacy in the acceptable boundaries of the SWP and CCP and their priorities, fuzzy... 

In this paper, by a simple example it is shown that existing market-based criteria alone cannot completely and correctly evaluate the transmission network expansion from market view. However criteria congestion cost (CC) and social welfare (SW) together are able to correctly evaluate transmission network from market view and so they are adopted for the market-based transmission expansion planning. To simply indicate the limits of CC and SW social welfare percentage (SWP) and congestion cost percentage (CCP) are defined. To consider uncertainty in bids of market producers and consumers, and also indeterminacy in the acceptable boundaries of the SWP and CCP and their priorities, fuzzy... 

In this paper we have proposed an evolutionary algorithm to induct fuzzy classification rules. The algorithm uses an ant colony optimization based local searcher to improve the quality of final fuzzy classification system. The proposed algorithm is performed on Intrusion Detection as a high-dimensional classification problem. Results show that the implemented evolutionary ACO-Based algorithm is capable of producing a reliable fuzzy rule based classifier for intrusion detection. © 2007 IEEE  

A novel approach for selecting proper cover images in steganography is presented in this paper. The proposed approach consists of two stages. The first stage is an evolutionary algorithm that extracts the signature of cover images against stego images in the form of fuzzy if-then rules. This algorithm is based on an iterative rule learning approach to construct an accurate fuzzy rule base. The rule base is generated in an incremental way by optimizing one fuzzy rule at a time using an evolutionary algorithm. In the second stage of the proposed approach, the fuzzy rules generated in the first stage are used for selecting suitable cover images for steganography. We applied our approach to some... 

Estimanon of the origin-destination trip danad matrix (O-D) plays a key role in travel analysis and transportation planning and operations. Many researchers have developed different O-D maths estimation mediods using traffic counts, which allow simple data collection as opposed to die costly traditional direct estimation methods based on home and roadside interviews. In mis papet, a new fuzzy O-D matrix estimation model (FODMEM) is proposed to estimate die O-D matrix from traffic count. A gradient-based aigoridnn. containing 3 fuzzy rule based approach to control die estimated O-D matrix changes, is proposed to solve FODMEM Since link data only represents a snapshot situation, resulting in... 

Trip distribution is considered as the second step in urban transportation planning. The important factors which affect trip distribution are the characteristics of origins and destinations and travel impedance between O/D. Trip distribution traditionally models with the deterministic variables although it seems affective variables in trip distribution molding are based on human perceptions. Since perceptions of people vary from one person to another, thus variables are imprecise and vague. Fuzzy approaches are proper tools of modeling non-deterministic variables. In this paper we present fuzzy estimation models of trip distribution for discretionary trip purposes including: shopping,... 

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... 

Route choice modeling is one of the most important parts of traffic assignment problem. Recently, this model is used to describe the reactions of drivers to Traveler Information Systems in order to develop accurate Advanced Traffic Management and Information System (ATMIS). Therefore accurate model is necessary. In this paper we proposed a new model based on Type-II fuzzy logic to model route choice problem. This model can take account of the imprecision, uncertainties and vagueness lying in the dynamic choice process and makes more accurate modeling of drivers' behavior than deterministic, stochastic and Type-I fuzzy models. In our proposed model we consider average speed and cost... 

Fuzzy functions are used to identify the structure of system models and reasoning with them. Fuzzy functions can be determined by any function identification method such as Least Square Estimates (LSE), Maximum Likelihood Estimates (MLE) or Support Vector Machine Estimates (SVM). However, estimating fuzzy functions using LSE method is structurally a new and unique approach for determining fuzzy functions. By using this approach, there is no need to know or to develop an in-depth understanding of essential concepts for developing and using the membership functions and selecting the t-norms, co-norms and implication operators. Furthermore, there is no need to apply fuzzification and... 

As benign tumors, warts are made through the mediation of Human Papillomavirus (HPV) and may grow on all parts of body, especially hands and feet. There are several treatment methods for this illness. However, none of them can heal all patients. Consequently, physicians are looking for more effective and customized treatments for each patient. They are endeavoring to discover which treatments have better impacts on a particular patient. The aim of this study is to identify the appropriate treatment for two common types of warts (plantar and common) and to predict the responses of two of the best methods (immunotherapy and cryotherapy) to the treatment. As an original work, the study was... 

In this paper, an adaptive critic-based neurofuzzy controller is presented for water level regulation of nuclear steam generators. The problem has been of great concern for many years as the steam generator is a highly nonlinear system showing inverse response dynamics especially at low operating power levels. Fuzzy critic-based learning is a reinforcement learning method based on dynamic programming. The only information available for the critic agent is the system feedback which is interpreted as the last action the controller has performed in the previous state. The signal produced by the critic agent is used alongside the backpropagation of error algorithm to tune online conclusion parts...