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Attribute reduction is one of the main issues in the theoretical research of rough set theory which is known as a NP-hard optimization problem. The objective is to find the minimal number of attributes from a large dataset. Hence it is difficult to solve to optimality. This paper proposes a composite neighbourhood structure approach to solve the attribute reduction problem that consists of two versions...
Advances in DNA microarray technology has motivated the research community to introduce sophisticated techniques for analyzing the resulted large-scale datasets. Biclustering techniques have been widely adapted for analyzing microarray gene expression data due to its ability to extract local patterns with a subset of genes that are similarly expressed over a subset of samples. Mostly, biclustering...
This paper addresses the problem of tuning the input and the output parameters of a fuzzy logic controller. A novel technique that combines Q(λ)-learning with a fuzzy inference system as a function approximation is proposed. The system learns autonomously without supervision or a priori training data. The proposed technique is applied to two different differential games. The proposed technique is...
Linear regression and classification techniques are very common in statistical data analysis but they are often able to extract from data only linear models, which can be a limitation in real data context. Aim of this study is to build an innovative procedure to overcome this defect. Initially, a multiple linear regression analysis using the best-subset algorithm was performed to determine the variables...
In this paper a new application of fuzzy logic to predict the performance of Titanium Aluminum Nitride (TiAlN) sputtering coating process is presented. Titanium Aluminum Nitride (TiAlN) coated material is widely used as a cutting tool in machining due to its excellent properties such as hardness, roughness and wear. A fuzzy logic model was proposed to predict the coating roughness with respect to...
Emergency physicians in small primary care hospitals seeing patients with acute neurological symptoms have difficulty differentiating ischemic from hemorrhagic strokes and from stroke mimics. Telestroke consults with experienced neurologists supplemented by computerized decision support may aid in this time critical situation. Here we present a Stroke Bayesian Network (SBN) based on a naïve Bayesian...
This work proposes a decision support technology to minimize risks while choosing among competitive investment projects. The technology combines two fuzzy-statistical methods, providing two stages of investment projects' evaluation. At the first stage preliminary selection of projects with small risks is made on the basis of the expertons method [2],[3]. The second stage makes more precise decisions...
Wind energy is becoming one of the most important and promising areas of renewable energy. During the past few years, wind energy generation underwent strong improvements in several fields including power electronics, mechanics, wind dynamics, etc. However, there is a high need to develop more intelligent control mechanisms that can handle the various sources of uncertainties encountered in wind turbines...
Evolutionary algorithms are a frequently used technique for designing morphology and controller of a robot. However, a significant challenge for evolutionary algorithms is premature convergence to local optima. Recently proposed Novelty Search algorithm introduces a radical idea that premature convergence to local optima can be avoided by ignoring the original objective and searching for any novel...
Using well-established techniques of Genetic Programming (GP), we automatically optimize image feature filters over several inputs and within transformation images, improving the Automatic Construction of Tree-Structural Image Transformation (ACTIT) system. Our objective is to also produce optimal solutions in substantially less computation time than require for generating features of ACTIT. We improved...
Since the outset of the deregulation of international financial markets in the 1980s, the frequency of currency crises has increased. Solely in the 1990s, five global storms of financial turmoil, also including collapses of the currency, have occurred. To date, crisis forecasting and monitoring of financial stability is still at a preliminary stage. This paper explores whether the application of the...
Speckle noise is one of the most critical disturbances that alter the quality of Synthetic Aperture Radar (SAR) coherent images. Before using SAR images in automatic target detection and recognition, the first step is to reduce the effect of speckle noise. Several adaptive and non-adaptive filters are widely used for despeckling in SAR images. In this paper, an adaptive mathematical morphological...
Genetic algorithm and particle swarm optimization are two methods which can be used to find the global extremum of cost functions. The solely performance of each method and their specific characteristics in finding the global extremum have been giving the idea of hybridization of these two methods to many researchers. In this paper a new hybrid algorithm named Serial Genetic Algorithm and Particle...
There is still an urgent need of finding a mathematical model which can provide an accurate relationship between the software project effort/cost and the cost drivers. A powerful algorithm which can optimize such a relationship via developing a mathematical relationship between model variables is urgently needed. In this paper, we explore the use of GP to develop a software cost estimation model utilizing...
This paper proposes a novel multiagent system for complex system modeling based on a dynamic fuzzy cognitive map approach. It aims to represent the domain knowledge and carry out the inference process regarding the uncertainty, distribution and dynamism that exist in most of real world problems. The proposed multiagent system architecture is able to model dynamic real world problems that almost contain...
Techniques exist to synthesize software architecture using genetic algorithms that employ transformations based on mutations and crossover. In this paper, we demonstrate that complementary crossover can significantly improve this technique. We study two versions of complementary crossover, one in which parents are selected so that they complement each other but the genes are inherited randomly from...
In this paper we propose an efficient and secure elliptic curve scalar multiplication algorithm over odd prime fields. For this purpose, we propose an explicit algorithm for short addition-subtraction chain method which utilizes a 2's Complement with window method. We term it as W2CASC. Our proposed scalar multiplication algorithm based on W2CASC algorithm has preceded by 12.7% to 28% 160 bit multiplier...
Intrusion Detection System (IDS) is an important and necessary component in ensuring network security and protecting network resources and infrastructures. In this paper, we effectively introduced intrusion detection system by using Principal Component Analysis (PCA) with Support Vector Machines (SVMs) as an approach to select the optimum feature subset. We verify the effectiveness and the feasibility...
Dimensionality reduction and feature selection in particular are known to be of a great help for making supervised learning more effective and efficient. Many different feature selection techniques have been proposed for the traditional settings, where each instance is expected to have a label. In multiple instance learning (MIL) each example or bag consists of a variable set of instances, and the...
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