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In this paper, we propose an effective four-stage approach that detects fire automatically. The proposed algorithm is composed of four stages. In the first stage, an approximate median method is used to detect moving regions. In the second stage, a fuzzy c-means (FCM) algorithm based on the color of fire is used to select candidate fire regions from these moving regions. In the third stage, a discrete...
Determining number of clusters present in a data set is an important problem in clustering. There exist very few techniques that can solve this problem satisfactorily. Some of these techniques rely on user supplied information, while some others use cluster validity indices which are expensive with regard to computation time. This paper proposes an alternative solution for the concerned problem that...
In recent years, piecewise linearization has developed as an attractive tool for the representation of various complex nonlinear systems. The piecewise linearization of individual functions provide a platform for the piecewise affine approximation of nonlinear systems containing a large number of scaler valued nonlinear functions. Inspite of the wide application of piecewise linearization, the optimal...
A high rate of expression of Endothelin protein in the placental cell is very much regulated by inhalation of tobacco smoke and leads to placental abnormalities subjected to birth failure. Our application developed using Image Processing, Nearest Neighbor algorithm (NN) and Genetic Algorithms (GA), automates the study of these proteins to assist pathologists and lab technicians in achieving a more...
We present an efficient genetic algorithm for mining multi-objective rules from large databases. Multi-objectives will conflict with each other, which makes it optimization problem that is very difficult to solve simultaneously. We propose a multi-objective evolutionary algorithm called improved niched Pareto genetic algorithm(INPGA), which not only accurate selects the candidates but also saves selection...
In the past, we proposed a time series segmentation approach by combining the clustering technique, the Discrete Wavelet Transformation (DWT) and the genetic algorithm to automatically find segments and patterns from a time series. In this paper, we propose a PIP-based evolutionary approach, which uses Perceptually Important Points (PIP) instead of DWT, to effectively adjust the length of subsequences...
In the process of codebook design of vector quantization, traditional LBG algorithm owns the advantage of fast convergence, but it is prone to local optimum and is influenced greatly by initial codebook. Given that the Genetic Algorithm has the capability to produce global optimal results, this paper proposes a new clustering algorithm GA-L based on GA and LBG to improve the quality of codebook. This...
Intrusion Detection Systems (IDS) allow to protect systems used by organizations against threats that emerges network connectivity by increasing. The main drawbacks of IDS are the number of alerts generated and failing. Thus in this paper an alert clustering and classification system are proposed. It is able to classify IDS alerts and reduces false positive alerts using clustering of genetic algorithms...
Scheduling heterogeneous tasks in a heterogeneous grid environment aims at effectively utilizing the resources and sharing the load among the available resources. Such a task assignment problem is NP-hard. This paper presents a Hybrid Particle Swarm Optimization - Genetic Algorithm (HPSO-GA) for solving the Task Assignment Problem. The novel Particle Swarm Optimization (PSO) implements GA operations...
The paper adopts the fuzzy c-means text mining method in lots of text mining methods. But aim at the defect that the initial value of the fuzzy c-means is more sensitivity and poor stability, an improved GAFCM text mining method has been put forward. GAFCM uses global search features of genetic algorithms to improve the fuzzy c-means. Finally, it has proved that the improved text mining method has...
Magnetic Resonance Imaging (MRI) is one of the best technologies currently being used for diagnosing brain tumor. Brain tumor is diagnosed at advanced stages with the help of the MRI image. Segmentation is an important process to extract suspicious region from complex medical images. Automatic detection of brain tumor through MRI can provide the valuable outlook and accuracy of earlier brain tumor...
Image thresholding is an important technique for image processing and pattern recognition. Several thresholding techniques have been proposed in the literature. In this paper for segmentation of magnetic resonance images, a novel method using a combination of the multilevel thresholding algorithm and the hierarchical evolutionary algorithm (HEA) is proposed. The HEA can be viewed as a variant of conventional...
Wireless sensor networks are emerged as a new technology in different applications to get information from environment in recent years. On of the most important challenges in this type of networks is energy shortage of sensors. Where as energy restriction, it should be mentioned a fundamental solution to providence energy consumption. The most suitable solution is clustering. In this paper the clustering...
Cluster analysis is used in several research areas to classify data sets in groups by their similar characteristics. Metaheuristic-based techniques, such as Genetic Algorithms (GAs) and Ant Colony Optimization (ACO), have been applied in order to increase the clustering algorithm performance. GA and ACO-based clustering algorithms are capable of efficiently and automatically forming natural groups...
Unequal Area Facility Layout Problem (UA-FLP) has been addressed by several methods. However, UA-FLP has only been solved regarding quantitative criteria. Our approach includes subjective features to UA-FLP, which are difficult to take into account with a classical heuristic optimization. For that, an Interactive Genetic Algorithm (IGA) is proposed that allows an interaction between the algorithm...
This paper describes a method using image processing and genetic algorithm-neural network (GA-NN) for automated Mycobacterium tuberculosis detection in tissues. The proposed method can be used to assist pathologists in tuberculosis (TB) diagnosis from tissue sections and replace the conventional manual screening process, which is time-consuming and labour-intensive. The approach consists of image...
The biclustering problem consists in simultaneously clustering rows and columns of a data matrix. The aim of this paper is to empirically assess the performance of cooperative coevolution as an alternative approach for coping with the task of discovering good and sizeable biclusters. For this purpose, two cooperative coevolutionary algorithms, one configured with genetic algorithms (GAs) and another...
This study proposes a novel classification technique of GA/k-prototypes in combination with a genetic algorithm to take the advantage of k-prototypes clustering mechanism for supporting the classification purpose. A genetic algorithm is used to adjust the weight applied to input attributes in order to enable a majority of the data records in each cluster to be with the same outcome class. We conduct...
Genetic algorithms are becoming increasingly valuable in solving large-scale, realistic, difficult problems, and new customer personalization is one of these problems. In this paper, a method combining GA based clustering algorithm with Collaborative Filtering CF-based Recommender system is proposed named Information Gain Clustering using Genetic Algorithm (IGCGA), which alleviates the problem of...
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