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Automatic detection of network intrusion is a challenging task because of increasing types of attacks. Many of the existing approaches either are rigid, inflexible designs tailored to a specific situation or require manual setting of design parameters such as the initial number of clusters. In this paper we allow the design parameters to be determined dynamically by adopting a layered hybrid architecture,...
This study used Kohonen network model Self-Organizing Feature map(SOFM) with Principal Components Analysis(PCA) and Back-Propagation Network(BPN) to predict Biochemical oxygen demand(BOD5) removal rate of water resource from the surface-flow constructed treatment wetlands at Yuli Township in Hualien County from December, 2005 to June, 2006. With this self-organizing map technology, the water quality...
An approach for an efficient clustering of 3D line segments based on an unsupervised competitive neural network is applied to a set of high resolution satellite image data in this paper. The unsupervised competitive neural network, called centroid neural network for clustering 3D line segments (CNN-3D), utilizes the characteristics of 3D line segments. Successful application of CNN-3D can lead accurate...
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...
Discovering clusters of varyingly shapes, sizes and densities in a data set is still a challenging problem for density-based algorithms. Recently presented approaches either require the input parameters involving the information about the structure of the data set, or are restricted to two-dimensional data. In this paper, we present a density-based clustering algorithm, which uses the fuzzy proximity...
A model of RBF neural network (RBFNN) is framed to solve the problem of identification of nonlinear system. In order to realize the structure identification of RBFNN, a kind of hybrid parameter optimization algorithm is proposed based on optimal selection cluster algorithm and PSO. By this algorithm, it is optimally gained the hidden layer node number of RBFNN in terms of input samples. Then the structure...
The generalization ability of neural network is an important aspect affecting its application. Meanwhile, the selection of training samples has a great impact on this ability. In order to improve the completeness of training samples, a method of samples self-learning of BP neural network based on clustering is put forward in this paper. By using the method of clustering, new samples can be collected...
This paper proposes a composite method for short-term load forecasting, which is based on fuzzy clustering wavelet decomposition and BP neural network. Firstly, the similar-day's load is selected as the input load based on the fuzzy clustering method; secondly, the wavelet method is applied to decompose the similar-day load into the low frequency and high frequency components, from which the feature...
A novel model of fuzzy clustering neural network is discussed, which synthesizes unsupervised fuzzy competitive learning algorithm and self-organized competitive network. Based on this model, an algorithm of abrupt video shot boundary detection is presented which is a two-stage clustering on a linear feature space. The experimental results obtained demonstrate that the algorithm is feasible and efficient.
Majority of the techniques that have been used for pattern discovery from Web Usage Data (WUD) are clustering methods. In e-commerce applications, clustering methods can be used for the purpose of generating marketing strategies, product offerings, personalization and Web site adaptation. A novel Partitional based approach for dynamically grouping Web users based on their Web access patterns using...
Clustered dot ordered dithering (CDOD) is one paradigm of digital half-toning that is employed for systems which cannot produce more than two levels at display. The conventional CDOD results in major limitations i.e. visually objectionable periodic patterns, false contouring and blurred appearance of the half-tone images. Cluster generation using artificial intelligence may be a potential solution...
We present a systematic study of the effect of size and shape on the spectral response of individual silver and gold nanoparticles. When developing nanoparticles as catalysts, their shape is very important. For a certain volume of material, nanoparticles make the best catalysts when they have a large surface area. It is a challenge to find the shape that has the largest surface area for its volume...
A novel neural network method to predict the spectral signature in the predicted meteorological image is presented here. Back propagation algorithm has been used in this work. Based on computation cost, three different dimensional feature vectors are provided from two consecutive images as input to neural net for training and testing. Various kinds of testing are made depending upon position of predicted...
An application in modeling a non-lineal system between temperature, humidity and urban airborne air pollution is presented. In this contribution, the implementation of cluster estimation method as a basis of a fuzzy model identification algorithm has been developed. Fuzzy clustering allowed partitioning this complex non-linear system into many linear sub-systems. Finally, comparison of the performance...
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...
When lines in a power system are constrained, the sensitivity of the power flows on these lines to generator output provides information about how the constraints divide the system and about the ability of sets of generators to increase revenue without increasing dispatch. Clustering is used to identify generators into groups with the potential for market advantage. In this paper, we discuss the implementation...
Researched and developed the methods and non-hierarchical clustering algorithms for determining the optimal initial number of clusters without any background information on the location of the clusters. The methods and algorithms are researched in the famous test set Iris.
An optical fiber displacement sensor based on Radial Basis Function neural network is proposed for enhancing accuracy and linear range. A Nearest Neighbor Clustering algorithm suitable for training RBF neural network in optical fiber displacement sensor is studied and implemented. The work method and process of sensor are described. Experimental results show that neural network method has higher precision...
In this paper, Structure and properties of neural networks with quadratic junction are presented. Unsupervised learning rules about the neural networks are given. Using this kind of neural networks, an ART-based hierarchical clustering algorithm is suggested. The algorithm can determine the number of clusters and clustering data. The time and space complexity of the algorithm are discussed. A 2-D...
A novel approach for an efficient extraction of rectangular boundaries from aerial image data is proposed in this paper. In this approach, a Centroid Neural Network (CNN) with a metric of line segments is utilized for connecting low-level linear structures or grouping similar objects. The proposed an approach, called hierarchical clustering method, utilizes the fact that rooftops of a building are...
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