Most current Intrusion Detection systems (IDS) examine all data features to detect intrusion. Also existing intrusion detection approaches have some limitations, namely impossibility to process a large number of audit...
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Most current Intrusion Detection systems (IDS) examine all data features to detect intrusion. Also existing intrusion detection approaches have some limitations, namely impossibility to process a large number of audit data for realtime operation, low detection and recognition accuracy. To overcome these limitations, we apply modular neural network models to detect and recognize attacks in computer networks. They are based on the combination of principal component analysis (PCA) neural networks and multilayer perceptrons (MLP). PCA networks are employed for important data extraction and to reduce high dimensional data vectors. We present two PCA neural networks for feature extraction: linear PCA (LPCA) and nonlinear PCA (NPCA). MLP is employed to detect and recognize attacks using feature-extracted data instead of original data. The proposed approaches are tested with the help of KDD-99 dataset. The experimental results demonstrate that the designed models are promising in terms of accuracy and computational time for real world intrusion detection.
Many techniques were used in order to detect and to predict epileptic seizures on the basis of electroencephalograms. One of the approaches for the prediction of the epileptic seizures is the use the chaos theory, nam...
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Many techniques were used in order to detect and to predict epileptic seizures on the basis of electroencephalograms. One of the approaches for the prediction of the epileptic seizures is the use the chaos theory, namely determination largest Lyapunov's exponent or correlation dimension of the scalp EEG signals. This paper presents the neural network technique for epilepsy detection. It is based on computing of the largest Lyapunov's exponent. The results of experiments are discussed.
An approach to development of fine-grain parallel algorithm of artificial neural network training using parallelization of computational operations of each elementary neuron is presented in this paper. A training algo...
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An approach to development of fine-grain parallel algorithm of artificial neural network training using parallelization of computational operations of each elementary neuron is presented in this paper. A training algorithm of back error propagation is described and parallel section of the algorithm is developed. The results of experimental research of the parallel algorithm are given using analysis of parallelization speedup and efficiency on parallel computer Origin 300.
A radical change (Feenstra, Holmer, Tromans, Moos and Mieritz 2003) in manufacturing is starting to occur. The Rapid Manufacturing, which can be defined as "the use of an additive manufacturing process to constru...
A radical change (Feenstra, Holmer, Tromans, Moos and Mieritz 2003) in manufacturing is starting to occur. The Rapid Manufacturing, which can be defined as "the use of an additive manufacturing process to construct parts that are used directly as finished products or components", is set to supersede many current uses of moulds and dies. Rapid Manufacturing (Wohlers 2003) is based on new additive manufacturing techniques that produce fully functional parts directly from a 3D CAD model without the use of tooling. The ambitious scope of the European Initiative CUSTOM-FIT, a Framework 6 Integrated Project, is to create a fully integrated system for the design, production and supply of individualized custom-products. Within the cases under study there are strong connections to sporting articles. This project is being funded by the European Commission over the next four years and a half and will become central to European research concerning Rapid Manufacturing (RM).
This book constitutes the refereed proceedings of the 38th Conference on Current Trends in Theory and Practice of computer Science, SOFSEM 2012, held in Špindlerův Mlýn, Czech Republic, in January 2012. The 43 re...
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ISBN:
(数字)9783642276606
ISBN:
(纸本)9783642276590
This book constitutes the refereed proceedings of the 38th Conference on Current Trends in Theory and Practice of computer Science, SOFSEM 2012, held in Špindlerův Mlýn, Czech Republic, in January 2012.
The 43 revised papers presented in this volume were carefully reviewed and selected from 121 submissions. The book also contains 11 invited talks, 10 of which are in full-paper length. The contributions are organized in topical sections named: foundations of computer science; software and Web engineering; cryptography, security, and verification; and artificial intelligence.
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