image classification is one of the pattern recognition techniques which are often used for categorizing the abnormal medical images into different groups. artificialneuralnetworks (ANN) is widely used for this autom...
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ISBN:
(纸本)9783642271823
image classification is one of the pattern recognition techniques which are often used for categorizing the abnormal medical images into different groups. artificialneuralnetworks (ANN) is widely used for this automated application owing to their numerous advantages. Despite the merits. one of the significant drawbacks of the ANN is the convergence problem. Specifically. in Kohonen neuralnetworks (KN) & Hopfield neuralnetworks (I-IN), the convergence to the stored patterns is not guaranteed which ultimately leads to misclassification on the input data. In this work, a hybrid approach namely. Kohonen-Hopfield neural network (KHN) is proposed to minimize the convergence problem for medical image classification applications. Experiments are conducted on KHN using abnormal Magnetic Resonance (MR) brain images from four classes. The performance of KHN is analyzed in terms of classification accuracy and convergence rate. Experimental results suggest promising results in terms of accuracy which indirectly indicates the minimization of convergence irregularities. A comparative analysis with other techniques is also performed to show the superior nature of the proposed approach.
An important way to reach a qualitative improvement of artificialneuralnetworks (ANNs) is to incorporate biological features in the networks. Our proposal introduces modularity at two different levels, first, at the...
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ISBN:
(纸本)9783642214981
An important way to reach a qualitative improvement of artificialneuralnetworks (ANNs) is to incorporate biological features in the networks. Our proposal introduces modularity at two different levels, first, at the network level and second, at the intrinsic level of the networks, generating neural network ensembles (NNEs). We designed three NNEs which incorporated new capacities with regard to the processing of missing data, introduced hybrid modularity, and also used modular ANNs for building the NNEs. We have investigated a suitable NNE design where selection and fusion are recurrently applied to a population of best combinations of classifiers. In this paper we explore the ability of the proposed NNE in different automated decision making applications, especially for those with inherent complexity in their information environment. We present some results on dementia diagnosis and on automatic pollutants detection.
On the one hand, face detection and recognition is an active interdisciplinary area of research that uses techniques from computer vision, imageprocessing and pattern recognition. On the other hand, neuralnetworks h...
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This study focuses on the development of a novel technique for the rapid generation of artificialneural network training data from video streams. Videos captured on an off-road terrain are used to train artificial ne...
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The paper presents basic notions of web mining, radial basis function (RBF) neuralnetworks and epsilon-insensitive support vector machine regression (epsilon-SVR) for the prediction of a time series for the website o...
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ISBN:
(数字)9783642239601
ISBN:
(纸本)9783642239601;9783642239595
The paper presents basic notions of web mining, radial basis function (RBF) neuralnetworks and epsilon-insensitive support vector machine regression (epsilon-SVR) for the prediction of a time series for the website of the University of Pardubice. The model includes pre-processing time series, design RBF neuralnetworks and epsilon-SVR structures, comparison of the results and time series prediction. The predictions concerning short, intermediate and long time series for various ratios of training and testing data. Prediction of web data can be benefit for a web server traffic as a complicated complex system.
Filtering method is applied to the images corrupted at the time of transmission due to several noises, with varying strengths and different noise probability. neural network based image filter is one of the most impor...
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ISBN:
(纸本)9783642184390
Filtering method is applied to the images corrupted at the time of transmission due to several noises, with varying strengths and different noise probability. neural network based image filter is one of the most important example of adaptive image filter. Adaptive neural network filter remove various types of noise such as Gaussian noise and impulsive noise. neuralnetworks are based on the concept of training or learning by examples and have already been applied in several domains of imageprocessing including image filtering. But training of those neuralnetworks consume much time before it is actually tested on such as image filtering. Applying parallelism to imageprocessing is increasingly practical and necessary, as our desktops are becoming multicore machines replacing single core. Therefore, this paper proposes a parallel approach named image decomposition parallel approach to train FLANN (Functional Link artificialneural Network). Well trained FLANN is used for rectifying the corrupted pixels to restore the image. Experimental results obtained through SPMD(Single Program Multiple Data) simulation enviromnent show that the proposed parallel approach to train the FLANN is feasible as it substantially reduces the training period and also make it an efficient filter to restore the image fairly well maintaining the quality of the filtered image. Hence, this method is suitable for real time image restoration applications.
In this paper we introduce a neural architecture for multiple scale color image segmentation on a Graphics processing Unit (GPU): the BioSPCIS (Bio-Inspired Stream processing Color image Segmentation) architecture. Bi...
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ISBN:
(纸本)9783642213267
In this paper we introduce a neural architecture for multiple scale color image segmentation on a Graphics processing Unit (GPU): the BioSPCIS (Bio-Inspired Stream processing Color image Segmentation) architecture. BioSPCIS has been designed according to the physiological organization of the cells on the mammalian visual system and psychophysical studies about the interaction of these cells for image segmentation. Quality of the segmentation was measured against hand-labelled segmentations from the Berkeley Segmentation Dataset. Using a stream processing model and hardware suitable for its execution, we are able to compute the activity of several neurons in the visual path system simultaneously. All the 100 test images in the Berkeley database can be processed in 5 minutes using this architecture.
In the quest for real time processing of hyperspectral images, this paper presents two artificial intelligence algorithms for target detection specially developed for their implementation over GPU and applied to a sea...
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This two-volume-set (CCIS 188 and CCIS 189) constitutes the refereed proceedings of the International conference on Digital Information processing and Communications, ICDIPC 2011, held in Ostrava, Czech Republic, in J...
ISBN:
(数字)9783642223891
ISBN:
(纸本)9783642223884
This two-volume-set (CCIS 188 and CCIS 189) constitutes the refereed proceedings of the International conference on Digital Information processing and Communications, ICDIPC 2011, held in Ostrava, Czech Republic, in July 2011. The 91 revised full papers of both volumes presented together with 4 invited talks were carefully reviewed and selected from 235 submissions. The papers are organized in topical sections on network security; Web applications; data mining; neuralnetworks; distributed and parallel processing; biometrics technologies; e-learning; information ethics; imageprocessing; information and data management; software engineering; data compression; networks; computer security; hardware and systems; multimedia; ad hoc network; artificial intelligence; signal processing; cloud computing; forensics; security; software and systems; mobile networking; and some miscellaneous topics in digital information and communications.
This paper presents a new modular neural network architecture that is used to build a system for pattern recognition based on the iris biometric measurement of persons. In this system, the properties of the person iri...
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ISBN:
(纸本)9783642253294;9783642253300
This paper presents a new modular neural network architecture that is used to build a system for pattern recognition based on the iris biometric measurement of persons. In this system, the properties of the person iris database are enhanced with imageprocessing methods, and the coordinates of the center and radius of the iris are obtained to make a cut of the area of interest by removing the noise around the iris. The inputs to the modular neural network are the processed iris images and the output is the number of the identified person. The integration of the modules was done with a type-2 fuzzy integrator at the level of the sub modules, and with a gating network at the level of the modules.
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