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This paper reports the investigations and experimental procedures conducted for designing an automatic sleep classification tool basedconly in the features extracted with wavelets from EEG, EMG and EOG (electro encephalo-mio- and oculo-gram) signals, without any visual aid or context-based evaluation. Real data collected from infants was processed and classified by several traditional and bio-inspired...
This paper presents a novel image edge detection method based on a simplified pulse coupled neural network with anisotropic interconnections (PCNNAI) by applying an anisotropic linking mechanism. PCNNAI utilizes the anisotropic linking mechanism to create an adaptive synaptic weight matrix to achieve the anisotropic interconnection model among neurons. Therefore, the neurons corresponding to edge...
In this paper we propose a method to build similarity relations into extended Rough Set Theory. Similarity is estimated using ideas from Granular computing and Case-base reasoning. A new measure is introduced in order to compute the quality of the similarity relation. This work presents a study of a case of a similarity relation based on a global similarity function between two objects, this function...
The research concerns computer-based clinical decision support for laryngopathies. The proposed computer tool is based on a speech signal analysis in the time domain using recurrent neural networks. Such networks have the ability of time series prediction because of their memory nodes as well as local recurrent connections. In our experiments we use the modified Elman-Jordan neural network. For this...
It is well known that the problem arising from high dimensionality of data should be considered in pattern recognition field. Face recognition databases are usually high dimensionality, especially when limited training samples are available for each subject. Traditional techniques perform dimensionality reduction are unable to solve this problem smoothly, which makes feature extraction task much difficult...
Feature selection is a very important preprocessing step in data classification. By applying it we are able to reduce the dimensionality of the problem by removing redundant or irrelevant data. High dimensional data sets are becoming usual nowadays specially in bio-informatics, biology, signal processing or text classification, increasing the need for efficient feature selection methods. In this paper...
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...
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...
According to some biological observations, generating output variability is one of the characteristics expected from a memory model. In this paper a BAM inspired chaotic model is used to mimic this functionality of the brain. Chaos gives the potential to create deterministic variability and control its degree of uncertainty. Using some time series generated by the trained network, largest lyapunov...
In order to improve the classifier performance in semantic image annotation, we propose a novel method which adopts learning vector quantization (LVQ) technique to optimize low level feature data extracted from given image. Some representative vectors are selected with LVQ to train support vector machine (SVM) classifier instead of using all feature data. Performance is compared between the methods...
A novel representation of Recurrent Artificial neural network is proposed for non-linear markovian and non-markovian control problems. The network architecture is inspired by Cartesian Genetic Programming. The neural network attributes namely weights, topology and functions are encoded using Cartesian Genetic Programming. The proposed algorithm is applied on the standard benchmark control problem:...
Co-occurrence data matrices arise frequently in various important applications such as a document clustering. By considering a multinomial mixture model, we present a new probabilistic Self-Organizing Map (SOM) for clustering and visualizing this kind of data. Contrary to SOM, our proposed learning algorithm optimizes an objective function. Its performances are evaluated by using Monte Carlo simulations...
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