Search results for "EURA"
showing 10 items of 3336 documents
Perceptual adaptive insensitivity for support vector machine image coding.
2005
Support vector machine (SVM) learning has been recently proposed for image compression in the frequency domain using a constant epsilon-insensitivity zone by Robinson and Kecman. However, according to the statistical properties of natural images and the properties of human perception, a constant insensitivity makes sense in the spatial domain but it is certainly not a good option in a frequency domain. In fact, in their approach, they made a fixed low-pass assumption as the number of discrete cosine transform (DCT) coefficients to be used in the training was limited. This paper extends the work of Robinson and Kecman by proposing the use of adaptive insensitivity SVMs [2] for image coding u…
Upport vector machines for nonlinear kernel ARMA system identification.
2006
Nonlinear system identification based on support vector machines (SVM) has been usually addressed by means of the standard SVM regression (SVR), which can be seen as an implicit nonlinear autoregressive and moving average (ARMA) model in some reproducing kernel Hilbert space (RKHS). The proposal of this letter is twofold. First, the explicit consideration of an ARMA model in an RKHS (SVM-ARMA 2k) is proposed. We show that stating the ARMA equations in an RKHS leads to solving the regularized normal equations in that RKHS, in terms of the autocorrelation and cross correlation of the (nonlinearly) transformed input and output discrete time processes. Second, a general class of SVM-based syste…
Nonlinear Pulse Shaping in Optical Fibres with a Neural Network
2020
We use a supervised machine-learning model based on a neural network to solve the direct and inverse problems relating to the shaping of optical pulses that occurs upon nonlinear propagation in optical fibres.
SIMULATING THE NUMBER OF ROMANIAN`S PUPILS AND STUDENT USING ARTIFICIAL INTELLIGENCE TECHNIQUES AND COUNTRY`S ECONOMIC ESTATE
2012
The authors present the result of a research that uses artificial neural networks in order to simulate the number of Romanian`s pupils and students (PAS), considering the country`s economic situation. The objective is to determine a better method to forecast the Romanian`s PAS considering its nonlinear behaviour. Also the ANN simulation offers an image about how inputs influence the PAS. In conclusion, the use of the ANN is considered a success and the authors determine the possibility that ANN research application be extended to other countries or even to the European zone.
A Random Neural Network for the Dynamic Multicast Problem
2004
This paper proposes a new heuristic for the dynamic version of the Steiner Tree Problem in Networks (SPN). The heuristic adopts a Random Neural Network (RNN) to improve solutions obtained by previously proposed dynamic algorithms. The Random Neural Network model is adapted to map the intrinsic features of the multicast transmission on a computer network. Exhaustive experiments are carried out to validate the proposed methodology.
Multilayer perceptron neural networks and radial-basis function networks as tools to forecast accumulation of deoxynivalenol in barley seeds contamin…
2011
The capacity of multi-layer perceptron artificial neural networks (MLP-ANN) and radial-basis function networks (RBFNs) to predict deoxynivalenol (DON) accumulation in barley seeds contaminated with Fusarium culmorum under different conditions has been assessed. Temperature (20-28 °C), water activity (0.94-0.98), inoculum size (7-15 mm diameter), and time were the inputs while DON concentration was the output. The dataset was used to train, validate and test many ANNs. Minimizing the mean-square error (MSE) was used to choose the optimal network. Single-layer perceptrons with low number of hidden nodes proved better than double-layer perceptrons, but the performance depended on the training …
Deep CNN for IIF Images Classification in Autoimmune Diagnostics
2019
The diagnosis and monitoring of autoimmune diseases are very important problem in medicine. The most used test for this purpose is the antinuclear antibody (ANA) test. An indirect immunofluorescence (IIF) test performed by Human Epithelial type 2 (HEp-2) cells as substrate antigen is the most common methods to determine ANA. In this paper we present an automatic HEp-2 specimen system based on a convolutional neural network method able to classify IIF images. The system consists of a module for features extraction based on a pre-trained AlexNet network and a classification phase for the cell-pattern association using six support vector machines and a k-nearest neighbors classifier. The class…
Information Abstraction from Crises Related Tweets Using Recurrent Neural Network
2016
Social media has become an important open communication medium during crises. The information shared about a crisis in social media is massive, complex, informal and heterogeneous, which makes extracting useful information a difficult task. This paper presents a first step towards an approach for information extraction from large Twitter data. In brief, we propose a Recurrent Neural Network based model for text generation able to produce a unique text capturing the general consensus of a large collection of twitter messages. The generated text is able to capture information about different crises from tens of thousand of tweets summarized only in a 2000 characters text.
A Damage Identification Approach for Offshore Jacket Platforms Using Partial Modal Results and Artificial Neural Networks
2018
This paper presents a damage identification method for offshore jacket platforms using partially measured modal results and based on artificial intelligence neural networks. Damage identification indices are first proposed combining information of six modal results and natural frequencies. Then, finite element models are established, and damages in structural members are assumed by reducing the structural elastic modulus. From the finite element analysis for a training sample, both the damage identification indices and the damages are obtained, and neural networks are trained. These trained networks are further tested and used for damage prediction of structural members. The calculation res…
Topology Inference and Signal Representation Using Dictionary Learning
2019
This paper presents a Joint Graph Learning and Signal Representation algorithm, called JGLSR, for simultaneous topology learning and graph signal representation via a learned over-complete dictionary. The proposed algorithm alternates between three main steps: sparse coding, dictionary learning, and graph topology inference. We introduce the “transformed graph” which can be considered as a projected graph in the transform domain spanned by the dictionary atoms. Simulation results via synthetic and real data show that the proposed approach has a higher performance when compared to the well-known algorithms for joint undirected graph topology inference and signal representation, when there is…