Search results for "NEURAL NETWORK"
showing 10 items of 1385 documents
Visual data mining with self-organising maps for ventricular fibrillation analysis
2012
Detection of ventricular fibrillation (VF) at an early stage is being deeply studied in order to lower the risk of sudden death and allows the specialist to have greater reaction time to give the patient a good recovering therapy. Some works are focusing on detecting VF based on numerical analysis of time-frequency distributions, but in general the methods used do not provide insight into the problem. However, this study proposes a new methodology in order to obtain information about this problem. This work uses a supervised self-organising map (SOM) to obtain visually information among four important groups of patients: VF (ventricular fibrillation), VT (ventricular tachycardia), HP (healt…
Neural Network Based Finite-Time Stabilization for Discrete-Time Markov Jump Nonlinear Systems with Time Delays
2013
Published version of an article in the journal: Abstract and Applied Analysis. Also available from the publisher at: http://dx.doi.org/10.1155/2013/359265 Open Access This paper deals with the finite-time stabilization problem for discrete-time Markov jump nonlinear systems with time delays and norm-bounded exogenous disturbance. The nonlinearities in different jump modes are parameterized by neural networks. Subsequently, a linear difference inclusion state space representation for a class of neural networks is established. Based on this, sufficient conditions are derived in terms of linear matrix inequalities to guarantee stochastic finite-time boundedness and stochastic finite-time stabi…
Synchronization of Uncertain Neural Networks with H8 Performance and Mixed Time-Delays
2011
An exponential H8 synchronization method is addressed for a class of uncertain master and slave neural networks with mixed time-delays, where the mixed delays comprise different neutral, discrete and distributed time-delays. An appropriate discretized Lyapunov-Krasovskii functional and some free weighting matrices are utilized to establish some delay-dependent sufficient conditions for designing a delayed state-feedback control as a synchronization law in terms of linear matrix inequalities under less restrictive conditions. The controller guarantees the exponential H8 synchronization of the two coupled master and slave neural networks regardless of their initial states. Numerical simulatio…
Notice of Violation of IEEE Publication Principles: New Delay-Dependent Exponential $H_{\infty}$ Synchronization for Uncertain Neural Networks With M…
2010
This paper establishes an exponential H infin synchronization method for a class of uncertain master and slave neural networks (MSNNs) with mixed time delays, where the mixed delays comprise different neutral, discrete, and distributed time delays. The polytopic and the norm-bounded uncertainties are separately taken into consideration. An appropriate discretized Lyapunov-Krasovskii functional and some free-weighting matrices are utilized to establish some delay-dependent sufficient conditions for designing delayed state-feedback control as a synchronization law in terms of linear matrix inequalities under less restrictive conditions. The controller guarantees the exponential H infin synchr…
Computer-Aided Diagnosis System with Backpropagation Artificial Neural Network—Improving Human Readers Performance
2016
This article presents the results of a study into possibility of artificial neural networks (ANNs) to classify cancer changes in mammographic images. Today’s Computer-Aided Detection (CAD) systems cannot detect 100 % of pathological changes. One of the properties of an ANN is generalized information —it can identify not only learned data but also data that is similar to training set. The combination of CAD and ANN could give better result and help radiologists to take the right decision.
Multilayer neural networks: an experimental evaluation of on-line training methods
2004
Artificial neural networks (ANN) are inspired by the structure of biological neural networks and their ability to integrate knowledge and learning. In ANN training, the objective is to minimize the error over the training set. The most popular method for training these networks is back propagation, a gradient descent technique. Other non-linear optimization methods such as conjugate directions set or conjugate gradient have also been used for this purpose. Recently, metaheuristics such as simulated annealing, genetic algorithms or tabu search have been also adapted to this context.There are situations in which the necessary training data are being generated in real time and, an extensive tr…
Towards to deep neural network application with limited training data: synthesis of melanoma's diffuse reflectance spectral images
2019
The goal of our study is to train artificial neural networks (ANN) using multispectral images of melanoma. Since the number of multispectral images of melanomas is limited, we offer to synthesize them from multispectral images of benign skin lesions. We used the previously created melanoma diagnostic criterion p'. This criterion is calculated from multispectral images of skin lesions captured under 526nm, 663nm, and 964nm LED illumination. We synthesize these three images from multispectral images of nevus so that the p' map matches the melanoma criteria (the values in the lesion area is >1, respectively). Demonstrated results show that by transforming multispectral images of benign nevus i…
Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance
2020
We present a pairwise learning to rank approach based on a neural net, called DirectRanker, that generalizes the RankNet architecture. We show mathematically that our model is reflexive, antisymmetric, and transitive allowing for simplified training and improved performance. Experimental results on the LETOR MSLR-WEB10K, MQ2007 and MQ2008 datasets show that our model outperforms numerous state-of-the-art methods, while being inherently simpler in structure and using a pairwise approach only.
Application of textile fibres from tire recycling in asphalt mixtures
2022
The tire rubber obtained from end-of-life car and truck tires has been successfully recycled, among other applications, in the asphalt industry by providing a mean to get asphalt mixtures with superior performance. Textile fibres are another component derived from tire recycling typically disposed of in landfills or used in energetic valorisation. This paper wants to re-ignite interest in this secondary product by evaluating its use as a valuable resource in asphalt mixtures. Indirect tensile tests, dynamic modulus, fatigue resistance, and permanent deformation tests were performed on a series of AC14 asphalt mixtures manufactured with two binders, namely 50/70 and 35/50 pen, using several …
Predictors of early dropout in treatment for gambling disorder: The role of personality disorders and clinical syndromes
2017
Several treatment options for gambling disorder (GD) have been tested in recent years; however dropout levels still remain high. This study aims to evaluate whether the presence of psychiatric comorbidities predicts treatment outcome according to Millon's evolutionary theory, following a six-month therapy for GD. The role of severity, duration of the disorder, typology of gambling (mainly online or offline) and pharmacological treatment were also analysed. The recruitment included 194 pathological gamblers (PGs) to be compared with 78 healthy controls (HCs). Psychological assessment included the South Oaks Gambling Screen and the Millon Clinical Multiaxial Inventory-III. The "treatment fail…