Search results for "NEURAL NETWORK"
showing 10 items of 1385 documents
Unsupervised tissue classification of brain MR images for voxel-based morphometry analysis
2016
In this article, a fully unsupervised method for brain tissue segmentation of T1-weighted MRI 3D volumes is proposed. The method uses the Fuzzy C-Means (FCM) clustering algorithm and a Fully Connected Cascade Neural Network (FCCNN) classifier. Traditional manual segmentation methods require neuro-radiological expertise and significant time while semiautomatic methods depend on parameter's setup and trial-and-error methodologies that may lead to high intraoperator/interoperator variability. The proposed method selects the most useful MRI data according to FCM fuzziness values and trains the FCCNN to learn to classify brain’ tissues into White Matter, Gray Matter, and Cerebro-Spinal Fluid in …
Semi-automated and interactive segmentation of contrast-enhancing masses on breast DCE-MRI using spatial fuzzy clustering
2022
Abstract Multiparametric Magnetic Resonance Imaging (MRI) is the most sensitive imaging modality for breast cancer detection and is increasingly playing a key role in lesion characterization. In this context, accurate and reliable quantification of the shape and extent of breast cancer is crucial in clinical research environments. Since conventional lesion delineation procedures are still mostly manual, automated segmentation approaches can improve this time-consuming and operator-dependent task by annotating the regions of interest in a reproducible manner. In this work, a semi-automated and interactive approach based on the spatial Fuzzy C-Means (sFCM) algorithm is proposed, used to segme…
Aspects and Potentiality of Unconventional Modelling of Processes in Sporting Events
1999
This paper describes how inexact processes as presented in sporting events can be recorded, analysed, and evaluated by means of neural networks and fuzzy modelling.
Deep Learning for Classifying Physical Activities from Accelerometer Data
2021
Physical inactivity increases the risk of many adverse health conditions, including the world’s major non-communicable diseases, such as coronary heart disease, type 2 diabetes, and breast and colon cancers, shortening life expectancy. There are minimal medical care and personal trainers’ methods to monitor a patient’s actual physical activity types. To improve activity monitoring, we propose an artificial-intelligence-based approach to classify the physical movement activity patterns. In more detail, we employ two deep learning (DL) methods, namely a deep feed-forward neural network (DNN) and a deep recurrent neural network (RNN) for this purpose. We evaluate the proposed models on two phy…
Convergence of Boobnov-Galerkin Method Exemplified
2004
In this Note, Boobnov–Galerkin’s method is proved to converge to an exact solution for an applied mechanics problem. We address in detail the interrelation of Boobnov–Galerkin method and the exact solution in the beam deflection problems. Namely, we show the coincidence of these two methods for clamped–clamped boundary conditions, using an alternative set of functions proposed by Filonenko-Borodich.12 Received 25 February 2003; accepted for publication 13 March 2004. Copyright c 2004 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. Copies of this paper may be made for personal or internal use, on condition that the copier pay the $10.00 per-copy fee to th…
Game analysis and control by means of continuously learning networks
2002
The paper deals with the question, if and how the process of learning can be modelled, analysed and maybe improved by means of Neural Networks. The problem is that most of the developed types of ne...
A QSAR study investigating the potential anti-HIV-1 effect of some acyclovir and ganciclovir analogs
2009
A QSAR study, involving the use of calculated physical-chemical properties (TSAR TM ), and the use of a neural network approach (TSAR TM ), has been performed on the potential anti-HIV-1 activity of a series of Acyclovir and Ganciclovir analogs. Model obtained allows reliable predictions for the anti-HIV-1 activity of these derivatives, and showed that the presence of the Ganciclovir chain in triazolopyrrolopyrimidine and pyrimidopyrrolopyrimidine series seems to increase the antiviral effect.
Adaptive Control of a Gas Turbine Engine for Axial Compressor Faults
1996
An adaptive control system for a gas turbine engine which diagnoses conditions of axial compressor faults is proposed and analyzed. Nonlinear models of the gas turbine, neural networks and genetic algorithms are used in this research. The adaptive control system minimizes the reduction in gas turbine performance deriving from non-destructive faults. In the absence of faults, the system automatically performs the optimization of the engine between overhauls. Improvements concern the definition of adaptive control with faults in progress, and the real-time optimization of the engine during its operative life between overhauls. Simulation results of the suggested control system are also discus…
Prediction of the Fundamental Period of Infilled RC Frame Structures Using Artificial Neural Networks
2015
The fundamental period is one of the most critical parameters for the seismic design of structures. There are several literature approaches for its estimation which often conflict with each other, making their use questionable. Furthermore, the majority of these approaches do not take into account the presence of infill walls into the structure despite the fact that infill walls increase the stiffness and mass of structure leading to significant changes in the fundamental period. In the present paper, artificial neural networks (ANNs) are used to predict the fundamental period of infilled reinforced concrete (RC) structures. For the training and the validation of the ANN, a large data set i…
Hardware implementation of real-time Extreme Learning Machine in FPGA: Analysis of precision, resource occupation and performance
2016
Extreme Learning Machine (ELM) on-chip learning is implemented on FPGA.Three hardware architectures are evaluated.Parametrical analysis of accuracy, resource occupation and performance is carried out. Display Omitted Extreme Learning Machine (ELM) proposes a non-iterative training method for Single Layer Feedforward Neural Networks that provides an effective solution for classification and prediction problems. Its hardware implementation is an important step towards fast, accurate and reconfigurable embedded systems based on neural networks, allowing to extend the range of applications where neural networks can be used, especially where frequent and fast training, or even real-time training…