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At least 685 records · Page 38Linked to original sources
PubMed1997

A neural network application to classification of health status of HIV/AIDS patients.

This paper presents an application of neural networks to classify and to predict the health status of HIV/AIDS patients. A neural network model in classifying both the well and not-well health status of HIV/AIDS patients is developed and evaluated in terms of validity and reliability of the test. Several different neural network topologies are applied to AIDS Cost and Utilization Survey (ACSUS) datasets in order to demonstrate the neural network's capability.

Activities of Daily Living↗
PubMedDate not supplied

[Principles and experiences for modeling chaotic attractors of heart rate fluctuations with artificial neural networks].

The aim of the present paper was, using artificial neural networks, to identify chaotic attractors presumed to be responsible for heart rate fluctuations. Chaotic behaviour is based on low-dimensional deterministic processes which are highly sensitive to initial conditions and therefore of limited predictability. Chaotic attractors produce orbits in the phase space where the points are dense. Following transformation of measured heart rate date into the phase space, such heart rate prediction characteristics were found in 6 adult rabbits. A low-dimensional deterministic model was designed by means of an artificial neural network [2*dimension of the time series +1) input neurons, 1 hidden layer, 1 output neuron and was successfully employed to predict the next 5 heart beats. Training of the neural network in terms of the prediction of the suspected chaotic orbits was dramatically improved by considering all possible prediction intervals within the prediction horizon. The trained models enabled the heart rate fluctuations to be distinguished during consciousness, under anaesthesia and additional vagal blockade.

Animals↗
PubMed1998

Detection of spikes with artificial neural networks using raw EEG.

Artificial neural networks (ANN) using raw electroencephalogram (EEG) data were developed and tested off-line to detect transient epileptiform discharges (spike and spike/wave) and EMG activity in an ongoing EEG. In the present study, a feedforward ANN with a variable number of input and hidden layer units and two output units was used to optimize the detection system. The ANN system was trained and tested with the backpropagation algorithm using a large data set of exemplars. The effects of different EEG time windows and the number of hidden layer neurons were examined using rigorous statistical tests for optimum detection sensitivity and selectivity. The best ANN configuration occurred with an input time window of 150 msec (30 input units) and six hidden layer neurons. This input interval contained information on the wave component of the epileptiform discharge which improved detection. Two-dimensional receiver operating curves were developed to define the optimum threshold parameters for best detection. Comparison with previous networks using raw EEG showed improvement in both sensitivity and selectivity. This study showed that raw EEG can be successfully used to train ANNs to detect epileptogenic discharges with a high success rate without resorting to experimenter-selected parameters which may limit the efficiency of the system.

Adolescent↗
PubMed1999

A memory-based self-generated basis function neural network.

The paper presents a novel memory-based Self-Generated Basis Function Neural Network (SGBFN) that is composed of small CMACs. The SGBFN requires much smaller memory space than the conventional CMAC and has an excellent learning convergence property compared to multilayer neural networks. Each CMAC in the new structure takes a subset of problem inputs as its inputs. Several CMACs that have different subsets of inputs form a submodule and a group of submodules form a neural network. The output of a submodule is the product of its CMACs' outputs. Each submodule implements a self-generated basis function, which is developed during the learning. The output of the neural network is the sum of the outputs from the submodules. Using only a subset of inputs in each CMAC significantly reduces the required memory space in high-dimensional modeling. With the same size of memory, the new structure is able to achieve a much smaller learning error compared to the conventional CMAC.

Artificial Intelligence↗
PubMed2003

[Fuzzy control of the physical training intensity based on neural-network].

Using computer technique, artificial neural network and fuzzy control theory, we have explored a real-time control method for the athlete's physical workload intensity in order that the goal of physical training can be reached effectively in accordance to the exercise plan. The technique could be useful for improving the efficiency of scientific physical training.

Algorithms↗
PubMed2004

An analog neural network implementation in fixed time of adjustable-order statistic filters and applications.

In this paper, we show a neural network implementation in fixed time of adjustable order statistic filters, including sorting, and adaptive-order statistic filters. All these networks accept an array of N numbers Xi = S(Xi)M(Xi)2E(Xi) as input (where S(Xi) is the sign of Xi, M(Xi) is the mantissa normalized to m digits, and Ex is the exponent) and employ two kinds of neurons, the linear and the threshold-logic neurons, with only integer weights (most of the weights being just +1 or -1) and integer threshold. Therefore, this will greatly facilitate the actual hardware implementation of the proposed neural networks using currently available very large scale integration technology. An application of using minimum filter in implementing a special neural network model neural network classifier (NNC) is given. With a classification problem of l classes C1, C2,.. ., C1, NNC classifies in fixed time an unknown vector to one class using a minimum-distance classification technique.

Computers, Analog↗
PubMed1994

A technique for using neural network analysis to perform survival analysis of censored data.

The purpose of this study was to demonstrate how a form of neural network analysis could be used to perform survival analysis on censored data, and to compare neural network analysis with the most commonly used technique for this type of analysis, Cox regression. In this study computer simulated data sets were used. The underlying rules connecting prognostic information to the hazard of death were defined to allow the construction of data sets with specific realistic properties that could be used to demonstrate situations in which neural network analysis had particular strengths in comparison with Cox regression modeling. Using these simulated data sets neural network analysis could produce successful predictive models, find interactions between variables, and recognize the importance of variables that contributed to the hazard rate as a complex function of the variables value and in situations where the proportionality of hazards assumption was violated. It was also demonstrated that neural network analysis was not a 'black box', but could lead to useful insights into the roles played by different prognostic variables in determining patient outcome.

Humans↗
PubMed2004

Predicting protein secondary structure by cascade-correlation neural networks.

The back-propagation neural network algorithm is a commonly used method for predicting the secondary structure of proteins. Whilst popular, this method can be slow to learn and here we compare it with an alternative: the cascade-correlation architecture. Using a constructive algorithm, cascade-correlation achieves predictive accuracies comparable to those obtained by back-propagation, in shorter time.

Algorithms↗
PubMed2001

Use of artificial neural networks in prostate cancer.

Artificial neural networks (ANNs) are a type of artificial intelligence software inspired by biological neuronal systems that can be used for nonlinear statistical modeling. In recent years, these applications have played an increasing role in predictive and classification modeling in medical research. We review the basic concepts behind ANNs and examine the role of this technology in selected applications in prostate cancer research.

Humans↗
PubMed1997

Real-time neural network based camera localization and its extension to mobile robot control.

The feasibility of using neural networks for camera localization and mobile robot control is investigated here. This approach has the advantages of eliminating the laborious and error-prone process of imaging system modeling and calibration procedures. Basically, two different approaches of using neural networks are introduced of which one is a hybrid approach combining neural networks and the pinhole-based analytic solution while the other is purely neural network based. These techniques have been tested and compared through both simulation and real-time experiments and are shown to yield more precise localization than analytic approaches. Furthermore, this neural localization method is also shown to be directly applicable to the navigation control of an experimental mobile robot along the hallway purely guided by a dark wall strip. It also facilitates multi-sensor fusion through the use of multiple sensors of different types for control due to the network's capability of learning without models.

Algorithms↗
PubMed2003

Assessment of a simple artificial neural network for predicting residual neuromuscular block.

BACKGROUND: Postoperative residual curarization (PORC) after surgery is common and its detection has a high error rate. Artificial neural networks are being used increasingly to examine complex data. We hypothesized that a neural network would enhance prediction of PORC. METHODS: In 40 previously reported patients, neuromuscular function, neuromuscular block/antagonist usage and time intervals were recorded throughout anaesthesia until tracheal extubation by an observer uninvolved in patient care. PORC was defined as significant 'fade' (train of four <0.7) at extubation. Neuromuscular function was classified as PORC (value=1) or no PORC (value=0). A back-propagation neural network was trained to assign similar values (0, 1) for prediction of PORC, by examining the impact of (i) the degree of spontaneous recovery at reversal, and (ii) the time since pharmacological reversal, using the jackknife method. Successful prediction was defined as attainment of a predicted value within 0.2 of the target value. RESULTS: Twenty-six patients (65%) had PORC at tracheal extubation. Clinical detection of PORC had a sensitivity of 0 and specificity of 1, with an indeterminate positive predictive value and a negative predictive value of 0.35. Using the artificial neural network, one patient with residual block and one with adequate neuromuscular function were incorrectly classified during the test phase, with no indeterminate predictions, giving an artificial neural network sensitivity of 0.96 (chi(2)=44, P<0.001) and specificity of 0.92 (P=1), with a positive predictive value of 0.96 and a negative predictive value of 0.93 (chi(2)=12, P<0.001). CONCLUSIONS: Neural network-based prediction, using readily available clinical measurements, is significantly better than human judgement in predicting recovery of neuromuscular function.

Adult↗
PubMed2001

A what-and-where fusion neural network for recognition and tracking of multiple radar emitters.

A neural network recognition and tracking system is proposed for classification of radar pulses in autonomous Electronic Support Measure systems. Radar type information is considered with position-specific information from active emitters in a scene. Type-specific parameters of the input pulse stream are fed to a neural network classifier trained on samples of data collected in the field. Meanwhile, a clustering algorithm is used to separate pulses from different emitters according to position-specific parameters of the input pulse stream. Classifier responses corresponding to different emitters are separated into tracks, or trajectories, one per active emitter, allowing for more accurate identification of radar types based on multiple views of emitter data along each emitter trajectory. Such a What-and-Where fusion strategy is motivated by a similar subdivision of labor in the brain. The fuzzy ARTMAP neural network is used to classify streams of pulses according to radar type using their functional parameters. Simulation results obtained with a radar pulse data set indicate that fuzzy ARTMAP compares favorably to several other approaches when performance is measured in terms of accuracy and computational complexity. Incorporation into fuzzy ARTMAP of negative match tracking (from ARTMAP-IC) facilitated convergence during training with this data set. Other modifications improved classification of data that include missing input pattern components and missing training classes. Fuzzy ARTMAP was combined with a bank of Kalman filters to group pulses transmitted from different emitters based on their position-specific parameters, and with a module to accumulate evidence from fuzzy ARTMAP responses corresponding to the track defined for each emitter. Simulation results demonstrate that the system provides a high level of performance on complex, incomplete and overlapping radar data.

Algorithms↗
PubMed1995

Human chromosome classification using multilayer perceptron neural network.

A multilayer perceptron (MLP) neural network (NN) has been studied for human chromosome classification. Only 10-20 examples were required for the MLP NN to reach its ultimate performance classifying chromosomes of 5 types. The empirical dependence of the entropic error on the number of examples was found to be highly comparable to the 1/t function. The principal component analysis (PCA) was used, both for network initialization and for feature reduction purposes. The PCA demonstrated the importance of retaining most of the image information whenever small training sets are used. The MLP NN classifier outperformed the Bayes piecewise classifier for all the cases tested. The MLP classifier was found to be almost unsusceptible to the ratio of the number of training vectors to the number of features, whereas the piecewise classifier was highly dependent on this ratio.

Algorithms↗
PubMed2006

Homeostatic plasticity improves signal propagation in continuous-time recurrent neural networks.

Continuous-time recurrent neural networks (CTRNNs) are potentially an excellent substrate for the generation of adaptive behaviour in artificial autonomous agents. However, node saturation effects in these networks can leave them insensitive to input and stop signals from propagating. Node saturation is related to the problems of hyper-excitation and quiescence in biological nervous systems, which are thought to be avoided through the existence of homeostatic plastic mechanisms. Analogous mechanisms are here implemented in a variety of CTRNN architectures and are shown to increase node sensitivity and improve signal propagation, with implications for robotics. These results lend support to the view that homeostatic plasticity may prevent quiescence and hyper-excitation in biological nervous systems.

Homeostasis↗
PubMed1997

Automatic segmentation and classification of outdoor images using neural networks.

The paper describes how neural networks may be used to segment and label objects in images. A self-organising feature map is used for the segmentation phase, and we quantify the quality of the segmentations produced as well as the contribution made by colour and texture features. A multi-layer perception is trained to label the regions produced by the segmentation process. It is shown that 91.1% of the image area is correctly classified into one of eleven categories which include cars, houses, fences, roads, vegetation and sky.

Automation↗
PubMed1993

Protein structure prediction system based on artificial neural networks.

Methods based on the neural network techniques are among the most accurate in the secondary structure prediction of globular proteins. Here the same principles have been used for the tertiary structure prediction problem. The map of dihedral phi and psi angles is divided into 10 by 10 squares each spanning 36 by 36 degrees. By predicting the classification of each residue in the protein chain in this map a rough tertiary structure can be deduced. A complete prediction system running on a cluster of workstations and a graphical user interface was developed.

Amino Acid Sequence↗
PubMed2000

Forecasts using neural network versus Box-Jenkins methodology for ambient air quality monitoring data.

This study explores ambient air quality forecasts using the conventional time-series approach and a neural network. Sulfur dioxide and ozone monitoring data collected from two background stations and an industrial station are used. Various learning methods and varied numbers of hidden layer processing units of the neural network model are tested. Results obtained from the time-series and neural network models are discussed and compared on the basis of their performance for 1-step-ahead and 24-step-ahead forecasts. Although both models perform well for 1-step-ahead prediction, some neural network results reveal a slightly better forecast without manually adjusting model parameters, according to the results. For a 24-step-ahead forecast, most neural network results are as good as or superior to those of the time-series model. With the advantages of self-learning, self-adaptation, and parallel processing, the neural network approach is a promising technique for developing an automated short-term ambient air quality forecast system.

Air Pollution, Indoor↗